The DOI matches its Crossref record; 92 of the 92 references it cites were found in a registry or an index.
Martini 3: a general purpose force field for coarse-grained molecular dynamics
Open accessAbstract
The coarse-grained Martini force field is widely used in biomolecular simulations. Here we present the refined model, Martini 3 ( http://cgmartini.nl ), with an improved interaction balance, new bead types and expanded ability to include specific interactions representing, for example, hydrogen bonding and electronic polarizability. The updated model allows more accurate predictions of molecular packing and interactions in general, which is exemplified with a vast and diverse set of applications, ranging from oil/water partitioning and miscibility data to complex molecular systems, involving protein-protein and protein-lipid interactions and material science applications as ionic liquids and aedamers.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“With over 800 different bead types and a recalibrated interaction matrix, Martini 3 offers an improved framework for CG MD simulations (Souza et al., 2021).”
From Molecular dynamics simulation of an entire cell (2023) · cites this paper as Souza et al., 2021
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
VMD: visual molecular dynamics
Humphrey, W., Dalke, A., & Schulten, K. (1996) · Journal of Molecular Graphics · doi:10.1016/0263-7855(96)00018-5 · Cited by 66,952
Abraham, M. J., Murtola, T., Schulz, R., Páll, S., Smith, J. C., Hess, B., & Lindahl, E. (2015) · SoftwareX · doi:10.1016/j.softx.2015.06.001 · Cited by 27,190
A smooth particle mesh Ewald method
Essmann, U., Perera, L., Berkowitz, M., Darden, T., Lee, H.-C., & Pedersen, L. (1995) · The Journal of Chemical Physics · doi:10.1063/1.470117 · Cited by 21,378
Polymorphic transitions in single crystals: A new molecular dynamics method
Parrinello, M., & Rahman, A. (1981) · Journal of Applied Physics · doi:10.1063/1.328693 · Cited by 20,009
GROMACS: Fast, flexible, and free
van der Spoel, D., Lindahl, E. R., Hess, B., Groenhof, G., Mark, A. E., & Berendsen, H. J. C. (2005) · Journal of Computational Chemistry · doi:10.1002/jcc.20291 · Cited by 19,461
Canonical sampling through velocity rescaling
Bussi, G., Donadio, D., & Parrinello, M. (2007) · The Journal of Chemical Physics · doi:10.1063/1.2408420 · Cited by 18,327
Computer "Experiments" on Classical Fluids. I. Thermodynamical Properties of Lennard-Jones Molecules
Verlet, L. (1967) · Physical Review · doi:10.1103/physrev.159.98 · Cited by 9,900
The MARTINI Force Field: Coarse Grained Model for Biomolecular Simulations
Marrink, . J., Risselada, H. J., Yefimov, S., Tieleman, D. P., & de Vries, A. H. (2007) · The Journal of Physical Chemistry B · doi:10.1021/jp071097f · Cited by 5,641
Self-citationThe MARTINI Coarse-Grained Force Field: Extension to Proteins
Monticelli, L., Kandasamy, S. K., Périole, X., Larson, R. G., Tieleman, D. P., & Marrink, . J. (2008) · Journal of Chemical Theory and Computation · doi:10.1021/ct700324x · Cited by 2,521
Self-citationCoarse Grained Model for Semiquantitative Lipid Simulations
Marrink, S. J., de Vries, A. H., & Mark, A. E. (2004) · The Journal of Physical Chemistry B · doi:10.1021/jp036508g · Cited by 2,257
Cited by
Sorted by citations
