Active learning of the thermodynamics-dynamics tradeoff in protein condensates

TL;DR

This study uses simulations and active learning to explore how protein sequence variations can independently tune the dynamics and thermodynamics of biomolecular condensates, revealing design principles for stimuli-responsive materials.

Contribution

It introduces an active learning approach to identify protein sequences that decouple condensate dynamics from thermodynamics, enabling independent control of these properties.

Findings

Homopolymer condensates show a strong correlation between stability and viscosity.

Active learning identifies heteropolymer sequences that break the correlation.

Sequence heterogeneity can independently tune condensate properties.

Abstract

Phase-separated biomolecular condensates exhibit a wide range of dynamical properties, which depend on the sequences of the constituent proteins and RNAs. However, it is unclear to what extent condensate dynamics can be tuned without also changing the thermodynamic properties that govern phase separation. Using coarse-grained simulations of intrinsically disordered proteins, we show that the dynamics and thermodynamics of homopolymer condensates are strongly correlated, with increased condensate stability being coincident with low mobilities and high viscosities. We then apply an "active learning" strategy to identify heteropolymer sequences that break this correlation. This data-driven approach and accompanying analysis reveal how heterogeneous amino-acid compositions and non-uniform sequence patterning map to a range of independently tunable dynamical and thermodynamic properties of biomolecular condensates. Our results highlight key molecular determinants governing the physical properties of biomolecular condensates and establish design rules for the development of stimuli-responsive biomaterials.