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Maximum Entropy Optimized Force Field for Intrinsically Disordered Proteins.

Andrew P LathamBin Zhang
Published in: Journal of chemical theory and computation (2019)
Intrinsically disordered proteins (IDPs) constitute a significant fraction of eukaryotic proteomes. High-resolution characterization of IDP conformational ensembles can help elucidate their roles in a wide range of biological processes but remains challenging both experimentally and computationally. Here, we present a generic algorithm to improve the accuracy of coarse-grained IDP models using a diverse set of experimental measurements. It combines maximum entropy optimization and least-squares regression to systematically adjust model parameters and improve the agreement between simulation and experiment. We successfully applied the algorithm to derive a transferable force field, which we term the maximum entropy optimized force field (MOFF), for de novo prediction of IDP structures. Statistical analysis of force field parameters reveals features of amino acid interactions not captured by potentials designed to work well for folded proteins. We anticipate its combination of efficiency and accuracy will make MOFF useful for studying the phase separation of IDPs, which drives the formation of various biological compartments.
Keyphrases
  • single molecule
  • high resolution
  • molecular dynamics
  • machine learning
  • amino acid
  • molecular dynamics simulations
  • preterm infants
  • mass spectrometry
  • neural network
  • gestational age