Publications

2024

1. Characterization of partial wetting by CMAS droplets using multiphase many-body dissipative particle dynamics and data-driven discovery based on PINNs. Kiyani, Elham; Kooshkbaghi, Mahdi; Shukla, Khemraj; Koneru; Rahul, B; Li, Zhen; Bravao, Luis; Ghoshal, Anindya; Karniadakis, George Em; Karttunen, Mikko.
   Accepted for publication in J. Fluid Mech. (2024).
   Preprint: https://arxiv.org/abs/2307.09142
   

   Abstract

   The molten sand, a mixture of calcia, magnesia, alumina, and silicate, known as CMAS, is characterized by its high viscosity, density, and surface tension. The unique properties of CMAS make it a challenging material to deal with in high-temperature applications, requiring innovative solutions and materials to prevent its buildup and damage to critical equipment. Here, we use multiphase many-body dissipative particle dynamics (mDPD) simulations to study the wetting dynamics of highly viscous molten CMAS droplets. The simulations are performed in three dimensions, with varying initial droplet sizes and equilibrium contact angles. We propose a coarse parametric ordinary differential equation (ODE) that captures the spreading radius behavior of the CMAS droplets. The ODE parameters are then identified based on the Physics-Informed Neural Network (PINN) framework. Subsequently, the closed form dependency of parameter values found by PINN on the initial radii and contact angles are given using symbolic regression. Finally, we employ Bayesian PINNs (B-PINNs) to assess and quantify the uncertainty associated with the discovered parameters. In brief, this study provides insight into spreading dynamics of CMAS droplets by fusing simple parametric ODE modeling and state-of-the-art machine learning techniques.

   
   

2. A Molecular Dynamics Simulation Study of the Effects of βGln114 Mutation on the Dynamic Behavior of the Catalytic Site of the Tryptophan Synthase. Roy, Anupom; Karttunen, Mikko. J. Chem. Inf. Model. 64, 983-1003 (2024)
   Web: https://doi.org/10.1021/acs.jcim.3c01966
   Preprint: https://doi.org/10.26434/chemrxiv-2023-4bhpv
   Data: https://doi.org/10.5281/zenodo.10339268
   

   Abstract

   L-tryptophan (L-Trp), a vital amino acid for the survival of various organisms, is synthesized by the enzyme tryptophan synthase (TS) in organisms such as eubacteria, archaebacteria, protista, fungi, and plantae. TS, a pyridoxal 5′-phosphate (PLP)- dependent enzyme, comprises α and β subunits that typically form an α2β2 tetramer. The enzyme’s activity is regulated by the conformational switching of its α and β subunits between the open (T state) and closed (R state) conformations. Many microorganisms rely on TS for growth and replication, making the enzyme and the L-Trp biosynthetic pathway potential drug targets. For instance, Mycobacterium tuberculosis, Chlamydiae bacteria, Streptococcus pneumoniae, Francisella tularensis, Salmonella bacteria, and Cryptosporidium parasitic protozoa depend on L-Trp synthesis. Antibiotic-resistant salmonella strains have emerged, underscoring the need for novel drugs targeting the L-Trp biosynthetic pathway, especially for salmonella-related infections. A single amino acid mutation can significantly impact enzyme function, affecting stability, conformational dynamics, and active or allosteric sites. These changes influence interactions, catalytic activity, and protein-ligand/protein-protein interactions. This study focuses on the impact of mutating the βGln114 residue on the catalytic and allosteric sites of TS. Extensive MD simulations were conducted on E(PLP), E(AEX1), E(A-A), and E(C3) forms of TS using the WT, βQ114A, and βQ114N versions. The results show that both the βQ114A and βQ114N mutations increase protein backbone RMSD fluctuations, destabilizing all TS forms. Conformational and hydrogen bond analyses suggest the significance of βGln114 drifting away from cofactor/intermediates and forming hydrogen bonds with water molecules necessary for L-Trp biosynthesis. The βQ114A mutation creates a gap between βAla114 and cofactor/intermediates, hindering hydrogen bond formation due to short sidechains, disrupting β-sites. Conversely, the βQ114N mutation positions βAsn114 closer to cofactor/intermediates, forming hydrogen bonds with O3 of cofactors/intermediates and nearby water molecules, potentially disrupting the L-Trp biosynthetic mechanism.

   
   

3. Chemoselective Staudinger Reactivity of Bis(Azido)Phosphines Supported with a \(\pi\)-Donating Imidazolin-2-Iminato Ligand. Lortie, John L.; Davies, Matthew; Boyle, Paul D.; Karttunen, Mikko; Ragogna, Paul J.
   Accepted for publication in Inorg. Chem. (2024).
   

   Abstract

   Synthesis and characterization of new P(III) and P(V) bis(azido)phosphines/phosphoranes supported by an N,N’-bis(2,6-diipopropylphenyl) imidazolin-2-iminato (IPrN) ligand, and their reactivity with various secondary and tertiary phosphines result in the formation of chiral and/or asymmetric mono(phosphinimino)azidophosphines via the Staudinger reaction. The reaction of IPrNP(N3)2 (2) or IPrNP(S)(N3)2 (4S) with an excess of tertiary phosphine resulted in the chemoselective formation of IPrNP(N3)(NPMe3) (7) or IPrNP(S)N3(NPR3) (5R), respectively. The chemoselective Staudinger reactivity was also observed in reactions using a secondary phosphine (HPCy2) to produce IPrNP(S)N3[NP(H)Cy2] (6a), which exists in an equilibrium with it tautomeric IPrNP(S)N3[N(H)PCy2] form (6b), as confirmed by 31P-31P Nuclear Overhauser Effect Spectroscopy (NOESY). Density Functional Theory (DFT) calculations point to a combination of energetically unfavorable lowest unoccupied molecular orbitals (LUMOs) and the accumulation of increasing negative charge at the terminal azido-nitrogen upon a single azide-to-phosphinimine conversion that gave rise to the observed chemoselectivity.

   
   

4. A Computational Investigation on Eumelanin-Drug Binding in Aqueous Solution. Soltani, Sepideh; Roy, Anupom; Urtti, Arto; Karttunen, Mikko. Submitted.
   Preprint: https://doi.org/10.26434/chemrxiv-2023-p16bg
   

   Abstract

   Melanin is a widely found natural pigment serving multiple physiological functions and having numerous applications in industries and pharmaceuticals. Due to the diverse structural properties of melanin, drug molecules exhibit varying degrees of affinity towards it. Consequently, drug molecules binding to melanin, including eumelanin, possess significant implications for drug delivery, biodistribution, and the treatment of various diseases. Here, we investigate allosteric binding between drugs and eumelanin using computational techniques such as molecular dynamics (MD) simulations, density functional theory (DFT) calculations, and free energy calculations. Eumelanin, composed of DHI and DHICA molecules, was utilized in different systems, including aggregated and random arrangements, with the addition of neutral or charged eumelanin and selected drug molecules (chloroquine, levofloxacin, timolol, methotrexate, and diclofenac). The MD simulations revealed conformational changes in both eumelanin and drug molecules upon interaction along with the creation of binding sites or cavities. Evaluation of binding free energy through Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) calculations indicated that neutral timolol and charged diclofenac exhibited the strongest binding to DHI aggregated bundles, while both neutral and charged methotrexate showed the strongest binding in random DHI systems. In contrast, neutral and charged chloroquine displayed the strongest binding in random systems with DHICA (neutral and charged) respectively. Following MD simulations, DFT calculations were employed to further investigate the strength of drug-eumelanin binding. By utilizing the drug-eumelanin poses obtained from MD simulations, DFT calculations demonstrated that the binding strength is influenced by the structural orientation and conformation of both the drug and eumelanin molecules. Overall, drug-eumelanin binding depends on various factors, including conformational changes in both the drug and eumelanin, the charges of the molecules, the presence of binding sites (especially in DHI eumelanin), the occurrence of π-π and hydrogen bond interactions, and the surrounding solvent environment.

   
   

5. Non-Stokesian dynamics of magnetic helical nanoswimmers under confinement. Fazeli, Alireza; Thakore, Vaibhav; Ala-Nissila, Tapio; Karttunen, Mikko. Submitted.
   Preprint: https://doi.org/10.48550/arXiv.2311.00839
   

   Abstract

   Electromagnetically propelled helical nanoswimmers offer great potential for nanorobotic applications. Here, the effect of confinement on their propulsion is characterized using lattice-Boltzmann simulations. Two principal mechanisms give rise to their forward motion under confinement: 1) pure swimming, and 2) the thrust created by the differential pressure due to confinement. Under strong confinement, they face greater rotational drag, but display a faster propulsion for fixed driving frequency in agreement with experimental findings. This is due to the increased differential pressure created by the boundary walls when they are sufficiently close to each other and the particle. Two new analytical relations are presented: 1) for predicting the swimming speed of an unconfined particle as a function of its angular speed and geometrical properties, and 2) an empirical expression to accurately predict the propulsion speed of a confined swimmer as a function of the degree of confinement and its unconfined swimming speed. At low driving frequencies and degrees of confinement, the systems retain the expected linear behavior consistent with the predictions of the Stokes equation. However, as the driving frequency and/or the degree of confinement increase, their impact on propulsion leads to increasing deviations from the Stokesian regime and emergence of nonlinear behavior.

   
   

6. Cholesterol Inhibits Oxygen Permeation Through Biological Membranes: Mechanism Against Double-Bond Peroxidation. Boonnoy, Phansiri; Bagheri, Behnaz; Dias, Cristiano; Karttunen, Mikko; Wong-ekkabut, Jirasak. Submitted.
   

   Abstract

   The presence of oxygen molecules (O\(_2\)) in biological membrane promotes lipid peroxidation of phospholipids with unsaturated acyl chains. On the another hand, cholesterol is considered to be an antioxidant molecule as it has a significant barrier effect on the permeation of O\(_2\) across membranes. However, a comprehensive explanation of how cholesterol affects the distribution and diffusion of O\(_2\) within lipid bilayers is yet to be established. In this study, we investigated the interaction of oxygen molecules with polyunsaturated lipid bilayers using molecular dynamics (MD) simulations. The degree of lipid unsaturation and the concentration of cholesterol were varied to study the permeation of O\(_2\). The free energy profile of O\(_2\) diffusing from the water phase to the lipid bilayer was calculated using biased umbrella MD simulations. The results show that O\(_2\) passively translocates into the membrane without changing the physical properties of the bilayer. Interestingly, in the unsaturated lipid bilayers the presence of cholesterol led to a significantly decreased permeation of O\(_2\) and an increase in the lipid chain order. Our results indicate that the hydroxyl groups of cholesterol strongly interact with the O\(_2\) molecules effectively inhibiting interactions between the oxygens and the double bonds in unsaturated lipid tails. These insights can help our understanding of how cholesterol functions as an antioxidant at a molecular level.

   
   

2023

1. A Framework Based on Symbolic Regression Coupled with eXtended Physics-Informed Neural Networks for Gray-Box Learning of Equations of Motion from Data. Kiyani, Elham; Shukla, Khemraj; Karniadakis, George Em; Karttunen, Mikko. Computer Methods in Applied Mechanics and Engineering 415, 116258 (2023).
   Web: https://doi.org/10.1016/j.cma.2023.116258
   Preprint: http://arxiv.org/abs/2305.10706
   

   Abstract

   We propose a framework and an algorithm to uncover the unknown parts of a nonlinear equation directly from data. The framework is based on eXtended Physics-Informed Neural Networks (X-PINNs), domain decomposition in space-time, and we augment the X-PINN method by imposing flux continuity across the domain interfaces. The well-known Allen-Cahn equation is used to demonstrate the approach. The Frobenius matrix norm is used to evaluate the accuracy of the X-PINN predictions and the results show excellent performance. In addition, symbolic regression, a machine learning (ML) technique, was used to determine the closed form of the unknown part of the equation from the data and the results confirm the accuracy of the X-PINNs based approach. To test the framework in a situation resembling real-world data, random noise was added to the datasets to mimic situations such as the presence of thermal noise or instrument errors. The results show that the framework is stable against significant amount of noise. As the final part, we determined the minimal amount of data that is needed for training the neural network. The framework was able to predict the correct form and coefficients of the underlying dynamical equation when at least 60% data was used for training.

   
   

2. Accurately computing protein~\(\textbf{pK}_\textbf{a}\) values using non-equilibrium alchemy. Wilson, Carter J.; Karttunen, Mikko; de Groot, Bert, Gapsys, Vytautas. J. Chem. Theory Comput. 19, 7833–7845 (2023).
   Web: https://doi.org/10.1021/acs.jctc.3c00721 (open access)
   Preprint: https://doi.org/10.26434/chemrxiv-2023-pqjrf
   

   Abstract

   The stability, solubility, and function of a protein depend both on its net charge and the protonation states of its individual residues. The \(\text{p}K_\text{a}\) is a measure of the tendency for a given residue to be (de)protonated at a specific pH. While the \(\text{p}K_\text{a}\) values can be resolved experimentally, theory and computation provide a compelling alternative. To this end, we assess the applicability of a non-equilibrium (NEQ) free energy method to the problem of \(\text{p}K_\text{a}\) prediction. On a dataset of 144 residues spanning 13 proteins, we report an average unsigned error of \(0.77\!\pm\!0.09\,\text{p}K\), \(0.69\!\pm\!0.09\,\text{p}K\), and \(0.52\!\pm\!0.04\,\text{p}K\) for aspartate, glutamate, and lysine, respectively. This is comparable to current state-of-the-art predictors and the accuracy recently reached using free energy perturbation methods (e.g., FEP+). Moreover, we demonstrate that our NEQ approach can accurately resolve the p\(K_\text{a}\) values of coupled residues and observe a substantial performance disparity associated with the lysine partial charges in Amber14SB/Amber99SB*-ILDN, for which an underused fix already exists.

   
   

3. Resolving coupled pH titrations using non-equilibrium free energy calculations. Wilson, Carter J.; de Groot, Bert, Gapsys, Vytautas. submitted (2023).
   Preprint: https://doi.org/10.26434/chemrxiv-2023-1c8rn
   

   Abstract

   In a protein, nearby titratable sites can be coupled: the (de)protonation of one may affect the other. The degree of this interaction depends on several factors and can influence the measured pKa. Here, we derive a formalism based on double free energy differences (ΔΔG) for quantifying the individual site pKa values of coupled residues. As ΔΔG values can be obtained by means of alchemical free energy calculations, the presented approach allows for a convenient estimation of coupled residue pKas in practice. We demonstrate that our approach and a previously proposed microscopic pKa formalism, can be combined with non-equilibrium (NEQ) alchemical free energy calculations to resolve pH-dependent protein pKa values. Toy models and both, regular and constant-pH molecular dynamics simulations, alongside experimental data, are used to validate this approach. Our results highlight the insights gleaned when coupling and microstate probabilities are analyzed and suggest extensions to more complex enzymatic contexts. Furthermore, we find that naïvely computed pKa values that ignore coupling, can be significantly improved when coupling is accounted for, in some cases reducing the error by half. In short, our results suggest that free energy methods can resolve the pKa values of both uncoupled and coupled residues.

   
   

4. Effect of oxidation on POPC lipid bilayers: Anionic carboxyl group plays a major role. Bagheri, Behnaz; Boonnoy, Phansiri; Wong-ekkabut, Jirasak; Karttunen, Mikko. PCCP, 25, 18310-18321 (2023) [open access].
   Web: https://doi.org/10.1039/D3CP01692G
   Movies: https://doi.org/10.5281/zenodo.7810558
   

   Abstract

   Polyunsaturated phospholipids are major targets of reactive oxygen species leading to formation of oxidized lipids. Oxidized phospholipids have a pronounced role in cell membrane damage. We investigated the effect of oxidation on physiological properties of phospholipid bilayers using atomistic molecular dynamics simulations. We studied phospholipid bilayer systems of 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) and its two stable oxidized products, 1-palmitoyl-2-(9’-oxo-nonanoyl)-sn-glycero-3-phosphocholine (PoxnoPC) and 1-palmitoyl-2-azelaoyl-sn-glycero-3-phosphocholine (PazePC). Structural properties of the POPC lipid bilayer upon the addition of PoxnoPC or PazePC with concentration ranging from 10% to 30% were described. The key finding is that PazePC lipids bend their polar tails toward the bilayer-water interface whereas PoxnoPC lipids orient their tail toward the bilayer interior. The bilayer thickness decreases such that the thickness reduction in bilayers containing PazePC is stronger than in bilayers containing PoxnoPC. The average area per lipid decreases with a stronger effect in bilayers containing PoxnoPC. The addition of PoxnoPC makes both POPC acyl chains slightly more ordered whereas the addition of PazePC reduces the order in the two POPC acyl chains. These structural changes lead to a deeper water penetration in bilayers containing PazePC, even with a moderate concentration of 10%, such that water penetrates deeper as the PazePC concentration increases. However, the addition of PoxnoPC to the POPC bilayer does not have a major influence on water penetration.

   
   

5. Effect of substrate heterogeneity and topology on epithelial tissue growth dynamics. Mazarei, Mahmood; Åström, Jan; Westerholm, Jan; Karttunen, Mikko. Phys. Rev. E 208, 054405 (2023).
   Web: https://dx.doi.org/10.1103/PhysRevE.108.054405
   Preprint: https://arxiv.org/abs/2303.10850
   Movies: https://doi.org/10.5281/zenodo.8112841
   

   Abstract

   Tissue growth kinetics and interface dynamics depend on the properties of the tissue environment and cell-cell interactions. In cellular environments, substrate heterogeneity and geometry arise from a variety factors, such as the structure of the extracellular matrix and nutrient concentration. We used the CellSim3D model, a kinetic division simulator, to investigate the growth kinetics and interface roughness dynamics of epithelial tissue growth on heterogeneous substrates with varying topologies. The results show that the presence of quenched disorder has a clear effect on the colony morphology and the roughness scaling of the interface in the moving interface regime. In a medium with quenched disorder, the tissue interface has a smaller interface roughness exponent, \(\alpha\), and a larger growth exponent, \(\beta\). The scaling exponents also depend on the topology of the substrate and cannot be categorized by well-known universality classes.

   
   

6. Designing architectured ceramics for transient thermal applications using finite element and deep learning. Kiyani, Elham; Sarvestani, Hamidreza Yazdani; Ravanbakhsh, Hossein; Behbahani, Razyeh; Ashrafi, Behnam; Rahmat, Meysam; Karttunen, Mikko. Modelling and Simulation in Materials Science and Engineering 32, 015001 (2023).
   Web: https://dx.doi.org/10.1088/1361-651X/ad073a
   Preprint: https://doi.org/10.48550/arXiv.2305.11632
   

   Abstract

   Topologically interlocking architectures can generate tough ceramics with attractive thermo-mechanical properties. This concept can make the material design pathway a challenging task, since modeling the whole design space is neither effective nor feasible. We propose an approach to design high-performance architectured ceramics using machine learning (ML) with data from finite element analysis (FEA). Convolutional neural networks (CNNs) and Multilayer Perceptrons (MLPs) are used as the deep learning approaches. A limited set of FEA simulation data containing a variety of architectural design parameters is used to train our neural networks, including learning how independent and dependent design parameters are related. A trained network is then used to predict the optimum structure from the configurations. A FEA simulation is run on the best predictions of both MLP and CNN algorithms to evaluate the performance of our networks. Although a limited amount of simulation data are available, our networks are effective in predicting the transient thermo-mechanical responses of possible panel designs. For example, the optimal design after using CNN prediction resulted in ≈ 30% improvement in terms of edge temperature.