Joseph, J. A., Reinhardt, A., Aguirre, A., Chew, P. Y., Russell, K. O., Espinosa, J. R., Garaizar, A., & Collepardo‐Guevara, R. (2021) · Nature Computational Science · doi:10.1038/s43588-021-00155-3 · Cited by 379
Piersimoni, L., Kastritis, P. L., Arlt, C., & Sinz, A. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00786 · Cited by 327
Bottom-up Coarse-Graining: Principles and Perspectives
Jin, J., Pak, A. J., Durumeric, A. E. P., Loose, T. D., & Voth, G. A. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c00643 · Cited by 321
Two decades of Martini: Better beads, broader scope
Marrink, S. J., Monticelli, L., Melo, M. N., Alessandri, R., Tieleman, D. P., & Souza, P. C. T. (2023) · WIREs Computational Molecular Science · doi:10.1002/wcms.1620 · Cited by 255
Self-citationFrallicciardi, J., Melcr, J., Siginou, P., Marrink, . J., & Poolman, B. (2022) · Nature Communications · doi:10.1038/s41467-022-29272-x · Cited by 275
Self-citationStructural basis of NINJ1-mediated plasma membrane rupture in cell death
Degen, M., Santos, J. C., Pluháčková, K., Cebrero, G., Ramos, S., Jankevicius, G., Hartenian, E., Guillerm, U., Mari, S. A., Kohl, B., et al. (2023) · Nature · doi:10.1038/s41586-023-05991-z · Cited by 280
Martini 3 Coarse‐Grained Force Field: Small Molecules
Alessandri, R., Barnoud, J., Gertsen, A. S., Patmanidis, I., de Vries, A. H., Souza, P. C. T., & Marrink, . J. (2021) · Advanced Theory and Simulations · doi:10.1002/adts.202100391 · Cited by 213
Self-citationSong, W., Corey, R. A., Ansell, T. B., Cassidy, C. K., Horrell, M. R., Duncan, A. L., Stansfeld, P. J., & Sansom, M. S. P. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.1c00708 · Cited by 197
Molecular dynamics simulation of an entire cell
Stevens, J. A., Grünewald, F., van Tilburg, P. A. M., König, M., Gilbert, B. R., Brier, T. A., Thornburg, Z. R., Luthey-Schulten, Z., & Marrink, S. J. (2023) · Frontiers in Chemistry · doi:10.3389/fchem.2023.1106495 · Cited by 156
“With over 800 different bead types and a recalibrated interaction matrix, Martini 3 offers an improved framework for CG MD simulations (Souza et al., 2021).”
MethodSelf-citationPolyply; a python suite for facilitating simulations of macromolecules and nanomaterials
Grünewald, F., Alessandri, R., Kroon, P. C., Monticelli, L., Souza, P. C. T., & Marrink, . J. (2022) · Nature Communications · doi:10.1038/s41467-021-27627-4 · Cited by 178
Self-citationImproving Martini 3 for Disordered and Multidomain Proteins
Thomasen, F. E., Pesce, F., Roesgaard, M. A., Tesei, G., & Lindorff‐Larsen, K. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.1c01042 · Cited by 143
Zhang, X., Ma, G., & Wei, W. (2021) · NPG Asia Materials · doi:10.1038/s41427-021-00320-0 · Cited by 157
Glycosaminoglycans: What Remains To Be Deciphered?
Pérez, S., Makshakova, O., Angulo, J., Bedini, E., Bisio, A., de Paz, J. L., Fadda, E., Guerrini, M., Hricovíni, M., Hricovı́ni, M., et al. (2023) · JACS Au · doi:10.1021/jacsau.2c00569 · Cited by 148
Perspective: Advances, Challenges, and Insight for Predictive Coarse-Grained Models
Noid, W. G. (2023) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.2c08731 · Cited by 145
Micropolarity governs the structural organization of biomolecular condensates
Ye, S., Latham, A. P., Tang, Y., Hsiung, C., Chen, J., Luo, F., Liu, Y., Zhang, B., & Zhang, X. (2023) · Nature Chemical Biology · doi:10.1038/s41589-023-01477-1 · Cited by 144
The Martini Model in Materials Science
