Computational design of conformation-biasing mutations to alter protein functions

Abstract

Conformational biasing (CB) is a rapid and streamlined computational method that uses contrastive scoring by inverse folding models to predict protein variants biased toward desired conformational states. We successfully validated CB across seven diverse datasets, identifying variants of K-Ras, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein, the β2 adrenergic receptor, and Src kinase with improved conformation-specific functions such as enhanced binding or enzymatic activity. Applying CB to the enzyme lipoic acid ligase (LplA), we uncovered a previously unknown mechanism controlling its promiscuous activity. Variants biased toward an "open" conformation state became more promiscuous, whereas "closed"-biased variants were more selective, enhancing LplA's utility for site-specific protein labeling with fluorophores in living cells. The speed and simplicity of CB make it a versatile tool for engineering protein dynamics with broad applications in basic research, biotechnology, and medicine.