Large-scale model quality assessment for improving protein tertiary structure prediction

By Renzhi Cao, Debswapna Bhattacharya, Badri Adhikari, Jilong Li, Jianlin Cheng. 2015

69 citations recorded by the source corpus.

Motivation: Sampling structural models and ranking them are the two major challenges of protein structure prediction. Traditional protein structure prediction methods generally use one or a few quality assessment (QA) methods to select the best-predicted models, which cannot consistently select relatively better models and rank a large number of models well. Results: Here, we develop a novel large-scale model QA method in conjunction with model clustering to rank and select protein structural models. It unprecedentedly applied 14 model QA methods to generate consensus model rankings, followed by model refinement based on model combination (i.e. averaging). Our experiment demonstrates that the large-scale model QA approach is more consistent and robust in selecting models of better quality than any individual QA method. Our method was blindly tested during the 11th Critical Assessment of Techniques for Protein Structure Prediction (CASP11) as MULTICOM group. It was officially ranked third out of all 143 human and server predictors according to the total scores of the first models predicted for 78 CASP11 protein domains and second according to the total scores of the best of the five models predicted for these domains. MULTICOM’s outstanding performance in the extremely competitive 2014 CASP11 experiment proves that our large-scale QA approach together with model clustering is a promising solution to one of the two major problems in protein structure modeling. Availability and implementation: The web server is available at: http://sysbio.rnet.missouri.edu/multicom_cluster/human/. Contact: chengji@missouri.edu

Subfields: Materials Chemistry, Molecular Biology

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Citations in the corpus

DockQ: A Quality Measure for Protein-Protein Docking Models (2016, methodology): “These methods have been successful in CASP for predicting the quality of protein structure models [14,15] and there is no reason to believe that they will not be as successful in predicting the quality of docking models.”
Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13 (2019, background): “…17-19 with our latest development in contact distance prediction empowered by deep learning and its application to template-FM and protein model ranking, 17,20,21 while having a routine update on its other components such as template library, template identification, and template-based modeling.”
Protein single-model quality assessment by feature-based probability density functions (2016, unknown): “232 6 700 1000 0 4823 (2) Dfire Dfire score”
Deep convolutional neural networks for predicting the quality of single protein structural models (2019, methodology): “There are two kinds of model quality assessment methods: local quality assessment 8-12 and global quality assessment 10,13-22 .”