ArticleJournal of chemical information and modeling2022
Fragment Hotspot Mapping to Identify Selectivity-Determining Regions between Related Proteins.
Article in Journal of chemical information and modeling, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Developments and challenges in hit progression within fragment-based drug discovery.Nature communications · 2026Review
- Discovering a novel dual specificity tyrosine-phosphorylation-regulated kinase 1A (DYRK1A) inhibitor and its impact on tau phosphorylation and amyloid-β formation.Journal of enzyme inhibition and medicinal chemistry · 2024Article
- Expanding FTMap for Fragment-Based Identification of Pharmacophore Regions in Ligand Binding Sites.Journal of chemical information and modeling · 2024Article
- TWN-FS method: A novel fragment screening method for drug discovery.Computational and structural biotechnology journal · 2023Article
- The druggable genome: Twenty years later.Frontiers in bioinformatics · 2022Article
- Computational drug discovery under RNA times.QRB discovery · 2022Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Selectivity is a crucial property in small molecule development. Binding site comparisons within a protein family are a key piece of information when aiming to modulate the selectivity profile of a compound. Binding site differences can be exploited to confer selectivity for a specific target, while shared areas can provide insights into polypharmacology. As the quantity of structural data grows, automated methods are needed to process, summarize, and present these data to users. We present a computational method that provides quantitative and data-driven summaries of the available binding site information from an ensemble of structures of the same protein. The resulting ensemble maps identify the key interactions important for ligand binding in the ensemble. The comparison of ensemble maps of related proteins enables the identification of selectivity-determining regions within a protein family. We applied the method to three examples from the well-researched human bromodomain and kinase families, demonstrating that the method is able to identify selectivity-determining regions that have been used to introduce selectivity in past drug discovery campaigns. We then illustrate how the resulting maps can be used to automate comparisons across a target protein family.
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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.