COFACTOR: an accurate comparative algorithm for structure-based protein function annotation

By Ambrish Roy, Jianyi Yang, Yang Zhang. 2012

696 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

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In Molecular Biology

1. Yang Zhang
2. Robin Pearce
3. Chengxin Zhang
4. Jianyi Yang
5. Yang Li
6. Wei Zheng
7. Eric W. Bell
8. Xiaogen Zhou

Citations in the corpus

• I-TASSER server: new development for protein structure and function predictions (2015, methodology): “The ligand-binding site in I-TASSER was previously predicted by COFACTOR, which deduces binding sites from homologous templates detected by global and local structure comparisons (8).”
• mlDEEPre: Multi-Functional Enzyme Function Prediction With Hierarchical Multi-Label Deep Learning (2019, methodology): “…bioinformatics task, a number of methods have been proposed to deal with the problem, based on structure similarity (Dobson and Doig, 2005; Roy et al., 2012; Yang et al., 2015), sequence similarity (Tian et al., 2004; Quester and Schomburg, 2011), or machine learning techniques (des…”
• I-TASSER-MTD: a deep-learning-based platform for multi-domain protein structure and function prediction (2022, methodology): “A binding site score >1 indicates a outstanding local match between the predicted and template binding sites based on a large-scale benchmark study 45,70 .”
• PrankWeb: a web server for ligand binding site prediction and visualization (2019, methodology): “This is the avenue taken by 3DLigandSite (11), COFAC-TOR (12,13), COACH (14) ISMBLAB-LIG (15) and LI-BRA (16).”
• COACH-D: improved protein–ligand binding sites prediction with refined ligand-binding poses through molecular docking (2018, positive): “Four of them are template-based methods: COFACTOR (6), FINDSITE (4), TM-SITE (7) and S-SITE (7).”
• DEEPre: sequence-based enzyme EC number prediction by deep learning (2017, methodology): “The COFACTOR dataset is used for cross-dataset validation.”
• A chronotherapeutics-applicable multi-target therapeutics based on AI: Example of therapeutic hypothermia (2022, methodology): “Four of these methods are COFAC-TOR [27], FINDSITE [28], TMSITE [29] and S-SITE [29].”
• PointSite: a point cloud segmentation tool for identification of protein ligand binding atoms (2019, neutral): “These datasets include B277 [19], DT198 [47], ASTEX85 [13], CHEN251 [6], COACH420 [44, 36], and a large dataset HOLO4k [38].”
• BioLiP: a semi-manually curated database for biologically relevant ligand–protein interactions (2012, methodology): “These data sets can be very useful for template-based protein-ligand docking (15,18), virtual ligand screening (16) and protein function annotations (17,19).”
• Protein Structure and Function Prediction Using I-TASSER (2015, methodology): “Both EC and GO ( UNIT 7.2 ; Blake and Harris, 2008) predictions are generated by CO-FACTOR (Roy et al., 2012), by global and local structural comparisons of the I-TASSER models with known proteins in the BioLiP function library.”
• COFACTOR: improved protein function prediction by combining structure, sequence and protein–protein interaction information (2017, methodology): “The structure-homology based GO prediction method by COFACTOR was described previously (4).”