The MARTINI Force Field: Coarse Grained Model for Biomolecular Simulations

VMD: visual molecular dynamics

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Coarse Grained Model for Semiquantitative Lipid Simulations

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Molecular Basis for Dimethylsulfoxide (DMSO) Action on Lipid Membranes

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Lipids Out of Equilibrium: Energetics of Desorption and Pore Mediated Flip-Flop

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‘GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers’

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Computer Simulation of Liquids

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Oxidized arachidonic and adrenic PEs navigate cells to ferroptosis

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Martini 3: A general purpose force field for coarse-grained molecular dynamics

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‘Improved parameters for the Martini coarse-grained protein force field’

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Computational Lipidomics with insane : A Versatile Tool for Generating Custom Membranes for Molecular Simulations

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Physical Principles of Nanoparticle Cellular Endocytosis

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Cardiolipin externalization to the outer mitochondrial membrane acts as an elimination signal for mitophagy in neuronal cells

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Perspective: Coarse-grained models for biomolecular systems

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Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development

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Membrane protein sequestering by ionic protein–lipid interactions

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Derivation and Systematic Validation of a Refined All-Atom Force Field for Phosphatidylcholine Lipids

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How Water’s Properties Are Encoded in Its Molecular Structure and Energies

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CHARMM-GUI Martini Maker for Coarse-Grained Simulations with the Martini Force Field

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Structure of the yeast F 1 F o -ATP synthase dimer and its role in shaping the mitochondrial cristae

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Multiscale simulation of soft matter systems–from the atomistic to the coarse-grained level and back

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Emerging Diversity in Lipid–Protein Interactions

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Lipids on the move: Simulations of membrane pores, domains, stalks and curves

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Polyunsaturated phospholipids facilitate membrane deformation and fission by endocytic proteins

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Polystyrene Nanoparticles Perturb Lipid Membranes

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AI-based structure prediction empowers integrative structural analysis of human nuclear pores

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Mobile Ions in Composite Solids

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Lipid bodies containing oxidatively truncated lipids block antigen cross-presentation by dendritic cells in cancer

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Characterization of Lipid-Protein Interactions and Lipid-Mediated Modulation of Membrane Protein Function through Molecular Simulation

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Martini coarse-grained force field: Extension to RNA

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Two decades of Martini: Better beads, broader scope

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Coarse-grained molecular modeling of non-ionic surfactant self-assembly

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Membrane Protein Structure, Function, and Dynamics: a Perspective from Experiments and Theory

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CHARMM‐GUI Martini Maker for modeling and simulation of complex bacterial membranes with lipopolysaccharides

Hsu, P., Bruininks, B. M. H., Jefferies, D., Cesar Telles de Souza, P., Lee, J., Patel, D. S., Marrink, S. J., Qi, Y., Khalid, S., & Im, W. (2017) · Journal of Computational Chemistry · doi:10.1002/jcc.24895 · Cited by 202

Disease‐linked TDP‐43 hyperphosphorylation suppresses TDP‐43 condensation and aggregation

Gruijs da Silva, L. A., Simonetti, F., Hutten, S., Riemenschneider, H., Sternburg, E. L., Pietrek, L. M., Gebel, J., Dötsch, V., Edbauer, D., Hummer, G., et al. (2022) · The EMBO Journal · doi:10.15252/embj.2021108443 · Cited by 192

Combining native and ‘omics’ mass spectrometry to identify endogenous ligands bound to membrane proteins

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Biomolecular simulations: from dynamics and mechanisms to computational assays of biological activity

Huggins, D. J., Biggin, P. C., Dämgen, M. A., Essex, J. W., Harris, S. A., Henchman, R. H., Khalid, S., Kuzmanic, A., Laughton, C. A., Michel, J., et al. (2019) · WIREs Computational Molecular Science · doi:10.1002/wcms.1393 · Cited by 169

Structure of Human ATG9A, the Only Transmembrane Protein of the Core Autophagy Machinery

Guardia, C. M., Tan, X.-F., Lian, T., Rana, M. S., Zhou, W., Christenson, E. T., Lowry, A. J., Faraldo-Gómez, J. D., Bonifacino, J. S., Jiang, J., et al. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.107837 · Cited by 163

Hydrophobic mismatch sorts SNARE proteins into distinct membrane domains

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Molecular modeling of protein materials: case study of elastin

