Protein structure selection based on consensus

In protein tertiary structure prediction, a crucial step is to select near-native structures from a large number of predicted structural models. Over the years, extensive research has been conducted for the protein structure selection problem with most approaches focusing on developing more accurate energy or scoring functions. Despite significant advances in this area, the discerning power of current approaches is still unsatisfactory. In this paper, we propose a novel consensus-based method that outperforms state-of-the-art scoring functions. Given a set of predicted models, the method first removes redundant models to derive a subset of reference models. Then a model is ranked based on its average pairwise similarity score to the set of reference models. Using the CASP8 data set containing a large collections of predicted models for 122 targets, we compared our method with an extensive collection of state-of-the-art scoring functions and showed its superior performance. The top models selected by our method are on average more than 8% better than the ones selected by the best competing method.