ArticleJournal of chemical information and modeling2010
Comprehensive structural and functional characterization of the human kinome by protein structure modeling and ligand virtual screening.
Article in Journal of chemical information and modeling, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 39 citations in OpenAlex.
- FRAGSITE2: A structure and fragment-based approach for virtual ligand screening.Protein science : a publication of the Protein Society · 2024Article
- Structure-based virtual screening and biological evaluation of novel small-molecule BTK inhibitors.Journal of enzyme inhibition and medicinal chemistry · 2022Article
- Artificial intelligence to guide precision anticancer therapy with multitargeted kinase inhibitors.BMC cancer · 2022Article
- Kinase Atlas: Druggability Analysis of Potential Allosteric Sites in Kinases.Journal of medicinal chemistry · 2019Review
- FINDSITEJournal of chemical information and modeling · 2018Article
- Predicting Ligand Binding Sites on Protein Surfaces by 3-Dimensional Probability Density Distributions of Interacting Atoms.PloS one · 2016Article
- AFAL: a web service for profiling amino acids surrounding ligands in proteins.Journal of computer-aided molecular design · 2014Article
- Modelling three-dimensional protein structures for applications in drug design.Drug discovery today · 2014Review
- Are predicted protein structures of any value for binding site prediction and virtual ligand screening?Current opinion in structural biology · 2013Review
- Kinome-wide activity modeling from diverse public high-quality data sets.Journal of chemical information and modeling · 2013Article
- FINDSITE(comb): a threading/structure-based, proteomic-scale virtual ligand screening approach.Journal of chemical information and modeling · 2013Article
- FINDSITE(X): a structure-based, small molecule virtual screening approach with application to all identified human GPCRs.Molecular pharmaceutics · 2012Article
- Evaluation of model quality predictions in CASP9.Proteins · 2011Article
- Prediction of ligand binding using an approach designed to accommodate diversity in protein-ligand interactions.PloS one · 2011Article
- Cross-reactivity virtual profiling of the human kinome by X-react(KIN): a chemical systems biology approach.Molecular pharmaceutics · 2010Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
The growing interest in the identification of kinase inhibitors, promising therapeutics in the treatment of many diseases, has created a demand for the structural characterization of the entire human kinome. At the outset of the drug development process, the lead-finding stage, approaches that enrich the screening library with bioactive compounds are needed. Here, protein structure based methods can play an important role, but despite structural genomics efforts, it is unlikely that the three-dimensional structures of the entire kinome will be available soon. Therefore, at the proteome level, structure-based approaches must rely on predicted models, with a key issue being their utility in virtual ligand screening. In this study, we employ the recently developed FINDSITE/Q-Dock ligand homology modeling approach, which is well-suited for proteome-scale applications using predicted structures, to provide extensive structural and functional characterization of the human kinome. Specifically, we construct structure models for the human kinome; these are subsequently subject to virtual screening against a library of more than 2 million compounds. To rank the compounds, we employ a hierarchical approach that combines ligand- and structure-based filters. Modeling accuracy is carefully validated using available experimental data with particularly encouraging results found for the ability to identify, without prior knowledge, specific kinase inhibitors. More generally, the modeling procedure results in a large number of predicted molecular interactions between kinases and small ligands that should be of practical use in the development of novel inhibitors. The data set is freely available to the academic community via a user-friendly Web interface at http://cssb.biology.gatech.edu/kinomelhm/ as well as at the ZINC Web site ( http://zinc.docking.org/applications/2010Apr/Brylinski-2010.tar.gz ).
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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.