Various physics- and data-driven sequence-dependent protein coarse-grained models have been developed to study biomolecular phase separation and elucidate the dominant physicochemical driving forces. Here we present Mpipi, a multiscale coarse-grained model that describes almost quantitatively the change in protein critical temperatures as a function of amino acid sequence. The model is parameterized from both atomistic simulations and bioinformatics data and accounts for the dominant role of π–π and hybrid cation–π/π–π interactions and the much stronger attractive contacts established by arginines than lysines. We provide a comprehensive set of benchmarks for Mpipi and seven other residue-level coarse-grained models against experimental radii of gyration and quantitative in vitro phase diagrams, demonstrating that Mpipi predictions agree well with experiments on both fronts. Moreover, Mpipi can account for protein–RNA interactions, correctly predicts the multiphase behavior of a charge-matched poly-arginine/poly-lysine/RNA system, and recapitulates experimental liquid–liquid phase separation trends for sequence mutations on FUS, DDX4 and LAF-1 proteins.
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All relevant supporting data are available in the figshare data repository at https://doi.org/10.6084/m9.figshare.1677281270. The data for this study were generated with the simulation codes and algorithms outlined in Supplementary Table 14, using the supporting code70, alongside standard command-line tools. Source data are provided with this paper.
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We thank D. Frenkel for useful comments on the manuscript, J. Mittal and G. L. Dignon for helping us implement the HPS-KR potential in LAMMPS and G. Tesei and K. Lindorff-Larsen for helping us debug our implementation of their potential. This project has received funding from the European Research Council under the European Union’s Horizon 2020 research and innovation programme (grant no. 803326; R.C.-G.). J.A.J. is a Junior Research Fellow at King’s College. R.C.-G. is an Advanced Fellow of the Winton Programme for the Physics of Sustainability. J.R.E. acknowledges funding from the Oppenheimer Fellowship of the University of Cambridge and the Roger Ekins Fellowship from Emmanuel College. A.G. is funded by the EPSRC (Doctoral Training Partnership, grant no. EP/N509620/1) and the Winton Programme for the Physics of Sustainability. P.Y.C. is funded by the University of Cambridge Ernest Oppenheimer Fund and the Winton Programme for the Physics of Sustainability. K.O.R. is funded by the EPSRC (Doctoral Training Partnership, grant no. EP/T517847/1). This work was performed using resources provided by the Cambridge Tier-2 system operated by the University of Cambridge Research Computing Service funded by EPSRC Tier-2 capital grant no. EP/P020259/1 (R.C.-G., J.A.J. and A.R.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
The authors declare no competing interests.
Peer review information Nature Computational Science thanks Hue Sun Chan and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. Handling editor: Jie Pan, in collaboration with the Nature Computational Science team.
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Joseph, J.A., Reinhardt, A., Aguirre, A. et al. Physics-driven coarse-grained model for biomolecular phase separation with near-quantitative accuracy. Nat Comput Sci 1, 732–743 (2021). https://doi.org/10.1038/s43588-021-00155-3