   
   

7. Reply to ‘On the existence of collective interactions’. Sowlati-Hashjin, Shahin; Šadek, Vojtěch; Sadjadi, Seyedabdolreza; Karttunen, Mikko; Martín-Pendás, Angel; Foroutan-Nejad, Cina. Nature Comms. 14, 3873 (2003)
   Web: https://doi.org/10.1038/s41467-023-39504-3
   

   Abstract

   Reply to the comment by Poater et al.

   
   

8. Molecular Dynamics Simulations of the Dissolution of Choline Chloride Nanocrystals in Ethylene Glycol at (and Near) the Deep Eutectic Ratio. Rafael Maglia de Souza, Mikko Karttunen, and Mauro Carlos Costa Ribeiro. submitted
   

   Abstract

   We conducted molecular dynamics (MD) simulations to investigate the dissolution process of tablet-shaped choline chloride (ChCl) nanocrystals in ethylene glycol (EG). In particular, we considered ChCl:EG ratios at (and near) the deep eutectic compositions of 1:2, 1:4, 1:6, 1:8, and 1:10. Dissolution of ChCl is observed to occur via three stages for all ratios: 1) A fast initial period where the most exposed ions are removed from the tablet-shaped nanocrystal until it becomes cylindrical, 2) a relatively long second stage, where dissolution follows a fixed rate law. This was fitted against classical dissolution models, which assume the rates to be proportional to the surface area from where ions detach. This was followed by a transition to the third stage, 3) in which ion diffusion from a diffusion layer surrounding the \ce{ChCl} nanocrystal controls the dissolution rate. The above contrasts to what has been observed for simple crystals (such as \ce{NaCl} and urea) dissolving in aqueous media under sink conditions: A second stage that lasts almost the entire simulation until a very small residual crystal forms and suddenly disintegrates. However, based on estimates we made on the thickness of the diffusion layer, our findings suggest that increasing the ratio of solvent decreases the size of the diffusion layer, which should vanish as it approaches the sink condition limit.

   
   
   

   Cover article: Elucidating Lipid Conformations in the Ripple Phase: Machine Learning Reveals Four Lipid Populations
   Biophys J Blog: Ripple Me This: Elucidating Lipid Conformations in the Ripple Phase

9. Elucidating Lipid Conformations in the Ripple Phase: Machine Learning Reveals Four Lipid Populations. Davies, Matthew; Reyes-Figueroa, A. D.; Gurtovenko, Andrey A.; Frankel, Daniel; Karttunen, Mikko. Biophys. J. 122, P442-450 (2023).
   Web: https://doi.org/10.1016/j.bpj.2022.11.024.
   Movies: https://doi.org/10.5281/zenodo.8103792
   Preprint: https://doi.org/10.1101/2021.11.25.470048

   Abstract

   A new mixed radial-angular, three-particle correlation function method in combination with unsupervised machine learning was applied to examine the emergence of the ripple phase in dipalmitoylphosphatidylcholine (DPPC) lipid bilayers using data from atomistic molecular dynamics simulations of system sizes ranging from 128 to 4096 lipids. Based on the acyl tail conformations, the analysis revealed the presence of four distinct conformational populations of lipids in the ripple phases of the DPPC lipid bilayers. The expected gel-like (ordered; L\(_\mathrm{o}\)) and fluid-like (disordered; L\(_\mathrm{d}\)) lipids are found along with their splayed tail equivalents (L\(_\mathrm{o,s}\) and L\(_\mathrm{d,s}\)). These lipids differ, based on their gauche distribution and tail packing. The disordered (L\(_\mathrm{d}\)) and disordered-splayed (L\(_\mathrm{d,s}\)) lipids spatially cluster in the ripple in the groove side, that is, in an asymmetric manner across the bilayer leaflets. The ripple phase does not contain large numbers of L\(_\mathrm{d}\) lipids; instead they only exist on the interface of the groove side of the undulation. The bulk of the groove side is a complex coexistence of L\(_\mathrm{o}\),L\(_\mathrm{o,s}\), and L\(_\mathrm{d,s}\) lipids. The convex side of the undulation contains predominantly L\(_\mathrm{o}\) lipids. Thus, the structure of the ripple phase is neither a simple coexistence of ordered and disordered lipids nor a coexistence of ordered interdigitating gel-like (L\(_\mathrm{o}\)) and ordered-splayed (L\(_\mathrm{o,s}\)) lipids, but instead a coexistence of an ordered phase and a complex mixed phase. Principal component analysis further confirmed the existence of the four lipid groups.

   
   

10. Investigation of Structure and Properties of Polymerizable Deep Eutectic Solvent Based on Choline Chloride and Acrylic Acid. Tolmachev, Dmitry; Nazarychev, Victor; Fedotova, Veronika; Vorobiov, Vitaly; Lukasheva, Natalia; Smirnov, Michael; Karttunen, Mikko. J. Mol. Liq. 370, 121030, (2023).
    https://doi.org/10.1016/j.molliq.2022.121030.

    Abstract

    Deep eutectic solvents (DESs) are multi-component solvents appearing in a broad range of applications. The next necessary step for the development of new DESs is understanding the molecular mechanisms of DES formation and the interactions that determine its structure and properties. In this work, we use multiscale simulations supported by experiments to investigate the detailed structure and properties of polymerizable DESs based on choline chloride and acrylic acid as a basis for creating inks for 3D printing. Thermodynamic and structural analyses show the physical mechanisms of DES formation in these materials: due to the significant size difference between the acrylic acid and choline ions, and favorable interactions between acrylic acid and the Cl- ions, the acrylic acid molecules are able to incorporate into the free spaces of the first coordination shells of the Cl- ions. As a consequence, the mixture has less volume than its individual components and this excess volume determines the negative value of the enthalpy of mixing. Structurally, the mixture is a network with the Cl- ions as nodes connecting the other DES components. This was confirmed by both the FTIR experiments and the atomistic MD simulations. The calculations show the necessity of correct accounting of excess enthalpy and entropy for determining DESs structures and other properties.

    
    
    

    Cover article: Combining Finite Element and Machine Learning…
    We combine ML and FEM to design and find the high-performance ceramics subjected to various thermo-mechanical loadings. These materials can be manufactured using a subtractive manufacturing scalable platform.

11. Combining Finite Element and Machine Learning Methods to Predict Structures of Architectured Interlocking Ceramics. Ravanbakhsh, Hossein; Behbahani, Razyeh; Sarvestani, Hamidreza Yazdani; Kiyani, Elham; Rahmat, Meysam; Karttunen, Mikko; Ashrafi, Behnam. Adv. Eng. Mater. 25, 2201408 (2023).
    Web: https://doi.org/10.1002/adem.202201408.

    Abstract

    Attaining optimum structural ceramic designs calls for an extensive search in a vast design space. Herein, the thermomechanical properties of interlocked ceramics are evaluated and an approach to assist their design under thermal shock loading is proposed. A combination of finite-element (FE) and machine learning (ML) methods is used to simulate behaviors of systems and then to sweep the vast domain of input combinations to determine the best-performing designs, respectively. First, FE modeling is done using a limited number of interlocking architectures with different design parameters via Comsol Multiphysics. The simulation data is used for training ML algorithms. Of the examined ML algorithms, Gaussian process regression (GPR), extreme gradient boosting (XGB), and neural networks (NN) more accurately predict the thermomechanical responses of the interlocking ceramics. After validation, the combination of FE and ML approaches is applied to thermal shielding and heat sink applications to find the optimal interlocked ceramics in terms of minimal out-of-plane deformation and maximal heat absorption, respectively. The results show the success of the approach in finding optimum designs in a space of more than 2 million cases. The striking success of the ML approach implies its promising potential for predicting physical properties of ceramics.

    
    

12. Identification of Catechins Binding Pockets in Monomeric Aβ42 Through Ensemble Docking and MD Simulations. Rohoullah Firouzi, Shahin Sowlati-Hashjin, Cecilia Chávez-García, Mitra Ashourie, Mohammad Hossein Karimi-Jafarif, Mikko Karttunen, Int. J. Mol. Sci. 24, 8161 (2023).
    Web: https://doi.org/10.3390/ijms24098161
    Preprint: https://doi.org/10.1101/2022.02.09.479729

    Abstract

    The assembly of the Amyloid-β peptide (Aβ) into toxic oligomers and fibrils is associated with Alzheimer’s disease and dementia. Therefore, disrupting amyloid assembly by direct targeting of the Aβ monomeric form with small molecules or antibodies is a promising therapeutic strategy. However, given the dynamic nature of Aβ, standard computational tools cannot be easily applied for high-throughput structure-based virtual screening in drug discovery projects. In the current study, we propose a computational pipeline ‒ in the framework of the ensemble docking strategy ‒ to identify catechins’ binding sites in monomeric Aβ42. It is shown that both hydrophobic aromatic interactions and hydrogen bonding are crucial for the binding of catechins to Aβ42. Also, it has been found that all the studied ligands, especially the EGCG, can act as potent inhibitors against amyloid aggregation by blocking the central hydrophobic region of the Aβ. Our findings are evaluated and confirmed with multi-microsecond MD simulations. Finally, it is suggested that our proposed pipeline, with low computational cost in comparison with MD simulations, is a suitable approach for the virtual screening of ligand libraries against Aβ.

    
    

13. Structure and Dynamics of the Rett Syndrome Protein, MeCP2. Noriyuki Kodera, Anna A. Kalashnikova, Mary E. Porter-Goff, Catherine A. Musselman, Cecilia Chávez-García, Mikko Karttunen, Borries Demeler, Tatiana G. Kutateladze, Toshio Ando and Jeffrey C. Hansen, submitted

    Abstract

    Methyl-CpG binding protein 2 (MeCP2) is a chromatin regulatory protein essential for brain development and activity in vertebrates. Specific missense and nonsense mutations in MeCP2 lead to the neurodevelopmental disorder, Rett syndrome (RTT). To understand the structure and dynamics of MeCP2 and gain insight into the molecular basis of RTT, we characterized MeCP2 properties using high speed atomic force microscopy and solution-state approaches. MeCP2 is an intrinsically disordered protein that displays highly dynamic behavior. MeCP2 transitions between a fully extended dumbbell-like structure with the methyl DNA binding domain (MBD) and C-terminal domain (CTD) at the extremities, and a compact structure where the MBD and CTD interact in cis. The MBD within the full length protein equilibrates between unfolded and well folded states. MBD−CTD interactions stabilize the MBD in its folded state and are essential for MeCP2 plasticity. The R106W, R133C, F155S and T158M RTT mutations all showed aberrant MBD dynamics compared to wild type. Our results indicate that MBD−CTD interactions in cis and the unfolding/refolding transition of the MBD are important features of MeCP2 structure that become dysregulated in RTT.

    
    

2022

1. Machine Learning-Driven Process of Alumina Ceramics Laser Machining. Behbahani, Razyeh; Sarvestani, Hamidreza Yazdani; Fatehi, Erfan; Kiyani, Elham; Ashrafi, Behnam; Karttunen, Mikko; Rahmat, Meysam. Phys. Scr. 98, 015834, (2022).
   Web: https://doi.org/10.1088/1402-4896/aca3da.
   Get it at Research Gate

   Abstract

   Laser machining is a highly flexible non-contact manufacturing technique that has been employed widely across academia and industry. Due to nonlinear interactions between light and matter, simulation methods are extremely crucial, as they help enhance the machining quality by offering comprehension of the inter-relationships between the laser processing parameters. On the other hand, experimental processing parameter optimization recommends a systematic, and consequently time-consuming, investigation of the available processing parameter space. An intelligent strategy is to employ machine learning (ML) techniques to capture the relationship between picosecond laser machining parameters for finding proper parameter combinations to create the desired cuts on industrial-grade alumina ceramic with deep, smooth and defect-free patterns. Laser parameters such as beam amplitude and frequency, scanner passing speed and the number of passes over the surface, as well as the vertical distance of the scanner from the sample surface, are used for predicting the depth, top width, and bottom width of the engraved channels using ML models. Owing to the complex correlation between laser parameters, it is shown that Neural Networks (NN) are the most efficient in predicting the outputs. Equipped with an ML model that captures the interconnection between laser parameters and the engraved channel dimensions, one can predict the required input parameters to achieve a target channel geometry. This strategy significantly reduces the cost and effort of experimental laser machining during the development phase without compromising accuracy or performance. The developed techniques can be applied to a wide range of ceramic laser machining processes.

   
   

   Featured in Chemistry World

   The article Collective Interactions among Organometallics Are Exotic Bonds Hidden on Lab Shelves was featured in Chemistry World: Strange new bonds found hiding in plain sight in common organometallics

2. Collective Interactions among Organometallics Are Exotic Bonds Hidden on Lab Shelves. Sowlati-Hashjin, Shahin; Šadek, Vojtěch; Sadjadi, Seyedabdolreza; Karttunen, Mikko; Martín-Pendás, Angel; Foroutan-Nejad, Cina. Nat. Commun. 13, 2069, (2022).
   Web: https://doi.org/10.1038/s41467-022-29504-0.
   Featured in Chemistry World

   Abstract

   Recent discovery of an unusual bond between Na and B in NaBH\(_3^−\) motivated us to look for potentially similar bonds, which remained unnoticed among systems isoelectronic with NaBH\(_3^−\). Here, we report a novel family of collective interactions and a measure called exchange-correlation interaction collectivity index (ICI\(_\mathrm{XC}\); \(ICI \in [0,1]\)) to characterize the extent of collective versus pairwise bonding. Unlike conventional bonds in which ICIXC remains close to one, in collective interactions ICI\(_\mathrm{XC}\) may approach zero. We show that collective interactions are commonplace among widely used organometallics, as well as among boron and aluminum complexes with the general formula [M\(^\mathrm{a+}\)AR\(_3\)]\(^\mathrm{b−}\) (A: C, B or Al). In these species, the metal atom interacts more efficiently with the substituents ® on the central atoms than the central atoms (A) upon forming efficient collective interactions. Furthermore, collective interactions were also found among fluorine atoms of XF\(_n\) systems (X: B or C). Some of organolithium and organomagnesium species have the lowest ICI\(_\mathrm{XC}\) among the more than 100 studied systems revealing the fact that collective interactions are rather a rule than an exception among organometallic species.

   
   

3. Machine-Learning-Based Data-Driven Discovery of Nonlinear Phase-Field Dynamics. Kiyani, Elham; Silber, Steven; Kooshkbaghi, Mahdi; Karttunen, Mikko. Phys. Rev. E 106, 065303, (2022).
   Web: https://doi.org/10.1103/PhysRevE.106.065303.

   Abstract

   One of the main questions regarding complex systems at large scales concerns the effective interactions and driving forces that emerge from the detailed microscopic properties. Coarse-grained models aim to describe complex systems in terms of coarse-scale equations with a reduced number of degrees of freedom. Recent developments in machine-learning algorithms have significantly empowered the discovery process of governing equations directly from data. However, it remains difficult to discover partial differential equations (PDEs) with high-order derivatives. In this paper, we present data-driven architectures based on a multilayer perceptron, a convolutional neural network (CNN), and a combination of a CNN and long short-term memory structures for discovering the nonlinear equations of motion for phase-field models with nonconserved and conserved order parameters. The well-known Allen-Cahn, Cahn-Hilliard, and phase-field crystal models were used as test cases. Two conceptually different types of implementations were used: (a) guided by physical intuition (such as the local dependence of the derivatives) and (b) in the absence of any physical assumptions (black-box model). We show that not only can we effectively learn the time derivatives of the field in both scenarios, but we can also use the data-driven PDEs to propagate the field in time and achieve results in good agreement with the original PDEs.

   
   

4. Structural Investigation of DHICA Eumelanin Using Density Functional Theory and Classical Molecular Dynamics Simulations. Soltani, Sepideh; Sowlati-Hashjin, Shahin; Tetsassi Feugmo, Conrard Giresse; Karttunen, Mikko. Molecules 27, 8417 (2022).
   Web: https://doi.org/10.3390/molecules27238417.
   Simulation parameters for melanin

   Abstract

   Eumelanin is an important pigment, for example, in skin, hair, eyes, and the inner ear. It is a highly heterogeneous polymer with 5,6-dihydroxyindole-2-carboxylic acid (DHICA) and 5,6-dihydroxyindole (DHI) building blocks, of which DHICA is reported as the more abundant in natural eumelanin. The DHICA-eumelanin protomolecule consists of three building blocks, indole-2-carboxylic acid-5,6-quinone (ICAQ), DHICA and pyrrole-2,3,5-tricarboxylic acid (PTCA). Here, we focus on the self-assembly of DHICA-eumelanin using multi-microsecond molecular dynamics (MD) simulations at various concentrations in aqueous solutions. The molecule was first parameterized using density functional theory (DFT) calculations. Three types of systems were studied: (1) uncharged DHICA-eumelanin, (2) charged DHICA-eumelanin corresponding to physiological pH, and (3) a binary mixture of both of the above protomolecules. In the case of uncharged DHICA-eumelanin, spontaneous aggregation occurred and water molecules were present inside the aggregates. In the systems corresponding to physiological pH, all the carboxyl groups are negatively charged and the DHICA-eumelanin model has a net charge of −4. The effect of K+ ions as counterions was investigated. The results show high probability of binding to the deprotonated oxygens of the carboxylate anions in the PTCA moiety. Furthermore, the K+ counterions increased the solubility of DHICA-eumelanin in its charged form. A possible explanation is that the charged protomolecules favor binding to the K+ ions rather than aggregating and binding to other protomolecules. The binary mixtures show aggregation of uncharged DHICA-eumelanins; unlike the charged systems with no aggregation, a few charged DHICA-eumelanins are present on the surface of the uncharged aggregation, binding to the K+ ions.

   
   

5. In Silico Testing of the Universality of Epithelial Tissue Growth. Mazarei, Mahmood; Åström, Jan; Westerholm, Jan; Karttunen, Mikko. Phys. Rev. E 106, L062402, (2022).
   Web: https://doi.org/10.1103/PhysRevE.106.L062402
   Preprint: https://arxiv.org/abs/2203.15883
   Github: CellSim3D: GPU Accelerated 3D Cell Simulator

   Abstract

   The universality of interfacial roughness in growing epithelial tissue has remained a controversial issue. Kardar-Parisi-Zhang (KPZ) and molecular beam epitaxy (MBE) universality classes have been reported among other behaviors including a total lack of universality. Here, we simulate tissues using the cellsim3d kinetic division model for deformable cells to investigate cell-colony scaling. With seemingly minor model changes, it can reproduce both KPZ- and MBE-like scaling in configurations that mimic the respective experiments. Tissue growth with strong cell-cell adhesion in a linear geometry is KPZ like, while weakly adhesive tissues in a radial geometry are MBE like. This result neutralizes the apparent scaling controversy.

