Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

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

Abstract

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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Rebalancing R, S and T beads.
Fig. 2: New chemical bead types, sublabels and applications.
Fig. 3: Improving packing, cavities and reducing protein stickiness.

Similar content being viewed by others

Data availability

Force-field parameters and procedures (for example, tutorials) are publicly available at http://cgmartini.nl. Simulation data (for example, trajectories) supporting the results of this paper are available from the corresponding authors upon reasonable request.

Code availability

Martinize2 (for which the manuscript is in preparation) and Martinate codes used in this work are publicly available at https://github.com/marrink-lab/. For more detailed information, see Supplementary Codes.

References

  1. Bottaro, S. & Lindorff-Larsen, K. Biophysical experiments and biomolecular simulations: a perfect match? Science 361, 355–360 (2018).

    Article  CAS  PubMed  Google Scholar 

  2. Ingólfsson, H. I. et al. The power of coarse graining in biomolecular simulations. Wiley Interdiscip. Rev. Comput. Mol. Sci. 4, 225–248 (2014).

    Article  PubMed  Google Scholar 

  3. Marrink, S. J., De Vries, A. H. & Mark, A. E. Coarse grained model for semiquantitative lipid simulations. J. Phys. Chem. B 108, 750–760 (2004).

    Article  CAS  Google Scholar 

  4. Marrink, S. J., Risselada, H. J., Yefimov, S., Tieleman, D. P. & de Vries, A. H. The MARTINI force field: coarse grained model for biomolecular simulations. J. Phys. Chem. B 111, 7812–7824 (2007).

    Article  CAS  PubMed  Google Scholar 

  5. Uusitalo, J. J., Ingólfsson, H. I., Akhshi, P., Tieleman, D. P. & Marrink, S. J. Martini coarse-grained force field: extension to DNA. J. Chem. Theory Comput. 11, 3932–3945 (2015).

    Article  CAS  PubMed  Google Scholar 

  6. Abellón-Ruiz, J. et al. Structural basis for maintenance of bacterial outer membrane lipid asymmetry. Nat. Microbiol. 2, 1616–1623 (2017).

    Article  PubMed  Google Scholar 

  7. Yen, H. Y. et al. PtdIns(4,5)P2 stabilizes active states of GPCRs and enhances selectivity of G-protein coupling. Nature 559, 423–427 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Van Eerden, F. J., Melo, M. N., Frederix, P. W. J. M., Periole, X. & Marrink, S. J. Exchange pathways of plastoquinone and plastoquinol in the photosystem II complex. Nat. Commun. 8, 15214 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Vögele, M., Köfinger, J. & Hummer, G. Hydrodynamics of diffusion in lipid membrane simulations. Phys. Rev. Lett. 120, 268104 (2018).

    Article  PubMed  Google Scholar 

  10. Agostino, M. D., Risselada, H. J., Lürick, A., Ungermann, C. & Mayer, A. A tethering complex drives the terminal stage of SNARE-dependent membrane fusion. Nature 551, 634–638 (2017).

    Article  PubMed  Google Scholar 

  11. Jeena, M. T. et al. Mitochondria localization induced self-assembly of peptide amphiphiles for cellular dysfunction. Nat. Commun. 8, 26 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Jiang, Z. et al. Subnanometre ligand-shell asymmetry leads to Janus-like nanoparticle membranes. Nat. Mater. 14, 912–917 (2015).

    Article  CAS  PubMed  Google Scholar 

  13. Maingi, V. et al. Stability and dynamics of membrane-spanning DNA nanopores. Nat. Commun. 8, 14784 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Frederix, P. W. J. M. et al. Exploring the sequence space for (tri-)peptide self-assembly to design and discover new hydrogels. Nat. Chem. 7, 30–37 (2015).