Alessandri, R., Grünewald, F., & Marrink, . J. (2021) · Advanced Materials · doi:10.1002/adma.202008635 · Cited by 136
Self-citationA coarse‐grained model for disordered and multi‐domain proteins
Cao, F., von Bülow, S., Tesei, G., & Lindorff‐Larsen, K. (2024) · Protein Science · doi:10.1002/pro.5172 · Cited by 134
Structural basis of mitochondrial membrane bending by the I–II–III2–IV2 supercomplex
Mühleip, A., Flygaard, R. K., Baradaran, R., Haapanen, O., Gruhl, T., Tobiasson, V., Maréchal, A., Sharma, V., & Amunts, A. (2023) · Nature · doi:10.1038/s41586-023-05817-y · Cited by 134
Nanocrystal Assemblies: Current Advances and Open Problems
Bassani, C. L., van Anders, G., Banin, U., Baranov, D., Chen, Q., Dijkstra, M., Dimitriyev, M. S., Efrati, E., Faraudo, J., Gang, O., et al. (2024) · ACS Nano · doi:10.1021/acsnano.3c10201 · Cited by 131
Structure of the human signal peptidase complex reveals the determinants for signal peptide cleavage
(2021) · doi:10.1016/j.molcel.2021.07.031 · Cited by 120
Self-citationMartinize2 and Vermouth provide a unified framework for molecular topology generation
Kroon, P. C., Grünewald, F., Barnoud, J., van Tilburg, M., Brasnett, C., Souza, P. C., Wassenaar, T. A., & Marrink, S. J. (2025) · eLife · doi:10.7554/elife.90627 · Cited by 124
Self-citationPrinciples Governing the Phase Separation of Multidomain Proteins
Mohanty, P., Kapoor, U., Devarajan, D. S., Phan, T. M., Rizuan, A., & Mittal, J. (2022) · Biochemistry · doi:10.1021/acs.biochem.2c00210 · Cited by 119
Chemically specific coarse‐graining of polymers: Methods and prospects
Dhamankar, S., & Webb, M. A. (2021) · Journal of Polymer Science · doi:10.1002/pol.20210555 · Cited by 118
Martini 3 Coarse-Grained Force Field for Carbohydrates
Grünewald, F., Punt, M. H., Jefferys, E. E., Vainikka, P. A., König, M., Virtanen, V., Meyer, T. A., Pezeshkian, W., Gormley, A. J., Karonen, M., et al. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c00757 · Cited by 116
Self-citationPatel, R. A., & Webb, M. A. (2023) · ACS Applied Bio Materials · doi:10.1021/acsabm.2c00962 · Cited by 121
Zhou, P., Xing, R., Li, Q., Li, J., Yuan, C., & Yan, X. (2023) · Matter · doi:10.1016/j.matt.2023.03.029 · Cited by 110
Current practices in the study of biomolecular condensates: a community comment
Alberti, S., Arosio, P., Best, R. B., Boeynaems, S., Cai, D., Collepardo‐Guevara, R., Dignon, G. L., Dimova, R., Elbaum‐Garfinkle, S., Fawzi, N. L., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-62055-8 · Cited by 116
Self-citationDevarajan, D. S., Wang, J., Szała-Mendyk, B., Rekhi, S., Nikoubashman, A., Kim, Y. C., & Mittal, J. (2024) · Nature Communications · doi:10.1038/s41467-024-46223-w · Cited by 117
Computational Approaches to Predict Protein–Protein Interactions in Crowded Cellular Environments
Grassmann, G., Miotto, M., Desantis, F., Rienzo, L. D., Tartaglia, G. G., Pastore, A., Ruocco, G., Monti, M. M., & Milanetti, E. (2024) · Chemical Reviews · doi:10.1021/acs.chemrev.3c00550 · Cited by 109
Hofmeister Effects Shine in Nanoscience
Wei, W.-C. (2023) · Advanced Science · doi:10.1002/advs.202302057 · Cited by 101
GōMartini 3: From large conformational changes in proteins to environmental bias corrections
Souza, P. C. T., Borges-Araújo, L., Brasnett, C., Moreira, R. A., Grünewald, F., Park, P., Wang, L., Razmazma, H., Borges-Araújo, A. C., Cofas‐Vargas, L. F., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-58719-0 · Cited by 101
