A Hybrid Consensus and Clustering Method for Protein Structure Selection
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, many methods have been proposed for the protein structure selection problem. Despite significant advances, the discerning power of current approaches is still unsatisfactory. In this paper, we propose a new algorithm, CC-Select, that combines consensus with clustering techniques. Given a set of predicted models, CC-Select first calculates a consensus score for each structure based on its average pair wise structural similarity to other models. Then, similar structures are grouped into clusters using multidimensional scaling and clustering algorithms. In each cluster, the one with the highest consensus score is selected as a candidate model. Using extensive benchmark sets of a large collection of predicted models, we compare CC-Select with existing state-of-the-art quality assessment methods and show significant improvement.