    Article  CAS  PubMed  Google Scholar 

  15. Bochicchio, D., Salvalaglio, M. & Pavan, G. M. Into the dynamics of a supramolecular polymer at submolecular resolution. Nat. Commun. 8, 147 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Stark, A. C., Andrews, C. T. & Elcock, A. H. Toward optimized potential functions for protein-protein interactions in aqueous solutions: osmotic second virial coefficient calculations using the MARTINI coarse-grained force field. J. Chem. Theory Comput. 9, 4176–4185 (2013).

    Article  CAS  Google Scholar 

  17. Javanainen, M., Martinez-Seara, H. & Vattulainen, I. Excessive aggregation of membrane proteins in the Martini model. PLoS ONE 12, e0187936 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Schmalhorst, P. S., Deluweit, F., Scherrers, R., Heisenberg, C.-P. & Sikora, M. Overcoming the limitations of the MARTINI force field in simulations of polysaccharides. J. Chem. Theory Comput. 13, 5039–5053 (2017).

    Article  CAS  PubMed  Google Scholar 

  19. Alessandri, R. et al. Pitfalls of the Martini model. J. Chem. Theory Comput. 15, 5448–5460 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Uusitalo, J. J., Ingólfsson, H. I., Marrink, S. J. & Faustino, I. Martini coarse-grained force field: extension to RNA. Biophys. J. 113, 246–256 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Ben-Naim, A. Molecular Theory of Solutions (Oxford Univ. Press, 2006).

  22. Ploetz, E. A., Bentenitis, N. & Smith, P. E. Kirkwood–Buff integrals for ideal solutions. J. Chem. Phys. 132, 164501 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Zych, A. J. & Iverson, B. L. Synthesis and conformational characterization of tethered, self-complexing 1,5-dialkoxynaphthalene/1,4,5,8-naphthalenetetracarboxylic diimide systems. J. Am. Chem. Soc. 122, 8898–8909 (2000).

    Article  CAS  Google Scholar 

  24. Gabriel, G. J. & Iverson, B. L. Aromatic oligomers that form hetero duplexes in aqueous solution. J. Am. Chem. Soc. 124, 15174–15175 (2002).

    Article  CAS  PubMed  Google Scholar 

  25. Liu, W. et al. Structural basis for allosteric regulation of GPCRs by sodium ions. Science 337, 232–236 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Gao, Z. G. & Ijzerman, A. P. Allosteric modulation of A(2A) adenosine receptors by amiloride analogues and sodium ions. Biochem. Pharmacol. 60, 669–676 (2000).

    Article  CAS  PubMed  Google Scholar 

  27. Okur, H. I. et al. Beyond the Hofmeister series: ion-specific effects on proteins and their biological functions. J. Phys. Chem. B 121, 1997–2014 (2017).

    Article  CAS  PubMed  Google Scholar 

  28. Dupont, D., Depuydt, D. & Binnemans, K. Overview of the effect of salts on biphasic ionic liquid/water solvent extraction systems: anion exchange, mutual solubility, and thermomorphic properties. J. Phys. Chem. B 119, 6747–6757 (2015).

    Article  CAS  PubMed  Google Scholar 

  29. Naert, P., Rabaey, K. & Stevens, C. V. Ionic liquid ion exchange: exclusion from strong interactions condemns cations to the most weakly interacting anions and dictates reaction equilibrium. Green Chem. 20, 4277–4286 (2018).

    Article  CAS  Google Scholar 

  30. Khan, H. M. et al. Capturing choline-aromatics cation–π interactions in the MARTINI force field. J. Chem. Theory Comput. 16, 2550–2560 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Tanaka, K., Caaveiro, J. M. M., Morante, K., González-Manãs, J. M. & Tsumoto, K. Structural basis for self-assembly of a cytolytic pore lined by protein and lipid. Nat. Commun. 6, 6337 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Huang, G., Willems, K., Soskine, M., Wloka, C. & Maglia, G. Electro-osmotic capture and ionic discrimination of peptide and protein biomarkers with FraC nanopores. Nat. Commun. 8, 935 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Alessandri, R., Uusitalo, J. J., De Vries, A. H., Havenith, R. W. A. & Marrink, S. J. Bulk heterojunction morphologies with atomistic resolution from coarse-grain solvent evaporation simulations. J. Am. Chem. Soc. 139, 3697–3705 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Chiu, M. Y., Jeng, U. S., Su, C. H., Liang, K. S. & Wei, K. H. Simultaneous use of small- and wide-angle X-ray techniques to analyze nanometerscale phase separation in polymer heterojunction solar cells. Adv. Mater. 20, 2573–2578 (2008).

