clustQ

Abstract

Structure of a protein largely determines its functional properties. Hence, the knowledge of the protein's 3D structure is an important aspect in determining solutions to fundamental biological problems. Structure prediction algorithms generally employ clustering algorithm to select the optimal model for a target from a large number of predicted confirmations (a.k.a. decoy). Despite significant advancement in clustering-based optimal decoy selection methods, these approaches often cannot deliver high performance in terms of the time taken to cluster large number of protein structures owing to the computational cost associated with pairwise structural superpositions. Here, we propose a superposition-free approach to protein decoy clustering, called clustQ, based on weighted internal distance comparisons. Experimental results suggest that the novel weighing scheme is helpful in both reproducing the decoy-native similarity score and estimating pairwise clustering based predicted quality score in a computationally efficient manner. clustQ attains performance comparable to the state-of-the-art multi-model decoy quality estimation methods participating in the latest Critical Assessment of protein Structure Prediction (CASP) experiments irrespective of target difficulty. Moreover, clustQ predicted score offers a unique way to reliably estimate target difficulty without the knowledge of the experimental structure. clustQ is freely available at http://watson.cse.eng.auburn.edu/clustQ/.