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Cited by 32Computational Drug Discovery MethodsProtein Degradation and Inhibitorsvaccines and immunoinformatics approaches

FRAGSITE: A Fragment-Based Approach for Virtual Ligand Screening

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Zhou, Hongyi, Cao, Hongnan, Skolnick, Jeffrey·2021·Journal of Chemical Information and Modeling
DOI 10.1021/acs.jcim.0c01160PMC PMC8243409
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Abstract

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 FINDSITE comb2.0 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 FINDSITE comb2.0 by integrating ligand fragment scores as encoded by molecular fingerprints with the global ligand similarity scores of FINDSITE comb2.0 . 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 FINDSITE comb2.0 . 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 FINDSITE comb2.0 . 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 .

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  • Using ligand homology modeling, low resolution structures are adequate for virtual ligand screening, with at most a 20% improvement in enrichment factor on going from lower resolution (TM-score > 0.4) to crystal structures.

    “Contrary to popular belief that high resolution structures are required, using ligand homology modeling,45–48 low resolution structures are adequate, with at most a 20% improvement in enrichment factor on going from lower resolution (TM-score > 0.4) to crystal structures.”

    From AlphaFold 2: Why It Works and Its Implications for Understanding the Relationships of Protein Sequence, Structure, and Function (2021) · cites this paper as [48]

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Research works printed but not linked: 2

  1. [46]Barreiro EJ Privileged Scaffolds in Medicinal Chemistry: An Introduction. In Privileged Scaffolds in Medicinal Chemistry: Design, Synthesis, Evaluation, Brase S, Ed.; Royal Society of Chemistry: 2016; Chapter 1, pp. 1– 15.
  2. [58]Davis J; Goadrich M The Relationship Between PrecisionRecall and ROC Curves. In ICML ‘06 Proceedings of the 23rd International Conference on Machine Learning, Pittsburgh, 2006; ACM New York, NY, USA Pittsburgh, 2006; pp. 233– 240.

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Authors
Zhou, H., Cao, H., & Skolnick, J.
Affiliations
Georgia Institute of Technology (US)
Corresponding author
Skolnick, Jeffrey
ORCID
Venue
Journal of Chemical Information and Modeling
Publisher
American Chemical Society (ACS)
Published
2021-03-16
Volume
61
Issue
4
Pages
2074-2089
ISSN
  • 1549-9596 (ISSN-L)
  • 1549-960X
DOI
10.1021/acs.jcim.0c01160
PMC
PMC8243409
OpenAlex
W3136158255
Cited by
32
References
69
Citations by year
2026: 5 · 2025: 2 · 2024: 7 · 2023: 7 · 2022: 6 · 2021: 5
FWCI
3.30
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Chemical Information and Modeling (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • National Institute of General Medical Sciences (R35GM118039)
  • Andrew McDonough B+ Foundation
Topics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Degradation and Inhibitors
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
Keywords
virtual ligand screening, fragment-based virtual screening, molecular fingerprints, dihydrofolate reductase, protein-ligand binding affinity prediction, drug discovery, enrichment factor
MeSH terms
Binding Sites, Ligands, Protein Binding, Protein Conformation, Proteins, Drug Discovery, Molecular Docking Simulation

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