    Article  CAS  Google Scholar 

  35. Petrov, D. & Zagrovic, B. Are current atomistic force fields accurate enough to study proteins in crowded environments? PLoS Comput. Biol. 10, e1003638 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Højgaard, C. et al. A soluble, folded protein without charged amino acid residues. Biochemistry 55, 3949–3956 (2016).

    Article  PubMed  Google Scholar 

  37. Ruckenstein, E. & Shulgin, I. L. Effect of salts and organic additives on the solubility of proteins in aqueous solutions. Adv. Colloid Interface Sci. 123–126, 97–103 (2006).

    Article  PubMed  Google Scholar 

  38. Zhou, F. X., Cocco, M. J., Russ, W. P., Brunger, A. T. & Engelman, D. M. Interhelical hydrogen bonding drives strong interactions in membrane proteins. Nat. Struct. Biol. 7, 154–160 (2000).

    Article  CAS  PubMed  Google Scholar 

  39. Zhou, F. X., Merianos, H. J., Brunger, A. T. & Engelman, D. M. Polar residues drive association of polyleucine transmembrane helices. Proc. Natl Acad. Sci. USA 98, 2250–2255 (2001).

    Article  CAS  PubMed  Google Scholar 

  40. Grau, B. et al. The role of hydrophobic matching on transmembrane helix packing in cells. Cell Stress 1, 90–106 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Chen, L., Merzlyakov, M., Cohen, T., Shai, Y. & Hristova, K. Energetics of ErbB1 transmembrane domain dimerization in lipid bilayers. Biophys. J. 96, 4622–4630 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Artemenko, E. O., Egorova, N. S., Arseniev, A. S. & Feofanov, A. V. Transmembrane domain of EphA1 receptor forms dimers in membrane-like environment. Biochim. Biophys. Acta 1778, 2361–2367 (2008).

    Article  CAS  PubMed  Google Scholar 

  43. Sarabipour, S. & Hristova, K. Glycophorin A transmembrane domain dimerization in plasma membrane vesicles derived from CHO, HEK 293T, and A431 cells. Biochim. Biophys. Acta - Biomembr. 1828, 1829–1833 (2013).

    Article  CAS  Google Scholar 

  44. Chen, L., Novicky, L., Merzlyakov, M., Hristov, T. & Hristova, K. Measuring the energetics of membrane protein dimerization in mammalian membranes. J. Am. Chem. Soc. 132, 3628–3635 (2010).

    Article  CAS  PubMed  Google Scholar 

  45. Nash, A., Notman, R. & Dixon, A. M. De novo design of transmembrane helix–helix interactions and measurement of stability in a biological membrane. Biochim. Biophys. Acta - Biomembr. 1848, 1248–1257 (2015).

    Article  CAS  Google Scholar 

  46. Finger, C. et al. The stability of transmembrane helix interactions measured in a biological membrane. J. Mol. Biol. 358, 1221–1228 (2006).

    Article  CAS  PubMed  Google Scholar 

  47. Hong, H., Blois, T. M., Cao, Z. & Bowie, J. U. Method to measure strong protein–protein interactions in lipid bilayers using a steric trap. Proc. Natl Acad. Sci. USA 107, 19802–19807 (2010).

    Article  CAS  PubMed  Google Scholar 

  48. Sparr, E. et al. Self-association of transmembrane α-helices in model membranes: importance of helix orientation and role of hydrophobic mismatch. J. Biol. Chem. 280, 39324–39331 (2005).

