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Cited by 76Protein Structure and DynamicsReceptor Mechanisms and SignalingComputational Drug Discovery Methods

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Allosteric Regulation at the Crossroads of New Technologies: Multiscale Modeling, Networks, and Machine Learning

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Verkhivker, Gennady M., Agajanian, Steve, Hu, Guang, Tao, Peng·2020·Frontiers in Molecular Biosciences
DOI 10.3389/fmolb.2020.00136PMC PMC7363947
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Abstract

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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Crossref · 10.3389/fmolb.2020.00136

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Figure 1 Integration of NMR experiments and computational approaches for experiment-guided analysis of allosteric states and mechanisms.
Figure 1 Integration of NMR experiments and computational approaches for experiment-guided analysis of allosteric states and mechanisms.CC BY · PubMed Central
Figure 2 Overview of the information-theoretic framework for modeling of allosteric states and communications. The upper panel presents structure-based community detection. The lower panel illustrates modeling of the dynamical flows on the MSM maps of states and hierarchical dynamics-based detection of allosteric states and persistent communities.
Figure 2 Overview of the information-theoretic framework for modeling of allosteric states and communications. The upper panel presents structure-based community detection. The lower panel illustrates modeling of the dynamical flows on the MSM maps of states and hierarchical dynamics-based detection of allosteric states and persistent communities.CC BY · PubMed Central
Figure 3 A general prototypical workflow of MSM approaches and ML modeling for detection and classification of functional allosteric states.
Figure 3 A general prototypical workflow of MSM approaches and ML modeling for detection and classification of functional allosteric states.CC BY · PubMed Central
Figure 4 An overview of data-intensive ML platform for allosteric research and allosteric drug discovery.
Figure 4 An overview of data-intensive ML platform for allosteric research and allosteric drug discovery.CC BY · PubMed Central

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Research works printed but not linked: 1

  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

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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.
ORCID
Venue
Frontiers in Molecular Biosciences
Publisher
Frontiers Media SA
Published
2020-07-09
Volume
7
Pages
136-136
ISSN
2296-889X (ISSN-L)
DOI
10.3389/fmolb.2020.00136
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
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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