Nonbonded terms extrapolated from nonlocal knowledge-based energy functions improve error detection in near-native protein structure models.

The accurate assessment of structural errors plays a key role in protein structure prediction, constitutes the first step of protein structure refinement, and has a major impact on subsequent functional inference from structural data. In this study, we assess and compare the ability of different full atom knowledge-based potentials to detect small and localized errors in comparative protein structure models of known accuracy. We have evaluated the effect of incorporating close nonbonded pairwise atom terms on the task of classifying residue modeling accuracy. Since the direct and unbiased derivation of close nonbonded terms from current experimental data is not possible, we extrapolated those terms from the corresponding pseudo-energy functions of a nonlocal knowledge-based potential. It is shown that this methodology clearly improves the detection of errors in protein models, suggesting that a proper description of close nonbonded terms is important to achieve a more complete and accurate description of native protein conformations. The use of close nonbonded terms directly derived from experimental data exhibited a poor performance, demonstrating that these terms cannot be accurately obtained by using the current data and methodology. Some external knowledge-based energy functions that are widely used in model assessment also performed poorly, which suggests that the benchmark of models and the specific error detection task tested in this study constituted a difficult challenge. The methodology presented here could be useful to detect localized structural errors not only in high-quality protein models, but also in experimental protein structures.