    Article  CAS  PubMed  Google Scholar 

  49. MacKenzie, K. R., Prestegard, J. H. & Engelman, D. M. Transmembrane helix dimer: structure and implications. Science 276, 131–133 (1997).

    Article  CAS  PubMed  Google Scholar 

  50. Trenker, R., Call, M. E. & Call, M. J. Crystal structure of the glycophorin A transmembrane dimer in lipidic cubic phase. J. Am. Chem. Soc. 137, 15676–15679 (2015).

    Article  CAS  PubMed  Google Scholar 

  51. Domański, J., Sansom, M. S. P., Stansfeld, P. J. & Best, R. B. Balancing force field protein–lipid interactions to capture transmembrane helix–helix association. J. Chem. Theory Comput. 14, 1706–1715 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Souza, P. C. T., Thallmair, S., Marrink, S. J. & Mera-Adasme, R. An allosteric pathway in copper, zinc superoxide dismutase unravels the molecular mechanism of the G93A amyotrophic lateral sclerosis-linked mutation. J. Phys. Chem. Lett. 10, 7740–7744 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Brini, E. et al. Systematic coarse-graining methods for soft matter simulations-a review. Soft Matter 9, 2108–2119 (2013).

    Article  CAS  Google Scholar 

  54. Foley, T. T., Shell, M. S. & Noid, W. G. The impact of resolution upon entropy and information in coarse-grained models. J. Chem. Phys. 143, 243104 (2015).

    Article  PubMed  Google Scholar 

  55. Wagner, J. W., Dama, J. F., Durumeric, A. E. P. & Voth, G. A. On the representability problem and the physical meaning of coarse-grained models. J. Chem. Phys. 145, 044108 (2016).

    Article  PubMed  Google Scholar 

  56. Wörner, S. J., Bereau, T., Kremer, K. & Rudzinski, J. F. Direct route to reproducing pair distribution functions with coarse-grained models via transformed atomistic cross correlations. J. Chem. Phys. 151, 244110 (2019).

    Article  PubMed  Google Scholar 

  57. Noid, W. G., Chu, J. W., Ayton, G. S. & Voth, G. A. Multiscale coarse-graining and structural correlations: connections to liquid-state theory. J. Phys. Chem. B. 111, 4116–4127 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Wu, Z., Cui, Q. & Yethiraj, A. Driving force for the association of hydrophobic peptides: the importance of electrostatic interactions in coarse-grained water models. J. Phys. Chem. Lett. 2, 1794–1798 (2011).

    Article  CAS  Google Scholar 

  59. Jin, J., Yu, A. & Voth, G. A. Temperature and phase transferable bottom-up coarse-grained models. J. Chem. Theory Comput. 16, 6823–6842 (2020).

    Article  CAS  PubMed  Google Scholar 

  60. Yesylevskyy, S. O., Schäfer, L. V., Sengupta, D. & Marrink, S. J. Polarizable water model for the coarse-grained MARTINI force field. PLoS Comput. Biol. 6, e1000810 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Michalowsky, J., Schäfer, L. V., Holm, C. & Smiatek, J. A refined polarizable water model for the coarse-grained MARTINI force field with long-range electrostatic interactions. J. Chem. Phys. 146, 054501 (2017).

    Article  PubMed  Google Scholar 

  62. Marrink, S. J. & Tieleman, D. P. Perspective on the Martini model. Chem. Soc. Rev. 42, 6801–22 (2013).

    Article  CAS  PubMed  Google Scholar 

  63. Bruininks, B. M. H., Souza, P. C. T. & Marrink, S. J. in Biomolecular Simulations: Methods and Protocols (eds Bonomi, M. & Camilloni, C.) 105–127 (Humana Press Inc., 2019).

  64. Liu, J. et al. Enhancing molecular n-type doping of donor-acceptor copolymers by tailoring side chains. Adv. Mater. 30, 1704630 (2018).

