Beta Turn Propensity and a Model Polymer Scaling Exponent Identify Disordered Proteins that Phase Separate

Abstract

The complex cellular milieu can spontaneously de-mix in a process controlled in part by proteins that are intrinsically disordered (ID). A proteins propensity to de-mix is thought to be driven by the preference for protein-protein rather than protein-solvent interactions. The hydrodynamic size of monomeric proteins, as quantified by the polymer scaling exponent (v), is driven by a similar balance. We hypothesize that mean v, as predicted by the protein sequence, will be smaller for proteins with a strong propensity to de-mix. To test this hypothesis, we analyzed protein databases containing subsets that are either folded, disordered, or disordered and known to spontaneously phase separate. We find that the phase separating disordered proteins, on average, have lower calculated values of v compared to their non-phase separating counterparts. Moreover, these proteins have a higher sequence-predicted propensity for {beta}-turns. Using a simple, surface areabased model, we propose a physical mechanism for this difference: transient {beta}-turn structures reduce the desolvation penalty of forming a protein-rich phase and increase exposure of atoms involved in {pi}/sp2 electronic interactions. By this mechanism, {beta}-turns act as energetically favored nucleation points, which may explain the increased propensity for turns in ID regions (IDRs) that are utilized biologically for phase separation. Phase separating IDRs, non-phase separating IDRs, and folded regions could be distinguished by combining v and {beta}-turn propensity, and we propose a new algorithm, ParSe (partition sequence), for predicting phase separating protein regions. ParSe is able to accurately identify folded, disordered, and phase-separating protein regions from the primary sequence.