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The ability to consistently distinguish real protein structures fromcomputationally generated model decoys is not yet a solved problem. One routeto distinguish real protein structures from decoys is to delineate theimportant physical features that specify a real protein. For example, it haslong been appreciated that the hydrophobic cores of proteins contributesignificantly to their stability. As a dataset of decoys to compare with realprotein structures, we studied submissions to the bi-annual CASP competition(specifically CASP11, 12, and 13), in which researchers attempt to predict thestructure of a protein only knowing its amino acid sequence. Our analysisreveals that many of the submissions possess cores that do not recapitulate thefeatures that define real proteins. In particular, the model structures appearmore densely packed (because of energetically unfavorable atomic overlaps),contain too few residues in the core, and have improper distributions ofhydrophobic residues throughout the structure. Based on these observations, wedeveloped a deep learning method, which incorporates key physical features ofprotein cores, to predict how well a computational model recapitulates the realprotein structure without knowledge of the structure of the target sequence. Byidentifying the important features of protein structure, our method is able torank decoys from the CASP competitions equally well, if not better than,state-of-the-art methods that incorporate many additional features.

Using physical features of protein core packing to distinguish real proteins from decoys