Dataset Information

Integrated biophysical approach to fragment screening and validation for fragment-based lead discovery.

ABSTRACT: In fragment-based drug discovery, the weak affinities exhibited by fragments pose significant challenges for screening. Biophysical techniques are used to address this challenge, but there is no clear consensus on which cascade of methods is best suited to identify fragment hits that ultimately translate into bound X-ray structures and provide bona fide starting points for synthesis. We have benchmarked an integrated biophysical approach for fragment screening and validation against Mycobacterium tuberculosis pantothenate synthetase. A primary screen of 1,250 fragments library was performed by thermal shift, followed by secondary screen using one-dimensional NMR spectroscopy (water ligand observed gradient spectroscopy and saturation transfer difference binding experiments) and ultimate hit validation by isothermal titration calorimetry and X-ray crystallography. Our multibiophysical approach identified three distinct binding sites for fragments and laid a solid foundation for successful structure-based elaboration into potent inhibitors.

SUBMITTER: Silvestre HL

PROVIDER: S-EPMC3740835 | biostudies-literature | 2013 Aug

REPOSITORIES: biostudies-literature

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Integrated biophysical approach to fragment screening and validation for fragment-based lead discovery.

Silvestre Hernani Leonardo HL Blundell Thomas L TL Abell Chris C Ciulli Alessio A

Proceedings of the National Academy of Sciences of the United States of America 20130719 32

In fragment-based drug discovery, the weak affinities exhibited by fragments pose significant challenges for screening. Biophysical techniques are used to address this challenge, but there is no clear consensus on which cascade of methods is best suited to identify fragment hits that ultimately translate into bound X-ray structures and provide bona fide starting points for synthesis. We have benchmarked an integrated biophysical approach for fragment screening and validation against Mycobacteriu ...[more]

PMID: 23872845

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Project description:To reduce time and cost, virtual ligand screening (VLS) often precedes experimental ligand screening in modern drug discovery. Traditionally, high-resolution structure-based docking approaches rely on experimental structures, while ligand-based approaches need known binders to the target protein and only explore their nearby chemical space. In contrast, our structure-based FINDSITEcomb2.0 approach takes advantage of predicted, low-resolution structures and information from ligands that bind distantly related proteins whose binding sites are similar to the target protein. Using a boosted tree regression machine learning framework, we significantly improved FINDSITEcomb2.0 by integrating ligand fragment scores as encoded by molecular fingerprints with the global ligand similarity scores of FINDSITEcomb2.0. The new approach, FRAGSITE, exploits our observation that ligand fragments, e.g., rings, tend to interact with stereochemically conserved protein subpockets that also occur in evolutionarily unrelated proteins. FRAGSITE was benchmarked on the 102 protein DUD-E set, where any template protein whose sequence identify >30% to the target was excluded. Within the top 100 ranked molecules, FRAGSITE improves VLS precision and recall by 14.3 and 18.5%, respectively, relative to FINDSITEcomb2.0. Moreover, the mean top 1% enrichment factor increases from 25.2 to 30.2. On average, both outperform state-of-the-art deep learning-based methods such as AtomNet. On the more challenging unbiased set LIT-PCBA, FRAGSITE also shows better performance than ligand similarity-based and docking approaches such as two-dimensional ECFP4 and Surflex-Dock v.3066. On a subset of 23 targets from DEKOIS 2.0, FRAGSITE shows much better performance than the boosted tree regression-based, vScreenML scoring function. Experimental testing of FRAGSITE's predictions shows that it has more hits and covers a more diverse region of chemical space than FINDSITEcomb2.0. For the two proteins that were experimentally tested, DHFR, a well-studied protein that catalyzes the conversion of dihydrofolate to tetrahydrofolate, and the kinase ACVR1, FRAGSITE identified new small-molecule nanomolar binders. Interestingly, one new binder of DHFR is a kinase inhibitor predicted to bind in a new subpocket. For ACVR1, FRAGSITE identified new molecules that have diverse scaffolds and estimated nanomolar to micromolar affinities. Thus, FRAGSITE shows significant improvement over prior state-of-the-art ligand virtual screening approaches. A web server is freely available for academic users at http:/sites.gatech.edu/cssb/FRAGSITE.

Dataset Information

Integrated biophysical approach to fragment screening and validation for fragment-based lead discovery.

Publications

Integrated biophysical approach to fragment screening and validation for fragment-based lead discovery.

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