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Structural protein-ligand interaction fingerprints (SPLIF) for structure-based virtual screening: method and
1Center for Integrative Chemical Biology and Drug Discovery, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill , Campus Box 7363, Chapel Hill, North Carolina 27599-7363, United States.
Journal of Chemical Information and Modeling
|August 14, 2014
Summary
We developed a new method called structural protein-ligand interaction fingerprints (SPLIF) to improve drug discovery. SPLIF accurately assesses how well potential drugs bind to proteins, outperforming existing techniques.
Area of Science:
- Computational chemistry
- Structural biology
- Drug discovery
Background:
- Accurate ligand-protein affinity prediction is crucial for structure-based virtual screening (SB-VS).
- Current methods face challenges in accuracy and affordability.
- Empirical postdocking filters using structure-activity data can enhance SB-VS.
Purpose of the Study:
- Introduce a novel postdocking filter, structural protein-ligand interaction fingerprints (SPLIF).
- Enable quantitative assessment of protein-ligand interactions based on 3D structural similarity.
- Improve the identification of active compounds in virtual screening campaigns.
Main Methods:
- Developed SPLIF based on 3D structural protein-ligand interactions.
- Evaluated SPLIF performance using 10 diverse datasets from the DUD-E database.
- Compared SPLIF against relevant standard postdocking filtering methods.
Main Results:
- SPLIF quantitatively assesses if docking poses mimic known ligand interactions.
- The method successfully rescues active compounds with initially poor docking scores.
- SPLIF demonstrated superior performance compared to standard methods across benchmark datasets.
Conclusions:
- SPLIF is an effective postdocking filter for structure-based virtual screening.
- This method enhances the accuracy and reliability of virtual screening.
- SPLIF offers a valuable tool for improving drug discovery pipelines.
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