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