   
   

6. Temperature-Resilient Anapole Modes Associated with TE Polarization in Semiconductor Nanowires. Thakore, Vaibhav; Ala-Nissila, Tapio; Karttunen, Mikko. Sci. Rep. 12, 21345, (2022).
   Web: https://doi.org/10.1038/s41598-022-25289-w
   Preprint: https://arxiv.org/abs/2203.14467

   Abstract

   Polarization-dependent scattering anisotropy of cylindrical nanowires has numerous potential applications in, for example, nanoantennas, photothermal therapy, thermophotovoltaics, catalysis, sensing, optical filters and switches. In all these applications, temperature-dependent material properties play an important role and often adversely impact performance depending on the dominance of either radiative or dissipative damping. Here, we employ numerical modeling based on Mie scattering theory to investigate and compare the temperature and polarization-dependent optical anisotropy of metallic (gold, Au) nanowires with indirect (silicon, Si) and direct (gallium arsenide, GaAs) bandgap semiconducting nanowires. Results indicate that plasmonic scattering resonances in semiconductors, within the absorption band, deteriorate with an increase in temperature whereas those occurring away from the absorption band strengthen as a result of the increase in phononic contribution. Indirect-bandgap thin (20nm) Si nanowires present low absorption efficiencies for both the transverse electric (TE, E⊥) and magnetic (TM, E∥) modes, and high scattering efficiencies for the TM mode at shorter wavelengths making them suitable as highly efficient scatterers. Temperature-resilient higher-order anapole modes with their characteristic high absorption and low scattering efficiencies are also observed in the semiconductor nanowires (r=125−130 nm) for the TE polarization. Herein, the GaAs nanowires present 3−7 times greater absorption efficiencies compared to the Si nanowires making them especially suitable for temperature-resilient applications such as scanning near-field optical microscopy (SNOM), localized heating, non-invasive sensing or detection that require strong localization of energy in the near field.

   
   

7. AlphaFold2: A Role for Disordered Protein/Region Prediction?. Wilson, Carter J.; Choy, Wing-Yiu; Karttunen, Mikko. Int. J. Mol. Sci. 23, 4591, (2022).
   Web: https://doi.org/10.3390/ijms23094591
   Proteins, structures and data from the manuscript is available at GitHub: SoftSimu/AlphaFoldDisorderData

   Abstract

   The development of AlphaFold2 marked a paradigm-shift in the structural biology community. Herein, we assess the ability of AlphaFold2 to predict disordered regions against traditional sequence-based disorder predictors. We find that AlphaFold2 performs well at discriminating disordered regions, but also note that the disorder predictor one constructs from an AlphaFold2 structure determines accuracy. In particular, a naïve, but non-trivial assumption that residues assigned to helices, strands, and H-bond stabilized turns are likely ordered and all other residues are disordered results in a dramatic overestimation in disorder; conversely, the predicted local distance difference test (pLDDT) provides an excellent measure of residue-wise disorder. Furthermore, by employing molecular dynamics (MD) simulations, we note an interesting relationship between the pLDDT and secondary structure, that may explain our observations and suggests a broader application of the pLDDT for characterizing the local dynamics of intrinsically disordered proteins and regions (IDPs/IDRs).

   
   

8. Fullerenes’ Interactions with Plasma Membranes: Insight from the MD Simulations. Nisoh, Nililla; Jarerattanachat, Viwan; Karttunen, Mikko; Wong-Ekkabut, Jirasak. Biomolecules 12, 639 (2022).
   Web: https://doi.org/10.3390/biom12050639.

   Abstract

   Understanding the interactions between carbon nanoparticles (CNPs) and biological membranes is critically important for applications of CNPs in biomedicine and toxicology. Due to the complexity and diversity of the systems, most molecular simulation studies have focused on the interactions of CNPs and single component bilayers. In this work, we performed coarse-grained molecular dynamic (CGMD) simulations to investigate the behaviors of fullerenes in the presence of multiple lipid components in the plasma membranes with varying fullerene concentrations. Our results reveal that fullerenes can spontaneously penetrate the plasma membrane. Interestingly, fullerenes prefer to locate themselves in the region of the highly unsaturated lipids that are enriched in the inner leaflet of the plasma membrane. This causes fullerene aggregation even at low concentrations. When increasing fullerene concentrations, the fullerene clusters grow, and budding may emerge at the inner leaflet of the plasma membrane. Our findings suggest by tuning the lipid composition, fullerenes can be loaded deeply inside the plasma membrane, which can be useful for designing drug carrier liposomes. Moreover, the mechanisms of how fullerenes perturb multicomponent cell membranes and how they directly enter the cell are proposed. These insights can help to determine fullerene toxicity in living cells.

   
   

   SoftSimu/SymPhas

   

   SymPhas is open source software available at GitHub

9. SymPhas —general Purpose Software for Phase‐field, Phase‐field Crystal, and Reaction‐diffusion Simulations. Silber, Steven A.; Karttunen, Mikko. Adv. Theory Simul. 5, 2100351, (2022).
   Web: https://doi.org/10.1002/adts.202100351
   Preprint: http://arxiv.org/abs/2109.02598
   GitHub respository for the open source code

   Abstract

   This work develops a new open source application programming interface (API) and software package called SymPhas for simulations of phase-field, phase-field crystal, and reaction-diffusion models, supporting up to three dimensions and an arbitrary number of fields. SymPhas delivers two novel program capabilities: 1) User specification of models from the associated dynamical equations in an unconstrained form and 2) extensive support for integrating user-developed discrete-grid-based numerical solvers into the API. The capability to specify general phase-field models is primarily achieved by developing a novel symbolic algebra functionality that can formulate mathematical expressions at compile time; is able to apply rules of symbolic algebra such as distribution, factoring, and automatic simplification; and support user-driven expression tree manipulation. A modular design based on the C++ template meta-programming paradigm is applied to the symbolic algebra library and general API implementation to minimize application runtime and increase the accessibility of the API for third party development. SymPhas is written in C/C++ and emphasizes high-performance capabilities via parallelization with OpenMP and the C++ standard library. SymPhas is equipped with a forward Euler solver and a semi-implicit Fourier spectral solver. Sample implementations and simulations of several phase-field models are presented, generated using the semi-implicit Fourier spectral solver.

   
   

10. Strengthening Cellulose Nanopaper via Deep Eutectic Solvent and Ultrasound-Induced Surface Disordering of Nanofibers. Batishcheva, Elizaveta V.; Sokolova, Darya N.; Fedotova, Veronika S.; Sokolova, Maria P.; Nikolaeva, Alexandra L.; Vakulyuk, Alexey Y.; Shakhbazova, Christina Y.; Ribeiro, Mauro Carlos Costa; Karttunen, Mikko; Smirnov, Michael A. Polymers 14, 78 (2021).
    Web: https://doi.org/10.3390/polym14010078.

    Abstract

    The route for the preparation of cellulose nanofiber dispersions from bacterial cellulose using ethylene glycol- or glycerol-based deep eutectic solvents (DES) is demonstrated. Choline chloride was used as a hydrogen bond acceptor and the effect of the combined influence of DES treatment and ultrasound on the thermal and mechanical properties of bacterial cellulose nanofibers (BC-NFs) is demonstrated. It was found that the maximal Young’s modulus (9.2 GPa) is achieved for samples prepared using a combination of ethylene glycol-based DES and ultrasound treatment. Samples prepared with glycerol-based DES combined with ultrasound exhibit the maximal strength (132 MPa). Results on the mechanical properties are discussed based on the structural investigations that were performed using FTIR, Raman, WAXD, SEM and AFM measurements, as well as the determination of the degree of polymerization and the density of BC-NF packing during drying with the formation of paper. We propose that the disordering of the BC-NF surface structure along with the preservation of high crystallinity bulk are the key factors leading to the improved mechanical and thermal characteristics of prepared BC-NF-based papers.

    
    

11. Computer Simulations of Deep Eutectic Solvents: Challenges, Solutions, and Perspectives. Tolmachev, Dmitry; Lukasheva, Natalia; Ramazanov, Ruslan; Nazarychev, Victor; Borzdun, Natalia; Volgin, Igor; Andreeva, Maria; Glova, Artyom; Melnikova, Sofia; Dobrovskiy, Alexey; Silber, Steven A.; Larin, Sergey; de Souza, Rafael Maglia; Ribeiro, Mauro Carlos Costa; Lyulin, Sergey; Karttunen, Mikko. Int. J. Mol. Sci. 23, 645 (2022).
    https://doi.org/10.3390/ijms23020645.

    Abstract

    Deep eutectic solvents (DESs) are one of the most rapidly evolving types of solvents, appearing in a broad range of applications, such as nanotechnology, electrochemistry, biomass transformation, pharmaceuticals, membrane technology, biocomposite development, modern 3D-printing, and many others. The range of their applicability continues to expand, which demands the development of new DESs with improved properties. To do so requires an understanding of the fundamental relationship between the structure and properties of DESs. Computer simulation and machine learning techniques provide a fruitful approach as they can predict and reveal physical mechanisms and readily be linked to experiments. This review is devoted to the computational research of DESs and describes technical features of DES simulations and the corresponding perspectives on various DES applications. The aim is to demonstrate the current frontiers of computational research of DESs and discuss future perspectives.

    
    

12. Highly Similar Sequence and Structure Yet Different Biophysical Behavior: A Computational Study of Two Triosephosphate Isomerases. Chávez-García, Cecilia; Karttunen, Mikko. J. Chem. Inf. Model. 62, 668–677, (2022).
    https://doi.org/10.1021/acs.jcim.1c01501
    Preprint: https://doi.org/10.1101/2021.10.13.464197

    Abstract

    Homodimeric triosephosphate isomerases (TIMs) from Trypanosoma cruzi (TcTIM) and Trypanosoma brucei (TbTIM) have markedly similar amino-acid sequences and three-dimensional structures. However, several of their biophysical parameters, such as their susceptibility to sulfhydryl agents and their reactivation speed after being denatured, have significant differences. The causes of these differences were explored with microsecond-scale molecular dynamics (MD) simulations of three different TIM proteins: TcTIM, TbTIM, and a chimeric protein, Mut1. We examined their electrostatic interactions and explored the impact of simulation length on them. The same salt bridge between catalytic residues Lys 14 and Glu 98 was observed in all three proteins, but key differences were found in other interactions that the catalytic amino acids form. In particular, a cation−π interaction between catalytic amino acids Lys 14 and His 96 and both a salt bridge and a hydrogen bond between catalytic Glu 168 and residue Arg 100 were only observed in TcTIM. Furthermore, although TcTIM forms less hydrogen bonds than TbTIM and Mut1, its hydrogen bond network spans almost the entire protein, connecting the residues in both monomers. This work provides new insight into the mechanisms that give rise to the different behavior of these proteins. The results also show the importance of long simulations.

    
    

13. Multiscale Computational Study of the Conformation of the Full-Length Intrinsically Disordered Protein MeCP2. Chávez-García, Cecilia; Hénin, Jérôme; Karttunen, Mikko. J. Chem. Inf. Model. 62, 958–970, (2022).
    https://doi.org/10.1021/acs.jcim.1c01354.

    Abstract

    The malfunction of the methyl-CpG binding protein 2 (MeCP2) is associated with the Rett syndrome, one of the most common causes of cognitive impairment in females. MeCP2 is an intrinsically disordered protein (IDP), making its experimental characterization a challenge. There is currently no structure available for the full-length MeCP2 in any of the databases, and only the structure of its MBD domain has been solved. We used this structure to build a full-length model of MeCP2 by completing the rest of the protein via ab initio modeling. Using a combination of all-atom and coarse-grained simulations, we characterized its structure and dynamics as well as the conformational space sampled by the ID and transcriptional repression domain (TRD) domains in the absence of the rest of the protein. The present work is the first computational study of the full-length protein. Two main conformations were sampled in the coarse-grained simulations: a globular structure similar to the one observed in the all-atom force field and a two-globule conformation. Our all-atom model is in good agreement with the available experimental data, predicting amino acid W104 to be buried, amino acids R111 and R133 to be solvent-accessible, and having a 4.1% α-helix content, compared to the 4% found experimentally. Finally, we compared the model predicted by AlphaFold to our Modeller model. The model was not stable in water and underwent further folding. Together, these simulations provide a detailed (if perhaps incomplete) conformational ensemble of the full-length MeCP2, which is compatible with experimental data and can be the basis of further studies, e.g., on mutants of the protein or its interactions with its biological partners.

    
    

14. Free Energy and Stacking of Eumelanin Nanoaggregates. Soltani, Sepideh; Sowlati-Hashjin, Shahin; Tetsassi Feugmo, Conrard Giresse; Karttunen, Mikko. J. Phys. Chem. B 126, 1805–1818, (2022).
    https://doi.org/10.1021/acs.jpcb.1c07884
    Preprint: https://doi.org/10.1101/2021.08.31.458381

    Abstract

    Eumelanin, a member of the melanin family, is a black-brown insoluble pigment. It possesses a broad range of properties such as antioxidation, free radical scavenging, photoprotection, and charge carrier transportation. Surprisingly, the exact molecular structure of eumelanin remains undefined. It is, however, generally considered to consist of two main building blocks, 5,6-dihydroxyindole (DHI) and 5,6- dihydroxyindole carboxylic acid (DHICA). We focus on DHI and report, for the first time, a computational investigation of the structural properties of DHI–eumelanin aggregates in aqueous solutions. First, multimicrosecond molecular dynamics (MD) simulations at different concentrations were performed to investigate the aggregation and ordering of tetrameric DHI–eumelanin protomolecules. This was followed by umbrella sampling (US) and density functional theory (DFT) calculations to study the physical mechanisms of stacking. Aggregation occurs through formation of nanoscale stacks and was observed in all systems. Further analyses showed that aggregation and coarsening of the domains is due to a decrease in hydrogen bonds between the eumelanins and water; while domains exist, there is no long-range order. The results show noncovalent stacks with the interlayer distance between eumelanin protomolecules being less than 3.5 Å. This is in good agreement with transmission electron microscopy data. Both free energy calculations and DFT revealed strong stacking interactions. The electrostatic potential map provides an explanation and a rationale for the slightly sheared relative orientations and, consequently, for the curved shapes of the nanoscale domains.

    
    

15. Osmotic Method for Calculating Surface Pressure of Monolayers in Molecular Dynamics Simulations. de Souza, Rafael Maglia; Romeu, Fábio Cavalcante; Ribeiro, Mauro Carlos Costa; Karttunen, Mikko; Dias, Luís Gustavo. J. Chem. Theory Comput. 18, 2042-2046 (2022).
    https://doi.org/10.1021/acs.jctc.2c00109.

    Abstract

    Surface pressure is a fundamental thermodynamic property related to the activity of molecules at interfaces. In molecular simulations, it is typically calculated from its definition: the difference between the surface tension of the air–water and air-surfactant interfaces. In this Letter, we show how to connect the surface pressure with a two-dimensional osmotic pressure and how to take advantage of this analogy to obtain a practical method of calculating surface pressure–area isotherms in molecular simulation. As a proof-of-concept, compression curves of zwitterionic and ionic surfactant monolayers were obtained using the osmotic approach and the curves were compared with the ones from the traditional pressure tensor-based scheme. The results shown an excellent agreement between both alternatives. Advantageously, the osmotic approach is simple to use and allows to obtain the surface pressure–area isotherm on the fly with a single simulation using equilibration stages.

    
    

16. Changes in the Local Conformational States Caused by Simple Na+ and K+ Ions in Polyelectrolyte Simulations: Comparison of Seven Force Fields with and without NBFIX and ECC Corrections. Lukasheva, Natalia; Tolmachev, Dmitry; Martinez-Seara, Hector; Karttunen, Mikko. Polymers 14, 252, (2022).
    https://doi.org/10.3390/polym14020252.

    Abstract

    Electrostatic interactions have a determining role in the conformational and dynamic behavior of polyelectrolyte molecules. In this study, anionic polyelectrolyte molecules, poly(glutamic acid) (PGA) and poly(aspartic acid) (PASA), in a water solution with the most commonly used K+ or Na+ counterions, were investigated using atomistic molecular dynamics (MD) simulations. We performed a comparison of seven popular force fields, namely AMBER99SB-ILDN, AMBER14SB, AMBER-FB15, CHARMM22*, CHARMM27, CHARMM36m and OPLS-AA/L, both with their native parameters and using two common corrections for overbinding of ions, the non-bonded fix (NBFIX), and electronic continuum corrections (ECC). These corrections were originally introduced to correct for the often-reported problem concerning the overbinding of ions to the charged groups of polyelectrolytes. In this work, a comparison of the simulation results with existing experimental data revealed several differences between the investigated force fields. The data from these simulations and comparisons with previous experimental data were then used to determine the limitations and strengths of these force fields in the context of the structural and dynamic properties of anionic polyamino acids. Physical properties, such as molecular sizes, local structure, and dynamics, were studied using two types of common counterions, namely potassium and sodium. The results show that, in some cases, both the macroion size and dynamics depend strongly on the models (parameters) for the counterions due to strong overbinding of the ions and charged side chain groups. The local structures and dynamics are more sensitive to dihedral angle parameterization, resulting in a preference for defined monomer conformations and the type of correction used. We also provide recommendations based on the results.

    
    

17. Micromagnetic Simulations of Clusters of Nanoparticles with Internal Structure: Application to Magnetic Hyperthermia. Behbahani, Razyeh; Plumer, Martin L.; Saika-Voivod, Ivan. Phys. Rev. Appl. 18, 034034, (2022).
    https://doi.org/10.1103/PhysRevApplied.18.034034.

    Abstract

    Micromagnetic simulation results on dynamic hysteresis loops of clusters of iron oxide nanoparticles (NPs) with internal structure composed of nanorods are compared with the widely used macrospin approximation. Such calculations allowing for nanorod-composed NPs is facilitated by a previously developed coarse-graining method based on the renormalization group approach. With a focus on applications to magnetic hyperthermia, we show that magnetostatic interactions improve the heating performance of NPs in chains and triangles, and reduce heating performance in fcc arrangements. Hysteresis loops of triangular and fcc systems of complex NPs are not recovered within the macrospin approximation, especially at smaller interparticle distances. For triangular arrangements, the macrospin approximation predicts that magnetostatic interactions reduce loop area, in contrast to the complex NP case. An investigation of the local hysteresis loops of individual NPs and macrospins in clusters reveals the impact of the geometry of their neighbors on individual versus collective magnetic response, inhomogeneous heating within clusters, and further differences between simulating NPs with internal structure and the use of the macrospin approximation. Capturing the internal physical and magnetic structure of NPs is thus important for some applications.

