Biomolecular phase separation through theoretical and computational microscope

Abstract

Biomolecular phase separation is a vital mechanism for orchestrating biomolecules within living cells. This crucial role has spurred an intense pursuit to comprehend the molecular underpinnings governing and regulating these processes. Computational methodologies offer a unique perspective, augmenting experimental techniques by providing detailed information that cannot be obtained otherwise. In this review, we briefly overview the theoretical and computational approaches to investigate biomolecular phase separation. As a short primer, we explain the factors driving and affecting phase separation of biomolecules, and then we delve into analytical and simulation methods used to study phase separation. We explain how analytical methods like the Flory–Huggins theory, random phase approximation, and graph‐based methods have been used to study phase behaviors of various proteins. We also discuss principles and applications of all‐atom simulations, coarse‐grained simulations, and field‐theoretical approaches. Additionally, we explore the recent advances in machine learning approach to predict phase separation of biomolecules.

Combinatorial entropy determines the early stages of nucleation

“…[6][7][8][9][10] Phase separation involves the aggregation of constituent molecules, making bond percolation a useful qualitative proxy. 11 We recently developed a graph-based simulation module to study polymer percolation behavior. 12 In this module, each associating unit ("monomer") is a node on a graph, with covalent bonds as permanent links.…”