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The MARTINI Coarse-Grained Force Field: Extension to Proteins
Abstract
Many biologically interesting phenomena occur on a time scale that is too long to be studied by atomistic simulations. These phenomena include the dynamics of large proteins and self-assembly of biological materials. Coarse-grained (CG) molecular modeling allows computer simulations to be run on length and time scales that are 2-3 orders of magnitude larger compared to atomistic simulations, providing a bridge between the atomistic and the mesoscopic scale. We developed a new CG model for proteins as an extension of the MARTINI force field. Here, we validate the model for its use in peptide-bilayer systems. In order to validate the model, we calculated the potential of mean force for each amino acid as a function of its distance from the center of a dioleoylphosphatidylcholine (DOPC) lipid bilayer. We then compared amino acid association constants, the partitioning of a series of model pentapeptides, the partitioning and orientation of WALP23 in DOPC lipid bilayers and a series of KALP peptides in dimyristoylphosphatidylcholine and dipalmitoylphosphatidylcholine (DPPC) bilayers. A comparison with results obtained from atomistic models shows good agreement in all of the tests performed. We also performed a systematic investigation of the partitioning of five series of polyalanine-leucine peptides (with different lengths and compositions) in DPPC bilayers. As expected, the fraction of peptides partitioned at the interface increased with decreasing peptide length and decreasing leucine content, demonstrating that the CG model is capable of discriminating partitioning behavior arising from subtle differences in the amino acid composition. Finally, we simulated the concentration-dependent formation of transmembrane pores by magainin, an antimicrobial peptide. In line with atomistic simulation studies, disordered toroidal pores are formed. In conclusion, the model is computationally efficient and effectively reproduces peptide-lipid interactions and the partitioning of amino acids and peptides in lipid bilayers.
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Citations
Cites
Kabsch, W., & Sander, C. (1983) · Biopolymers · doi:10.1002/bip.360221211 · Cited by 15,320
Coarse 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
Coarse grained protein- lipid model with application to lipoprotein particles
Shih, A. Y., Arkhipov, A., Freddolino, L., & Schulten, K. (2006) · The Journal of Physical Chemistry B · doi:10.1021/jp0550816 · Cited by 267
Cited by
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Allen, M. P., & Tildesley, D. J. (2017) · doi:10.1093/oso/9780198803195.001.0001 · Cited by 20,945
Martini 3: A general purpose force field for coarse-grained molecular dynamics
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., et al. (2021) · Nature Methods · doi:10.1038/s41592-021-01098-3 · Cited by 1,551
Self-citation‘Improved parameters for the Martini coarse-grained protein force field’
de Jong, D. H., Singh, G., Bennett, W. F. D., Arnarez, C., Wassenaar, T. A., Schäfer, L. V., Periole, X., Tieleman, D. P., & Marrink, S. J. (2013) · Journal of Chemical Theory and Computation · doi:10.1021/ct300646g · Cited by 1,486
Self-citationBiomolecular Simulation: A Computational Microscope for Molecular Biology
Dror, R. O., Dirks, R. M., Grossman, J. P., Xu, H., & Shaw, D. E. (2012) · Annual Review of Biophysics · doi:10.1146/annurev-biophys-042910-155245 · Cited by 1,198
Wassenaar, T. A., Ingólfsson, H. I., Böckmann, R. A., Tieleman, D. P., & Marrink, S. J. (2015) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5b00209 · Cited by 1,174
Self-citationCoarse-Grained Protein Models and Their Applications
Kmiecik, S., Gront, D., Kolinski, M., Wieteska, L., Dawid, A. E., & Kolinski, A. (2016) · Chemical Reviews · doi:10.1021/acs.chemrev.6b00163 · Cited by 1,071
Lipid Organization of the Plasma Membrane
Ingólfsson, H. I., Melo, M. N., van Eerden, F. J., Arnarez, C., Lopez, C. A., Wassenaar, T. A., Periole, X., de Vries, A. H., Tieleman, D. P., & Marrink, S. J. (2014) · Journal of the American Chemical Society · doi:10.1021/ja507832e · Cited by 931
Self-citationPerspective: Coarse-grained models for biomolecular systems
Noid, W. G. (2013) · The Journal of Chemical Physics · doi:10.1063/1.4818908 · Cited by 893
Advances in protein structure prediction and design
Kuhlman, B., & Bradley, P. (2019) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-019-0163-x · Cited by 806
Lin, J., Zhang, H., Chen, Z., & Zheng, Y. (2010) · ACS Nano · doi:10.1021/nn1010792 · Cited by 638
RNA Structural Dynamics As Captured by Molecular Simulations: A Comprehensive Overview
Šponer, J., Bussi, G., Krepl, M., Banáš, P., Bottaro, S., Cunha, R. A., Gil-Ley, A., Pinamonti, G., Poblete, S., Jurečka, P., et al. (2018) · Chemical Reviews · doi:10.1021/acs.chemrev.7b00427 · Cited by 599
The power of coarse graining in biomolecular simulations
Ingólfsson, H. I., Lopez, C. A., Uusitalo, J. J., de Jong, D. H., Gopal, S. M., Periole, X., & Marrink, S. J. (2014) · WIREs Computational Molecular Science · doi:10.1002/wcms.1169 · Cited by 572
Self-citationComputer simulation study of fullerene translocation through lipid membranes
Wong-Ekkabut, J., Baoukina, S., Triampo, W., Tang, I.-M., Tieleman, D. P., & Monticelli, L. (2008) · Nature Nanotechnology · doi:10.1038/nnano.2008.130 · Cited by 506
Self-citationCHARMM-GUI Martini Maker for Coarse-Grained Simulations with the Martini Force Field
Qi, Y., Ingólfsson, H. I., Cheng, X., Lee, J., Marrink, S. J., & Im, W. (2015) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5b00513 · Cited by 494
Self-citationMultiscale simulation of soft matter systems–from the atomistic to the coarse-grained level and back
