Beta turn propensity and a model polymer scaling exponent identify disordered proteins that phase separate

Abstract

<jats:title>Abstract</jats:title><jats:p>The complex cellular milieu can spontaneously de-mix in a process driven in part by proteins that are intrinsically disordered (ID). We hypothesized that protein self-interactions that determine the polymer scaling exponent, <jats:italic>v</jats:italic>, of monomeric ID proteins (IDPs), also facilitate de-mixing transitions into phase separated assemblies. We analyzed a protein database containing subsets that are folded, ID, or IDPs identified previously to spontaneously phase separate. We found that the subsets differentiated into distinct protein classes according to sequence-based calculations of <jats:italic>v</jats:italic> and, surprisingly, the propensity in the sequence for adopting the β-turn. Structure-based simulations find that transient β-turn structures reduce the desolvation penalty of forming a protein-rich phase. By this mechanism, β-turns act as energetically favored nucleation points, which may explain the increased propensity for turns in IDPs that are utilized biologically for phase separation.</jats:p>

An interpretable machine learning algorithm to predict disordered protein phase separation based on biophysical interactions

“…Another, ParSe, combines two physical features, hydrodynamic size of monomeric proteins and beta-turn propensity estimated from polymer models, to predict phase separation propensity, however uses only composition and not residue context when making predictions. [43] A third, PSAP, uses compositional bias of phase-separating proteins and 10 amino-acid biochemical features as features for a random forest classifier with a 0.89 AUROC (area under the receiver operating characteristics curve), yet also lacks residue context in the prediction. [44]…”