Self-citationIngólfsson, H. I., Neale, C., Carpenter, T. S., Shrestha, R., López, C. A., Tran, T. H., Oppelstrup, T., Bhatia, H., Stanton, L. G., Zhang, X., et al. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2113297119 · Cited by 96
Conformational ensembles of intrinsically disordered proteins and flexible multidomain proteins
Thomasen, F. E., & Lindorff‐Larsen, K. (2022) · Biochemical Society Transactions · doi:10.1042/bst20210499 · Cited by 95
Applications of Molecular Dynamics Simulations in Drug Discovery
AlRawashdeh, S., & Barakat, K. (2023) · Methods in molecular biology · doi:10.1007/978-1-0716-3441-7_7 · Cited by 96
Tsanai, M., Frederix, P. W. J. M., Schroer, C. F. E., Souza, P. C. T., & Marrink, . J. (2021) · Chemical Science · doi:10.1039/d1sc00374g · Cited by 87
Self-citationMartini 3 Coarse-Grained Force Field for Cholesterol
Borges-Araújo, L., Borges-Araújo, A. C., Öztürk, T. N., Ramirez-Echemendía, D. P., Fábián, B., Carpenter, T. S., Thallmair, S., Barnoud, J., Ingólfsson, H. I., Hummer, G., et al. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c00547 · Cited by 87
Self-citationWinkeljann, B., Keul, D. C., & Merkel, O. M. (2022) · Journal of Controlled Release · doi:10.1016/j.jconrel.2022.12.008 · Cited by 87
Identification and assessment of cardiolipin interactions with E. coli inner membrane proteins
Corey, R. A., Song, W., Duncan, A. L., Ansell, T. B., Sansom, M. S. P., & Stansfeld, P. J. (2021) · Science Advances · doi:10.1126/sciadv.abh2217 · Cited by 80
Intra-Lysosomal Peptide Assembly for the High Selectivity Index against Cancer
Jana, B., Jin, S., Go, E. M., Cho, Y., Kim, D., Kim, S., Kwak, S. K., & Ryu, J. (2023) · Journal of the American Chemical Society · doi:10.1021/jacs.3c04467 · Cited by 81
Pragmatic Coarse-Graining of Proteins: Models and Applications
Borges-Araújo, L., Patmanidis, I., Singh, A. P., Santos, L. H., Sieradzan, A. K., Vanni, S., Czaplewski, C. R., Pantano, S., Shinoda, W., Monticelli, L., et al. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c00733 · Cited by 80
Self-citationKaila, V. R. I. (2021) · Accounts of Chemical Research · doi:10.1021/acs.accounts.1c00524 · Cited by 77
Predicting Biomolecular Binding Kinetics: A Review
Wang, J., Do, H. N., Koirala, K., & Miao, Y. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c01085 · Cited by 77
Stable peptide-assembled nanozyme mimicking dual antifungal actions
Yuan, Y., Chen, L., Song, K., Cheng, M., Fang, L., Kong, L., Yu, L., Wang, R., Fu, Z., Sun, M., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-50094-6 · Cited by 77
Zhan, Y., Hu, X., Li, Y., Wang, Y., Chen, H., Omolo, C. A., Govender, T., Li, H., Huang, F., Shi, L., et al. (2023) · Advanced Functional Materials · doi:10.1002/adfm.202214299 · Cited by 75
Theory and Practice of Coarse-Grained Molecular Dynamics of Biologically Important Systems
Liwo, A., Czaplewski, C. R., Sieradzan, A. K., Lipska, A. G., Samsonov, S. A., & Murarka, R. K. (2021) · Biomolecules · doi:10.3390/biom11091347 · Cited by 74
The Martini 3 Lipidome: Expanded and Refined Parameters Improve Lipid Phase Behavior
Pedersen, K. B., Ingólfsson, H. I., Ramirez-Echemendía, D. P., Borges-Araújo, L., Andreasen, M. D., Empereur‐mot, C., Melcr, J., Öztürk, T. N., Bennett, W. F., Kjølbye, L. R., et al. (2025) · ACS Central Science · doi:10.1021/acscentsci.5c00755 · Cited by 80
