ACS Central ScienceBiophysicsPeer-reviewed journal article
Chemically-informed coarse-graining of electrostatic forces in charge-richbiomolecular condensates
Andrés R. TejedoriD, Anne Aguirre Gonzalez, Maria Julia MaristanyiD, Pin Yu ChewiD, Kieran O. RusselliD, Jorge Ramı́reziD, Jorge R. EspinosaiD, Rosana Collepardo‐GuevaraiD
Abstract
Biomolecular condensates composed of highly charged biomolecules like DNA, RNA, chromatin, and nucleic-acid binding proteins are ubiquitous in the cell nucleus. The biophysical properties of these charge-rich condensates are largely regulated by electrostatic interactions. Residue-resolution coarse-grained models that describe solvent and ions implicitly are widely used to gain mechanistic insights into the biophysical properties of condensates, offering transferability, computational efficiency, and accurate predictions for many systems. However, their predictive accuracy diminishes for charge-rich condensates due to the implicit treatment of solvent and ions. Here, we present the Mpipi-Recharged model, a residue-resolution coarse-grained model that improves the description of charge effects in biomolecular condensates containing disordered proteins, multi-domain proteins, and/or disordered RNAs. Mpipi-Recharged maintains the computational efficiency of its predecessor--the Mpipi model--by still treating solvent and ions implicitly, but improves its accuracy by incorporating a pair-specific asymmetric electrostatic potential informed by atomistic simulations in explicit solvent and ions. We show that such asymmetric coarse-graining of electrostatic forces is needed to recapitulate the stronger mean-field impact of associative interactions between opposite-charge pairs over the repulsion among equally charged pairs revealed by our atomistic simulations. Mpipi-Recharged shows excellent agreement with the experimental phase behavior of highly charged systems, capturing subtle effects challenging to model without explicit solvation, such as the impact of charge blockiness, stoichiometry changes, and salt concentration variation. By offering improved predictions for charge-rich biomolecular condensates, Mpipi-Recharged extends the computational tools available to investigate the physicochemical mechanisms regulating biomolecular condensates.
- research focus: protein folding & dynamics
- research focus: chromatin & epigenetic regulation
- research focus: cell signaling & trafficking
- research focus: development & morphogenesis
- approach: deep learning
- approach: statistical model
- Citations
- 66
- FWCI
- 15.10
- Field percentile
- top 1%
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