2021

1. Fine-Tuning the Polarizable CL&Pol Force Field for the Deep Eutectic Solvent Ethaline. Maglia de Souza, Rafael; Karttunen, Mikko; Ribeiro, Mauro Carlos Costa. J. Chem. Inf. Model. 61, 5938–5947, (2021).
   https://doi.org/10.1021/acs.jcim.1c01181
   Preprint: http://arxiv.org/abs/2109.14007

2. Polymerizable Choline- and Imidazolium-Based Ionic Liquids Reinforced with Bacterial Cellulose for 3D-Printing. Smirnov, Michael A.; Fedotova, Veronika S.; Sokolova, Maria P.; Nikolaeva, Alexandra L.; Elokhovsky, Vladimir Yu; Karttunen, Mikko. Polymers 13, (2021).
   https://doi.org/10.3390/polym13183044.

3. Nanocomposite of Fullerenes and Natural Rubbers: MARTINI Force Field Molecular Dynamics Simulations. Kitjanon, Jiramate; Khuntawee, Wasinee; Phongphanphanee, Saree; Sutthibutpong, Thana; Chattham, Nattaporn; Karttunen, Mikko; Wong-ekkabut, Jirasak. Polymers 13, 4044, (2021).
   https://doi.org/10.3390/polym13224044.

4. Role of Cholesterol Flip-Flop in Oxidized Lipid Bilayers. Boonnoy, Phansiri; Jarerattanachat, Viwan; Karttunen, Mikko; Wong-Ekkabut, Jirasak. Biophys. J. (2021).
   https://doi.org/10.1016/j.bpj.2021.08.036.

5. Biphasic Proton Transport Mechanism for Uncoupling Proteins. Ardalan, Afshan; Sowlati-Hashjin, Shahin; Oduwoye, Habib; Uwumarenogie, Stephanie O.; Karttunen, Mikko; Smith, Matthew D.; Jelokhani-Niaraki, Masoud. J. Phys. Chem. B 125, 9130–9144, (2021).
   https://doi.org/10.1021/acs.jpcb.1c04766.

6. Electromagnetic Response of Nanoparticles with a Metallic Core and a Semiconductor Shell. Seyedheydari, Fahime; Conley, Kevin M.; Thakore, Vaibhav; Karttunen, Mikko; Sihvola, Ari; Ala-Nissila, Tapio. J. Phys. Commun. 5, 015002, (2021).
   https://doi.org/10.1088/2399-6528/abd4c4.

7. Silica-Silicon Composites for near-Infrared Reflection: A Comprehensive Computational and Experimental Study. Conley, Kevin; Moosakhani, Shima; Thakore, Vaibhav; Ge, Yanling; Lehtonen, Joonas; Karttunen, Mikko; Hannula, Simo-Pekka; Ala-Nissila, Tapio. Ceram. Int. 47, 16833–16840, (2021).
   https://doi.org/10.1016/j.ceramint.2021.02.257
   Preprint: https://arxiv.org/abs/2009.13805

8. Functional Oligomeric Forms of Uncoupling Protein 2: Strong Evidence for Asymmetry in Protein and Lipid Bilayer Systems. Ardalan, Afshan; Sowlati-Hashjin, Shahin; Uwumarenogie, Stephanie O.; Fish, Michael; Mitchell, Joel; Karttunen, Mikko; Smith, Matthew D.; Jelokhani-Niaraki, Masoud. J. Phys. Chem. B 125, 169–183, (2021).
   https://doi.org/10.1021/acs.jpcb.0c09422
   Preprint: https://doi.org/10.1101/430835

9. Jamming and Force Distribution in Growing Epithelial Tissue. Madhikar, Pranav; Åström, Jan; Baumeier, Björn; Karttunen, Mikko. Phys. Rev. Research 3, 023129, (2021).
   https://doi.org/10.1103/PhysRevResearch.3.023129.

10. Development of Coarse-Grained Force Field to Investigate Sodium-Ion Transport Mechanisms in Cyanoborate-Based Ionic Liquid. Maglia de Souza, Rafael; Lourenço, Tuanan C.; Amaral de Siqueira, Leonardo José; Karttunen, Mikko; Da Silva, Juarez L. F.; Dias, Luis Gustavo. J. Mol. Liq. 338, 116648, (2021).
    https://doi.org/10.1016/j.molliq.2021.116648.

    Editor’s Choice

    

    Editor’s Choice: Effects of Amino Acid Side-Chain Length and Chemical Structure on Anionic Polyglutamic and Polyaspartic Acid Cellulose-Based Polyelectrolyte Brushes

11. Effects of Amino Acid Side-Chain Length and Chemical Structure on Anionic Polyglutamic and Polyaspartic Acid Cellulose-Based Polyelectrolyte Brushes. Tolmachev, Dmitry; Mamistvalov, George; Lukasheva, Natalia; Larin, Sergey; Karttunen, Mikko. Polymers 13, (2021).
    https://doi.org/10.3390/polym13111789.

12. KEAP1 Cancer Mutants: A Large-Scale Molecular Dynamics Study of Protein Stability. Wilson, Carter J.; Chang, Megan; Karttunen, Mikko; Choy, Wing-Yiu. Int. J. Mol. Sci. 22, (2021).
    https://doi.org/10.3390/ijms22105408.

13. Exploring the Conformational Landscape of the Neh4 and Neh5 Domains of Nrf2 Using Two Different Force Fields and Circular Dichroism. Chang, Megan; Wilson, Carter J.; Karunatilleke, Nadun Chanaka; Moselhy, Mohamed Hesham; Karttunen, Mikko; Choy, Wing-Yiu. J. Chem. Theory Comput. 17, 3145–3156, (2021).
    https://doi.org/10.1021/acs.jctc.0c01243.

14. In Silico and in Vitro Design of Cordycepin Encapsulation in Liposomes for Colon Cancer Treatment. Khuntawee, Wasinee; Amornloetwattana, Rawiporn; Vongsangnak, Wanwipa; Namdee, Katawut; Yata, Teerapong; Karttunen, Mikko; Wong-ekkabut, Jirasak. RSC Adv. 11, 8475–8484, (2021).
    https://doi.org/10.1039/D1RA00038A.

15. Experimental and Computational Observations of Immunogenic Cobalt Porphyrin Lipid Bilayers: Nanodomain-Enhanced Antigen Association. Federizon, Jasmin; Feugmo, Conrard Giresse Tetsassi; Huang, Wei-Chiao; He, Xuedan; Miura, Kazutoyo; Razi, Aida; Ortega, Joaquin; Karttunen, Mikko; Lovell, Jonathan F. Pharmaceutics 13, 98, (2021).
    https://doi.org/10.3390/pharmaceutics13010098.

    

    Cover article: How to Control Interactions of Cellulose-Based Biomaterials with Skin: The Role of Acidity in the Contact Area

16. How to Control Interactions of Cellulose-Based Biomaterials with Skin: The Role of Acidity in the Contact Area. Gurtovenko, Andrey A.; Karttunen, Mikko. Soft Matter 17, 6507–6518, (2021).
    https://doi.org/10.1039/d1sm00608h.

17. Electrostatic Fields in Biophysical Chemistry. Sowlati-Hashjin, Shahin; Karttunen, Mikko; Matta, Chérif F. Chapter 7, pp. 225-262 in Electric Fields and Structure-Reactivity Aspects: Combined Theoretical and Experimental Perspectives, Sason Shaik and Thijs Stuyver (Eds.), RSC Publications, 2021.
    Article: https://doi.org/10.1039/9781839163043-00225
    Book: https://doi.org/10.1039/9781839163043

2020

1. Self-Assembly of Phosphocholine Derivatives Using the ELBA Coarse-Grained Model: Micelles, Bicelles, and Reverse Micelles. de Souza, R. M.; Ratochinski, R. H.; Karttunen, Mikko; Dias, L. G. J. Chem. Inf. Model. 60, 522–536, (2020).
   https://doi.org/10.1021/acs.jcim.9b00790.

2. Formation of Aggregates, Icosahedral Structures and Percolation Clusters of Fullerenes in Lipids Bilayers: The Key Role of Lipid Saturation. Nisoh, Nililla; Jarerattanachat, Viwan; Karttunen, Mikko; Wong-Ekkabut, Jirasak. Biochim. Biophys. Acta Biomembr. 1862, 183328, (2020).
   https://doi.org/10.1016/j.bbamem.2020.183328
   Preprint: https://doi.org/10.1101/2020.02.12.946152

   

   Cover article: Insights into the Polyhexamethylene Biguanide (PHMB) Mechanism of Action on Bacterial Membrane and DNA: A Molecular Dynamics Study

3. Insights into the Polyhexamethylene Biguanide (PHMB) Mechanism of Action on Bacterial Membrane and DNA: A Molecular Dynamics Study. Sowlati-Hashjin, Shahin; Carbone, Paola; Karttunen, Mikko. J. Phys. Chem. B 124, 4487–4497, (2020).
   https://doi.org/10.1021/acs.jpcb.0c02609
   Preprint: https://doi.org/10.1101/2020.03.25.007732

4. Cholesterol Sequestration by Xenon Nano Bubbles Leads to Lipid Raft Destabilization. Reyes-Figueroa, A. D.; Karttunen, Mikko; Ruiz-Suárez, J. C. Soft Matter 16, 9655–9661, (2020).
   https://doi.org/10.1039/d0sm01256d
   Preprint: https://doi.org/10.1101/2020.05.04.077727

5. Molecular Dynamics Simulations of Polymer-Ionic Liquid (1-Ethyl-3-Methylimidazolium Tetracyanoborate) Ternary Electrolyte for Sodium and Potassium Ion Batteries. de Souza, Rafael Maglia; de Siqueira, Leonardo José Amaral; Karttunen, Mikko; Dias, Luis Gustavo. J. Chem. Inf. Model. 60, 485–499, (2020).
   https://doi.org/10.1021/acs.jcim.9b00750.

6. Grafted Dipolar Chains: Dipoles and Restricted Freedom Lead to Unexpected Hairpins. Glova, Artyom D.; Larin, Sergey V.; Nazarychev, Victor M.; Karttunen, Mikko; Lyulin, Sergey V. Macromolecules 53, 29–38, (2020).
   https://doi.org/10.1021/acs.macromol.9b02288.

   

   Cover article: Directing near-Infrared Photon Transport with Core@shell Particles

7. Directing near-Infrared Photon Transport with Core@shell Particles. Conley, Kevin M.; Thakore, Vaibhav; Seyedheydari, Fahime; Karttunen, Mikko; Ala-Nissila, Tapio. AIP Adv. 10, 095128, (2020).
   https://doi.org/10.1063/5.0015553.

8. Influence of Calcium Binding on Conformations and Motions of Anionic Polyamino Acids. Effect of Side Chain Length. Tolmachev, Dmitry; Lukasheva, Natalia; Mamistvalov, George; Karttunen, Mikko. Polymers 12, 1279, (2020).
   https://doi.org/10.3390/polym12061279.

9. Overbinding and Qualitative and Quantitative Changes Caused by Simple Na+ and K+ Ions in Polyelectrolyte Simulations: Comparison of Force Fields with and without NBFIX and ECC Corrections. Tolmachev, D. A.; Boyko, O. S.; Lukasheva, N. V.; Martinez-Seara, H.; Karttunen, Mikko. J. Chem. Theory Comput. 16, 677–687, (2020).
   https://doi.org/10.1021/acs.jctc.9b00813.

10. Coarse-Grained Modeling of Cell Division in 3D: Influence of Density, Medium Viscosity, and Inter-Membrane Friction on Cell Growth and Nearest Neighbor Distribution. Madhikar, Pranav; Åström, Jan; Westerholm, Jan; Baumeier, Björn; Karttunen, Mikko. Soft Mater. 18, 150–162, (2020).
    https://doi.org/10.1080/1539445X.2019.1706565.

11. Symmetry-Breaking Transitions in the Early Steps of Protein Self-Assembly. La Rosa, Carmelo; Condorelli, Marcello; Compagnini, Giuseppe; Lolicato, Fabio; Milardi, Danilo; Do, Trang Nhu; Karttunen, Mikko; Pannuzzo, Martina; Ramamoorthy, Ayyalusamy; Fraternali, Franca; Collu, Francesca; Rezaei, Human; Strodel, Birgit; Raudino, Antonio. Eur. Biophys. J. 49, 175–191, (2020).
    https://doi.org/10.1007/s00249-020-01424-1.

    

    Cover article: Manipulation of Diatomic Molecules with Oriented External Electric Fields: Linear Correlations in Atomic Properties Lead to Nonlinear Molecular Responses

12. Manipulation of Diatomic Molecules with Oriented External Electric Fields: Linear Correlations in Atomic Properties Lead to Nonlinear Molecular Responses. Sowlati-Hashjin, Shahin; Karttunen, Mikko; Matta, Chérif F. J. Phys. Chem. A 124, 4720–4731, (2020).
    https://doi.org/10.1021/acs.jpca.0c02569.

2019

1. Propulsion and Controlled Steering of Magnetic Nanohelices. Alcanzare, Maria Michiko; Karttunen, Mikko; Ala-Nissila, Tapio. Soft Matter 15, 1684–1691, (2019).
   https://doi.org/10.1039/c8sm00037a.

2. Molecular Dynamics Study of Natural Rubber-Fullerene Composites: Connecting Microscopic Properties to Macroscopic Behavior. Khuntawee, Wasinee; Sutthibutpong, Thana; Phongphanphanee, Saree; Karttunen, Mikko; Wong-Ekkabut, Jirasak. Phys. Chem. Chem. Phys. 21, 19403–19413, (2019).
   https://doi.org/10.1039/c9cp03155c.

3. Thermoplasmonic Response of Semiconductor Nanoparticles: A Comparison with Metals. Thakore, Vaibhav; Tang, Janika; Conley, Kevin; Ala-Nissila, Tapio; Karttunen, Mikko. Adv. Theory Simul. 2, 1800100, (2019).
   https://doi.org/10.1002/adts.201800100.

4. Chemical Modification of Nanocrystalline Cellulose for Improved Interfacial Compatibility with Poly(lactic Acid). Averianov, Ilia V.; Stepanova, Mariia A.; Gofman, Iosif V.; Nikolaeva, Alexandra L.; Korzhikov-Vlakh, Viktor A.; Karttunen, Mikko; Korzhikova-Vlakh, Evgenia G. Mendeleev Commun. 29, 220–222, (2019).
   https://doi.org/10.1016/j.mencom.2019.03.036.

5. Controlled On-Off Switching of Tight-Binding Hydrogen Bonds between Model Cell Membranes and Acetylated Cellulose Surfaces. Gurtovenko, Andrey A.; Karttunen, Mikko. Langmuir 35, 13753–13760, (2019).
   https://doi.org/10.1021/acs.langmuir.9b02453.

6. Dependence of Fullerene Aggregation on Lipid Saturation due to a Balance between Entropy and Enthalpy. Nalakarn, Pornkamon; Boonnoy, Phansiri; Nisoh, Nililla; Karttunen, Mikko; Wong-Ekkabut, Jirasak. Sci. Rep. 9, 1037, (2019).
   https://doi.org/10.1038/s41598-018-37659-4.

   Featured in Nature Chemical Biology

   The article Membrane Disruption by Very Long Chain Fatty Acids during Necroptosis was featured in Nat. Chem. Biol.: Poked to Death

7. Membrane Disruption by Very Long Chain Fatty Acids during Necroptosis. Parisi, Laura R.; Sowlati-Hashjin, Shahin; Berhane, Ilyas A.; Galster, Samuel L.; Carter, Kevin A.; Lovell, Jonathan F.; Chemler, Sherry R.; Karttunen, Mikko; Atilla-Gokcumen, G. Ekin. ACS Chem. Biol. 14, 2286–2294, (2019).
   https://doi.org/10.1021/acschembio.9b00616.

8. PGlu-Modified Nanocrystalline Cellulose Improves Mechanical Properties, Biocompatibility, and Mineralization of Polyester-Based Composites. Stepanova, Mariia; Averianov, Ilia; Serdobintsev, Mikhail; Gofman, Iosif; Blum, Natalya; Semenova, Natalya; Nashchekina, Yuliya; Vinogradova, Tatiana; Korzhikov-Vlakh, Viktor; Karttunen, Mikko; Korzhikova-Vlakh, Evgenia. Materials 12, 3435, (2019).
   https://doi.org/10.3390/ma12203435.

   Featured in Resolv News

   The article The Extracellular Gate Shapes the Energy Profile of an ABC Exporter was featured in Resolve News.: How to engine break a cell’s drug carrier

9. The Extracellular Gate Shapes the Energy Profile of an ABC Exporter. Hutter, Cedric A. J.; Timachi, M. Hadi; Hürlimann, Lea M.; Zimmermann, Iwan; Egloff, Pascal; Göddeke, Hendrik; Kucher, Svetlana; Štefanić, Saša; Karttunen, Mikko; Schäfer, Lars V.; Bordignon, Enrica; Seeger, Markus A. Nat. Commun. 10, 2260, (2019).
   https://doi.org/10.1038/s41467-019-09892-6.

   Editor’s Choice PCCP HOT ARTICLE

10. Quantifying Correlations between Mutational Sites in the Catalytic Subunit of γ-Secretase. Chávez-García, Cecilia; Aguayo-Ortiz, Rodrigo; Dominguez, Laura. J. Mol. Graph. Model. 88, 221–227, (2019).
    https://doi.org/10.1016/j.jmgm.2019.02.002.

11. Mineralization of Phosphorylated Cellulose: Crucial Role of Surface Structure and Monovalent Ions for Optimizing Calcium Content. Lukasheva, Natalia V.; Tolmachev, Dmitry A.; Karttunen, Mikko. Phys. Chem. Chem. Phys. 21, 1067–1077, (2019).
    https://doi.org/10.1039/c8cp05767b.

2018

1. Phospholipid-Cellulose Interactions: Insight from Atomistic Computer Simulations for Understanding the Impact of Cellulose-Based Materials on Plasma Membranes. Gurtovenko, Andrey A.; Mukhamadiarov, Evgenii I.; Kostritskii, Andrei Yu; Karttunen, Mikko. J. Phys. Chem. B 122, 9973–9981, (2018).
   https://doi.org/10.1021/acs.jpcb.8b07765.

2. Improved General-Purpose Five-Point Model for Water: TIP5P/2018. Khalak, Yuriy; Baumeier, Björn; Karttunen, Mikko. J. Chem. Phys. 149, 224507, (2018).
   https://doi.org/10.1063/1.5070137.

3. Does α-Tocopherol Flip-Flop Help to Protect Membranes Against Oxidation?. Boonnoy, Phansiri; Karttunen, Mikko; Wong-Ekkabut, Jirasak. J. Phys. Chem. B 122, 10362–10370, (2018).
   https://doi.org/10.1021/acs.jpcb.8b09064.