Self-citationMultifaceted Computational Modeling in Glycoscience
Pérez, S., & Makshakova, O. (2022) · Chemical Reviews · doi:10.1021/acs.chemrev.2c00060 · Cited by 70
Thermodynamically stable plumber’s nightmare structures in block copolymers
Lee, H., Kwon, S., Min, J., Jin, S., Hwang, J. H., Lee, E. J., Lee, W. B., & Park, M. J. (2024) · Science · doi:10.1126/science.adh0483 · Cited by 71
Phospholipids are imported into mitochondria by VDAC, a dimeric beta barrel scramblase
Jahn, H., Bartoš, L., Dearden, G. I., Dittman, J. S., Holthuis, J. C. M., Vácha, R., & Menon, A. K. (2023) · Nature Communications · doi:10.1038/s41467-023-43570-y · Cited by 74
Theoretical and Data-Driven Approaches for Biomolecular Condensates
Saar, K. L., Qian, D., Good, L. L., Morgunov, A. S., Collepardo‐Guevara, R., Best, R. B., & Knowles, T. P. J. (2023) · Chemical Reviews · doi:10.1021/acs.chemrev.2c00586 · Cited by 66
Self-citationMartinize2 and Vermouth: Unified Framework for Topology GenerationPreprint
Kroon, P. C., Grunewald, F., Barnoud, J., van Tilburg, M., Souza, P. C. T., Wassenaar, T. A., & Marrink, . J. (2023) · eLife · doi:10.7554/elife.90627.1 · Cited by 71
Self-citationThe CL &Pol polarizable force field for the simulation of ionic liquids and eutectic solvents
Goloviznina, K., Gong, Z., & Pádua, A. A. H. (2021) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1572 · Cited by 67
Potter, T. D., Barrett, E. L., & Miller, M. A. (2021) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.1c00322 · Cited by 69
Huang, Y., Gao, Q., Li, C., Chen, X., Li, X., He, Y., Jin, Q., & Ji, J. (2021) · Advanced Functional Materials · doi:10.1002/adfm.202109011 · Cited by 67
OpenMSCG: A Software Tool for Bottom-Up Coarse-Graining
Peng, Y., Pak, A. J., Durumeric, A. E. P., Sahrmann, P. G., Mani, S., Jin, J., Loose, T. D., Beiter, J. R., & Voth, G. A. (2023) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.3c04473 · Cited by 67
Rescaling protein-protein interactions improves Martini 3 for flexible proteins in solution
Thomasen, F. E., Skaalum, T., Kumar, A., Srinivasan, S., Vanni, S., & Lindorff‐Larsen, K. (2024) · Nature Communications · doi:10.1038/s41467-024-50647-9 · Cited by 69
Structural basis for sugar perception by Drosophila gustatory receptors
Ma, D., Hu, M., Yang, X., Liu, Q., Ye, F., Cai, W., Wang, Y., Xu, X., Chang, S., Wang, R., et al. (2024) · Science · doi:10.1126/science.adj2609 · Cited by 71
Tan, X., Chen, M., Zhang, J., Li, S., Zhang, H., Yang, L., Sun, T. S., Qian, X., & Feng, G. (2024) · Advanced Energy Materials · doi:10.1002/aenm.202400564 · Cited by 67
Advances in computational methods for ligand binding kinetics
Sohraby, F., & Nunes‐Alves, A. (2022) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2022.11.003 · Cited by 65
Navigating protein landscapes with a machine-learned transferable coarse-grained model
Charron, N. E., Bonneau, K., Pasos-Trejo, A. S., Guljas, A., Chen, Y., Musil, F., Venturin, J., Gusew, D., Zaporozhets, I., Krämer, A., et al. (2025) · Nature Chemistry · doi:10.1038/s41557-025-01874-0 · Cited by 93
Understanding the cell: Future views of structural biology
Beck, M., Covino, R., Hänelt, I., & Müller-McNicoll, M. (2024) · Cell · doi:10.1016/j.cell.2023.12.017 · Cited by 71
Martini3-IDP: improved Martini 3 force field for disordered proteins