Peter, C., & Kremer, K. (2009) · Soft Matter · doi:10.1039/b912027k · Cited by 465
Large-Scale Molecular Dynamics Simulations of Self-Assembling Systems
Klein, M. L., & Shinoda, W. (2008) · Science · doi:10.1126/science.1157834 · Cited by 416
Martini Coarse-Grained Force Field: Extension to Carbohydrates
López, C. A., Rzepiela, A. J., de Vries, A. H., Dijkhuizen, L., Hünenberger, P. H., & Marrink, S. J. (2009) · Journal of Chemical Theory and Computation · doi:10.1021/ct900313w · Cited by 411
Self-citationLee, H., de Vries, A. H., Marrink, S.-J., & Pastor, R. W. (2009) · The Journal of Physical Chemistry B · doi:10.1021/jp9058966 · Cited by 398
Self-citationPolystyrene Nanoparticles Perturb Lipid Membranes
Rossi, G., Barnoud, J., & Monticelli, L. (2014) · The Journal of Physical Chemistry Letters · doi:10.1021/jz402234c · Cited by 373
Self-citationAI-based structure prediction empowers integrative structural analysis of human nuclear pores
Mosalaganti, S., Obarska-Kosinska, A., Siggel, M., Taniguchi, R., Turoňová, B., Zimmerli, C. E., Buczak, K., Schmidt, F. H., Margiotta, E., Mackmull, M.-T., et al. (2022) · Science · doi:10.1126/science.abm9506 · Cited by 355
Reversible self-assembly of superstructured networks
Freeman, R., Han, M., Álvarez, Z., Lewis, J. A., Wester, J. R., Stephanopoulos, N., McClendon, M. T., Lynsky, C., Godbe, J. M., Sangji, H., et al. (2018) · Science · doi:10.1126/science.aat6141 · Cited by 353
Martini Coarse-Grained Force Field: Extension to DNA
Uusitalo, J. J., Ingólfsson, H. I., Akhshi, P., Tieleman, D. P., & Marrink, S. J. (2015) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5b00286 · Cited by 320
Self-citationMuller, M. P., Jiang, T., Sun, C., Lihan, M., Pant, S., Mahinthichaichan, P., Trifan, A., & Tajkhorshid, E. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00608 · Cited by 282
Martini coarse-grained force field: Extension to RNA
Uusitalo, J. J., Ingólfsson, H. I., Marrink, S. J., & Faustino, I. (2017) · Biophysical Journal · doi:10.1016/j.bpj.2017.05.043 · Cited by 259
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-citationA review of advancements in coarse-grained molecular dynamics simulations
Joshi, S. Y., & Deshmukh, S. A. (2021) · Molecular Simulation · doi:10.1080/08927022.2020.1828583 · Cited by 266
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
Surface electrostatics govern the emulsion stability of biomolecular condensates
Welsh, T. J., Krainer, G., Espinosa, J. R., Joseph, J. A., Sridhar, A., Jahnel, M., Arter, W. E., Saar, K. L., Alberti, S., Collepardo-Guevara, R., et al. (2020) · doi:10.1101/2020.04.20.047910 · Cited by 168
Hydrophobic mismatch sorts SNARE proteins into distinct membrane domains
Milovanovic, D., Honigmann, A., Koike, S., Göttfert, F., Pähler, G., Junius, M., Müllar, S., Diederichsen, U., Janshoff, A., Grubmüller, H., et al. (2015) · Nature Communications · doi:10.1038/ncomms6984 · Cited by 162
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
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
A 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
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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
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Chiavazzo, E., Covino, R., Coifman, R. R., Gear, C. W., Georgiou, A. S., Hummer, G., & Kevrekidis, I. G. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1621481114 · Cited by 125
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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
(2023) · doi:10.1038/s41467-023-41586-y · Cited by 118
Comparison of mechanistic transport cycle models of ABC exporters
Szöllősi, D., Rose-Sperling, D., Hellmich, U. A., & Stockner, T. (2018) · Biochimica et Biophysica Acta (BBA) - Biomembranes · doi:10.1016/j.bbamem.2017.10.028 · Cited by 107
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Janke, J. J., Bennett, W. F. D., & Tieleman, D. P. (2014) · Langmuir · doi:10.1021/la501962n · Cited by 87
Self-citationCoarse-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
Smirnova, Y. G., Risselada, H. J., & Müller, M. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1818200116 · Cited by 81
Pugliese, R., Fontana, F., Marchini, A., & Gelain, F. (2018) · Acta Biomaterialia · doi:10.1016/j.actbio.2017.11.026 · Cited by 65
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
Periole, X. (2017) · Chemical Reviews · doi:10.1021/acs.chemrev.6b00344 · Cited by 58
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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
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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
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Thota, N., & Jiang, J. (2015) · Frontiers in Materials · doi:10.3389/fmats.2015.00064 · Cited by 38
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
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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
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
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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
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
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Metadata
- Authors
- Monticelli, L., Kandasamy, S. K., Periole, X., Larson, R. G., Tieleman, D. P., & Marrink, S.-J.
- Venue
- Journal of Chemical Theory and Computation
- Publisher
- American Chemical Society (ACS)
- Published
- 2008-04-16
- Volume
- 4
- Issue
- 5
- Pages
- 819-834
- ISSN
- 1549-9618 (ISSN-L)
- 1549-9626
- OpenAlex
- W2051864856
- Cited by
- 2,489
- References
- 3
- FWCI
- 31.02
- Type
- journal article
- Language
- en
- Open access
- closed
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