Identify High-Quality Protein Structural Models by EnhancedK-Means

Abstract

Background.One critical issue in protein three-dimensional structure prediction using either ab initio or comparative modeling involves identification of high-quality protein structural models from generated decoys. Currently, clustering algorithms are widely used to identify near-native models; however, their performance is dependent upon different conformational decoys, and, for some algorithms, the accuracy declines when the decoy population increases.Results.Here, we proposed two enhancedK-means clustering algorithms capable of robustly identifying high-quality protein structural models. The first one employs the clustering algorithm SPICKER to determine the initial centroids for basicK-means clustering (SK-means), whereas the other employs squared distance to optimize the initial centroids (K-means++). Our results showed thatSK-means andK-means++ were more robust as compared with SPICKER alone, detecting 33 (59%) and 42 (75%) of 56 targets, respectively, with template modeling scores better than or equal to those of SPICKER.Conclusions.We observed that the classicK-means algorithm showed a similar performance to that of SPICKER, which is a widely used algorithm for protein-structure identification. BothSK-means andK-means++ demonstrated substantial improvements relative to results from SPICKER and classicalK-means.