AI Article Synopsis
- Template-based and template-free methods are used for predicting protein-protein complex structures, with each having its advantages depending on the availability of reliable templates.
- A new method called ZING combines predictions from both SPRING (template-based) and ZDOCK (template-free) and has shown improved performance, achieving a success rate of 68.2% compared to 52.1% and 35.9% for SPRING and ZDOCK, respectively.
- ZING is publicly available for download on GitHub, and supplementary information can be found in Bioinformatics online.
Article Abstract
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 And Implementation: 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.
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Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7523679 | PMC |
| http://dx.doi.org/10.1093/bioinformatics/btz623 | DOI Listing |
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