Sequence, phase behavior and dynamics in protein condensates: an eternal triangle revealed by machine learning

While biomolecular condensates have been identified in a wide range of intracellular compartments, debate continues over the utility of such “droplets” and the connection between sequence characteristics, phase behavior, and condensate dynamics. Prior experimental observations and physical arguments suggest that the phase-separating propensity of a protein is strongly correlated to the dynamics in a protein-rich phase. Inspired by this work, we endeavor to understand to what extent we can exploit sequence patterns to break this correlation and what are the characteristics of any sequences that “break the mold.” To navigate the complexity in both sequence design and understanding the characteristics of designed condensates, we heavily rely on and demonstrate the utility of techniques from machine learning. In aggregate, we hope to augment our understanding of existing biological systems and importantly highlight opportunities for functional soft materials design, including phase-separating protein condensates with “tunable” dynamical properties.