Intracellular membraneless organelles correspond to the droplet phase upon liquid-liquid
phase separation (LLPS) of mixtures of proteins and possibly RNA, and mediate myriad
cellular functions [1]. Cells use a variety of biochemical signals such as expression level
and posttranslational modification to regulate droplet formation and dissolution. Our
study focuses on elucidating the physical basis of phase behaviors associated with
cellular functions of membraneless organelles, using three complementary approaches.
First, we use colloids and polymers, respectively, as models for structured and disordered
proteins, to investigate both the common basis for protein phase separation and the
unique characteristics of structured and disordered proteins in LLPS. Disordered proteins
are characterized by both extensive attraction throughout the sequence and low energetic
cost from steric repulsion, contributing to easy observation of phase separation. Second,
we use multi-component patchy particles to investigate the wide range of effects of
ancillary macromolecular species on the droplet formation of driver proteins. Third, we
have developed a powerful computational method called FMAP for determining liquidliquid
phase equilibria [2,3]. By using fast Fourier transform to efficiently evaluate
protein-protein interactions, FMAP enables an atomistic representation of the protein
molecules. Application to three γ-crystalins reveals how minor variations in amino-acid
sequence, similar to those from posttranslational modifications and disease-associated
mutations, lead to drastic differences in critical temperature. These studies contribute to
both qualitative and quantitative understanding on the phase behaviors of membraneless
organelles and their regulation and dysregulation.

1. S. Qin and H.-X. Zhou (2017). Protein folding, binding, and droplet formation in celllike
conditions. Curr. Opin. Struct. Biol. 43, 28-37.
2. S. Qin and H.-X. Zhou (2014). Further development of the FFT-based method for
atomistic modeling of protein folding and binding under crowding: optimization of
accuracy and speed. J. Chem. Theory Comput. 10, 2824-2835.
3. S. Qin and H.-X. Zhou (2016). Fast method for computing chemical potentials and
liquid-liquid phase equilibria of macromolecular solutions. J. Phys. Chem. B. 120, 8164-
8174.