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Aguirre Gonzalez, A.

Publications and source records attributed to Aguirre Gonzalez, A..

2 recordsLinked to original sources

Chemically-informed coarse-graining of electrostatic forces in charge-richbiomolecular condensates

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.

biophysics↗

Universal predictive scaling laws of phase separation of prion-like low complexity domains

Proteins containing prion-like low complexity domains (PLDs) are common drivers of the formation of biomolecular condensates and are prone to misregulation due to amino acid mutations. Here, we exploit the accuracy of our residue-resolution coarse-grained model, Mpipi, to quantify the impact of amino acid mutations on the stability of 140 PLD mutants from six proteins (hnRNPA1, TDP43, FUS, EWSR1, RBM14, and TIA1). Our simulations reveal the existence of scaling laws that quantify the range of change in the critical solution temperature of PLDs as a function of the number and type of amino acid sequence mutations. These rules are consistent with the physicochemical properties of the mutations and extend across the entire family tested, suggesting that scaling laws can be used as tools to predict changes in the stability of PLD condensates. Our work offers a quantitative lens into how the emergent behavior of PLD solutions varies in response to physicochemical changes of single PLD molecules.

biophysics↗