ArticleStructure (London, England : 1993)2012
Recognizing protein-ligand binding sites by global structural alignment and local geometry refinement.
Article in Structure (London, England : 1993), 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers.
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Who cites it
61 citing papers in PubMed.
- A graphic and command line protocol for quick and accurate comparisons of protein and nucleic acid structures with US-align.Nature protocols · 2026Review
- Computational Ligand-Binding Site Prediction.Advances in experimental medicine and biology · 2026Review
- Computational approaches to druggable site identification: Current status and future perspective.Acta pharmaceutica Sinica. B · 2026Review
- Databases of ligand-binding pockets and protein-ligand interactions.Computational and structural biotechnology journal · 2024Review
- MecE, MecB, and MecC proteins orchestrate methyl group transfer during dichloromethane fermentation.Applied and environmental microbiology · 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
- Recent Progress of Protein Tertiary Structure Prediction.Molecules (Basel, Switzerland) · 2024Review
- Intrinsic disorder in the open reading frame 2 of hepatitis E virus: a protein with multiple functions beyond viral capsid.Journal, genetic engineering & biotechnology · 2023Article
- Machine learning assessment of the binding region as a tool for more efficient computational receptor-ligand docking.Journal of molecular liquids · 2022Article
- Identification of Iron-Sulfur (Fe-S) Cluster and Zinc (Zn) Binding Sites Within Proteomes Predicted by DeepMind's AlphaFold2 Program Dramatically Expands the Metalloproteome.Journal of molecular biology · 2022Article
- Simulation and Machine Learning Methods for Ion-Channel Structure Determination, Mechanistic Studies and Drug Design.Frontiers in pharmacology · 2022Review
- Role of "dual-personality" fragments in HEV adaptation-analysis of Y-domain region.Journal, genetic engineering & biotechnology · 2021Article
- Identification and Classification of Novel Genetic Variants: En Route to the Diagnosis of Primary Ciliary Dyskinesia.International journal of molecular sciences · 2021Article
- Toward the solution of the protein structure prediction problem.The Journal of biological chemistry · 2021Review
- Article
- Virtual Screening of Human Class-A GPCRs Using Ligand Profiles Built on Multiple Ligand-Receptor Interactions.Journal of molecular biology · 2020Article
- EDock: blind protein-ligand docking by replica-exchange monte carlo simulation.Journal of cheminformatics · 2020Article
- ClusPro LigTBM: Automated Template-based Small Molecule Docking.Journal of molecular biology · 2020Article
- Exploring the computational methods for protein-ligand binding site prediction.Computational and structural biotechnology journal · 2020Review
1 more citing papers are in PubMed but not listed here.
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Authors and funding
2 authors.
Funding
Abstract
Proteins perform functions through interacting with other molecules. However, structural details for most of the protein-ligand interactions are unknown. We present a comparative approach (COFACTOR) to recognize functional sites of protein-ligand interactions using low-resolution protein structural models, based on a global-to-local sequence and structural comparison algorithm. COFACTOR was tested on 501 proteins, which harbor 582 natural and drug-like ligand molecules. Starting from I-TASSER structure predictions, the method successfully identifies ligand-binding pocket locations for 65% of apo receptors with an average distance error 2 Å. The average precision of binding-residue assignments is 46% and 137% higher than that by FINDSITE and ConCavity. In CASP9, COFACTOR achieved a binding-site prediction precision 72% and Matthews correlation coefficient 0.69 for 31 blind test proteins, which was significantly higher than all other participating methods. These data demonstrate the power of structure-based approaches to protein-ligand interaction predictions applicable for genome-wide structural and functional annotations.
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