Wang, L., Brasnett, C., Borges-Araújo, L., Souza, P. C. T., & Marrink, . J. (2025) · Nature Communications · doi:10.1038/s41467-025-58199-2 · Cited by 70
Self-citationShort Peptide Self-Assembly in the Martini Coarse-Grain Force Field Family
van Teijlingen, A., Smith, M. C., & Tuttle, T. (2023) · Accounts of Chemical Research · doi:10.1021/acs.accounts.2c00810 · Cited by 67
Róg, T., Girych, M., & Bunker, A. (2021) · Pharmaceuticals · doi:10.3390/ph14101062 · Cited by 66
Adaptive Sampling Methods for Molecular Dynamics in the Era of Machine Learning
Kleiman, D. E., Nadeem, H., & Shukla, D. (2023) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.3c04843 · Cited by 70
SPICA Force Field for Proteins and Peptides
Kawamoto, S., Liu, H., Miyazaki, Y., Seo, S., Dixit, M. K., Devane, R. H., MacDermaid, C. M., Fiorin, G., Klein, M. L., & Shinoda, W. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.1c01207 · 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. (2023) · Structure · doi:10.1016/j.str.2023.02.006 · Cited by 60
Self-citationPérez‐Sancho, J., Smokvarska, M., Dubois, G., Glavier, M., Sritharan, S., Moraes, T. D. S., Moreau, H., Dietrich, V., Platre, M. P., Paterlini, A., et al. (2025) · Cell · doi:10.1016/j.cell.2024.11.034 · Cited by 60
Borges-Araújo, L., Souza, P. C. T., Fernandes, F., & Melo, M. N. (2021) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.1c00615 · Cited by 56
Self-citationCoiled-coil domains are sufficient to drive liquid-liquid phase separation in protein models
Ramirez, D. A., Hough, L. E., & Shirts, M. R. (2024) · Biophysical Journal · doi:10.1016/j.bpj.2024.02.007 · Cited by 53
Zozoulenko, I. V., Franco‐Gonzalez, J. F., Gueskine, V., Mehandzhiyski, A. Y., Modarresi, M., Rolland, N., & Tybrandt, K. (2021) · Macromolecules · doi:10.1021/acs.macromol.1c00444 · Cited by 54
Multiscale simulations reveal TDP-43 molecular-level interactions driving condensation
Ingólfsson, H. I., Rizuan, A., Liu, X., Mohanty, P., Souza, P. C. T., Marrink, . J., Bowers, M. T., Mittal, J., & Berry, J. M. (2023) · Biophysical Journal · doi:10.1016/j.bpj.2023.10.016 · Cited by 52
Self-citationLipid scrambling is a general feature of protein insertases
Li, D., Rocha-Roa, C., Schilling, M. A., Reinisch, K. M., & Vanni, S. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2319476121 · Cited by 63
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
Self-citationMapping Mechanostable Pulling Geometries of a Therapeutic Anticalin/CTLA-4 Protein Complex
Liu, Z., Moreira, R. A., Dujmović, A., Liu, H., Yang, B., Poma, A. B., & Nash, M. A. (2021) · Nano Letters · doi:10.1021/acs.nanolett.1c03584 · Cited by 50
A computational study of cellulose regeneration: Coarse-grained molecular dynamics simulations
Pang, J., Mehandzhiyski, A. Y., & Zozoulenko, I. V. (2023) · Carbohydrate Polymers · doi:10.1016/j.carbpol.2023.120853 · Cited by 52
Han, R., Ye, Z., Zhang, Y., Cheng, Y., Zheng, Y., & Ouyang, D. (2023) · Asian Journal of Pharmaceutical Sciences · doi:10.1016/j.ajps.2023.100811 · Cited by 50
Zhang, M., Zhu, Z., Pan, F., Zhou, Q., Zhao, L., & Zhao, L. (2025) · Food Hydrocolloids · doi:10.1016/j.foodhyd.2025.111135 · Cited by 50
Conjugating Ligands to an Equilibrated Nanoparticle Protein Corona Enables Cell Targeting in Serum
Stordy, B. P., Zhang, Y., Sepahi, Z., Khatami, M. H., Kim, P. M., & Chan, W. C. W. (2022) · Chemistry of Materials · doi:10.1021/acs.chemmater.2c01168 · Cited by 49
