Physics-driven coarse-grained model for biomolecular phase separation with near-quantitative accuracy

Jerelle A. Joseph, Aleks Reinhardt, Anne Aguirre, Pin Yu Chew, Kieran O. Russell, Jorge R. Espinosa, Adiran Garaizar, Rosana Collepardo-Guevara

Research output: Contribution to journalArticlepeer-review

85 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)732-743
Number of pages12
JournalNature Computational Science
Issue number11
StatePublished - Nov 2021
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • Computer Science Applications
  • Computer Networks and Communications


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