Integrating ab initio and template-based algorithms for protein-protein complex structure prediction
UMass Chan Affiliations
Program in Bioinformatics and Integrative BiologyDocument Type
Journal ArticlePublication Date
2019-08-08Keywords
Amino Acids, Peptides, and ProteinsBiochemistry, Biophysics, and Structural Biology
Bioinformatics
Computational Biology
Integrative Biology
Statistics and Probability
Metadata
Show full item recordAbstract
MOTIVATION: Template-based and template-free methods have both been widely used in predicting the structures of protein-protein complexes. Template-based modeling is effective when a reliable template is available, while template-free methods are required for predicting the binding modes or interfaces that have not been previously observed. Our goal is to combine the two methods to improve computational protein-protein complex structure prediction. RESULTS: Here we present a method to identify and combine high-confidence predictions of a template-based method (SPRING) with a template-free method (ZDOCK). Cross-validated using the protein-protein docking benchmark version 5.0, our method (ZING) achieved a success rate of 68.2%, outperforming SPRING and ZDOCK, with success rates of 52.1% and 35.9% respectively, when the top 10 predictions were considered per test case. In conclusion, a statistics-based method that evaluates and integrates predictions from template-based and template-free methods is more successful than either method independently. AVAILABILITY: ZING is available for download as a Github repository (https://github.com/weng-lab/ZING.git). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.Source
Bioinformatics. 2019 Aug 8. pii: btz623. doi: 10.1093/bioinformatics/btz623. [Epub ahead of print] Link to article on publisher's site
DOI
10.1093/bioinformatics/btz623Permanent Link to this Item
http://hdl.handle.net/20.500.14038/25860PubMed ID
31393558Related Resources
ae974a485f413a2113503eed53cd6c53
10.1093/bioinformatics/btz623