title: Massive integration of diverse protein quality assessment methods to improve template based modeling in CASP11.
abstract: Model evaluation and selection is an important step and a big challenge in template-based protein structure prediction. Individual model quality assessment methods designed for recognizing some specific properties of protein structures often fail to consistently select good models from a model pool because of their limitations. Therefore, combining multiple complimentary quality assessment methods is useful for improving model ranking and consequently tertiary structure prediction. Here, we report the performance and analysis of our human tertiary structure predictor (MULTICOM) based on the massive integration of 14 diverse complementary quality assessment methods that was successfully benchmarked in the 11th Critical Assessment of Techniques of Protein Structure prediction (CASP11). The predictions of MULTICOM for 39 template-based domains were rigorously assessed by six scoring metrics covering global topology of Cα trace, local all-atom fitness, side chain quality, and physical reasonableness of the model. The results show that the massive integration of complementary, diverse single-model and multi-model quality assessment methods can effectively leverage the strength of single-model methods in distinguishing quality variation among similar good models and the advantage of multi-model quality assessment methods of identifying reasonable average-quality models. The overall excellent performance of the MULTICOM predictor demonstrates that integrating a large number of model quality assessment methods in conjunction with model clustering is a useful approach to improve the accuracy, diversity, and consequently robustness of template-based protein structure prediction. Proteins 2016; 84(Suppl 1):247-259. © 2015 Wiley Periodicals, Inc.
authors.affiliation: Department of Computer Science, University of Missouri, Columbia, Missouri, 65211.
authors.forename: Renzhi
authors.initials: R
authors.lastname: Cao
authors.affiliation: Department of Computer Science, University of Missouri, Columbia, Missouri, 65211.
authors.forename: Debswapna
authors.initials: D
authors.lastname: Bhattacharya
authors.affiliation: Department of Computer Science, University of Missouri, Columbia, Missouri, 65211.
authors.forename: Badri
authors.initials: B
authors.lastname: Adhikari
authors.affiliation: Department of Computer Science, University of Missouri, Columbia, Missouri, 65211.
authors.forename: Jilong
authors.initials: J
authors.lastname: Li
authors.affiliation: Department of Computer Science, University of Missouri, Columbia, Missouri, 65211. chengji@missouri.edu.|Informatics Institute, University of Missouri, Columbia, Missouri, 65211. chengji@missouri.edu.
authors.forename: Jianlin
authors.initials: J
authors.lastname: Cheng
mesh_terms: D000465:Algorithms; D019985:Benchmarking; D019295:Computational Biology / Q000379:methods / Q000706:statistics & numerical data*; D003198:Computer Simulation; D030562:Databases, Protein; D006801:Humans; D020407:Internet; D008958:Models, Molecular; D015233:Models, Statistical; D017510:Protein Folding; D054730:Protein Interaction Domains and Motifs; D017433:Protein Structure, Secondary; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry*; D011786:Quality Control; D012984:Software; D040681:Structural Homology, Protein; D013816:Thermodynamics
chemical_list: D011506:Proteins
keywords: CASP; integration; model quality assessment; protein structure prediction; template-based modeling