A unified intermediate-resolution framework for modeling biomolecular condensates at near-cellular complexity
- 1University of Massachusetts, Amherst
Abstract
Biomolecular condensates, formed through phase separation of multivalent biomolecules, are central to cellular organization and function. Here, we present a unified, physics-based intermediate-resolution framework for efficient and quantitative simulation of biomolecular phase separation at near-cellular complexity. The framework integrates the HyRes protein and iConNA nucleic acid models to explicitly describe major driving forces and capture transient local and global structural features underlying phase transitions. With well-balanced hydrogen bonding, electrostatic, cation-{pi}, and hydrophobic interactions, as well as nucleic acid base stacking, base pairing, and ion-mediated effects, the framework accurately predicts saturation concentrations of over 60 disordered proteins (within tenfold) and phase diagrams of homotypic RNA and heterotypic protein-nucleic acid condensates. We further introduce iConMetabolome, a machine learning-enabled library of cellular metabolites that captures metabolite-driven phase separation and reproduces experimental partitioning trends across condensates. This unified platform enables the dissection of the mechanisms and regulation of condensation in biology and disease.
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BibTeX
@misc{li2026unified,
title = {{A unified intermediate-resolution framework for modeling biomolecular condensates at near-cellular complexity}},
author = {S. Li and J. Fong Ng and J. Chen},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.15.751913},
url = {https://www.biorxiv.org/content/10.64898/2026.09.15.751913v1},
note = {CC BY-NC},
}
RIS
TY - UNPB
TI - A unified intermediate-resolution framework for modeling biomolecular condensates at near-cellular complexity
AU - S. Li
AU - J. Fong Ng
AU - J. Chen
PY - 2026
DA - 2026/09/15
PB - bioRxiv
DO - 10.64898/2026.09.15.751913
UR - https://www.biorxiv.org/content/10.64898/2026.09.15.751913v1
AB - Biomolecular condensates, formed through phase separation of multivalent biomolecules, are central to cellular organization and function. Here, we present a unified, physics-based intermediate-resolution framework for efficient and quantitative simulation of biomolecular phase separation at near-cellular complexity. The framework integrates the HyRes protein and iConNA nucleic acid models to explicitly describe major driving forces and capture transient local and global structural features underlying phase transitions. With well-balanced hydrogen bonding, electrostatic, cation-{pi}, and hydrophobic interactions, as well as nucleic acid base stacking, base pairing, and ion-mediated effects, the framework accurately predicts saturation concentrations of over 60 disordered proteins (within tenfold) and phase diagrams of homotypic RNA and heterotypic protein-nucleic acid condensates. We further introduce iConMetabolome, a machine learning-enabled library of cellular metabolites that captures metabolite-driven phase separation and reproduces experimental partitioning trends across condensates. This unified platform enables the dissection of the mechanisms and regulation of condensation in biology and disease.
ER -