    Article  Google Scholar 

  65. Vazquez-Salazar, L. I., Selle, M., de Vries, A., Marrink, S. J. & Souza, P. C. T. Martini coarse-grained models of imidazolium-based ionic liquids: from nanostructural organization to liquid–liquid extraction. Green Chem. 22, 7376–7386 (2020).

    Article  CAS  Google Scholar 

  66. Souza, P. C. T. et al. Protein–ligand binding with the coarse-grained Martini model. Nat. Commun. 11, 3714 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. López, C. A. et al. Martini coarse-grained force field: extension to carbohydrates. J. Chem. Theory Comput. 5, 3195–3210 (2009).

    Article  PubMed  Google Scholar 

  68. Monticelli, L. et al. The MARTINI coarse-grained force field: extension to proteins. J. Chem. Theory Comput. 4, 819–834 (2008).

    Article  CAS  PubMed  Google Scholar 

  69. Grunewald, F., Rossi, G., de Vries, A. H., Marrink, S. J. & Monticelli, L. Transferable MARTINI model of poly(ethylene oxide). J. Phys. Chem. B 122, 7436–7449 (2018).

    Article  CAS  PubMed  Google Scholar 

  70. de Jong, D. H. et al. Improved parameters for the martini coarse-grained protein force field. J. Chem. Theory Comput. 9, 687–97 (2013).

    Article  PubMed  Google Scholar 

  71. Herzog, F. A., Braun, L., Schoen, I. & Vogel, V. Improved side chain dynamics in MARTINI simulations of protein–lipid interfaces. J. Chem. Theory Comput. 12, 2446–2458 (2016).

    Article  CAS  PubMed  Google Scholar 

  72. Poma, A. B., Cieplak, M. & Theodorakis, P. E. Combining the MARTINI and structure-based coarse-grained approaches for the molecular dynamics studies of conformational transitions in proteins. J. Chem. Theory Comput. 13, 1366–1374 (2017).

    Article  CAS  PubMed  Google Scholar 

  73. Periole, X., Cavalli, M., Marrink, S.-J. & Ceruso, M. A. Combining an elastic network with a coarse-grained molecular force field: structure, dynamics, and intermolecular recognition. J. Chem. Theory Comput. 5, 2531–2543 (2009).

    Article  CAS  PubMed  Google Scholar 

  74. Wassenaar, T. A., Ingólfsson, H. I., Böckmann, R. A., Tieleman, D. P. & Marrink, S. J. Computational lipidomics with insane: a versatile tool for generating Custom membranes for molecular simulations. J. Chem. Theory Comput. 11, 2144–2155 (2015).

    Article  CAS  PubMed  Google Scholar 

  75. Melo, M. N., Ingólfsson, H. I. & Marrink, S. J. Parameters for Martini sterols and hopanoids based on a virtual-site description. J. Chem. Phys. 143, 243152 (2015).

    Article  CAS  PubMed  Google Scholar 

  76. López, C. A., Sovova, Z., van Eerden, F. J., de Vries, A. H. & Marrink, S. J. Martini force field parameters for glycolipids. J. Chem. Theory Comput. 9, 1694–1708 (2013).

    Article  PubMed  Google Scholar 

  77. Carpenter, T. S. et al. Capturing phase behavior of ternary lipid mixtures with a refined Martini coarse-grained force field. J. Chem. Theory Comput. 14, 6050–6062 (2018).

    Article  CAS  PubMed  Google Scholar 

  78. de Jong, D. H., Baoukina, S., Ingólfsson, H. I. & Marrink, S. J. Martini straight: boosting performance using a shorter cutoff and GPUs. Comput. Phys. Commun. 199, 1–7 (2016).

    Article  Google Scholar 

  79. Hockney, R. W., Goel, S. P. & Eastwood, J. W. Quiet high-resolution computer models of a plasma. J. Comput. Phys. 14, 148–158 (1974).

    Article  Google Scholar 

  80. Páll, S. & Hess, B. A flexible algorithm for calculating pair interactions on SIMD architectures. Comput. Phys. Commun. 184, 2641–2650 (2013).