Tarakanova, A., & Buehler, M. J. (2013) · Modelling and Simulation in Materials Science and Engineering · doi:10.1088/0965-0393/21/6/063001 · Cited by 160

Microstructure of Catalyst Layers in PEM Fuel Cells Redefined: A Computational Approach

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CG2AT2: An enhanced fragment-based approach for serial multi-scale molecular dynamics simulations

Vickery, O. N., & Stansfeld, P. J. (2021) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.1c00295 · Cited by 150

Solvent-Exposed Tails as Prestalk Transition States for Membrane Fusion at Low Hydration

Smirnova, Y. G., Marrink, S.-J., Lipowsky, R., & Knecht, V. (2010) · Journal of the American Chemical Society · doi:10.1021/ja910050x · Cited by 149

Accelerating the prediction and discovery of peptide hydrogels with human-in-the-loop

Xu, T., Wang, J., Zhao, S., Chen, D., Zhang, H., Fang, Y., Kong, N., Zhou, Z., Li, W., & Wang, H. (2023) · Nature Communications · doi:10.1038/s41467-023-39648-2 · Cited by 132

Intrinsic map dynamics exploration for uncharted effective free-energy landscapes

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Martinize2 and Vermouth provide a unified framework for molecular topology generation

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Structure of the human signal peptidase complex reveals the determinants for signal peptide cleavage

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Martini 3 Coarse-Grained Force Field for Carbohydrates

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How good are coarse-grained polymer models? A comparison for atactic polystyrene

Karimi‐Varzaneh, H. A., van der Vegt, N. F. A., Müller‐Plathe, F., & Carbone, P. (2012) · ChemPhysChem · doi:10.1002/cphc.201200111 · Cited by 114

Cholesterol in bilayers with PUFA chains: doping with DMPC or POPC results in sterol reorientation and membrane-domain formation

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Comparison of mechanistic transport cycle models of ABC exporters

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Free energies of membrane stalk formation from a lipidomics perspective

Poojari, C. S., Scherer, K. C., & Hub, J. S. (2021) · Nature Communications · doi:10.1038/s41467-021-26924-2 · Cited by 98

Oleic Acid Phase Behavior from Molecular Dynamics Simulations

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Coarse-grained modeling of polystyrene in various environments by iterative Boltzmann inversion

Bayramoglu, B., & Faller, R. (2012) · Macromolecules · doi:10.1021/ma301280b · Cited by 86

Structural basis for membrane attack complex inhibition by CD59

Couves, E. C., Gardner, S., Voisin, T. B., Bickel, J. K., Stansfeld, P. J., Tate, E. W., & Bubeck, D. (2023) · Nature Communications · doi:10.1038/s41467-023-36441-z · Cited by 85

Thermodynamically reversible paths of the first fusion intermediate reveal an important role for membrane anchors of fusion proteins

Smirnova, Y. G., Risselada, H. J., & Müller, M. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1818200116 · Cited by 81

Branched peptides integrate into self-assembled nanostructures and enhance biomechanics of peptidic hydrogels

Pugliese, R., Fontana, F., Marchini, A., & Gelain, F. (2018) · Acta Biomaterialia · doi:10.1016/j.actbio.2017.11.026 · Cited by 65

MagiC: software package for multiscale modeling

Mirzoev, A., & Lyubartsev, A. P. (2013) · Journal of Chemical Theory and Computation · doi:10.1021/ct301019v · Cited by 62

Mechanics of membrane fusion/pore formation

Fuhrmans, M., Marelli, G., Smirnova, Y. G., & Müller, M. (2015) · Chemistry and Physics of Lipids · doi:10.1016/j.chemphyslip.2014.07.010 · Cited by 65

Dispersion of Nafion Ionomer Aggregates in 1-Propanol/Water Solutions: Effects of Ionomer Concentration, Alcohol Content, and Salt Addition

Mabuchi, T., Huang, S.-F., & Tokumasu, T. (2020) · Macromolecules · doi:10.1021/acs.macromol.9b02725 · Cited by 64

Molecular architecture and dynamics of SARS-CoV-2 envelope by integrative modeling