   SoftSimu/CellSim3D

   

   CellSim3D: GPU Accelerated 3D Cell Simulator is open source software available at GitHub

4. CellSim3D: GPU Accelerated Software for Simulations of Cellular Growth and Division in Three Dimensions. Madhikar, Pranav; Åström, Jan; Westerholm, Jan; Karttunen, Mikko. Comput. Phys. Commun. 232, 206–213, (2018).
   https://doi.org/10.1016/j.cpc.2018.05.024.

5. Prediction of Binding Energy of Keap1 Interaction Motifs in the Nrf2 Antioxidant Pathway and Design of Potential High-Affinity Peptides. Karttunen, Mikko; Choy, Wing-Yiu; Cino, Elio A. J. Phys. Chem. B 122, 5851–5859, (2018).
   https://doi.org/10.1021/acs.jpcb.8b03295.

6. Lipopeptide Daptomycin: Interactions with Bacterial and Phospholipid Membranes, Stability of Membrane Aggregates and Micellation in Solution. Liu, Bin; Karttunen, Mikko. Biochim. Biophys. Acta Biomembr. 1860, 1949–1954, (2018).
   https://doi.org/10.1016/j.bbamem.2018.03.028.

7. Atomistic Mechanism of Large-Scale Conformational Transition in a Heterodimeric ABC Exporter. Göddeke, Hendrik; Timachi, M. Hadi; Hutter, Cedric A. J.; Galazzo, Laura; Seeger, Markus A.; Karttunen, Mikko; Bordignon, Enrica; Schäfer, Lars V. J. Am. Chem. Soc. 140, 4543–4551, (2018).
   https://doi.org/10.1021/jacs.7b12944.

8. Scale-Dependent Miscibility of Polylactide and Polyhydroxybutyrate: Molecular Dynamics Simulations. Glova, Artyom D.; Falkovich, Stanislav G.; Dmitrienko, Daniil I.; Lyulin, Alexey V.; Larin, Sergey V.; Nazarychev, Victor M.; Karttunen, Mikko; Lyulin, Sergey V. Macromolecules 51, 552–563, (2018).
   https://doi.org/10.1021/acs.macromol.7b01640.

2017

1. Controlled Propulsion and Separation of Helical Particles at the Nanoscale. Alcanzare, Maria Michiko T.; Thakore, Vaibhav; Ollila, Santtu T. T.; Karttunen, Mikko; Ala-Nissila, Tapio. Soft Matter 13, 2148–2154, (2017).
   https://doi.org/10.1039/c6sm02437h.

2. Molecular Dynamics Simulations and Kelvin Probe Force Microscopy to Study of Cholesterol-Induced Electrostatic Nanodomains in Complex Lipid Mixtures. Drolle, E.; Bennett, W. F. D.; Hammond, K.; Lyman, E.; Karttunen, M.; Leonenko, Z. Soft Matter 13, 355–362, (2017).
   https://doi.org/10.1039/c6sm01350c.

3. Alpha-Tocopherol Inhibits Pore Formation in Oxidized Bilayers. Boonnoy, Phansiri; Karttunen, Mikko; Wong-Ekkabut, Jirasak. Phys. Chem. Chem. Phys. 19, 5699–5704, (2017).
   https://doi.org/10.1039/c6cp08051k.

4. Non-Conformal Coarse-Grained Potentials for Water. Rodríguez-López, Tonalli; Khalak, Yuriy; Karttunen, Mikko. J. Chem. Phys. 147, 134108, (2017).
   https://doi.org/10.1063/1.4985914.

5. Intermolecular Singlet and Triplet Exciton Transfer Integrals from Many-Body Green’s Functions Theory. Wehner, Jens; Baumeier, Björn. J. Chem. Theory Comput. 13, 1584–1594, (2017).
   https://doi.org/10.1021/acs.jctc.6b00935.

6. A Molecular Dynamics Study of Conformations of Beta-Cyclodextrin and Its Eight Derivatives in Four Different Solvents. Khuntawee, Wasinee; Karttunen, Mikko; Wong-Ekkabut, Jirasak. Phys. Chem. Chem. Phys. 19, 24219–24229, (2017).
   https://doi.org/10.1039/c7cp04009a.

7. Design of Hydrated Porphyrin-Phospholipid Bilayers with Enhanced Magnetic Resonance Contrast. Shao, Shuai; Do, Trang Nhu; Razi, Aida; Chitgupi, Upendra; Geng, Jumin; Alsop, Richard J.; Dzikovski, Boris G.; Rheinstädter, Maikel C.; Ortega, Joaquin; Karttunen, Mikko; Spernyak, Joseph A.; Lovell, Jonathan F. Small 13, 1602505, (2017).
   https://doi.org/10.1002/smll.201602505.

2016

1. Getting Excited: Challenges in Quantum-Classical Studies of Excitons in Polymeric Systems. Bagheri, Behnaz; Baumeier, Björn; Karttunen, Mikko. Phys. Chem. Chem. Phys. 18, 30297–30304, (2016).
   https://doi.org/10.1039/c6cp02944b.

2. Solvent Effects on Optical Excitations of Poly Para Phenylene Ethynylene Studied by QM/MM Simulations Based on Many-Body Green’s Functions Theory. Bagheri, B.; Karttunen, M.; Baumeier, B. Eur. Phys. J. Spec. Top. 225, 1743–1756, (2016).
   https://doi.org/10.1140/epjst/e2016-60144-5.

3. Molecular Dynamics Simulation of Water Permeation through the Alpha-Hemolysin Channel. Wong-Ekkabut, Jirasak; Karttunen, Mikko. J. Biol. Phys. 42, 133–146, (2016).
   https://doi.org/10.1007/s10867-015-9396-x.

4. The Good, the Bad and the User in Soft Matter Simulations. Wong-ekkabut, Jirasak; Karttunen, Mikko. Biochimica et Biophysica Acta (BBA) - Biomembranes 1858, 2529–2538, (2016).
   https://doi.org/10.1016/j.bbamem.2016.02.004.

5. Effect of Cholesterol on Cellular Uptake of Cancer Drugs Pirarubicin and Ellipticine. Zhang, Lei; Bennett, W. F. Drew; Zheng, Tao; Ouyang, Ping-Kai; Ouyang, Xinping; Qiu, Xueqing; Luo, Anqi; Karttunen, Mikko; Chen, P. J. Phys. Chem. B 120, 3148–3156, (2016).
   https://doi.org/10.1021/acs.jpcb.5b12337.

6. Ab Initio Calculations of Optical Properties of Silver Clusters: Cross-over from Molecular to Nanoscale Behavior. Titantah, John T.; Karttunen, Mikko. Eur. Phys. J. B 89, 125, (2016).
   https://doi.org/10.1140/epjb/e2016-70065-y.

7. Binding of Disordered Peptides to Kelch: Insights from Enhanced Sampling Simulations. Do, Trang Nhu; Choy, Wing-Yiu; Karttunen, Mikko. J. Chem. Theory Comput. 12, 395–404, (2016).
   https://doi.org/10.1021/acs.jctc.5b00868.

8. Characterization of the Free State Ensemble of the CoRNR Box Motif by Molecular Dynamics Simulations. Cino, Elio A.; Choy, Wing-Yiu; Karttunen, Mikko. J. Phys. Chem. B 120, 1060–1068, (2016).
   https://doi.org/10.1021/acs.jpcb.5b11565.

2015

1. Folding and Insertion Thermodynamics of the Transmembrane WALP Peptide. Bereau, Tristan; Bennett, W. F. Drew; Pfaendtner, Jim; Deserno, Markus; Karttunen, Mikko. J. Chem. Phys. 143, 243127, (2015).
   https://doi.org/10.1063/1.4935487.

2. Hydroxyapatite Growth Inhibition Effect of Pellicle Statherin Peptides. Xiao, Y.; Karttunen, M.; Jalkanen, J.; Mussi, M. C. M.; Liao, Y.; Grohe, B.; Lagugné-Labarthet, F.; Siqueira, W. L. J. Dent. Res. 94, 1106–1112, (2015).
   https://doi.org/10.1177/0022034515586769.

3. Hydrophobicity: Effect of Density and Order on Water’s Rotational Slowing down. Titantah, John Tatini; Karttunen, Mikko. Soft Matter 11, 7977–7985, (2015).
   https://doi.org/10.1039/c5sm00930h.

4. Crossovers in Supercooled Solvation Water: Effects of Hydrophilic and Hydrophobic Interactions. Titantah, John Tatini; Karttunen, Mikko. EPL 110, 38006, (2015).
   https://doi.org/10.1209/0295-5075/110/38006.

5. Lipid Monolayer Disruption Caused by Aggregated Carbon Nanoparticles. Nisoh, Nililla; Karttunen, Mikko; Monticelli, Luca; Wong-ekkabut, Jirasak. RSC Adv. 5, 11676–11685, (2015).
   https://doi.org/10.1039/C4RA17006G.

6. Design and Characterization of a Multifunctional pH-Triggered Peptide C8 for Selective Anticancer Activity. Lu, Sheng; Bennett, W. F. Drew; Ding, Yong; Zhang, Lei; Fan, Helen Y.; Zhao, Danyang; Zheng, Tao; Ouyang, Ping-Kai; Li, Jason; Wu, Yan; Xu, Wen; Chu, Dafeng; Yuan, Yongfang; Heerklotz, Heiko; Karttunen, Mikko; Chen, P. Adv. Healthc. Mater. 4, 2709–2718, (2015).
   https://doi.org/10.1002/adhm.201500636.

7. Bilayer Deformation, Pores, and Micellation Induced by Oxidized Lipids. Boonnoy, Phansiri; Jarerattanachat, Viwan; Karttunen, Mikko; Wong-Ekkabut, Jirasak. J. Phys. Chem. Lett. 6, 4884–4888, (2015).
   https://doi.org/10.1021/acs.jpclett.5b02405.

8. Molecular dynamics simulation of surfactant monolayers, Bin Liu, Jirasak Wong-ekkabut, Mikko Karttunen, in “Computational Methods for Complex Liquid-Fluid Interfaces”, Rahni, Karbaschi, Miller (Eds.). Taylor & Francis (2015).
   Article: Chapter 11
   Book: https://doi.org/10.1201/b19337

9. Molecular-scale computational techniques in interfacial science, Trang Nhu Do, Jari Jalkanen, Mikko Karttunen, in “Computational Methods for Complex Liquid-Fluid Interfaces”, Rahni, Karbaschi, Miller (Eds.). Taylor & Francis (2015).
   Article: Chapter 6
   Book: https://doi.org/10.1201/b19337

2014

1. Molecular Dynamics Simulations of Lipid Membranes with Lateral Force: Rupture and Dynamic Properties. Xie, Jun Yu; Ding, Guang Hong; Karttunen, Mikko. Biochimica et Biophysica Acta (BBA) - Biomembranes 1838, 994?1002, (2014).
   https://doi.org/10.1016/j.bbamem.2013.12.011.

2. In Situ Nanoparticle Size Measurements of Gas-Borne Silicon Nanoparticles by Time-Resolved Laser-Induced Incandescence. Sipkens, T. A.; Mansmann, R.; Daun, K. J.; Petermann, N.; Titantah, J. T.; Karttunen, M.; Wiggers, H.; Dreier, T.; Schulz, C. Appl. Phys. B 116, 623–636, (2014).
   https://doi.org/10.1007/s00340-013-5745-2.

3. Dehydroergosterol as an Analogue for Cholesterol: Why It Mimics Cholesterol so Well-or Does It?. Pourmousa, Mohsen; Róg, Tomasz; Mikkeli, Risto; Vattulainen, Llpo; Solanko, Lukasz M.; Wüstner, Daniel; List, Nanna Holmgaard; Kongsted, Jacob; Karttunen, Mikko. J. Phys. Chem. B 118, 7345–7357, (2014).
   https://doi.org/10.1021/jp406883k.

   Porphyrins got media attention

   • Highlighted in Photonics Media: Nanoballoons and Lasers: A New Cancer Fighter

   • Science Daily: Fighting cancer with lasers, nanoballoons that pop

   • Genetic Engineering & Biotechnology news: Real-Life Fantastic Voyage via Cancer-Fighting Nanoballoon

   • Cambridge Times: A fantastic voyage for chemotherapy drugs

   • SUNY Buffalo: Fighting cancer with lasers and nanoballoons that pop

4. Porphyrin-Phospholipid Liposomes Permeabilized by near-Infrared Light. Carter, Kevin A.; Shao, Shuai; Hoopes, Matthew I.; Luo, Dandan; Ahsan, Bilal; Grigoryants, Vladimir M.; Song, Wentao; Huang, Haoyuan; Zhang, Guojian; Pandey, Ravindra K.; Geng, Jumin; Pfeifer, Blaine A.; Scholes, Charles P.; Ortega, Joaquin; Karttunen, Mikko; Lovell, Jonathan F. Nat. Commun. 5, 3546, (2014).
   https://doi.org/10.1038/ncomms4546.

5. Biopolymer Filtration in Corrugated Nanochannels. Ollila, Santtu T.; Denniston, Colin; Karttunen, Mikko; Ala-Nissila, Tapio. Phys. Rev. Lett. 112, 118301, (2014).
   https://doi.org/10.1103/physrevlett.112.118301.

6. Molecular Dynamics Study of DNA Oligomers under Angled Pulling. Naserian-Nik, A. M.; Tahani, M.; Karttunen, M. RSC Adv. 4, 10751, (2014).
   https://doi.org/10.1039/c3ra45604h.

7. A New Model for Cell Division and Migration with Spontaneous Topology Changes. Mkrtchyan, Anna; Åström, Jan; Karttunen, Mikko. Soft Matter 10, 4332–4339, (2014).
   https://doi.org/10.1039/c4sm00489b.

8. Modeling the Behavior of Confined Colloidal Particles under Shear Flow. Mackay, F. E.; Pastor, K.; Karttunen, M.; Denniston, C. Soft Matter 10, 8724–8730, (2014).
   https://doi.org/10.1039/c4sm01812e.

9. Molecular Dynamics Simulations of DPPC/CTAB Monolayers at the Air/Water Interface. Liu, Bin; Hoopes, Matthew I.; Karttunen, Mikko. J. Phys. Chem. B 118, 11723-11737, (2014).
   https://doi.org/10.1021/jp5050892.

   Editor’s Choice

10. Melatonin Directly Interacts with Cholesterol and Alleviates Cholesterol Effects in Dipalmitoylphosphatidylcholine Monolayers. Choi, Youngjik; Attwood, Simon J.; Hoopes, Matthew I.; Drolle, Elizabeth; Karttunen, Mikko; Leonenko, Zoya. Soft Matter 10, 206, (2014).
    https://doi.org/10.1039/c3sm52064a.

11. Micelle Fragmentation and Wetting in Confined Flow. Habibi, Mona; Denniston, Colin; Karttunen, Mikko. EPL 108, 28005, (2014).
    https://doi.org/10.1209/0295-5075/108/28005.

12. Accelerating the Conformational Sampling of Intrinsically Disordered Proteins. Do, Trang Nhu; Choy, Wing-Yiu; Karttunen, Mikko. J. Chem. Theory Comput. 10, 5081–5094, (2014).
    https://doi.org/10.1021/ct5004803.

13. Classical Electrostatics for Biomolecular Simulations. Cisneros, G. Andrés; Karttunen, Mikko; Ren, Pengyu; Sagui, Celeste. Chem. Rev. 114, 779–814, (2014).
    https://doi.org/10.1021/cr300461d.

14. Long Molecular Dynamics Simulations of Intrinsically Disordered Proteins Reveal Preformed Structural Elements for Target Binding. Cino, Elio, A.; Karttunen, Mikko; Choy, Wing-Yiu. In Computational Approaches to Protein Dynamics - From Quantum to Coarse-Grained Methods (Taylor & Francis), M. Fuxreiter (Ed.).
    Article: Chapter 8
    Book: https://doi.org/10.1201/b17979

2013

1. Hydrodynamic Forces Implemented into LAMMPS through a Lattice-Boltzmann Fluid. Mackay, F. E.; Ollila, S. T. T.; Denniston, C. Comput. Phys. Commun. 184, 2021–2031, (2013).
   https://doi.org/10.1016/j.cpc.2013.03.024.

2. Analytical Model and Multiscale Simulations of Aβ Peptide Aggregation in Lipid Membranes: Towards a Unifying Description of Conformational Transitions, Oligomerization and Membrane Damage. Pannuzzo, Martina; Milardi, Danilo; Raudino, Antonio; Karttunen, Mikko; Rosa, Carmelo La. Phys. Chem. Chem. Phys. 15, 8940–8951, (2013).
   https://doi.org/10.1039/c3cp44539a.

3. α-Helical Structures Drive Early Stages of Self-Assembly of Amyloidogenic Amyloid Polypeptide Aggregate Formation in Membranes. Pannuzzo, Martina; Raudino, Antonio; Milardi, Danilo; La Rosa, Carmelo; Karttunen, Mikko. Sci. Rep. 3, 2781, (2013).
   https://doi.org/10.1038/srep02781.

4. Multiphase Density Functional Theory Parameterization of the Interatomic Potential for Silver and Gold. Titantah, John T.; Karttunen, Mikko. Eur. Phys. J. B 86, (2013).
   https://doi.org/10.1140/epjb/e2013-40067-6.

5. Water Dynamics: Relation between Hydrogen Bond Bifurcations, Molecular Jumps, Local Density & Hydrophobicity. Titantah, John Tatini; Karttunen, Mikko. Sci. Rep. 3, 2991, (2013).
   https://doi.org/10.1038/srep02991.

6. Early Stages of Interactions of Cell-Penetrating Peptide Penetratin with a DPPC Bilayer. Pourmousa, Mohsen; Karttunen, Mikko. Chem. Phys. Lipids 169, 85–94, (2013).
   https://doi.org/10.1016/j.chemphyslip.2013.02.011.

7. Hydrodynamic Effects on Confined Polymers. Ollila, Santtu T. T.; Denniston, Colin; Karttunen, Mikko; Ala-Nissila, Tapio. Soft Matter 9, 3478–3487, (2013).
   https://doi.org/10.1039/C3SM27410A.

8. Pulling of Double-Stranded DNA by Atomic Force Microscopy: A Simulation in Atomistic Details. Naserian-Nik, A. M.; Tahani, M.; Karttunen, M. RSC Adv. 3, 10516, (2013).
   https://doi.org/10.1039/c3ra23213a.

9. Molecular Dynamics Study of Oxidized Lipid Bilayers in NaCl Solution. Jarerattanachat, Viwan; Karttunen, Mikko; Wong-Ekkabut, Jirasak. J. Phys. Chem. B 117, 8490–8501, (2013).
   https://doi.org/10.1021/jp4040612.

10. Phase-Field-Crystal Model for Magnetocrystalline Interactions in Isotropic Ferromagnetic Solids. Faghihi, Niloufar; Provatas, Nikolas; Elder, K. R.; Grant, Martin; Karttunen, Mikko. Physical Review E 88, 032407, (2013).
    https://doi.org/10.1103/PhysRevE.88.032407.

