FINDSITEcomb2.0: A New Approach for Virtual Ligand Screening of Proteins and Virtual Target Screening of Biomolecules
Open accessAbstract
Computational approaches for predicting protein–ligand interactions can facilitate drug lead discovery and drug target determination. We have previously developed a threading/structural-based approach, FINDSITE comb, for the virtual ligand screening of proteins that has been extensively experimentally validated. Even when low resolution predicted protein structures are employed, FINDSITE comb has the advantage of being faster and more accurate than traditional high-resolution structure-based docking methods. It also overcomes the limitations of traditional QSAR methods that require a known set of seed ligands that bind to the given protein target. Here, we further improve FINDSITE comb by enhancing its template ligand selection from the PDB/DrugBank/ChEMBL libraries of known protein–ligand interactions by (1) parsing the template proteins and their corresponding binding ligands in the DrugBank and ChEMBL libraries into domains so that the ligands with falsely matched domains to the targets will not be selected as template ligands; (2) applying various thresholds to filter out falsely matched template structures in the structure comparison process and thus their corresponding ligands for template ligand selection. With a sequence identity cutoff of 30% of target to templates and modeled target structures, FINDSITE comb2.0 is shown to significantly improve upon FINDSITE comb on the DUD-E benchmark set by increasing the 1% enrichment factor from 16.7 to 22.1, with a p -value of 4.3 × 10 –3 by the Student t -test. With an 80% sequence identity cutoff of target to templates for the DUD-E set and modeled target structures, FINDSITE comb2.0, having a 1% ROC enrichment factor of 52.39, also outperforms state-of-the-art methods that employ machine learning such as a deep convolutional neural network, CNN, with an enrichment of 29.65. Thus, FINDSITE comb2.0 represents a significant improvement in the state-of-the-art. The FINDSITE comb2.0 web service is freely available for academic users at http://pwp.gatech.edu/cssb/FINDSITE-COMB-2 .
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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 [47]
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Mitović, N., Milošević, M., Ristić, M., Dimitrijević, L., Kovačević, S., Ostojić, J. N., & Stankovic, M. S. (2024) · Zdravstvena zastita · doi:10.5937/zdravzast53-52914
Milošević, M., Mitović, N., Ristić, M., Dimitrijević, L., Kovačević, S., Ostojić, J. N., Stanojević, M., & Spasić, S. (2024) · Zdravstvena zastita · doi:10.5937/zdravzast53-52916
- Authors
- Zhou, H., Cao, H., & Skolnick, J.
- Affiliations
- Georgia Institute of Technology (US)
- Corresponding author
- Skolnick, Jeffrey
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2018-10-02
- Volume
- 58
- Issue
- 11
- Pages
- 2343-2354
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- PMC
- PMC6437778
- OpenAlex
- W2894906875
- Cited by
- 39
- References
- 58
- Citations by year
- 2026: 1 · 2024: 8 · 2023: 6 · 2022: 8 · 2021: 6 · 2020: 5 · 2019: 5
- FWCI
- 2.99
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of Chemical Information and Modeling (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of General Medical Sciences (1R35 GM118039)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Keywords
- virtual ligand screening, protein-ligand interactions, protein threading, structure-based virtual screening, drug target identification, web services
- MeSH terms
- Binding Sites, Humans, Ligands, Protein Binding, Protein Conformation, Proteins, Software, Databases, Protein, Drug Discovery, Molecular Docking Simulation, Databases, Pharmaceutical
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