ArticleProceedings of the National Academy of Sciences of the United States of America2017
Structure-based prediction of ligand-protein interactions on a genome-wide scale.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Label-Free Measurement of Ligand Interactions Using SABRE Hyperpolarization at Low Magnetic Fields.Analytical chemistry · 2026Article
- Discovery of Two GSK3β Inhibitors fromCurrent computer-aided drug design · 2026Article
- Review
- Predicting multiple conformations of ligand binding sites in proteins suggests that AlphaFold2 may remember too much.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- MAGPIE: An interactive tool for visualizing and analyzing protein-ligand interactions.Protein science : a publication of the Protein Society · 2024Article
- LVPocket: integrated 3D global-local information to protein binding pockets prediction with transfer learning of protein structure classification.Journal of cheminformatics · 2024Article
- Q-BioLiP: A Comprehensive Resource for Quaternary Structure-based Protein-ligand Interactions.Genomics, proteomics & bioinformatics · 2024Article
- PrePCI: A structure- and chemical similarity-informed database of predicted protein compound interactions.Protein science : a publication of the Protein Society · 2023Article
- Defining A Global Map of Functional Group-based 3D Ligand-binding Motifs.Genomics, proteomics & bioinformatics · 2022Article
- A novel graph convolutional neural network for predicting interaction sites on protein kinase inhibitors in phosphorylation.Scientific reports · 2022Article
- Identifying Novel Drug Targets by iDTPnd: A Case Study of Kinase Inhibitors.Genomics, proteomics & bioinformatics · 2021Article
- Characterization of Mercury-Resistant Rhizobacteria for Plant Growth Promotion: An In Vitro and In Silico Approach.Current microbiology · 2021Article
- Computational insights of phytochemical-driven disruption of RNA-dependent RNA polymerase-mediated replication of coronavirus: a strategic treatment plan against coronavirus disease 2019.New microbes and new infections · 2021Article
- Protein functional annotation of simultaneously improved stability, accuracy and false discovery rate achieved by a sequence-based deep learning.Briefings in bioinformatics · 2020Article
- HMI-PRED: A Web Server for Structural Prediction of Host-Microbe Interactions Based on Interface Mimicry.Journal of molecular biology · 2020Article
- ClusPro LigTBM: Automated Template-based Small Molecule Docking.Journal of molecular biology · 2020Article
- Article
- Strategy for the Biosynthesis of Short Oligopeptides: Green and Sustainable Chemistry.Biomolecules · 2019Review
- Computational methods and tools for binding site recognition between proteins and small molecules: from classical geometrical approaches to modern machine learning strategies.Journal of computer-aided molecular design · 2019Article
- In silico DNA methylation analysis identifies potential prognostic biomarkers in type 2 papillary renal cell carcinoma.Cancer medicine · 2019Article
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4 authors.
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Abstract
We report a template-based method, LT-scanner, which scans the human proteome using protein structural alignment to identify proteins that are likely to bind ligands that are present in experimentally determined complexes. A scoring function that rapidly accounts for binding site similarities between the template and the proteins being scanned is a crucial feature of the method. The overall approach is first tested based on its ability to predict the residues on the surface of a protein that are likely to bind small-molecule ligands. The algorithm that we present, LBias, is shown to compare very favorably to existing algorithms for binding site residue prediction. LT-scanner's performance is evaluated based on its ability to identify known targets of Food and Drug Administration (FDA)-approved drugs and it too proves to be highly effective. The specificity of the scoring function that we use is demonstrated by the ability of LT-scanner to identify the known targets of FDA-approved kinase inhibitors based on templates involving other kinases. Combining sequence with structural information further improves LT-scanner performance. The approach we describe is extendable to the more general problem of identifying binding partners of known ligands even if they do not appear in a structurally determined complex, although this will require the integration of methods that combine protein structure and chemical compound databases.
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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.