11. Effect of Melatonin and Cholesterol on the Structure of DOPC and DPPC Membranes. Drolle, E.; Kučerka, N.; Hoopes, M. I.; Choi, Y.; Katsaras, J.; Karttunen, M.; Leonenko, Z. Biochim. Biophys. Acta 1828, 2247–2254, (2013).
    https://doi.org/10.1016/j.bbamem.2013.05.015.

12. Thermal Accommodation Coefficients for Laser-Induced Incandescence Sizing of Metal Nanoparticles in Monatomic Gases. Daun, K. J.; Sipkens, T. A.; Titantah, J. T.; Karttunen, M. Appl. Phys. B 112, 409–420, (2013).
    https://doi.org/10.1007/s00340-013-5508-0.

13. Conformational Biases of Linear Motifs. Cino, Elio A.; Choy, Wing-Yiu; Karttunen, Mikko. J. Phys. Chem. B 117, 15943–15957, (2013).
    https://doi.org/10.1021/jp407536p.

14. Binding of Disordered Proteins to a Protein Hub. Cino, Elio A.; Killoran, Ryan C.; Karttunen, Mikko; Choy, Wing-Yiu. Sci. Rep. 3, 2305, (2013).
    https://doi.org/10.1038/srep02305.

15. Classical Molecular Dynamics in a Nutshell. Hug, Susanna. Methods Mol. Biol. 924, 127–152, (2013). In: Monticelli, L., Salonen, E. (eds) Biomolecular Simulations. Methods in Molecular Biology, vol 924. Humana Press, Totowa, NJ
    Article: https://doi.org/10.1007/978-1-62703-017-5_6
    Book: https://doi.org/10.1007/978-1-62703-017-5

16. Molecular Dynamic Studies of Transportan Interacting with a DPPC Lipid Bilayer. Pourmousa, Mohsen; Wong-ekkabut, Jirasak; Patra, Michael; Karttunen, Mikko. J. Phys. Chem. B 117, 230–241, (2013).
    https://doi.org/10.1021/jp310255r.

2012

1. Comparison of Secondary Structure Formation Using 10 Different Force Fields in Microsecond Molecular Dynamics Simulations. Cino, Elio A.; Choy, Wing-Yiu; Karttunen, Mikko. J. Chem. Theory Comput. 8, 2725–2740, (2012).
   https://doi.org/10.1021/ct300323g.

2. Long-Time Correlations and Hydrophobe-Modified Hydrogen-Bonding Dynamics in Hydrophobic Hydration. Titantah, John Tatini; Karttunen, Mikko. J. Am. Chem. Soc. 134, 9362–9368, (2012).
   https://doi.org/10.1021/ja301908a.

3. Assessment of Common Simulation Protocols for Simulations of Nanopores, Membrane Proteins, and Channels. Wong-Ekkabut, Jirasak; Karttunen, Mikko. J. Chem. Theory Comput. 8, 2905–2911, (2012).
   https://doi.org/10.1021/ct3001359.

4. Mechanism of inhibition of calcium oxalate crystal growth by an osteopontin phosphopeptide. Hug, Susanna; Grohe, Bernd; Jalkanen, Jari; Chan, Brian; Galarreta, Betty; Vincent, Krista; Lagugné-Labarthet, François; Lajoie, Gilles; Goldberg, Harvey A.; Karttunen, Mikko; Hunter, Graeme K.Soft Matter 8, 1226–1233, (2012).
   https://doi.org/10.1039/c1sm06232h.

5. Molecular Dynamics, Crystallography and Mutagenesis Studies on the Substrate Gating Mechanism of Prolyl Oligopeptidase. Kaszuba, Karol; Róg, Tomasz; Danne, Reinis; Canning, Peter; Fülöp, Vilmos; Juhász, Tünde; Szeltner, Zoltán; Pierre, J. F. St; García-Horsman, Arturo; Männistö, Pekka T.; Karttunen, Mikko; Hokkanen, Jyrki; Bunker, Alex. Biochimie 94, 1398–1411, (2012).
   https://doi.org/10.1016/j.biochi.2012.03.012.

6. Cationic Dimyristoylphosphatidylcholine and Dioleoyloxytrimethylammonium Propane Lipid Bilayers: Atomistic Insight for Structure and Dynamics. Zhao, Wei; Gurtovenko, Andrey A.; Vattulainen, Ilpo; Karttunen, Mikko. J. Phys. Chem. B 116, 269–276, (2012).
   https://doi.org/10.1021/jp210619q.

7. Molecular Dynamics Simulations of the Bacterial ABC Transporter SAV1866 in the Closed Form. St-Pierre, Jean-François; Bunker, Alex; Róg, Tomasz; Karttunen, Mikko; Mousseau, Normand. J. Phys. Chem. B 116, 2934–2942, (2012).
   https://doi.org/10.1021/jp209126c.

8. Combined Depletion and Electrostatic Forces in Polymer-Induced Membrane Adhesion: A Theoretical Model. Raudino, Antonio; Pannuzzo, Martina; Karttunen, Mikko. J. Chem. Phys. 136, 055101, (2012).
   https://doi.org/10.1063/1.3678836.

   SoftSimu/cashew

   

   Cashew molecular dynamics simulator for the 3d Mercedes-Benz model is available at GitHub.

9. A Molecular Dynamics Implementation of the 3D Mercedes-Benz Water Model. Hynninen, T.; Dias, C. L.; Mkrtchyan, A.; Heinonen, V.; Karttunen, M.; Foster, A. S.; Ala-Nissila, T. Comput. Phys. Commun. 183, 363–369, (2012).
   https://doi.org/10.1016/j.cpc.2011.09.008.

10. Mimicking the Biomolecular Control of Calcium Oxalate Monohydrate Crystal Growth: Effect of Contiguous Glutamic Acids. Grohe, Bernd; Hug, Susanna; Langdon, Aaron; Jalkanen, Jari; Rogers, Kem A.; Goldberg, Harvey A.; Karttunen, Mikko; Hunter, Graeme K. Langmuir 28, 12182–12190, (2012).
    https://doi.org/10.1021/la3018985.

11. Molecular Dynamics Simulation of Thermal Accommodation Coefficients for Laser-Induced Incandescence Sizing of Nickel Particles. Daun, K. J.; Titantah, J. T.; Karttunen, M. Appl. Phys. B 107, 221–228, (2012).
    https://doi.org/10.1007/s00340-012-4896-x.

12. Effects of Molecular Crowding on the Dynamics of Intrinsically Disordered Proteins. Cino, Elio A.; Karttunen, Mikko; Choy, Wing-Yiu. PLoS One 7, e49876, (2012).
    https://doi.org/10.1371/journal.pone.0049876.

13. Stiffness Transition in Anisotropic Fiber Nets. Åström, J. A.; Kumar, P. B. Sunil; Karttunen, Mikko. Physical Review E 86, (2012).
    https://doi.org/10.1103/PhysRevE.86.021922.

2011

1. Fluctuating Lattice-Boltzmann Model for Complex Fluids. Ollila, Santtu T. T.; Denniston, Colin; Karttunen, Mikko; Ala-Nissila, Tapio. J. Chem. Phys. 134, 064902, (2011).
   https://doi.org/10.1063/1.3544360.

2. Use of Umbrella Sampling to Calculate the Entrance/Exit Pathway for Z-Pro-Prolinal Inhibitor in Prolyl Oligopeptidase. St-Pierre, Jean-François; Karttunen, Mikko; Mousseau, Normand; Róg, Tomasz; Bunker, Alex. J. Chem. Theory Comput. 7, 1583–1594, (2011).
   https://doi.org/10.1021/ct1007058.

3. Matrix Gla Protein Inhibits Ectopic Calcification by a Direct Interaction with Hydroxyapatite Crystals. O’Young, Jason; Liao, Yinyin; Xiao, Yizhi; Jalkanen, Jari; Lajoie, Gilles; Karttunen, Mikko; Goldberg, Harvey A.; Hunter, Graeme K. J. Am. Chem. Soc. 133, 18406–18412, (2011).
   https://doi.org/10.1021/ja207628k.

4. Study of PEGylated Lipid Layers as a Model for PEGylated Liposome Surfaces: Molecular Dynamics Simulation and Langmuir Monolayer Studies. Stepniewski, Michał; Pasenkiewicz-Gierula, Marta; Róg, Tomasz; Danne, Reinis; Orlowski, Adam; Karttunen, Mikko; Urtti, Arto; Yliperttula, Marjo; Vuorimaa, Elina; Bunker, Alex. Langmuir 27, 7788–7798, (2011).
   https://doi.org/10.1021/la200003n.

5. Simulations of Micellization of Sodium Hexyl Sulfate. Sammalkorpi, M.; Sanders, S.; Panagiotopoulos, A. Z.; Karttunen, M.; Haataja, M. J. Phys. Chem. B 115, 1403–1410, (2011).
   https://doi.org/10.1021/jp109882r.

6. Low Density Lipoprotein: Structure, Dynamics, and Interactions of apoB-100 with Lipids. Murtola, Teemu; Vuorela, Timo A.; Hyvönen, Marja T.; Marrink, Siewert-Jan; Karttunen, Mikko; Vattulainen, Ilpo. Soft Matter 7, 8135–8141, (2011).
   https://doi.org/10.1039/C1SM05367A.

7. Hydrophobicity within the Three-Dimensional Mercedes-Benz Model: Potential of Mean Force. Dias, Cristiano L.; Hynninen, Teemu; Ala-Nissila, Tapio; Foster, Adam S.; Karttunen, Mikko. J. Chem. Phys. 134, 065106, (2011).
   https://doi.org/10.1063/1.3537734.

8. Hydrophobic Interactions in the Formation of Secondary Structures in Small Peptides. Dias, Cristiano L.; Karttunen, Mikko; Chan, Hue Sun. Physical Review E 84, (2011).
   https://doi.org/10.1103/PhysRevE.84.041931.

9. Microsecond Molecular Dynamics Simulations of Intrinsically Disordered Proteins Involved in the Oxidative Stress Response. Cino, Elio A.; Wong-ekkabut, Jirasak; Karttunen, Mikko; Choy, Wing-Yiu. PLoS One 6, e27371, (2011).
   https://doi.org/10.1371/journal.pone.0027371.

10. Citrate Modulates Calcium Oxalate Crystal Growth by Face-Specific Interactions. Grohe, Bernd; O’Young, Jason; Langdon, Aaron; Karttunen, Mikko; Goldberg, Harvey A.; Hunter, Graeme K. Cells Tissues Organs 194, 176–181, (2011).
    https://doi.org/10.1159/000324338.

2010

1. Reply to the Comment by Graziano on “The Hydrophobic Effect and Its Role in Cold Denaturation”. Dias, Cristiano L.; Ala-Nissila, Tapio; Wong-ekkabut, Jirasak; Vattulainen, Ilpo; Grant, Martin; Karttunen, Mikko. Cryobiology 60, 356–357, (2010).
   https://doi.org/10.1016/j.cryobiol.2010.03.006.

3. Roles of Electrostatics and Conformation in Protein-Crystal Interactions. Azzopardi, Paul V.; O’Young, Jason; Lajoie, Gilles; Karttunen, Mikko; Goldberg, Harvey A.; Hunter, Graeme K. PLoS One 5, e9330, (2010).
   https://doi.org/10.1371/journal.pone.0009330.

4. Role of Lipids in Spheroidal High Density Lipoproteins. Vuorela, Timo; Catte, Andrea; Niemelä, Perttu S.; Hall, Anette; Hyvönen, Marja T.; Marrink, Siewert-Jan; Karttunen, Mikko; Vattulainen, Ilpo. PLoS Comput. Biol. 6, e1000964, (2010).
   https://doi.org/10.1371/journal.pcbi.1000964.

5. Effects of the Lipid Bilayer Phase State on the Water Membrane Interface. Stepniewski, Michał; Bunker, Alex; Pasenkiewicz-Gierula, Marta; Karttunen, Mikko; Róg, Tomasz. J. Phys. Chem. B 114, 11784–11792, (2010).
   https://doi.org/10.1021/jp104739a.

6. Molecular Dynamics Simulations Reveal Fundamental Role of Water as Factor Determining Affinity of Binding of Beta-Blocker Nebivolol to beta(2)-Adrenergic Receptor. Kaszuba, Karol; Róg, Tomasz; Bryl, Krzysztof; Vattulainen, Ilpo; Karttunen, Mikko. J. Phys. Chem. B 114, 8374–8386, (2010).
   https://doi.org/10.1021/jp909971f.

7. Static Charges Cannot Drive a Continuous Flow of Water Molecules through a Carbon Nanotube. Wong-ekkabut, Jirasak; Miettinen, Markus S.; Dias, Cristiano; Karttunen, Mikko. Nat. Nanotechnol. 5, 555–557, (2010).
   https://doi.org/10.1038/nnano.2010.152.

8. Cholesterol Induces Specific Spatial and Orientational Order in Cholesterol/Phospholipid Membranes. Martinez-Seara, Hector; Róg, Tomasz; Karttunen, Mikko; Vattulainen, Ilpo; Reigada, Ramon. PLoS One 5, e11162, (2010).
   https://doi.org/10.1371/journal.pone.0011162.

   

   Featured in Phys Rev Focus: Cutting Ice with a Nanowire

9. Cutting Ice: Nanowire Regelation. Hynninen, Teemu; Heinonen, Vili; Dias, Cristiano L.; Karttunen, Mikko; Foster, Adam S.; Ala-Nissila, Tapio. Phys. Rev. Lett. 105, (2010).
   https://doi.org/10.1103/PhysRevLett.105.086102.

10. The Flexible Polyelectrolyte Hypothesis of Protein-Biomineral Interaction. Hunter, Graeme K.; O’Young, Jason; Grohe, Bernd; Karttunen, Mikko; Goldberg, Harvey A. Langmuir 26, 18639–18646, (2010).
    https://doi.org/10.1021/la100401r.

11. Role of Glycolipids in Lipid Rafts: A View through Atomistic Molecular Dynamics Simulations with Galactosylceramide. Hall, Anette; Róg, Tomasz; Karttunen, Mikko; Vattulainen, Ilpo. J. Phys. Chem. B 114, 7797–7807, (2010).
    https://doi.org/10.1021/jp912175d.

12. The Hydrophobic Effect and Its Role in Cold Denaturation. Dias, Cristiano L.; Ala-Nissila, Tapio; Wong-ekkabut, Jirasak; Vattulainen, Ilpo; Grant, Martin; Karttunen, Mikko. Cryobiology 60, 91–99, (2010).
    https://doi.org/10.1016/j.cryobiol.2009.07.005.

13. Phosphorylation of Ser136 Is Critical for Potent Bone Sialoprotein-Mediated Nucleation of Hydroxyapatite Crystals. Baht, Gurpreet S.; O’Young, Jason; Borovina, Antonia; Chen, Hong; Tye, Coralee E.; Karttunen, Mikko; Lajoie, Gilles A.; Hunter, Graeme K.; Goldberg, Harvey A. Biochem. J 428, 385–395, (2010).
    https://doi.org/10.1042/BJ20091864.

14. Myosin Motor Mediated Contraction Is Enough to Produce Cytokinesis in the Absence of Polymerisation. Åström, Jan A.; von Alfthan, Sebastian; Kumar, P. B. Sunil; Karttunen, Mikko. Soft Matter 6, 5375, (2010).
    https://doi.org/10.1039/c0sm00134a.

2009

1. Lipid Domain Morphologies in Phosphatidylcholine-Ceramide Monolayers. Karttunen, Mikko; Haataja, Mikko P.; Säily, Matti; Vattulainen, Ilpo; Holopainen, Juha M. Langmuir 25, 4595–4600, (2009).
   https://doi.org/10.1021/la803377s.

2. Why Is the Sn-2 Chain of Monounsaturated Glycerophospholipids Usually Unsaturated Whereas the Sn-1 Chain Is Saturated? Studies of 1-Stearoyl-2-Oleoyl-Sn-Glycero-3-Phosphatidylcholine (SOPC) and 1-Oleoyl-2-Stearoyl-Sn-Glycero-3-Phosphatidylcholine (OSPC) Membranes with and without Cholesterol. Martinez-Seara, Hector; Róg, Tomasz; Karttunen, Mikko; Vattulainen, Ilpo; Reigada, Ramon. J. Phys. Chem. B 113, 8347–8356, (2009).
   https://doi.org/10.1021/jp902131b.

3. Molecular Dynamics Study of Prolyl Oligopeptidase with Inhibitor in Binding Cavity. Kaszuba, K.; Rog, T.; St Pierre, J. F.; Mannisto, P. T.; Karttunen, M.; Bunker, A. SAR QSAR Environ. Res. 20, 595–609, (2009).
   https://doi.org/10.1080/10629360903438198.

4. Phosphorylation of Osteopontin Peptides Mediates Adsorption to and Incorporation into Calcium Oxalate Crystals. O’Young, Jason; Chirico, Sara; Tarhuni, Nehal Al; Grohe, Bernd; Karttunen, Mikko; Goldberg, Harvey A.; Hunter, Graeme K. Cells Tissues Organs 189, 51–55, (2009).
   https://doi.org/10.1159/000151724.

5. Mitochondrial Membranes with Mono- and Divalent Salt: Changes Induced by Salt Ions on Structure and Dynamics. Pöyry, Sanja; Róg, Tomasz; Karttunen, Mikko; Vattulainen, Ilpo. J. Phys. Chem. B 113, 15513–15521, (2009).
   https://doi.org/10.1021/jp905915m.

6. Role of Cardiolipins in the Inner Mitochondrial Membrane: Insight Gained through Atom-Scale Simulations. Róg, Tomasz; Martinez-Seara, Hector; Munck, Nana; Oresic, Matej; Karttunen, Mikko; Vattulainen, Ilpo. J. Phys. Chem. B 113, 3413–3422, (2009).
   https://doi.org/10.1021/jp8077369.

7. Water Isotope Effect on the Phosphatidylcholine Bilayer Properties: A Molecular Dynamics Simulation Study. Róg, Tomasz; Murzyn, Krzysztof; Milhaud, Jeannine; Karttunen, Mikko; Pasenkiewicz-Gierula, Marta. J. Phys. Chem. B 113, 2378–2387, (2009).
   https://doi.org/10.1021/jp8048235.

8. Multiscale Modeling of Emergent Materials: Biological and Soft Matter. Murtola, Teemu; Bunker, Alex; Vattulainen, Ilpo; Deserno, Markus; Karttunen, Mikko. Phys. Chem. Chem. Phys. 11, 1869–1892, (2009).
   https://doi.org/10.1039/b818051b.

9. Ionic Surfactant Aggregates in Saline Solutions: Sodium Dodecyl Sulfate (SDS) in the Presence of Excess Sodium Chloride (NaCl) or Calcium Chloride (CaCl(2)). Sammalkorpi, Maria; Karttunen, Mikko; Haataja, Mikko. J. Phys. Chem. B 113, 5863–5870, (2009).
   https://doi.org/10.1021/jp901228v.

