title: NEW MDS AND CLUSTERING BASED ALGORITHMS FOR PROTEIN MODEL QUALITY ASSESSMENT AND SELECTION.
abstract: In protein tertiary structure prediction, assessing the quality of predicted models is an essential task. Over the past years, many methods have been proposed for the protein model quality assessment (QA) and selection problem. Despite significant advances, the discerning power of current methods is still unsatisfactory. In this paper, we propose two new algorithms, CC-Select and MDS-QA, based on multidimensional scaling andk-means clustering. For the model selection problem, CC-Select combines consensus with clustering techniques to select the best models from a given pool. Given a set of predicted models, CC-Select first calculates a consensus score for each structure based on its average pairwise 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. For the QA problem, MDS-QA combines single-model scoring functions with consensus to determine more accurate assessment score for every model in a given pool. Using extensive benchmark sets of a large collection of predicted models, we compare the two algorithms with existing state-of-the-art quality assessment methods and show significant improvement.
authors.affiliation: Bioinformatics and Systems Medicine Laboratory, Vanderbilt University Nashville, TN 37203, USA.
authors.forename: Qingguo
authors.initials: Q
authors.lastname: Wang
authors.affiliation: Computer Science Department, University of Illinois at Urbana-Champaign Urbana, IL 61801, USA.
authors.forename: Charles
authors.initials: C
authors.lastname: Shang
authors.affiliation: Computer Science Department, University of Missouri Columbia, MO 65211, USA.
authors.forename: Dong
authors.initials: D
authors.lastname: Xu
authors.affiliation: Computer Science Department, University of Missouri Columbia, MO 65211, USA.
authors.forename: Yi
authors.initials: Y
authors.lastname: Shang
keywords: Protein tertiary structure prediction; clustering; consensus method; model quality assessment; multidimensional scaling