Selecting near-native structures from decoys using maximal cliques

Protein structure prediction is one of the most important subjects in computational structural biology. In the process of protein structure prediction, many structure decoys are obtained. It has remained an unsolved and challenging problem to select the best model from the structure decoys that are closest to the native structure. One of the important methods for selecting the near-native structure is by clustering the structure decoys. The traditional methods simply use clustering methods which are usually not appropriate in the high dimensional conformation space. Here we propose a method based on maximal cliques in graph theory to solve this problem. The similarities between the decoys are first computed using TM-score, and a graph is built using the shared nearest neighbor (SNN) information among the decoys. Then the maximal cliques of the graph are found and the centroids of these maximal cliques are selected as near-native structures. The experiments show that, compared to the traditional methods, the proposed method can select better near-native structures which have higher similarities with the native structures.