Pezeshkian, W., Grünewald, F., Narykov, O., Lu, S., Arkhipova, V., Solodovnikov, A., Wassenaar, T. A., Marrink, S. J., & Korkin, D. (2023) · Structure · doi:10.1016/j.str.2023.02.006 · Cited by 60

Molecular architecture and dynamics of SARS-CoV-2 envelope by integrative modeling

Pezeshkian, W., Grünewald, F., Narykov, O., Lu, S., Arkhipova, V., Solodovnikov, A., Wassenaar, T. A., Marrink, S. J., & Korkin, D. (2021) · doi:10.1101/2021.09.15.459697 · Cited by 60

Interplay of G Protein-Coupled Receptors with the Membrane: Insights from Supra-Atomic Coarse Grain Molecular Dynamics Simulations

Periole, X. (2017) · Chemical Reviews · doi:10.1021/acs.chemrev.6b00344 · Cited by 58

Coarse-Grained Modeling of Polyethylene Melts: Effect on Dynamics

Peters, B. L., Salerno, K. M., Agrawal, A., Perahia, D., & Grest, G. S. (2017) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00241 · Cited by 58

Facilitating CG Simulations with MAD: The MArtini Database Server

Hilpert, C., Beranger, L., Souza, P. C. T., Vainikka, P. A., Nieto, V., Marrink, S. J., Monticelli, L., & Launay, G. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c01375 · Cited by 51

Crosstalk between regulatory elements in disordered TRPV4 N-terminus modulates lipid-dependent channel activity

Goretzki, B., Wiedemann, C., McCray, B. A., Schäfer, S. L., Jansen, J., Tebbe, F., Mitrovic, S.-A., Nöth, J., Cabezudo, A. C., Donohue, J. K., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-39808-4 · Cited by 51

Targeting oncogenic KRAS with molecular brush-conjugated antisense oligonucleotides

Wang, D., Wang, Q., Wang, Y., Chen, P., Lu, X., Jia, F., Sun, Y., Sun, T., Zhang, L., Che, F., et al. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2113180119 · Cited by 43

Insertases scramble lipids: Molecular simulations of MTCH2

Bartoš, L., Menon, A. K., & Vácha, R. (2024) · Structure · doi:10.1016/j.str.2024.01.012 · Cited by 42

Effective Molecular Dynamics from Neural Network-Based Structure Prediction Models

Jussupow, A., & Kaila, V. R. I. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c01027 · Cited by 42

Synthetic Membrane Shaper for Controlled Liposome Deformation

De Franceschi, N., Pezeshkian, W., Fragasso, A., Bruininks, B. M. H., Tsai, S., Marrink, S. J., & Dekker, C. (2023) · ACS Nano · doi:10.1021/acsnano.2c06125 · Cited by 40

Glimps: A machine learning approach to resolution transformation for multiscale modeling

Louison, K. A., Dryden, I. L., & Laughton, C. A. (2021) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.1c00735 · Cited by 35

GENESIS CGDYN: large-scale coarse-grained MD simulation with dynamic load balancing for heterogeneous biomolecular systems

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Computational amphiphilic materials for drug delivery

Thota, N., & Jiang, J. (2015) · Frontiers in Materials · doi:10.3389/fmats.2015.00064 · Cited by 38

Coarse-Grained Parameterization of Nucleotide Cofactors and Metabolites: Protonation Constants, Partition Coefficients, and Model Topologies

Sousa, F. M., Lima, L. M. P., Arnarez, C., Pereira, M. M., & Melo, M. N. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c01077 · Cited by 30

Sensing their plasma membrane curvature allows migrating cells to circumvent obstacles

Sitarska, E., Almeida, S. D., Beckwith, M. S., Stopp, J., Czuchnowski, J., Siggel, M., Roessner, R., Tschanz, A., Ejsing, C., Schwab, Y., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-41173-1 · Cited by 34

‘Assembly and analysis of cell-scale membrane envelopes’

Vermaas, J. V., Mayne, C. G., Shinn, E., & Tajkhorshid, E. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c01050 · Cited by 30

ATP-induced asymmetric pre-protein folding as a driver of protein translocation through the Sec machinery.

