ArticleJournal of computer-aided molecular design2013
eFindSite: improved prediction of ligand binding sites in protein models using meta-threading, machine learning and auxiliary ligands.
Article in Journal of computer-aided molecular design, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- Comparative evaluation of methods for the prediction of protein-ligand binding sites.Journal of cheminformatics · 2024Article
- Effect of preprocessing and simulation parameters on the performance of molecular docking studies.Journal of molecular modeling · 2023Article
- DrugRep: an automatic virtual screening server for drug repurposing.Acta pharmacologica Sinica · 2023Article
- Estimating the Similarity between Protein Pockets.International journal of molecular sciences · 2022Review
- GraphSite: Ligand Binding Site Classification with Deep Graph Learning.Biomolecules · 2022Article
- Machine learning assessment of the binding region as a tool for more efficient computational receptor-ligand docking.Journal of molecular liquids · 2022Article
- Pocket2Drug: An Encoder-Decoder Deep Neural Network for the Target-Based Drug Design.Frontiers in pharmacology · 2022Article
- GraphDTI: A robust deep learning predictor of drug-target interactions from multiple heterogeneous data.Journal of cheminformatics · 2021Article
- CATH functional families predict functional sites in proteins.Bioinformatics (Oxford, England) · 2021Article
- Machine learning approaches and databases for prediction of drug-target interaction: a survey paper.Briefings in bioinformatics · 2021Review
- Proteins and Their Interacting Partners: An Introduction to Protein-Ligand Binding Site Prediction Methods with a Focus on FunFOLD3.Methods in molecular biology (Clifton, N.J.) · 2021Article
- BionoiNet: ligand-binding site classification with off-the-shelf deep neural network.Bioinformatics (Oxford, England) · 2020Article
- Exploring the computational methods for protein-ligand binding site prediction.Computational and structural biotechnology journal · 2020Review
- CB-Dock: a web server for cavity detection-guided protein-ligand blind docking.Acta pharmacologica Sinica · 2020Article
- Binding site matching in rational drug design: algorithms and applications.Briefings in bioinformatics · 2019Review
- Hinge Region in DNA Packaging Terminase pUL15 of Herpes Simplex Virus: A Potential Allosteric Target for Antiviral Drugs.Biomolecules · 2019Article
- Elucidating the druggability of the human proteome with eFindSite.Journal of computer-aided molecular design · 2019Article
- DeepDrug3D: Classification of ligand-binding pockets in proteins with a convolutional neural network.PLoS computational biology · 2019Article
- P2Rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure.Journal of cheminformatics · 2018Article
- eModel-BDB: a database of comparative structure models of drug-target interactions from the Binding Database.GigaScience · 2018Article
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2 authors.
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Abstract
Molecular structures and functions of the majority of proteins across different species are yet to be identified. Much needed functional annotation of these gene products often benefits from the knowledge of protein-ligand interactions. Towards this goal, we developed eFindSite, an improved version of FINDSITE, designed to more efficiently identify ligand binding sites and residues using only weakly homologous templates. It employs a collection of effective algorithms, including highly sensitive meta-threading approaches, improved clustering techniques, advanced machine learning methods and reliable confidence estimation systems. Depending on the quality of target protein structures, eFindSite outperforms geometric pocket detection algorithms by 15-40 % in binding site detection and by 5-35 % in binding residue prediction. Moreover, compared to FINDSITE, it identifies 14 % more binding residues in the most difficult cases. When multiple putative binding pockets are identified, the ranking accuracy is 75-78 %, which can be further improved by 3-4 % by including auxiliary information on binding ligands extracted from biomedical literature. As a first across-genome application, we describe structure modeling and binding site prediction for the entire proteome of Escherichia coli. Carefully calibrated confidence estimates strongly indicate that highly reliable ligand binding predictions are made for the majority of gene products, thus eFindSite holds a significant promise for large-scale genome annotation and drug development projects. eFindSite is freely available to the academic community at http://www.brylinski.org/efindsite .
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