10. Ordering Effects of Cholesterol and Its Analogues. Róg, Tomasz; Pasenkiewicz-Gierula, Marta; Vattulainen, Ilpo; Karttunen, Mikko. Biochimica et Biophysica Acta (BBA) - Biomembranes 1788, 97–121, (2009).
    https://doi.org/10.1016/j.bbamem.2008.08.022.

11. Systematic Coarse Graining from Structure Using Internal States: Application to Phospholipid/cholesterol Bilayer. Murtola, Teemu; Karttunen, Mikko; Vattulainen, Ilpo. J. Chem. Phys. 131, 055101, (2009).
    https://doi.org/10.1063/1.3167405.

12. Ion Dynamics in Cationic Lipid Bilayer Systems in Saline Solutions. Miettinen, Markus S.; Gurtovenko, Andrey A.; Vattulainen, Ilpo; Karttunen, Mikko. J. Phys. Chem. B 113, 9226–9234, (2009).
    https://doi.org/10.1021/jp810233q.

13. Three-Dimensional “Mercedes-Benz” Model for Water. Dias, Cristiano L.; Ala-Nissila, Tapio; Grant, Martin; Karttunen, Mikko. J. Chem. Phys. 131, 054505, (2009).
    https://doi.org/10.1063/1.3183935.

14. Aster Formation and Rupture Transition in Semi-Flexible Fiber Networks with Mobile Cross-Linkers. Åström, Jan A.; Kumar, P. B. Sunil; Karttunen, Mikko. Soft Matter 5, 2869–2874, (2009).
    https://doi.org/10.1039/b815892d.

15. Nonlinear Driven Response of a Phase-Field Crystal in a Periodic Pinning Potential. Achim, C. V.; Ramos, J. A. P.; Karttunen, M.; Elder, K. R.; Granato, E.; Ala-Nissila, T.; Ying, S. C. Phys. Rev. E 79, 011606, (2009).
    https://doi.org/10.1103/PhysRevE.79.011606.

2008

1. Agent-Based Modelling of Glucose Transport. Van Gaalen, R. D.; Karttunen, Mikko. J. Comput. Interdiscip. Sci. 1, (2008).
   https://doi.org/10.6062/jcis.2008.01.01.0004.

2. Molecular Dynamics Simulations of the Enzyme Catechol-O-Methyltransferase: Methodological issues. Bunker, A.; Männistö, P. T.; Pierre, J. F. St; Róg, T.; Pomorski, P.; Stimson, L.; Karttunen, M. SAR QSAR Environ. Res. 19, 179–189, (2008).
   https://doi.org/10.1080/10629360701843318.

3. Significance of Cholesterol Methyl Groups. Pöyry, Sanja; Róg, Tomasz; Karttunen, Mikko; Vattulainen, Ilpo. J. Phys. Chem. B 112, 2922–2929, (2008).
   https://doi.org/10.1021/jp7100495.

4. Comparison of Cholesterol and Its Direct Precursors along the Biosynthetic Pathway: Effects of Cholesterol, Desmosterol and 7-Dehydrocholesterol on Saturated and Unsaturated Lipid Bilayers. Róg, Tomasz; Vattulainen, Ilpo; Jansen, Maurice; Ikonen, Elina; Karttunen, Mikko. J. Chem. Phys. 129, 154508, (2008).
   https://doi.org/10.1063/1.2996296.

5. Phase Diagram of Pinned Lattices in the Phase Field Crystal Model. Achim, C. V.; Karttunen, M.; Elder, K. R.; Granato, E.; Ala-Nissila, T.; Ying, S. C. J. Phys. Conf. Ser. 100, 072001, (2008).
   https://doi.org/10.1088/1742-6596/100/7/072001.

6. Structure of Spheroidal HDL Particles Revealed by Combined Atomistic and Coarse-Grained Simulations. Catte, Andrea; Patterson, James C.; Bashtovyy, Denys; Jones, Martin K.; Gu, Feifei; Li, Ling; Rampioni, Aldo; Sengupta, Durba; Vuorela, Timo; Niemelä, Perttu; Karttunen, Mikko; Marrink, Siewert Jan; Vattulainen, Ilpo; Segrest, Jere P. Biophys. J. 94, 2306–2319, (2008).
   https://doi.org/10.1529/biophysj.107.115857.

7. Interplay of Unsaturated Phospholipids and Cholesterol in Membranes: Effect of the Double-Bond Position. Martinez-Seara, Hector; Róg, Tomasz; Pasenkiewicz-Gierula, Marta; Vattulainen, Ilpo; Karttunen, Mikko; Reigada, Ramon. Biophys. J. 95, 3295–3305, (2008).
   https://doi.org/10.1529/biophysj.108.138123.

8. Influence of Ethanol on Lipid Membranes: From Lateral Pressure Profiles to Dynamics and Partitioning. Terama, Emma; Ollila, O. H. Samuli; Salonen, Emppu; Rowat, Amy C.; Trandum, Christa; Westh, Peter; Patra, Michael; Karttunen, Mikko; Vattulainen, Ilpo. J. Phys. Chem. B 112, 4131–4139, (2008).
   https://doi.org/10.1021/jp0750811.

9. Role of Phosphatidylglycerols in the Stability of Bacterial Membranes. Zhao, Wei; Róg, Tomasz; Gurtovenko, Andrey A.; Vattulainen, Ilpo; Karttunen, Mikko. Biochimie 90, 930–938, (2008).
   https://doi.org/10.1016/j.biochi.2008.02.025.

10. Micelle Fission through Surface Instability and Formation of an Interdigitating Stalk. Sammalkorpi, Maria; Karttunen, Mikko; Haataja, Mikko. J. Am. Chem. Soc. 130, 17977–17980, (2008).
    https://doi.org/10.1021/ja8077413.

11. Replacing the Cholesterol Hydroxyl Group with the Ketone Group Facilitates Sterol Flip-Flop and Promotes Membrane Fluidity. Róg, Tomasz; Stimson, Lorna M.; Pasenkiewicz-Gierula, Marta; Vattulainen, Ilpo; Karttunen, Mikko. J. Phys. Chem. B 112, 1946–1952, (2008).
    https://doi.org/10.1021/jp075078h.

12. Nonpolar Interactions between Trans-Membrane Helical EGF Peptide and Phosphatidylcholines, Sphingomyelins and Cholesterol. Molecular Dynamics Simulation Studies. Róg, Tomasz; Murzyn, Krzysztof; Karttunen, Mikko; Pasenkiewicz-Gierula, Marta. J. Pept. Sci. 14, 374–382, (2008).
    https://doi.org/10.1002/psc.936.

13. Dynamical Scaling Exponents for Polymer Translocation through a Nanopore. Luo, Kaifu; Ollila, Santtu; Huopaniemi, Ilkka; Ala-Nissila, Tapio; Pomorski, Pawel; Karttunen, Mikko; Ying, See-Chen; Bhattacharya, Aniket. Physical Review E 78, (2008).
    https://doi.org/10.1103/PhysRevE.78.050901.

14. Electrostatics in Biomolecular Simulations: Where Are We Now and Where Are We Heading?. Karttunen, Mikko; Rottler, Jörg; Vattulainen, Ilpo; Sagui, Celeste. Curr. Top. Membr. 60, 49–89, (2008).
    https://doi.org/10.1016/s1063-5823(08)00002-1.

15. Lateral Diffusion in Lipid Membranes through Collective Flows. Falck, Emma; Róg, Tomasz; Karttunen, Mikko; Vattulainen, Ilpo. J. Am. Chem. Soc. 130, 44–45, (2008).
    https://doi.org/10.1021/ja7103558.

16. Microscopic Mechanism for Cold Denaturation. Dias, Cristiano L.; Ala-Nissila, Tapio; Karttunen, Mikko; Vattulainen, Ilpo; Grant, Martin. Phys. Rev. Lett. 100, 118101, (2008).
    https://doi.org/10.1103/PhysRevLett.100.118101.

17. Strain Hardening, Avalanches, and Strain Softening in Dense Cross-Linked Actin Networks. Åström, Jan A.; Kumar, P. B. Sunil; Vattulainen, Ilpo; Karttunen, Mikko. Physical Review E 77, 051913, (2008).
    https://doi.org/10.1103/PhysRevE.77.051913.

18. Influence of Cis Double-Bond Parametrization on Lipid Membrane Properties: How Seemingly Insignificant Details in Force-Field Change Even Qualitative Trends. Martinez-Seara, Hector; Róg, Tomasz; Karttunen, Mikko; Reigada, Ramon; Vattulainen, Ilpo. J. Chem. Phys. 129, 105103, (2008).
    https://doi.org/10.1063/1.2976443.

19. Systematic Approach to Coarse-Graining of Molecular Descriptions and Interactions with Applications to Lipid Membranes. Murtola, Teemu; Vattulainen, Ilpo; Karttunen, Mikko. In Coarse-Graining of Condensed Phase and Biomolecular Systems. Gregory A. Voth (Ed.). CRC Press, Boca Raton, FL.
    Article: Chapter 7
    Book: https://doi.org/10.1201/9781420059564
    Article at Research Gate

2007

1. Assessing the Nature of Lipid Raft Membranes. Niemelä, Perttu S.; Ollila, Samuli; Hyvönen, Marja T.; Karttunen, Mikko; Vattulainen, Ilpo. PLoS Comput. Biol. 3, e34, (2007).
   https://doi.org/10.1371/journal.pcbi.0030034.

2. Long-Range Interactions and Parallel Scalability in Molecular Simulations. Patra, Michael; Hyvönen, Marja T.; Falck, Emma; Sabouri-Ghomi, Mohsen; Vattulainen, Ilpo; Karttunen, Mikko. Comput. Phys. Commun. 176, 14–22, (2007).
   https://doi.org/10.1016/j.cpc.2006.07.017.

3. Role of Sterol Type on Lateral Pressure Profiles of Lipid Membranes Affecting Membrane Protein Functionality: Comparison between Cholesterol, Desmosterol, 7-Dehydrocholesterol and Ketosterol. Ollila, O. H. Samuli; Róg, Tomasz; Karttunen, Mikko; Vattulainen, Ilpo. J. Struct. Biol. 159, 311–323, (2007).
   https://doi.org/10.1016/j.jsb.2007.01.012.

4. Insight into the Putative Specific Interactions between Cholesterol, Sphingomyelin, and Palmitoyl-Oleoyl Phosphatidylcholine. Aittoniemi, Jussi; Niemelä, Perttu S.; Hyvönen, Marja T.; Karttunen, Mikko; Vattulainen, Ilpo. Biophys. J. 92, 1125–1137, (2007).
   https://doi.org/10.1529/biophysj.106.088427.

5. Enhanced Dielectrophoresis of Nanocolloids by Dimer Formation. Salonen, E.; Terama, E.; Vattulainen, I.; Karttunen, M. Europhys. Lett. 78, 48004+, (2007).
   https://doi.org/10.1209/0295-5075/78/48004.

6. Atomic-Scale Structure and Electrostatics of Anionic Palmitoyloleoylphosphatidylglycerol Lipid Bilayers with Na+ Counterions. Zhao, Wei; Róg, Tomasz; Gurtovenko, Andrey A.; Vattulainen, Ilpo; Karttunen, Mikko. Biophys. J. 92, 1114–1124, (2007).
   https://doi.org/10.1529/biophysj.106.086272.

7. Stearic Acid Spin Labels in Lipid Bilayers: Insight through Atomistic Simulations. Stimson, Lorna; Dong, Lei; Karttunen, Mikko; Wisniewska, Anna; Dutka, Małgorzata; Róg, Tomasz. J. Phys. Chem. B 111, 12447–12453, (2007).
   https://doi.org/10.1021/jp0746796.

8. Structural Properties of Ionic Detergent Aggregates: A Large-Scale Molecular Dynamics Study of Sodium Dodecyl Sulfate. Sammalkorpi, Maria; Karttunen, Mikko; Haataja, Mikko. J. Phys. Chem. B 111, 11722–11733, (2007).
   https://doi.org/10.1021/jp072587a.

9. What Happens If Cholesterol Is Made Smoother: Importance of Methyl Substituents in Cholesterol Ring Structure on Phosphatidylcholine-Sterol Interaction. Róg, Tomasz; Pasenkiewicz-Gierula, Marta; Vattulainen, Ilpo; Karttunen, Mikko. Biophys. J. 92, 3346–3357, (2007).
   https://doi.org/10.1529/biophysj.106.095497.

10. Glycolipid Membranes through Atomistic Simulations: Effect of Glucose and Galactose Head Groups on Lipid Bilayer Properties. Róg, Tomasz; Vattulainen, Ilpo; Bunker, Alex; Karttunen, Mikko. J. Phys. Chem. B 111, 10146–10154, (2007).
    https://doi.org/10.1021/jp0730895.

11. Reptational Dynamics in Dissipative Particle Dynamics Simulations of Polymer Melts. Nikunen, Petri; Vattulainen, Ilpo; Karttunen, Mikko. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 75, 036713, (2007).
    https://doi.org/10.1103/PhysRevE.75.036713.

12. Effect of Double Bond Position on Lipid Bilayer Properties: Insight through Atomistic Simulations. Martinez-Seara, Hector; Róg, Tomasz; Pasenkiewicz-Gierula, Marta; Vattulainen, Ilpo; Karttunen, Mikko; Reigada, Ramon. J. Phys. Chem. B 111, 11162–11168, (2007).
    https://doi.org/10.1021/jp071894d.

13. Control of Calcium Oxalate Crystal Growth by Face-Specific Adsorption of an Osteopontin Phosphopeptide. Grohe, Bernd; O’Young, Jason; Ionescu, D. Andrei; Lajoie, Gilles; Rogers, Kem A.; Karttunen, Mikko; Goldberg, Harvey A.; Hunter, Graeme K. J. Am. Chem. Soc. 129, 14946–14951, (2007).
    https://doi.org/10.1021/ja0745613.

14. Coarse-Grained Model for Phospholipid/cholesterol Bilayer Employing Inverse Monte Carlo with Thermodynamic Constraints. Murtola, Teemu; Falck, Emma; Karttunen, Mikko; Vattulainen, Ilpo. J. Chem. Phys. 126, 075101, (2007).
    https://doi.org/10.1063/1.2646614.

2006

1. Dynamics of Water at Membrane Surfaces: Effect of Headgroup Structure. Murzyn, Krzysztof; Zhao, Wei; Karttunen, Mikko; Kurdziel, Marcin; Róg, Tomasz. Biointerphases 1, 98–105, (2006).
   https://doi.org/10.1116/1.2354573.

2. Significance of Sterol Structural Specificity. Desmosterol Cannot Replace Cholesterol in Lipid Rafts. Vainio, Saara; Jansen, Maurice; Koivusalo, Mirkka; Róg, Tomasz; Karttunen, Mikko; Vattulainen, Ilpo; Ikonen, Elina. J. Biol. Chem. 281, 348–355, (2006).
   https://doi.org/10.1074/jbc.M509530200.

3. Influence of Pyrene-Labeling on Fluid Lipid Membranes. Repáková, Jarmila; Holopainen, Juha M.; Karttunen, Mikko; Vattulainen, Ilpo. J. Phys. Chem. B 110, 15403–15410, (2006).
   https://doi.org/10.1021/jp061300r.

   SoftSimu/Stencils2D

   

   The associated Mathematica file for stencils for 2D differential operators is available at GitHub.

4. Stencils with Isotropic Discretization Error for Differential Operators. Patra, Michael; Karttunen, Mikko. Numer. Methods Partial Differ. Equ. 22, 936–953, (2006).
   https://doi.org/10.1002/num.20129.

5. Transient Ordered Domains in Single-Component Phospholipid Bilayers. Murtola, Teemu; Róg, Tomasz; Falck, Emma; Karttunen, Mikko; Vattulainen, Ilpo. Phys. Rev. Lett. 97, (2006).
   https://doi.org/10.1103/PhysRevLett.97.238102.

6. Molecular Dynamics Study of Charged Dendrimers in Salt-Free Solution: Effect of Counterions. Gurtovenko, Andrey A.; Lyulin, Sergey V.; Karttunen, Mikko; Vattulainen, Ilpo. J. Chem. Phys. 124, 094904, (2006).
   https://doi.org/10.1063/1.2166396.

7. Interaction of Fusidic Acid with Lipid Membranes: Implications to the Mechanism of Antibiotic Activity. Falck, Emma; Hautala, Jari T.; Karttunen, Mikko; Kinnunen, Paavo K. J.; Patra, Michael; Saaren-Seppälä, Heikki; Vattulainen, Ilpo; Wiedmer, Susanne K.; Holopainen, Juha M. Biophys. J. 91, 1787–1799, (2006).
   https://doi.org/10.1529/biophysj.106.084525.

8. Under the Influence of Alcohol: The Effect of Ethanol and Methanol on Lipid Bilayers. Patra, Michael; Salonen, Emppu; Terama, Emma; Vattulainen, Ilpo; Faller, Roland; Lee, Bryan W.; Holopainen, Juha; Karttunen, Mikko. Biophys. J. 90, 1121–1135, (2006).
   https://doi.org/10.1529/biophysj.105.062364.

9. Tilt: Major Factor in Sterols’ Ordering Capability in Membranes. Aittoniemi, Jussi; Róg, Tomasz; Niemelä, Perttu; Pasenkiewicz-Gierula, Marta; Karttunen, Mikko; Vattulainen, Ilpo. J. Phys. Chem. B 110, 25562–25564, (2006).
   https://doi.org/10.1021/jp064931u.

10. Phase Diagram and Commensurate-Incommensurate Transitions in the Phase Field Crystal Model with an External Pinning Potential. Achim, C.; Karttunen, M.; Elder, K.; Granato, E.; Ala-Nissila, T.; Ying, S. Physical Review E 74, 072001, (2006).
    https://doi.org/10.1103/PhysRevE.74.021104.

11. Cell Aggregation: Packing Soft Grains. Åström, J. A.; Karttunen, M. Physical Review E 73, 062301, (2006).
    https://doi.org/10.1103/PhysRevE.73.062301.

12. The Influence of Lateral and Terminal Substitution on the Structure of a Liquid Crystal Dendrimer in Nematic Solution: A Computer Simulation Study. Wilson, Mark R.; Stimson, Lorna M.; Ilnytskyi, Jaroslav M. Liq. Cryst. 33, 1167–1175, (2006).
    https://doi.org/10.1080/02678290600973113.

13. Cholesterol-Sphingomyelin Interactions: A Molecular Dynamics Simulation Study. Róg, Tomasz; Pasenkiewicz-Gierula, Marta. Biophys. J. 91, 3756–3767, (2006).
    https://doi.org/10.1529/biophysj.106.080887.

14. Cholesterol Effects on a Mixed-Chain Phosphatidylcholine Bilayer: A Molecular Dynamics Simulation Study. Róg, T.; Pasenkiewicz-Gierula, M. Biochimie 88, 449–460, (2006).
    https://doi.org/10.1016/j.biochi.2005.10.005.

