Enhancing simulations of disordered protein ensembles with chemical shift restraints in coarse-grained models

With more than one third of the human proteome being structurally disordered, intrinsically disordered proteins (IDPs) are key contributors to complex cellular functions, despite their highly dynamic conformational ensembles remaining elusive to conventional techniques in structural biology. Coarse-grained (CG) simulation methods thus serve as a gold standard to refine IDP ensembles. These methods, when relying on elastic network models or rigid body to sample secondary structure, are however inherently limited in their capability to model and sample transient secondary structure at a residue-level resolution as informed by experimental data. This fundamental limitation persists despite experimental evidence suggesting that transient structural propensities are a fundamental hallmark of IDP function. Through an artificial neural network capable of predicting backbone chemical shifts from C_α-only protein conformations, we present a generalized framework which does not rely on dihedral parameterization to equip one bead-per-aminoacid CG force fields with the ability to sample functional elements of secondary structure. When applied on a large set of IDPs in isolation or condensates, our approach correctly recapitulates both local and global arrangements, yielding experimentally informed ensembles that can inform structure-mediated function and resolve force field limitations. This can yield a fruitful exploration of ensemble-to-function relationships in IDPs, allowing to explore phenomena such as secondary structure propensity and nucleation in condensates, conformationally selected binding motifs across disordered ensembles or conformational preferences of sequence-specific short-linear motifs.