ArticleNucleic acids research2012
COFACTOR: an accurate comparative algorithm for structure-based protein function annotation.
Article in Nucleic acids research, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 322 papers.
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Who cites it
322 citing papers in PubMed.
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- DPFunc: accurately predicting protein function via deep learning with domain-guided structure information.Nature communications · 2025Article
- Machine Learning for Protein Function Prediction.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Computational identification of natural inhibitors targeting GroEL inFrontiers in cellular and infection microbiology · 2025Article
- Decoding the cross-immune pressure: Dengue's role in SARS-CoV-2 evolution.Computational and structural biotechnology journal · 2025Article
- Machine Learning Techniques to Infer Protein Structure and Function from Sequences: A Comprehensive Review.Methods in molecular biology (Clifton, N.J.) · 2025Review
- cAMP-independent Crp homolog adds to the multi-layer regulatory network inFrontiers in cellular and infection microbiology · 2025Article
262 more citing papers are in PubMed but not listed here.
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3 authors.
Funding
Abstract
We have developed a new COFACTOR webserver for automated structure-based protein function annotation. Starting from a structural model, given by either experimental determination or computational modeling, COFACTOR first identifies template proteins of similar folds and functional sites by threading the target structure through three representative template libraries that have known protein-ligand binding interactions, Enzyme Commission number or Gene Ontology terms. The biological function insights in these three aspects are then deduced from the functional templates, the confidence of which is evaluated by a scoring function that combines both global and local structural similarities. The algorithm has been extensively benchmarked by large-scale benchmarking tests and demonstrated significant advantages compared to traditional sequence-based methods. In the recent community-wide CASP9 experiment, COFACTOR was ranked as the best method for protein-ligand binding site predictions. The COFACTOR sever and the template libraries are freely available at http://zhanglab.ccmb.med.umich.edu/COFACTOR.
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