Capturing secondary structure in coarse grained intrinsically disordered proteins with simulations driven by chemical shifts

Abstract A major challenge when investigating intrinsically disordered proteins (IDPs) pertains to understanding how secondary structure formation across otherwise disordered ensembles, relates to function. While coarse-grained (CG) simulation methods are instrumental to describe ensemble-to-function relationships in IDPs, they remain largely incapable of sampling secondary structure. We here present NapshiftC α , an artificial neural network capable of modelling NMR chemical shifts from C α -mapped CG protein conformations, leveraging its predictions to restrain simulations and yield experimentally-informed IDP ensembles that contain secondary structure. We applied NapshiftC α on a large set of proteins in isolation or condensed phase, and found that, when secondary structure is present, CS incorporation yields improved agreements with smFRET spectroscopy and resolves the over-reliance of CG models on near linear conformers. By incorporating observables that are routinely collected for IDPs but never coupled to CG simulations, NapshiftC α ultimately informs on both local and global conformational features of function-defining IDP ensembles.