Physics-based generative model of curvature sensing peptides; distinguishing sensors from binders
van Hilten, N., Methorst, J., Verwei, N., & Risselada, H. J. (2023) · Science Advances · doi:10.1126/sciadv.ade8839 · Cited by 48
Vainikka, P. A., Thallmair, S., Souza, P. C. T., & Marrink, . J. (2021) · ACS Sustainable Chemistry & Engineering · doi:10.1021/acssuschemeng.1c06521 · Cited by 47
Self-citationSchmitt, S., Fleckenstein, F., Hasse, H., & Stephan, S. (2023) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.2c07997 · Cited by 48
Gasdermin-A3 pore formation propagates along variable pathways
Mari, S. A., Pluháčková, K., Pipercevic, J., Leipner, M., Hiller, S., Engel, A. H., & Müller, D. J. (2022) · Nature Communications · doi:10.1038/s41467-022-30232-8 · Cited by 54
Shao, H., Jin, Z., Lin, X., Zhou, Y., Xue, Y., Hong, W., Lin, X., Jia, X., & Fan, Y. (2023) · Biomaterials · doi:10.1016/j.biomaterials.2023.122183 · Cited by 49
Perspectives on High-Throughput Ligand/Protein Docking With Martini MD Simulations
Souza, P. C. T., Limongelli, V., Wu, S., Marrink, . J., & Monticelli, L. (2021) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2021.657222 · Cited by 52
Self-citationCoarse-Grained Modeling of Ion-Containing Polymers
Zhu, Z., Luo, X., & Paddison, S. J. (2022) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00913 · Cited by 47
Structural basis of dimerization of chemokine receptors CCR5 and CXCR4
Marino, D. D., Conflitti, P., Motta, S., & Limongelli, V. (2023) · Nature Communications · doi:10.1038/s41467-023-42082-z · Cited by 45
Cabezudo, A. C., Athanasiou, C., Tsengenes, A., & Wade, R. C. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c00950 · Cited by 46
Larsen, A. H., John, L., Sansom, M. S. P., & Corey, R. A. (2022) · Bioscience Reports · doi:10.1042/bsr20211406 · Cited by 46
What Can We Learn about PEDOT:PSS Morphology from Molecular Dynamics Simulations of Ionic Diffusion?
Sedghamiz, T., Mehandzhiyski, A. Y., Modarresi, M., Linares, M., & Zozoulenko, I. V. (2023) · Chemistry of Materials · doi:10.1021/acs.chemmater.3c00873 · Cited by 43
Atomistic Insights into Organization of RNA-Loaded Lipid Nanoparticles
Paloncýová, M., Šrejber, M., Čechová, P., Kührová, P., Zaoral, F., & Otyepka, M. (2023) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.2c07671 · Cited by 44
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
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
Cárdenas, H., Kamrul-Bahrin, M. A. H., Seddon, D., Othman, J., Cabral, J. T., Mejı́a, A., Shahruddin, S., Matar, O. K., & Müller, E. A. (2024) · Journal of Colloid and Interface Science · doi:10.1016/j.jcis.2024.07.002 · Cited by 42
Xie, X. N., Li, T., Ma, L., Wu, J., Qi, Y., Yang, B., Li, Z., Yang, Z., Zhang, K., Chu, Z., et al. (2025) · Nature Chemistry · doi:10.1038/s41557-025-01837-5 · Cited by 46
Ramezanpour, M., & Tieleman, D. P. (2022) · Langmuir · doi:10.1021/acs.langmuir.2c00430 · Cited by 44
Self-citationMartinize2 and Vermouth: Unified Framework for Topology GenerationPreprint
Kroon, P. C., Grünewald, F., Barnoud, J., van Tilburg, M., Brasnett, C., Souza, P. C. T., Wassenaar, T. A., & Marrink, . J. (2025) · eLife · doi:10.7554/elife.90627.3 · Cited by 47
Self-citationTillotson, M. J., Diamantonis, N. I., Buda, C., Bolton, L. W., & Müller, E. A. (2023) · Physical Chemistry Chemical Physics · doi:10.1039/d2cp05423j · Cited by 43
