ArticleNucleic acids research2018
COACH-D: improved protein-ligand binding sites prediction with refined ligand-binding poses through molecular docking.
Article in Nucleic acids research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 134 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
134 citing papers in PubMed, 2 syntheses or guidelines pooled it, 290 citations in OpenAlex.
- Advances in the Application of Protein Language Modeling for Nucleic Acid Protein Binding Site Prediction.Genes · 2024Pooled it
- Iron dysregulation in COVID-19 and reciprocal evolution of SARS-CoV-2: Natura nihil frustra facit.Journal of cellular biochemistry · 2022Pooled it
- TripleBind: a generalizable deep learning framework for protein-nucleic acid and protein-ligand binding sites prediction based on pre-trained protein language models.Molecular diversity · 2026Article
- Protein-DNA Binding Sites Prediction via Integrating Pretrained Large Language Models and Contrastive Learning.Interdisciplinary sciences, computational life sciences · 2026Article
- Protective Effects of the Ethanolic Leaf Extract ofMolecules (Basel, Switzerland) · 2026Article
- DeepPANB: integrating protein language model with PaiNN equivariant graph neural networks for prediction of protein-nucleic acid binding sites.Briefings in bioinformatics · 2026Article
- Protein-nucleic acid binding site prediction using interpretable Kolmogorov-Arnold networks with hypergraph representation learning.Bioinformatics (Oxford, England) · 2026Article
- RGTBind: RBF-gate graph transformer with spatially biased attention for protein-DNA binding-site prediction.Journal of molecular modeling · 2026Article
- A protocol for single-sequence protein-RNA complex structure prediction using ProRNA3D-single.STAR protocols · 2026Article
- Potential antidepressant properties of aminophylline in male mice exposed to chronic restraint stress.Pharmacological reports : PR · 2026Article
- Discovery of a KLHL41 Ligand for Muscle Specific Protein Degradation.Nature communications · 2026Article
- Exploring the functional and immunological diversity of the fatty acid-binding protein (FABP) family in Fasciola hepatica.Scientific reports · 2026Article
- Predicting protein-nucleic acid interactions via protein language models with biophysical and evolutionary priors.iScience · 2026Article
- Artificial Intelligence Powers Protein Functional Annotation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Comparative pangenomics of Pasteurella multocida uncovers adhesins specific to capsular serogroup B strains associated with bovine host tropism.BMC microbiology · 2026Article
- DGSite: A Well-Calibrated Deep Learning Framework for Protein-RNA Binding Site Prediction.ACS omega · 2026Article
- Accurate Identification of Protein Binding Sites for All Drug Modalities Using ALLSites.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- ViralBindPredict: empowering viral protein-ligand binding sites through deep learning and protein sequence-derived insights.GigaScience · 2026Article
- Copper biosorption by Serratia plymuthica: crucial role of tightly bound extracellular polymeric substances in planktonic and biofilm systems.Biodegradation · 2026Article
- Computational approaches to druggable site identification: Current status and future perspective.Acta pharmaceutica Sinica. B · 2026Review
74 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
Abstract
The identification of protein-ligand binding sites is critical to protein function annotation and drug discovery. The consensus algorithm COACH developed by us represents one of the most efficient approaches to protein-ligand binding sites prediction. One of the most commonly seen issues with the COACH prediction are the low quality of the predicted ligand-binding poses, which usually have severe steric clashes to the protein structure. Here, we present COACH-D, an enhanced version of COACH by utilizing molecular docking to refine the ligand-binding poses. The input to the COACH-D server is the amino acid sequence or the three-dimensional structure of a query protein. In addition, the users can also submit their own ligand of interest. For each job submission, the COACH algorithm is first used to predict the protein-ligand binding sites. The ligands from the users or the templates are then docked into the predicted binding pockets to build their complex structures. Blind tests show that the algorithm significantly outperforms other ligand-binding sites prediction methods. Benchmark tests show that the steric clashes between the ligand and the protein structures in the COACH models are reduced by 85% after molecular docking in COACH-D. The COACH-D server is freely available to all users at http://yanglab.nankai.edu.cn/COACH-D/.
Indexed as
Identifiers
What OpenQuestion holds
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.