Assessment of template‐based modeling of protein structure in CASP11. (15th June 2016)

• Record Type: Journal Article
• Authors: Modi, Vivek; Xu, Qifang; Adhikari, Sam; Dunbrack, Roland L.
• Is Part Of: Proteins. Volume 84(2016)Supplement 1
• Page Start: 200
• Page End: 220
• Publication Date: 2016-06-15
• Subjects: protein structure prediction -- CASP -- template‐based modeling -- homology modeling -- structural biology; Proteins -- Periodicals; Proteins -- Periodicals; 572.6
• DOI: 10.1002/prot.25049
• Languages: English
• ISSNs: 0887-3585

Abstract

ABSTRACT: We present the assessment of predictions submitted in the template‐based modeling (TBM) category of CASP11 (Critical Assessment of Protein Structure Prediction). Model quality was judged on the basis of global and local measures of accuracy on all atoms including side chains. The top groups on 39 human‐server targets based on model 1 predictions were LEER, Zhang, LEE, MULTICOM, and Zhang‐Server. The top groups on 81 targets by server groups based on model 1 predictions were Zhang‐Server, nns, BAKER‐ROSETTASERVER, QUARK, and myprotein‐me. In CASP11, the best models for most targets were equal to or better than the best template available in the Protein Data Bank, even for targets with poor templates. The overall performance in CASP11 is similar to the performance of predictors in CASP10 with slightly better performance on the hardest targets. For most targets, assessment measures exhibited bimodal probability density distributions. Multi‐dimensional scaling of an RMSD matrix for each target typically revealed a single cluster with models similar to the target structure, with a mode in the GDT‐TS density between 40 and 90, and a wide distribution of models highly divergent from each other and from the experimental structure, with density mode at a GDT‐TS value of ∼20. The models in this peak in the density were either compact models with entirely the wrong fold, or highly non‐compact models. The results argue for a density‐driven approach in future CASP TBM assessments that accounts for the bimodal nature of these distributions instead of Z scores, which assume a unimodal, Gaussian distribution. Proteins 2016; 84(Suppl 1):200–220. © 2016 Wiley Periodicals, Inc.