15. Influence of the Disulfide Bond Configuration on the Dynamics of the Spin Label Attached to Cytochrome c. Murzyn, Krzysztof; Róg, Tomasz; Blicharski, Wojciech; Dutka, Małgorzata; Pyka, Janusz; Szytula, Sebastian; Froncisz, Wojciech. Proteins 62, 1088–1100, (2006).
    https://doi.org/10.1002/prot.20838.

16. Effective Ac Response of Graded Colloidal Suspensions. Wei, En-Bo; Dong, L.; Yu, K. W. J. Appl. Phys. 99, 054101, (2006).
    https://doi.org/10.1063/1.2177378.

17. Giant Enhancement of Optical Nonlinearity in Multilayer Metallic Films. Huang, J. P.; Dong, L.; Yu, K. W. J. Appl. Phys. 99, 053503, (2006).
    https://doi.org/10.1063/1.2175477.

2005

1. Effect of Monovalent Salt on Cationic Lipid Membranes As Revealed by Molecular Dynamics Simulations. Gurtovenko, Andrey A.; Miettinen, Markus; Karttunen, Mikko; Vattulainen, Ilpo. J. Phys. Chem. B 109, 21126–21134, (2005).
   https://doi.org/10.1021/jp053667m.

2. Modelling Glycolipids: Take One,. Róg, Tomasz; Vattulainen, Ilpo; Karttunen, Mikko. Cellular and Molecular Biololgy Letters 10, 625–630, (2005).

3. Reply to ``Comment on the Use of the Method of Images for Calculating Electromagnetic Responses of Interacting Spheres’’. Huang, J. P.; Yu, K. W.; Gu, G. Q.; Karttunen, M.; Dong, L. Phys. Rev. E 72, 023402, (2005).
   https://doi.org/10.1103/PhysRevE.72.023402.

4. Dielectrophoresis of Nanocolloids: A Molecular Dynamics Study. Salonen, E.; Terama, E.; Vattulainen, I.; Karttunen, M. Eur. Phys. J. E 18, 133–142, (2005).
   https://doi.org/10.1140/epje/i2004-10157-2.

5. Response to Comment by Almeida et Al.: Free Area Theories for Lipid Bilayers–Predictive or Not?. Falck, Emma; Patra, Michael; Karttunen, Mikko; Hyvönen, Marja T.; Vattulainen, Ilpo. Biophys. J. 89, 745–752, (2005).
   https://doi.org/10.1529/biophysj.105.065714.

6. Spectral Representation of the Effective Dielectric Constant of Graded Composites. Dong, L.; Karttunen, Mikko; Yu, K. W. Phys. Rev. E 72, 016613, (2005).
   https://doi.org/10.1103/PhysRevE.72.016613.

7. Exploring the Effect of Xenon on Biomembranes. Stimson, Lorna M.; Vattulainen, Ilpo; Róg, Tomasz; Karttunen, Mikko. Cell. Mol. Biol. Lett. 10, 563–569, (2005).

8. Coarse-Grained Simulation Studies of a Liquid Crystal Dendrimer: Towards Computational Predictions of Nanoscale Structure through Microphase Separation. Hughes, Zak E.; Wilson, Mark R.; Stimson, Lorna M. Soft Matter 1, 436–443, (2005).
   https://doi.org/10.1039/b511082c.

9. Multipole Polarizability of a Graded Spherical Particle. Dong, L.; Huang, J. P.; Yu, K. W.; Gu, G. Q. The European Physical Journal B 48, 439–444, (2005).
   https://doi.org/10.1140/epjb/e2005-00419-5.

10. Free Volume Properties of Sphingomyelin, DMPC, DPPC, and PLPC Bilayers. Kupiainen, Mikko; Falck, Emma; Ollila, Samuli; Niemelä, Perttu; Gurtovenko, Andrey A.; Hyvönen, Marja T.; Karttunen, Mikko; Vattulainen, Ilpo. Journal of Computational and Theoretical Nanoscience 2, 401-413 (2005).
    Article

2004

1. Turing Systems as Models of Complex Pattern Formation. Leppänen, Teemu; Karttunen, Mikko; Barrio, R. A.; Kaski, Kimmo. Braz. J. Phys. 34, (2004).
   https://doi.org/10.1590/s0103-97332004000300006.

2. Cationic DMPC/DMTAP Lipid Bilayers: Molecular Dynamics Study. Gurtovenko, Andrey A.; Patra, Michael; Karttunen, Mikko; Vattulainen, Ilpo. Biophys. J. 86, 3461–3472, (2004).
   https://doi.org/10.1529/biophysj.103.038760.

3. Electrokinetic Behavior of Two Touching Inhomogeneous Biological Cells and Colloidal Particles: Effects of Multipolar Interactions. Huang, J. P.; Karttunen, Mikko; Yu, K. W.; Dong, L.; Gu, G. Q. Phys. Rev. E 69, 051402, (2004).
   https://doi.org/10.1103/PhysRevE.69.051402.

4. Morphological Transitions and Bistability in Turing Systems. Leppänen, Teemu; Karttunen, Mikko; Barrio, R. A.; Kaski, Kimmo. Phys. Rev. E 70, 066202, (2004).
   https://doi.org/10.1103/physreve.70.066202.

5. Coarse-Grained Model for Phospholipid/cholesterol Bilayer Employing Inverse Monte Carlo with Thermodynamic Constraints. Murtola, Teemu; Falck, Emma; Karttunen, Mikko; Vattulainen, Ilpo. J. Chem. Phys. 121, 9156–9165, (2004).
   https://doi.org/10.1063/1.1803537.

6. Structural Effects of Small Molecules on Phospholipid Bilayers Investigated by Molecular Simulations. Lee, Bryan W.; Faller, Roland; Sum, Amadeu K.; Vattulainen, Ilpo; Patra, Michael; Karttunen, Mikko. Fluid Phase Equilib. 225, 63–68, (2004).
   https://doi.org/10.1016/j.fluid.2004.07.008.

7. Systematic Comparison of Force Fields for Microscopic Simulations of NaCl in Aqueous Solutions: Diffusion, Free Energy of Hydration, and Structural Properties. Patra, Michael; Karttunen, Mikko. J. Comput. Chem. 25, 678–689, (2004).
   https://doi.org/10.1002/jcc.10417.

8. Lipid Bilayers Driven to a Wrong Lane in Molecular Dynamics Simulations by Subtle Changes in Long-Range Electrostatic Interactions. Patra, Michael; Karttunen, Mikko; Hyvönen, Marja T.; Falck, Emma; Vattulainen, Ilpo. J. Phys. Chem. B 108, 4485–4494, (2004).
   https://doi.org/10.1021/jp031281a.

9. Anomalously Slow Phase Transitions in Self-Gravitating Systems. Ispolatov, I.; Karttunen, M. Physical Review E 70, (2004).
   https://doi.org/10.1103/PhysRevE.70.026102.

10. Nonlinear Alternating Current Responses of Dipolar Fluids. Huang, J.; Yu, K.; Karttunen, Mikko. Physical Review E 70, (2004).
    https://doi.org/10.1103/PhysRevE.70.011403.

11. Lessons of Slicing Membranes: Interplay of Packing, Free Area, and Lateral Diffusion in Phospholipid/cholesterol Bilayers. Falck, Emma; Patra, Michael; Karttunen, Mikko; Hyvönen, Marja T.; Vattulainen, Ilpo. Biophys. J. 87, 1076–1091, (2004).
    https://doi.org/10.1529/biophysj.104.041368.

12. Impact of Cholesterol on Voids in Phospholipid Membranes. Falck, Emma; Patra, Michael; Karttunen, Mikko; Hyvönen, Marja T.; Vattulainen, Ilpo. J. Chem. Phys. 121, 12676, (2004).
    https://doi.org/10.1063/1.1824033.

13. Crumpling of a Stiff Tethered Membrane. Åström, J. A.; Timonen, J.; Karttunen, Mikko. Phys. Rev. Lett. 93, (2004).
    https://doi.org/10.1103/PhysRevLett.93.244301.

14. Many-Body Dipole-Induced Dipole Model for Electrorheological Fluids. Ji-Ping, Huang; Kin-Wah, Yu. Chinese Phys. 13, 1065, (2004).
    https://doi.org/10.1088/1009-1963/13/7/017.

15. Spatio-Temporal Dynamics in a Turing Model. Leppänen, T.; Karttunen, M.; Barrio, R. A.; Kaski, K. Unifying Themes in Complex Systems. 2011, pp 215–222.
    https://doi.org/10.1007/978-3-642-17635-7_26.

16. Optical Nonlinearity Enhancement of Graded Metal-Dielectric Composite Films. Huang, J. P.; Dong, L.; Yu, K. W. EPL 67, 854–858, (2004).
    https://doi.org/10.1209/epl/i2004-10107-8.

17. The Theory of Turing Pattern Formation. Leppänen, Teemu. Current Topics in Physics 199–227 (2005).
    https://doi.org/10.1142/9781860947209_0011.

18. Dielectric Response of Graded Spherical Particles of Anisotropic Materials. Dong, L.; Huang, J. P.; Yu, K. W.; Gu, G. Q. J. Appl. Phys. 95, 621–624, (2004).
    https://doi.org/10.1063/1.1633648.

19. Modeling of biologically motivated soft matter systems. Vattulainen Ilpo; Karttunen, Mikko. Handbook of Theoretical and Computational Nanotechnology, edited by M. Rieth and W. Schommers (American Scientific Publishers).
    Article

2003

1. The Effect of Noise on Turing Patterns. Leppänen, T.; Karttunen, M.; Barrio, R. A.; Kaski, K. Progr. Theoret. Phys. 150, 367–370, (2003).
   https://doi.org/10.1143/PTPS.150.367.

2. Dimensionality Effects in Turing Pattern Formation. Leppänen, Teemu; Karttunen, Mikko; Kaski, Kimmo; Barrio, Rafael A. Int. J. Mod. Phys. B 17, 5541–5553, (2003).
   https://doi.org/10.1142/S0217979203023240.

3. Dielectrophoresis of Charged Colloidal Suspensions. Huang, J. P.; Karttunen, Mikko; Yu, K. W.; Dong, L. Phys. Rev. E 67, 021403, (2003).
   https://doi.org/10.1103/PhysRevE.67.021403.

4. Characterization of Sphingosine−Phosphatidylcholine Monolayers: Effects of DNA. Säily, V. Matti J.; Alakoskela, Juha-Matti; Ryhänen, Samppa J.; Karttunen, Mikko; Kinnunen, Paavo K. J. Langmuir 19, 8956–8963, (2003).
   https://doi.org/10.1021/la034307y.

5. Stability of Charge Inversion, Thomson Problem, and Application to Electrophoresis. Patra, Michael; Patriarca, Marco; Karttunen, Mikko. Phys. Rev. 67, 031402, (2003).
   https://doi.org/10.1103/PhysRevE.67.031402.

6. Molecular Dynamics Simulations of Lipid Bilayers: Major Artifacts due to Truncating Electrostatic Interactions. Patra, M.; Karttunen, M.; Hyvönen, M. T.; Falck, E.; Lindqvist, P.; Vattulainen, I. Biophys. J. 84, 3636–3645, (2003).
   https://doi.org/10.1016/S0006-3495(03)75094-2.

7. How Would You Integrate the Equations of Motion in Dissipative Particle Dynamics Simulations?. Nikunen, P.; Karttunen, M.; Vattulainen, I. Comput. Phys. Commun. 153, 407–423, (2003).
   https://doi.org/10.1016/S0010-4655(03)00202-9.

8. Collapses and Explosions in Self-Gravitating Systems. Ispolatov, I.; Karttunen, M. Phys. Rev. E 68, 036117, (2003).
   https://doi.org/10.1103/PhysRevE.68.036117.

9. Electrorotation in Graded Colloidal Suspensions. Huang, J. P.; Yu, K. W.; Gu, G. Q.; Karttunen, Mikko. Phys. Rev. E 67, 051405, (2003).
   https://doi.org/10.1103/PhysRevE.67.051405.

10. Decay Rate Distributions of Disordered Slabs and Application to Random Lasers. Patra, M. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 67, 016603, (2003).
    https://doi.org/10.1103/PhysRevE.67.016603.

2002

1. Instabilities and Resistance Fluctuations in Thin Accelerated Superconducting Rings. Karttunen, Mikko; Elder, K. R.; Tarlie, Martin B.; Grant, Martin. Phys. Rev. E 66, 026115, (2002).
   https://doi.org/10.1103/PhysRevE.66.026115.

2. Integration Schemes for Dissipative Particle Dynamics Simulations: From Softly Interacting Systems towards Hybrid Models. Vattulainen, I.; Karttunen, M.; Besold, G.; Polson, J. M. J. Chem. Phys. 116, 3967–3979, (2002).
   https://doi.org/10.1063/1.1450554.

3. A New Dimension to Turing Patterns. Leppänen, Teemu; Karttunen, M.; Kaski, Kimmo; Barrio, Rafael A.; Zhang, Limei. Physica D 168-169, 35–44, (2002).
   https://doi.org/10.1016/S0167-2789(02)00493-1.

4. On Coarse-Graining by the Inverse Monte Carlo Method: Dissipative Particle Dynamics Simulations Made to a Precise Tool in Soft Matter Modeling. Lyubartsev, Alexander P.; Karttunen, Mikko; Vattulainen, Ilpo; Laaksonen, Aatto. Soft Mater. 1, 121–137, (2002).
   https://doi.org/10.1081/SMTS-120016746.

5. Effects of Quenched Impurities on Surface Diffusion, Spreading, and Ordering of O/W(110). Nikunen, P.; Vattulainen, I.; Ala-Nissila, T. J. Chem. Phys. 117, 6757–6765, (2002).
   https://doi.org/10.1063/1.1505856.

Older papers

1. Towards Better Integrators for Dissipative Particle Dynamics Simulations. 1. Besold, G.; Vattulainen, I., I.; Karttunen, M.; Polson, J. M. Phys. Rev. E Stat. Phys. Plasmas Fluids Relat. Interdiscip. Topics 62, R7611–R7614, (2000).
   https://doi.org/10.1103/physreve.62.r7611.

   Abstract

   Coarse-grained models that preserve hydrodynamics provide a natural approach to study collective properties of soft-matter systems. Here, we demonstrate that commonly used integration schemes in dissipative particle dynamics give rise to pronounced artifacts in physical quantities such as the compressibility and the diffusion coefficient. We assess the quality of these integration schemes, including variants based on a recently suggested self-consistent approach, and examine their relative performance. Implications of integrator-induced effects are discussed.

   
   

2. Defects, Order, and Hysteresis in Driven Charge-Density Waves. Karttunen, Mikko; Haataja, Mikko; Elder, K. R.; Grant, Martin. Phys. Rev. Lett. 83, 3518–3521, (1999).
   https://doi.org/10.1103/PhysRevLett.83.3518.

   Abstract

   We model driven two-dimensional charge-density waves in random media via a modified Swift-Hohenberg equation, which includes both amplitude and phase fluctuations of the condensate. As the driving force is increased, the defect density first increases and then decreases. Furthermore, we find switching phenomena, due to the formation of channels of dislocations. These results are in qualitative accord with recent dynamical x-ray scattering experiments by Ringland et al. [Phys. Rev. Lett. 82, 1923 (1999)] and transport experiments by Lemay et al. [Phys. Rev. Lett. 83, 2793 (1999)].

   
   

3. Nucleation, Growth, and Scaling in Slow Combustion. Karttunen, Mikko; Provatas, Nikolas; Ala-Nissila, Tapio; Grant, Martin. J. Stat. Phys. 90, 1401–1411, (1998).
   https://doi.org/10.1023/A:1023243831128.

   Abstract

   We study the nucleation and growth of flame fronts in slow combustion. This is modeled by a set of reaction-diffusion equations for the temperature field, coupled to a background of reactants and augmented by a term describing random temperature fluctuations for ignition. We establish connections between this model and the classical theories of nucleation and growth of droplets from a metastable phase. Our results are in good agreement with theoretical predictions.

   
   

4. The Internet Pilot to Physics: An Open Information System for Physics Research and Education. Karttunen, Mikko; Holmlund, Kenneth; Nowotny, Günther. Int. J. Mod. Phys. C 8, 3–17, (1997).
   https://doi.org/10.1142/S0129183197000035.

   Abstract

   In this article we discuss the effort made by the Internet Pilot to Physics (TIPTOP) project to develop a uniform and open information infrastructure for physics research and education. We discuss concepts such as communication in research and education, the importance of distributed indexing and catalogs, and briefly the use of new technology such as VRML and Java. We also stress the importance of developing and using standardized protocols and formats such as the Summary Object Interchangeable Format (SOIF) and Thematic Uniform Resource Agents (TURA). TIPTOP has rapidly grown to be one of the most popular physics Internet knowledge servers, and the recently established collaboration with the European Physical Society is an important step forward in developing a stable framework of high quality information for researchers and educators.

   
   

5. Residual Stresses in Plastic Random Systems. Alava, M. J.; Karttunen, M. E. J.; Niskanen, K. J. EPL 32, 143, (1995).
   https://doi.org/10.1209/0295-5075/32/2/009.

   Abstract

   We show that yielding in elastic plastic materials creates residual stresses when local disorder is present. The intensity of these stresses grows with the external stress and degree of initial disorder. The one-dimensional model we employ also yields a discontinuous transition to perfect plasticity when the lateral stress transfer approaches zero. In the limit of vanishing but positive coupling we obtain the same critical value of stress or surface energy, as reported earlier for the two-dimensional case corresponding to the (1 + 1)-D minimal-interface problem.

   
   

6. Fracture in Mesoscopic Disordered Systems. Karttunen, M. E.; Niskanen, K. J.; Kaski, K.Phys. Rev. B Condens. Matter 49, 9453–9459, (1994).
   https://doi.org/10.1103/physrevb.49.9453.

   Abstract

   A simple mechanical model of planar fibrous materials with mesoscopic disorder is introduced and analyzed. In this scalar model a shear modulus controls the stress transfer in the transverse direction. The system is studied using the effective medium approximation and computer simulations; the comparison between them is quite favorable. In the disorder-controlled regime the stress-strain relation, the number of broken cells at the onset of crack propagation, and the length of the final crack scale with the system size as \(L^2\), \(L^{1.7}\), and \(L\), respectively. The mechanical properties are controlled by the interplay between disorder and shear modulus, which is studied in detail.

   
   

7. Molecular Dynamics of a Microscopic Droplet on Solid Surface. Nieminen, J. A.; Abraham, D. B.; Karttunen, M.; Kaski, K. Phys. Rev. Lett. 69, 124–127, (1992).
   https://doi.org/10.1103/PhysRevLett.69.124.

   Abstract

   The spreading of a microscopic nonvolatile liquid drop on a homogeneous solid substrate has been studied by molecular dynamics simulations of Lennard-Jones systems. The substrate is assumed to be continuous without atomic structure and exerting a long-range Lennard-Jones potential on liquid particles in the drop. A molecular precursor film is observed to spread initially with nearly constant speed, crossing over later to diffusive spreading. These results are compared with recent molecular dynamics results of a volatile liquid on a structured substrate.