Computational design of amino-acid sequence variations that modulate the liquid-liquid phase separation of intrinsically disordered proteins

Liquid-liquid phase separation (LLPS) is an important mechanism that contributes to intracellular organization via the formation of biomolecular condensates. From a theoretical and computational perspective, the development of accurate and transferable coarse-grained models that allow us to elucidate the molecular mechanisms driving condensate formation in the cytoplasm and nucleoplasm with attainable computational cost is highly desirable.  We recently developed a multiscale coarse-grained model, termed ‘Mpipi‘ [1] that predicts phase diagrams of proteins in excellent quantitative agreement with experiments [2]. Mpipi provides a balanced parametrization of interaction strengths between different types of amino acids, accounting for the dominant role of π-based interactions. Here, we combine Mpipi with a genetic algorithm [3] to rationally design amino acid sequence mutations with desired LLPS properties. We propose a set of experimentally-testable amino-acid sequence variations of intrinsically disordered proteins that can either promote or inhibit their phase separation.

[1] Joseph, Reinhardt et al., Physics driven coarse-grained model for biomolecular phase separation with near-quantitative accuracy, Nat. Comput. Sci., 2021 (in press).

[2] Bremer et al, Deciphering how naturally occurring sequence features impact the phase behaviors of disordered prion-like domains, bioRxiv, 2021.

[3] Lichtinger et al, Targeted modulation of protein liquid–liquid phase separation by evolution of amino-acid sequence, Plos CompBiol, 2021