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    Hot Spot Analysis for Driving the Development of Hits into Leads in Fragment-Based Drug Discovery
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    † Department of Biomedical Engineering, Boston University, 44 Cummington Street, Boston, Massachusetts 02215, United States
    ‡ Department of Chemistry, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, United States
    *E-mail: [email protected]; [email protected]
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    Journal of Chemical Information and Modeling

    Cite this: J. Chem. Inf. Model. 2012, 52, 1, 199–209
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    https://doi.org/10.1021/ci200468p
    Published December 7, 2011
    Copyright © 2011 American Chemical Society
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    Abstract

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    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.

    Copyright © 2011 American Chemical Society

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    Journal of Chemical Information and Modeling

    Cite this: J. Chem. Inf. Model. 2012, 52, 1, 199–209
    Click to copy citationCitation copied!
    https://doi.org/10.1021/ci200468p
    Published December 7, 2011
    Copyright © 2011 American Chemical Society
    Request reuse permissions

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