    Article  Google Scholar 

  81. Verlet, L. Computer ‘experiments’ on classical fluids. I. Thermodynamical properties of Lennard–Jones molecules. Phys. Rev. 159, 98–103 (1967).

    Article  CAS  Google Scholar 

  82. Tironi, I. G., Sperb, R., Smith, P. E. & Van Gunsteren, W. F. A generalized reaction field method for molecular dynamics simulations. J. Chem. Phys. 102, 5451–5459 (1995).

    Article  CAS  Google Scholar 

  83. Essmann, U. et al. A smooth particle mesh Ewald method. J. Chem. Phys. 103, 8577–8593 (1995).

    Article  CAS  Google Scholar 

  84. Bussi, G., Donadio, D. & Parrinello, M. Canonical sampling through velocity rescaling. J. Chem. Phys. 126, 014101 (2007).

    Article  PubMed  Google Scholar 

  85. Parrinello, M. & Rahman, A. Polymorphic transitions in single crystals: a new molecular dynamics method. J. Appl. Phys. 52, 7182–7190 (1981).

    Article  CAS  Google Scholar 

  86. Abraham, M. J. et al. GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2, 19–25 (2015).

    Article  Google Scholar 

  87. Van Der Spoel, D. et al. GROMACS: fast, flexible, and free. J. Comput. Chem. 26, 1701–1718 (2005).

    Article  Google Scholar 

  88. Wassenaar, T. A., Ingólfsson, H. I., Prieß, M., Marrink, S. J. & Schäfer, L. V. Mixing MARTINI: electrostatic coupling in hybrid atomistic-coarse-grained biomolecular simulations. J. Phys. Chem. B. 117, 3516–3530 (2013).

    Article  CAS  PubMed  Google Scholar 

  89. Wassenaar, T. A. et al. High-throughput simulations of dimer and trimer assembly of membrane proteins. The DAFT approach. J. Chem. Theory Comput. 11, 2278–91 (2015).

    Article  CAS  PubMed  Google Scholar 

  90. Humphrey, W., Dalke, A. & Schulten, K. VMD—visual molecular dynamics. J. Molec. Graph. 14, 33–38 (1996).

    Article  CAS  Google Scholar 

  91. Gowers, R. J. et al. MDAnalysis: a Python package for the rapid analysis of molecular dynamics simulations. in Proc. 15th Python Sci. Conference 98–105 (2016).

Download references

Acknowledgements

We thank all members of the S.J.M. group and also external users for testing Martini 3 in its open-beta version. In particular, we thank C. F. E. Schroer, P. W. J. M. Frederix, W. Pezeshkian, M. N. Melo, H. I. Ingólfsson, M. Tsanai, M. König, P. A. Vainikka, T. Zijp, L. Gaifas, J. H. van der Woude, M. Espinoza Cangahuala, M. Scharte, J. Cruiming, L. M. van der Sleen, V. Verduijn, A. H. Beck Frederiksen, B. Schiøtt, M. Sikora, P. Schmalhorst, R. A. Moreira, A. B. Poma, K. Pluhackova, C. Arnarez, C. A. López, E. Jefferys and M. S. P. Sansom for their preliminary tests with a lot of different systems including aedamers, sugars, amino acids, deep eutectic solvents, lipids, peptides and proteins. We also thank the Center for Information Technology of the University of Groningen for providing access to the Peregrine high-performance computing cluster. We acknowledge the National Computing Facilities Foundation of The Netherlands Organization for Scientific Research (NWO), CSC–IT Center for Science Ltd (Espoo, Finland) and CINES (France) for providing computing time. Work in the S.J.M. group was supported by an European Research Council advanced grant no. ‘COMP-MICR-CROW-MEM’. R.A. thanks the NWO (Graduate Programme Advanced Materials, no. 022.005.006) for financial support. L.M. acknowledges the Institut National de la Santé et de la Recherche Medicale and the Agence Nationale de la Recherche for funding (grant no. ANR-17-CE11-0003) and GENCI-CINES for computing time (grant no. A0060710138). S.T. acknowledges the support from the European Commission via a Marie Skłodowska-Curie Actions individual fellowship (MicroMod-PSII, grant agreement no. 748895). M.J. thanks the Emil Aaltonen foundation for financial support. I.V. thanks the Academy of Finland (Center of Excellence program (grant no. 307415)), Sigrid Juselius Foundation, the Helsinki Institute of Life Science fellow program and the HFSP (research grant no. RGP0059/2019). R.B.B. and J.D. were supported by the intramural research program of the NIDDK, NIH. Their work used the computational resources of the NIH HPC Biowulf cluster (http://hpc.nih.gov). H.M.-S. acknowledges the Czech Science Foundation (grant no. 19-19561S). J.B. acknowledges funding from the TOP grant from S.J.M. (NWO) and the EPSRC program grant no. EP/P021123/1. Work in D.P.T.’s group is supported by the Natural Sciences and Engineering Research Council (Canada) and Compute Canada, funded by the Canada Foundation for Innovation. D.P.T. acknowledges further support from the Canada Research Chairs program. N.R. acknowledges funding from the Norwegian Research Council (FRIMEDBIO nos. 251247 and 288008) and computational resources from UNINETT SIGMA2 AS (grant no. NN4700K). H.M.K. acknowledges funding from the University of Calgary through the ‘Eyes High Postdoctoral Fellowship’ program.

