ArticleBioinformatics (Oxford, England)2020
Integrating ab initio and template-based algorithms for protein-protein complex structure prediction.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- AlphaFold and Docking Approaches for Antibody-Antigen and Other Targets: Insights From CAPRI Rounds 47-55.Proteins · 2026Article
- Review and Comparative Analysis of Methods and Advancements in Predicting Protein Complex Structure.Interdisciplinary sciences, computational life sciences · 2024Review
- PLM-GAN: A Large-Scale Protein Loop Modeling Using pix2pix GAN.ACS omega · 2024Article
- Fast and accurate genome-wide predictions and structural modeling of protein-protein interactions using Galaxy.BMC bioinformatics · 2023Article
- Benchmarking AlphaFold for protein complex modeling reveals accuracy determinants.Protein science : a publication of the Protein Society · 2022Article
- Review
- Computational Enzyme Engineering Pipelines for Optimized Production of Renewable Chemicals.Frontiers in bioengineering and biotechnology · 2021Review
- Application of docking methodologies to modeled proteins.Proteins · 2020Article
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4 authors.
Funding
Abstract
motivationTemplate-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.
resultsHere, 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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