A threading-based method (FINDSITE) for ligand-binding site prediction and functional annotation
Open accessAbstract
The detection of ligand-binding sites is often the starting point for protein function identification and drug discovery. Because of inaccuracies in predicted protein structures, extant binding pocket-detection methods are limited to experimentally solved structures. Here, FINDSITE, a method for ligand-binding site prediction and functional annotation based on binding-site similarity across groups of weakly homologous template structures identified from threading, is described. For crystal structures, considering a cutoff distance of 4 Å as the hit criterion, the success rate is 70.9% for identifying the best of top five predicted ligand-binding sites with a ranking accuracy of 76.0%. Both high prediction accuracy and ability to correctly rank identified binding sites are sustained when approximate protein models (<35% sequence identity to the closest template structure) are used, showing a 67.3% success rate with 75.5% ranking accuracy. In practice, FINDSITE tolerates structural inaccuracies in protein models up to a rmsd from the crystal structure of 8–10 Å. This is because analysis of weakly homologous protein models reveals that about half have a rmsd from the native binding site <2 Å. Furthermore, the chemical properties of template-bound ligands can be used to select ligand templates associated with the binding site. In most cases, FINDSITE can accurately assign a molecular function to the protein model.
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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 [45]
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Self-citation
- Authors
- Brylinski, M., & Skolnick, J.
- Affiliations
- Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, 250 14th Street NW, Atlanta, GA 30318
- Georgia Institute of Technology (US)
- ORCID
- Skolnick, Jeffrey
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2008-01-08
- Volume
- 105
- Issue
- 1
- Pages
- 129-134
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC2224172
- OpenAlex
- W2144258433
- Cited by
- 333
- References
- 48
- Citations by year
- 2026: 8 · 2025: 11 · 2024: 21 · 2023: 15 · 2022: 20 · 2021: 20 · 2020: 16 · 2019: 16 · 2018: 16 · 2017: 15 · 2016: 22 · 2015: 20 · 2014: 15 · 2013: 27 · 2012: 21
- FWCI
- 6.49
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Civil War Book Review (submittedVersion)
- Funders
- National Institute of General Medical Sciences (GM-48835, R01GM048835)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Keywords
- ligand binding site prediction, protein threading, protein annotation, protein structure prediction
- MeSH terms
- Algorithms, Binding Sites, Biophysics, Ligands, Models, Molecular, Molecular Conformation, Protein Binding, Protein Conformation, Proteins, Software, Reproducibility of Results, Models, Statistical, Crystallography, X-Ray, Computational Biology, Protein Interaction Mapping
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