期刊
PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
卷 77, 期 -, 页码 29-49出版社
WILEY
DOI: 10.1002/prot.22551
关键词
homology modeling; protein structure prediction; all-atom contacts; full-model assessment
资金
- National Institutes of Health [GM073930, GM073919]
- NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES [R01GM073919, R01GM073930] Funding Source: NIH RePORTER
For template-based modeling in the CASP8 Critical Assessment of Techniques for Protein Structure Prediction, this work develops and applies six new full-model metrics. They are designed to complement and add value to the traditional template-based assessment by the global distance test (GDT) and related scores (based on multiple superpositions of C alpha atoms between target structure and predictions labeled Model 1). The new metrics evaluate each predictor group on each target, using all atoms of their best model with above-average GDT. Two metrics evaluate how protein-like the predicted model is: the MolProbity score used for validating experimental structures, and a mainchain reality score using all-atom steric clashes, bond length and angle outliers, and backbone dihedrals. Four other new metrics evaluate match of model to target for mainchain and side-chain hydrogen bonds, side-chain end positioning, and side-chain rotamers. Group-average Z-score across the six full-model measures is averaged with group-average GDT Z-score to produce the overall ranking for full-model, high-accuracy performance. Separate assessments are reported for specific aspects of predictor-group performance, such as robustness of approximately correct template or fold identification, and self-scoring ability at identifying the best of their models. Fold identification is distinct from but correlated with group-average GDT Z-score if target difficulty is taken into account, whereas self-scoring is done best by servers and is uncorrelated with GDT performance. Outstanding individual models on specific targets are identified and discussed. Predictor groups excelled at different aspects, highlighting the diversity of current methodologies. However, good full-model scores correlate robustly with high C alpha accuracy.
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