ArticlePLoS computational biology2009
Predicting protein ligand binding sites by combining evolutionary sequence conservation and 3D structure.
Article in PLoS computational biology, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 212 papers.
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Who cites it
212 citing papers in PubMed.
- Advances and opportunities for computational interrogation of plant proteins.The Plant journal : for cell and molecular biology · 2026Review
- Deconvolving mutation effects on protein stability and function with disentangled protein language models.bioRxiv : the preprint server for biology · 2026Article
- Immobilized horseradish peroxidase hybrid biocatalysts for degradation of pharmaceuticals and other emerging micropollutants.PloS one · 2026Article
- Computational Ligand-Binding Site Prediction.Advances in experimental medicine and biology · 2026Review
- Integrative proteome-wide structural analysis and high-throughput docking identify broad-spectrum antiviral scaffolds against Zika, Yellow Fever, West Nile, Saint Louis encephalitis, and Usutu viruses.Frontiers in cellular and infection microbiology · 2026Article
- The Human Omnibus of Targetable Pockets.Journal of cheminformatics · 2025Article
- Leveraging large language models for literature-driven prioritization of protein binding pockets.Bioinformatics (Oxford, England) · 2025Article
- Evaluation of Small-Molecule Binding Site Prediction Methods on Membrane-Embedded Protein Interfaces.Journal of chemical information and modeling · 2025Article
- Article
- CrypToth: Cryptic Pocket Detection through Mixed-Solvent Molecular Dynamics Simulations-Based Topological Data Analysis.Journal of chemical information and modeling · 2025Article
- Biochemical Characterization of Disease-Associated Variants of Human Ornithine Transcarbamylase.ACS chemical biology · 2025Article
- CrypTothML: An Integrated Mixed-Solvent Molecular Dynamics Simulation and Machine Learning Approach for Cryptic Site Prediction.International journal of molecular sciences · 2025Article
- AlloBench: A Data Set Pipeline for the Development and Benchmarking of Allosteric Site Prediction Tools.ACS omega · 2025Article
- Computational identification of natural inhibitors targeting GroEL inFrontiers in cellular and infection microbiology · 2025Article
- Databases of ligand-binding pockets and protein-ligand interactions.Computational and structural biotechnology journal · 2024Review
- Comparative evaluation of methods for the prediction of protein-ligand binding sites.Journal of cheminformatics · 2024Article
- Combined Physics- and Machine-Learning-Based Method to Identify Druggable Binding Sites Using SILCS-Hotspots.Journal of chemical information and modeling · 2024Article
- Turbocharging protein binding site prediction with geometric attention, inter-resolution transfer learning, and homology-based augmentation.BMC bioinformatics · 2024Article
- Article
- LVPocket: integrated 3D global-local information to protein binding pockets prediction with transfer learning of protein structure classification.Journal of cheminformatics · 2024Article
152 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors.
Funding
Abstract
Identifying a protein's functional sites is an important step towards characterizing its molecular function. Numerous structure- and sequence-based methods have been developed for this problem. Here we introduce ConCavity, a small molecule binding site prediction algorithm that integrates evolutionary sequence conservation estimates with structure-based methods for identifying protein surface cavities. In large-scale testing on a diverse set of single- and multi-chain protein structures, we show that ConCavity substantially outperforms existing methods for identifying both 3D ligand binding pockets and individual ligand binding residues. As part of our testing, we perform one of the first direct comparisons of conservation-based and structure-based methods. We find that the two approaches provide largely complementary information, which can be combined to improve upon either approach alone. We also demonstrate that ConCavity has state-of-the-art performance in predicting catalytic sites and drug binding pockets. Overall, the algorithms and analysis presented here significantly improve our ability to identify ligand binding sites and further advance our understanding of the relationship between evolutionary sequence conservation and structural and functional attributes of proteins. Data, source code, and prediction visualizations are available on the ConCavity web site (http://compbio.cs.princeton.edu/concavity/).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.