Integrating ab initio and template-based algorithms for protein-protein complex structure prediction
Program in Bioinformatics and Integrative Biology
Amino Acids, Peptides, and Proteins | Biochemistry, Biophysics, and Structural Biology | Bioinformatics | Computational Biology | Integrative Biology | Statistics and Probability
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.
DOI of Published Version
Bioinformatics. 2019 Aug 8. pii: btz623. doi: 10.1093/bioinformatics/btz623. [Epub ahead of print] Link to article on publisher's site
Bioinformatics (Oxford, England)
Vangaveti S, Vreven T, Zhang Y, Weng Z. (2019). Integrating ab initio and template-based algorithms for protein-protein complex structure prediction. Program in Bioinformatics and Integrative Biology Publications. https://doi.org/10.1093/bioinformatics/btz623. Retrieved from https://escholarship.umassmed.edu/bioinformatics_pubs/153