Suchergebnisse

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. ; We thank Veronica Ryan and Nicolas L. Fawzi for sharing the PRE data for FUS LCD, FUS12E LCD, and A2 LCD as well as Robert Konrat for sharing the intramolecular PRE data for Osteopontin. We thank Robert B. Best for sharing data on compaction of IDPs; Gregory L. Dignon and Jeetain Mittal for help setting up simulations with the HPS model; and Tanja Mittag, Massimiliano Bonomi, and Benjamin Schuler for helpful discussions. We acknowledge funding from the BRAINSTRUC structural biology initiative from the Lundbeck Foundation (Grant R155-2015-2666) and acknowledge access to computational resources from the Resource for Biomolecular Simulations (ROBUST) (supported by Novo Nordisk Foundation Grant NNF18OC0032608) and Biocomputing Core Facility at the Department of Biology, University of Copenhagen. T.K.S. acknowledges support from the Novo Scholarship Programme 2021. This project has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie Grant Agreement 101025063. ; Peer reviewed