Corey, R. A., Ahdash, Z., Shah, A., Pyle, E., Allen, W. J., Fessl, T., Lovett, J. E., Politis, A., & Collinson, I. (2019) · eLife · doi:10.7554/elife.41803 · Cited by 30

ExEnDiff: An Experiment-Guided Diffusion Model for Protein Conformational Ensemble Generation

Liu, Y., Yu, Z., Lindsay, R. J., Lin, G., Chen, M., Sahoo, A., & Hanson, S. M. (2025) · PRX Life · doi:10.1103/prxlife.3.023013 · Cited by 28

ddcMD: A fully GPU-accelerated molecular dynamics program for the Martini force field

Zhang, X., Sundram, S., Oppelstrup, T., Kokkila-Schumacher, S. I. L., Carpenter, T. S., Ingólfsson, H. I., Streitz, F. H., Lightstone, F. C., & Glosli, J. N. (2020) · The Journal of Chemical Physics · doi:10.1063/5.0014500 · Cited by 25

From Membrane Composition to Antimicrobial Strategies: Experimental and Computational Approaches to AMP Design and Selectivity

Rossetti, P., Trollmann, M. F. W., Wichmann, C., Gutsmann, T., Eggeling, C., & Böckmann, R. A. (2026) · Small · doi:10.1002/smll.202411476 · Cited by 25

Nanoparticle transport across model cellular membranes: when do solubility-diffusion models break down?

Smith, D. J., Leal, L. G., Mitragotri, S., & Shell, M. S. (2018) · Journal of Physics D: Applied Physics · doi:10.1088/1361-6463/aacac9 · Cited by 23

Molecular dynamics of the recruitment of immunoreceptor signaling module DAP12 homodimer to lipid raft boundary regulated by PIP2

Dong, R., Tan, Y., Fan, A., Liao, Z., Liu, H., & Wei, P. (2020) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.9b11095 · Cited by 22

‘Asynchronous reciprocal coupling of Martini 2.2 coarse-grained and CHARMM36 all-atom simulations in an automated multiscale framework’

López, C. A., Zhang, X., Aydin, F., Shrestha, R., Van, Q. N., Stanley, C. B., Carpenter, T. S., Nguyen, K., Patel, L. A., Chen, D., et al. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c00168 · Cited by 18

Characterization of the membrane interactions of phospholipase Cγ reveals key features of the active enzyme

Le Huray, K. I. P., Bunney, T. D., Pinotsis, N., Kalli, A. C., & Katan, M. (2022) · Science Advances · doi:10.1126/sciadv.abp9688 · Cited by 21

Membrane contact probability: An essential and predictive character for the structural and functional studies of membrane proteins

Wang, L., Zhang, J., Wang, D., & Song, C. (2022) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1009972 · Cited by 18

Intrinsically disordered region amplifies membrane remodeling to augment selective ER-phagy

Poveda-Cuevas, S. A., Lohachova, K., Markusic, B., Dikic, I., Hummer, G., & Bhaskara, R. M. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2408071121 · Cited by 18

Coarse-Grained Model of Phytic Acid for Predicting the Supramolecular Architecture of Ionically Cross-Linked Chitosan Hydrogels

Visan, R. M., & Angelescu, D. G. (2023) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.3c02115 · Cited by 15

Critical residues and motifs for homodimerization of the first transmembrane domain of the plasma membrane glycoprotein CD36

Wei, P., Sun, F.-D., Zuo, L.-M., Qu, J., Chen, P., Xu, L.-D., & Luo, S.-Z. (2017) · Journal of Biological Chemistry · doi:10.1074/jbc.m117.779595 · Cited by 11

Structure of the Human Signal Peptidase Complex Reveals the Determinants for Signal Peptide Cleavage

Liaci, A. M., Steigenberger, B., Tamara, S., de Souza, P. C. T., Gröllers-Mulderij, M., Ogrissek, P., Marrink, S. J., Scheltema, R. A., & Förster, F. (2020) · doi:10.1101/2020.11.11.378711 · Cited by 10

ATP-Bound State of the Uncoupling Protein 1 (UCP1) from Molecular Simulations

Jacobsen, L., Lydersen, L., & Khandelia, H. (2023) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.3c03473 · Cited by 10

Free Energy of Membrane Pore Formation and Stability from Molecular Dynamics Simulations

Rivel, T., Biriukov, D., Kabelka, I., & Vácha, R. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01960 · Cited by 9

Spontaneous transmembrane pore formation by short-chain synthetic peptide

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