The DOI matches its Crossref record; 360 of the 361 references it cites were found in a registry or an index.
Allosteric Regulation at the Crossroads of New Technologies: Multiscale Modeling, Networks, and Machine Learning
Open accessAbstract
Allosteric regulation is a common mechanism employed by complex biomolecular systems for regulation of activity and adaptability in the cellular environment, serving as an effective molecular tool for cellular communication. As an intrinsic but elusive property, allostery is a ubiquitous phenomenon where binding or disturbing of a distal site in a protein can functionally control its activity and is considered as the "second secret of life." The fundamental biological importance and complexity of these processes require a multi-faceted platform of synergistically integrated approaches for prediction and characterization of allosteric functional states, atomistic reconstruction of allosteric regulatory mechanisms and discovery of allosteric modulators. The unifying theme and overarching goal of allosteric regulation studies in recent years have been integration between emerging experiment and computational approaches and technologies to advance quantitative characterization of allosteric mechanisms in proteins. Despite significant advances, the quantitative characterization and reliable prediction of functional allosteric states, interactions, and mechanisms continue to present highly challenging problems in the field. In this review, we discuss simulation-based multiscale approaches, experiment-informed Markovian models, and network modeling of allostery and information-theoretical approaches that can describe the thermodynamics and hierarchy allosteric states and the molecular basis of allosteric mechanisms. The wealth of structural and functional information along with diversity and complexity of allosteric mechanisms in therapeutically important protein families have provided a well-suited platform for development of data-driven research strategies. Data-centric integration of chemistry, biology and computer science using artificial intelligence technologies has gained a significant momentum and at the forefront of many cross-disciplinary efforts. We discuss new developments in the machine learning field and the emergence of deep learning and deep reinforcement learning applications in modeling of molecular mechanisms and allosteric proteins. The experiment-guided integrated approaches empowered by recent advances in multiscale modeling, network science, and machine learning can lead to more reliable prediction of allosteric regulatory mechanisms and discovery of allosteric modulators for therapeutically important protein targets.
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“AlphaFold, which predicts single ranked structures for a protein sequence, is unable to address directly allosteric mechanisms, which are based on the populations of conformational states in the ensembles.”
From AlphaFold, Artificial Intelligence (AI), and Allostery (2022) · cites this paper as [107]
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Research works printed but not linked: 1
- Verkhivker, 2016, Dissecting allosteric regulatory mechanisms of the Hsp90 chaperone interactions with the protein kinase clients: Integrating structural bioinformatics with multiscale atomistic simulations and biophysical experiments, Abst. Pap. Am. Chem. Soc., 251, 2
Cited by
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AlphaFold, Artificial Intelligence (AI), and Allostery
Nussinov, R., Zhang, M., Liu, Y., & Jang, H. (2022) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.2c04346 · Cited by 190
Allostery: Allosteric Cancer Drivers and Innovative Allosteric Drugs
Nussinov, R., Zhang, M., Maloney, R., Liu, Y., Tsai, C.-J., & Jang, H. (2022) · Journal of Molecular Biology · doi:10.1016/j.jmb.2022.167569 · Cited by 66
Machine learning approaches in predicting allosteric sites
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Metadata
- Authors
- Verkhivker, G. M., Agajanian, S., Hu, G., & Tao, P.
- Affiliations
- Chapman University (US)
- Soochow University (CN)
- Southern Methodist University (US)
- Corresponding author
- Verkhivker, Gennady M.
- Venue
- Frontiers in Molecular Biosciences
- Publisher
- Frontiers Media SA
- Published
- 2020-07-09
- Volume
- 7
- Pages
- 136-136
- ISSN
- 2296-889X (ISSN-L)
- PMC
- PMC7363947
- OpenAlex
- W3041792613
- Cited by
- 76
- References
- 361
- Citations by year
- 2026: 8 · 2025: 11 · 2024: 15 · 2023: 13 · 2022: 15 · 2021: 10 · 2020: 4
- FWCI
- 1.01
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Molecular Biosciences (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Chapman University Digital Commons (Chapman University) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Chapman University
- National Institute of General Medical Sciences (R15GM122013)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Receptor Mechanisms and Signaling
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Keywords
- allosteric regulation, allosteric proteins, allosteric modulators, multiscale modeling, Markov model, protein-protein interaction network, information theory, deep learning, deep reinforcement learning, allosteric mechanism
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