ArticleJournal of chemical information and modeling2012
Hot spot analysis for driving the development of hits into leads in fragment-based drug discovery.
Article in Journal of chemical information and modeling, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- E-FTMap: A Protein Structure Based Pharmacophore Identification Server for Guiding Fragment Expansion.Journal of molecular biology · 2025Article
- Conservation of Hot Spots and Ligand Binding Sites in Protein Models by AlphaFold2.Journal of chemical information and modeling · 2024Article
- Fragment library screening by X-ray crystallography and binding site analysis on thioredoxin glutathione reductase of Schistosoma mansoni.Scientific reports · 2024Article
- Antiviral Agents: Structural Basis of Action and Rational Design.Sub-cellular biochemistry · 2024Review
- Article
- Druggable hot spots in trypanothione reductase: novel insights and opportunities for drug discovery revealed by DRUGpy.Journal of computer-aided molecular design · 2021Article
- Article
- Review
- Structural analysis of mycobacterial homoserine transacetylases central to methionine biosynthesis reveals druggable active site.Scientific reports · 2019Article
- Ensemble docking to difficult targets in early-stage drug discovery: Methodology and application to fibroblast growth factor 23.Chemical biology & drug design · 2018Article
- Virtual screening for inhibitors of the human TSLP:TSLPR interaction.Scientific reports · 2017Article
- A computationally identified compound antagonizes excess FGF-23 signaling in renal tubules and a mouse model of hypophosphatemia.Science signaling · 2016Article
- Article
- Focused grid-based resampling for protein docking and mapping.Journal of computational chemistry · 2016Article
- New Frontiers in Druggability.Journal of medicinal chemistry · 2015Article
- Lessons from Hot Spot Analysis for Fragment-Based Drug Discovery.Trends in pharmacological sciences · 2015Review
- PoLi: A Virtual Screening Pipeline Based on Template Pocket and Ligand Similarity.Journal of chemical information and modeling · 2015Article
- AlphaSpace: Fragment-Centric Topographical Mapping To Target Protein-Protein Interaction Interfaces.Journal of chemical information and modeling · 2015Article
- Ligand deconstruction: Why some fragment binding positions are conserved and others are not.Proceedings of the National Academy of Sciences of the United States of America · 2015Article
- The FTMap family of web servers for determining and characterizing ligand-binding hot spots of proteins.Nature protocols · 2015Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Fragment-based drug design (FBDD) starts with finding fragment-sized compounds that are highly ligand efficient and can serve as a core moiety for developing high-affinity leads. Although the core-bound structure of a protein facilitates the construction of leads, effective design is far from straightforward. We show that protein mapping, a computational method developed to find binding hot spots and implemented as the FTMap server, provides information that complements the fragment screening results and can drive the evolution of core fragments into larger leads with a minimal loss or, in some cases, even a gain in ligand efficiency. The method places small molecular probes, the size of organic solvents, on a dense grid around the protein and identifies the hot spots as consensus clusters formed by clusters of several probes. The hot spots are ranked based on the number of probe clusters, which predicts the binding propensity of the subsites and hence their importance for drug design. Accordingly, with a single exception the main hot spot identified by FTMap binds the core compound found by fragment screening. The most useful information is provided by the neighboring secondary hot spots, indicating the regions where the core can be extended to increase its affinity. To quantify this information, we calculate the density of probes from mapping, which describes the binding propensity at each point, and show that the change in the correlation between a ligand position and the probe density upon extending or repositioning the core moiety predicts the expected change in ligand efficiency.
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