The DOI matches its Crossref record; 86 of the 86 references it cites were found in a registry or an index.
Physics-driven coarse-grained model for biomolecular phase separation with near-quantitative accuracy
Open accessAbstract
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. Combining bioinformatics data and atomistic simulations, this study develops a sequence-dependent coarse-grained model for biomolecular phase separation. This model achieves a quantitative agreement with experimental observations. Extensive benchmarks exemplify its performance.
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Cites
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“…particular system using fine-grained simulations as a reference, often derived in a systematic way using, for instance, iterative Boltzmann inversion 30,31 or force matching 32,33 , and transferable, either bottom-up or top-down parameterizations, aiming to achieve a generally applicable potential.”
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Cited by
Phase Transitions of Associative Biomacromolecules
Pappu, R. V., Cohen, S. R., Dar, F., Farag, M., & Kar, M. (2023) · Chemical Reviews · doi:10.1021/acs.chemrev.2c00814 · Cited by 367
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
Design of intrinsically disordered protein variants with diverse structural properties
Pesce, F., Bremer, A., Tesei, G., Hopkins, J. B., Grace, C. R., Mittag, T., & Lindorff-Larsen, K. (2024) · Science Advances · doi:10.1126/sciadv.adm9926 · Cited by 60
Lambourne, L., Mattioli, K., Santoso, C., Sheynkman, G., Inukai, S., Kaundal, B., Berenson, A., Spirohn-Fitzgerald, K., Bhattacharjee, A., Rothman, E., et al. (2025) · Molecular Cell · doi:10.1016/j.molcel.2025.03.004 · Cited by 42
(2024) · doi:10.1038/s41467-024-47654-1 · Cited by 39
Metadata
- Authors
- Joseph, J. A., Reinhardt, A., Aguirre, A., Chew, P. Y., Russell, K. O., Espinosa, J. R., Garaizar, A., & Collepardo-Guevara, R.
- Venue
- Nature Computational Science
- Published
- 2021-11-22
- Volume
- 1
- Issue
- 11
- Pages
- 732-743
- ISSN
- 2662-8457 (ISSN-L)
- OpenAlex
- W3215858345
- Cited by
- 382
- References
- 86
- FWCI
- 11.61
- Type
- journal article
- Language
- en
- Open access
- green
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