Accurate model of liquid–liquid phase behavior of intrinsically disordered proteins from optimization of single-chain properties

Significance Cells may compartmentalize proteins via a demixing process known as liquid–liquid phase separation (LLPS), which is often driven by intrinsically disordered proteins (IDPs) and regions. Protein condensates arising from LLPS may develop into insoluble protein aggregates, as in neurodegenerative diseases and cancer. Understanding the process of formation, dissolution, and aging of protein condensates requires models that accurately capture the underpinning interactions at the residue level. In this work, we leverage data from biophysical experiments on IDPs in dilute solution to develop a sequence-dependent model which predicts conformational and phase behavior of diverse and unrelated protein sequences with good accuracy. Using the model, we gain insight into the coupling between chain compaction and LLPS propensity. Many intrinsically disordered proteins (IDPs) may undergo liquid–liquid phase separation (LLPS) and participate in the formation of membraneless organelles in the cell, thereby contributing to the regulation and compartmentalization of intracellular biochemical reactions. The phase behavior of IDPs is sequence dependent, and its investigation through molecular simulations requires protein models that combine computational efficiency with an accurate description of intramolecular and intermolecular interactions. We developed a general coarse-grained model of IDPs, with residue-level detail, based on an extensive set of experimental data on single-chain properties. Ensemble-averaged experimental observables are predicted from molecular simulations, and a data-driven parameter-learning procedure is used to identify the residue-specific model parameters that minimize the discrepancy between predictions and experiments. The model accurately reproduces the experimentally observed conformational propensities of a set of IDPs. Through two-body as well as large-scale molecular simulations, we show that the optimization of the intramolecular interactions results in improved predictions of protein self-association and LLPS.