PMIpred: a physics-informed web server for quantitative protein–membrane interaction prediction
van Hilten, N., Verwei, N., Methorst, J., Nase, C., Bernatavicius, A., & Risselada, H. J. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae069 · Cited by 41
Metadata
- Authors
- Souza, P. C. T., Alessandri, R., Barnoud, J., Thallmair, S., Faustino, I., Grünewald, F., Patmanidis, I., Abdizadeh, H., Bruininks, B. M. H., Wassenaar, T. A., Kroon, P. C., Melcr, J., Nieto, V., Corradi, V., Khan, H. M., Domański, J., Javanainen, M., Martinez-Seara, H., Reuter, N., Best, R. B., Vattulainen, I., Monticelli, L., Periole, X., Tieleman, D. P., de Vries, A. H., & Marrink, S. J.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- University of Groningen (NL)
- University of Calgary (CA)
- National Institutes of Health (US)
- Tampere University (FI)
- University of Bergen (NO)
- University of Helsinki (FI)
- Corresponding author
- Souza, Paulo C. T.
- Marrink, Siewert J.
- ORCID
- Souza, Paulo C. T.
- Alessandri, Riccardo
- Barnoud, Jonathan
- Thallmair, Sebastian
- Faustino, Ignacio
- Grünewald, Fabian
- Patmanidis, Ilias
- Abdizadeh, Haleh
- Bruininks, Bart M. H.
- Wassenaar, Tsjerk A.
- Kroon, Peter C.
- Melcr, Josef
- Nieto, Vincent
- Corradi, Valentina
- Khan, Hanif M.
- Domański, Jan
- Javanainen, Matti
- Martinez-Seara, Hector
- Reuter, Nathalie
- Best, Robert B.
- Vattulainen, Ilpo
- Monticelli, Luca
- Tieleman, D. Peter
- Marrink, Siewert J.
- Venue
- Nature Methods
- Published
- 2021-03-29
- Volume
- 18
- Issue
- 4
- Pages
- 382-388
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC12554258
- OpenAlex
- W3148042916
- Cited by
- 1,551
- References
- 92
- Citations by year
- 2026: 370 · 2025: 392 · 2024: 298 · 2023: 270 · 2022: 146 · 2021: 73
- FWCI
- 117.73
- Type
- journal article
- Language
- en
- Open access
- green
- research.rug.nl
- Licence
- Locations
- Nature Methods (publishedVersion)
- PubMed (publishedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (publishedVersion, other-oa)
- Europe PMC (PubMed Central) (acceptedVersion)
- HAL (submittedVersion, other-oa)
- Työväentutkimus Vuosikirja (submittedVersion)
- Lirias (KU Leuven) (submittedVersion)
- Bristol Research (University of Bristol) (submittedVersion, other-oa)
- Explore Bristol Research (submittedVersion, other-oa)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, other-oa)
- Funders
- Agence Nationale de la Recherche (ANR-17-CE11-0003)
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (022.005.006)
- National Institutes of Health (Z99 DK999999)
- Engineering and Physical Sciences Research Council (EP/P021123/1)
- National Institute of Diabetes and Digestive and Kidney Diseases (ZIADK075104)
- Topics
- Physical Sciences › Chemistry › Physical and Theoretical Chemistry › Advanced Physical and Chemical Molecular Interactions
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Lipid Membrane Structure and Behavior
- Keywords
- Martini 3 force field, coarse-grained molecular dynamics, hydrogen bonding, electronic polarizability, oil-water partitioning, miscibility, protein-protein interaction, protein-lipid interactions, ionic liquids, biomolecular simulation
- MeSH terms
- Hydrogen Bonding, Lipid Bilayers, Thermodynamics, Molecular Dynamics Simulation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review