Recent computational advances in the identification of allosteric sites in proteins
Abstract
Allosteric modulators have the potential to fine-tune protein functional activity. Therefore, the targeting of allosteric sites, as a strategy in drug design, is gaining increasing attention. Currently, it is not trivial to find and characterize new allosteric sites by experimental approaches. Alternatively, computational approaches are useful in helping researchers analyze and select potential allosteric sites for drug discovery. Here, we review state-of-the-art computational approaches directed at predicting putative allosteric sites in proteins, along with examples of successes in identifying allosteric sites utilizing these methods. We also discuss the challenges in developing reliable methods for predicting allosteric sites and tactics to resolve demanding tasks.
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Allosteric modulators can change protein/substrate affinity in a highly predictable manner by stabilizing target proteins in inactive or active state, leading to desirable controllability.
“Allosteric modulators can change the protein/substrate affinity in a highly predictable manner by stabilizing target proteins in an inactive or active state, leading to desirable controllability.”
From Recent advances in targeting the “undruggable” proteins: from drug discovery to clinical trials (2023) · cites this paper as [238]
Allosteric ligands do not have to compete with high-affinity substrates, making it simpler to develop allosteric modulators.
“Firstly, allosteric ligands do not have to compete with high-affinity substrates, making it simpler to develop allosteric modulators.”
From Recent advances in targeting the “undruggable” proteins: from drug discovery to clinical trials (2023) · cites this paper as [238]
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- Authors
- Lu, S., Huang, W., & Zhang, J.
- Affiliations
- Shanghai Jiao Tong University (CN)
- Corresponding author
- Zhang, Jian
- Venue
- Drug Discovery Today
- Publisher
- Elsevier BV
- Published
- 2014-10
- Volume
- 19
- Issue
- 10
- Pages
- 1595-1600
- ISSN
- 1359-6446 (ISSN-L)
- OpenAlex
- W2052046618
- Cited by
- 102
- References
- 60
- Citations by year
- 2026: 5 · 2025: 3 · 2024: 4 · 2023: 6 · 2022: 3 · 2021: 8 · 2020: 5 · 2019: 16 · 2018: 10 · 2017: 11 · 2016: 17 · 2015: 14
- FWCI
- 1.14
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Drug Discovery Today (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (81322046, 81302698)
- 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 › Machine Learning in Bioinformatics
- Keywords
- allosteric site, allosteric modulators, allosteric site prediction, protein structure, drug target identification, computer-aided drug design, protein function modulation
- MeSH terms
- Allosteric Site, Protein Conformation, Proteins, Computational Biology, Drug Discovery, Molecular Dynamics Simulation
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