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Protein structure prediction: making AWSEM AWSEM‐ER by adding evolutionary restraints
Authors:Brian J Sirovetz  Nicholas P Schafer  Peter G Wolynes
Institution:1. Center for Theoretical Biological Physics, Rice University, Houston, Texas;2. Department of Chemistry, Rice University, Houston, Texas;3. Department of Physics, Rice University, Houston, Texas;4. Department of Biosciences, Rice University, Houston, Texas
Abstract:Protein sequences have evolved to fold into functional structures, resulting in families of diverse protein sequences that all share the same overall fold. One can harness protein family sequence data to infer likely contacts between pairs of residues. In the current study, we combine this kind of inference from coevolutionary information with a coarse‐grained protein force field ordinarily used with single sequence input, the Associative memory, Water mediated, Structure and Energy Model (AWSEM), to achieve improved structure prediction. The resulting Associative memory, Water mediated, Structure and Energy Model with Evolutionary Restraints (AWSEM‐ER) yields a significant improvement in the quality of protein structure prediction over the single sequence prediction from AWSEM when a sufficiently large number of homologous sequences are available. Free energy landscape analysis shows that the addition of the evolutionary term shifts the free energy minimum to more native‐like structures, which explains the improvement in the quality of structures when performing predictions using simulated annealing. Simulations using AWSEM without coevolutionary information have proved useful in elucidating not only protein folding behavior, but also mechanisms of protein function. The success of AWSEM‐ER in de novo structure prediction suggests that the enhanced model opens the door to functional studies of proteins even when no experimentally solved structures are available.
Keywords:coevolution  contact prediction  energy landscape theory  hybrid model  knowledge‐based model  physically motivated potential  sequence covariation
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