Author information

Authors and Affiliations

Authors

Contributions

P.C.T.S. and S.J.M. conceived the project with suggestions from R.A., A.H.V., J.B. and S.T. P.C.T.S. generated and optimized all bead parameters. P.C.T.S., R.A. and J.B. generated the topology and bonded parameters of all CG models with suggestions from S.T. and I.F. P.C.T.S., R.A., A.H.V. and F.G. performed the simulations and analysis involving transfer free energies, solvent and polymer properties. P.C.T.S., S.T., J.B. and J.M. performed the simulations and analysis involving lipid bilayers. P.C.T.S., I.F. and R.A. performed the simulations and analysis involving nucleobases. P.C.T.S., I.P. and A.H.V. generated the models and performed the simulations and analysis involving aedamers. P.C.T.S. and F.G. generated the models and performed the simulations and analysis involving ionic liquids and ionic water solutions. R.A. generated the models and performed the simulations and analysis involving bulk heterojunctions, with suggestions from L.M. regarding the fullerene model. P.C.T.S., J.B., H.A., R.A., B.M.H.B., S.T., J.M., V.N., X.P., M.J., H.M.K., J.D., V.C. and H.M.-S. performed the simulations and analysis involving amino acids, peptides and proteins. J.B., T.A.W., P.C.K. and S.T. developed some tools and scripts used to generate the CG models and to run the molecular dynamics simulations. L.M., R.B.B., P.T., N.R., I.V., A.H.V. and S.J.M. provided guidance and supervision in the studies performed by their respective group members and collaborators. P.C.T.S. and S.J.M. wrote the main manuscript, with contributions from all the authors. P.C.T.S. prepared the figures with contributions from R.A., B.M.H.B., H.M.K. and A.H.V. P.C.T.S. wrote the Methods with contributions from all the authors. P.C.T.S. wrote the Supplementary Information, with contributions from all the authors. All the authors revised and approved the final version of the manuscript, Methods and Supplementary Information.

Corresponding authors

Correspondence to Paulo C. T. Souza or Siewert J. Marrink.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Methods thanks the anonymous reviewers for their contribution for the peer review of this work. Arunima Singh was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Notes, Results and Codes.

Reporting Summary

Supplementary Table 1

Comparison between simulation and experimental results

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Souza, P.C.T., Alessandri, R., Barnoud, J. et al. Martini 3: a general purpose force field for coarse-grained molecular dynamics. Nat Methods 18, 382–388 (2021). https://doi.org/10.1038/s41592-021-01098-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41592-021-01098-3

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing