ArticlePLoS computational biology2016
Normal Modes Expose Active Sites in Enzymes.
Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Decoding allosteric landscapes: computational methodologies for enzyme modulation and drug discovery.RSC chemical biology · 2025Review
- Convergence and equilibrium in molecular dynamics simulations.Communications chemistry · 2024Article
- Specifics of Metabolite-Protein Interactions and Their Computational Analysis and Prediction.Methods in molecular biology (Clifton, N.J.) · 2023Review
- In Silico Methods for Identification of Potential Active Sites of Therapeutic Targets.Molecules (Basel, Switzerland) · 2022Review
- Identification of a Novel Therapeutic Target against XDRMicroorganisms · 2021Article
- Coupling dynamics and evolutionary information with structure to identify protein regulatory and functional binding sites.Proteins · 2019Article
- Why Proteins are Big: Length Scale Effects on Equilibria and Kinetics.The protein journal · 2019Review
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate prediction of active sites is an important tool in bioinformatics. Here we present an improved structure based technique to expose active sites that is based on large changes of solvent accessibility accompanying normal mode dynamics. The technique which detects EXPOsure of active SITes through normal modEs is named EXPOSITE. The technique is trained using a small 133 enzyme dataset and tested using a large 845 enzyme dataset, both with known active site residues. EXPOSITE is also tested in a benchmark protein ligand dataset (PLD) comprising 48 proteins with and without bound ligands. EXPOSITE is shown to successfully locate the active site in most instances, and is found to be more accurate than other structure-based techniques. Interestingly, in several instances, the active site does not correspond to the largest pocket. EXPOSITE is advantageous due to its high precision and paves the way for structure based prediction of active site in enzymes.
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Registered trials
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