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Conformational Dynamics and Protein Allostery: Molecular Simulations, Network Modeling and Machine Learning Approaches

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Biophysics".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 2095

Special Issue Editor

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Guest Editor
Schmid College of Science and Technology, Computational and Data Science, School of Pharmacy, Chapman University, Orange, CA 92866, USA
Interests: computational biology; structural bioinformatics; computational virology; computational systems biology; theoretical and computational approaches for studies of protein dynamics; mechanisms of allosteric regulation; network modeling of biomolecular systems; machine learning; allosteric drug discovery and engineering of allosteric functions; AI and machine learning approaches for exploring allosteric landscapes and drug design
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Special Issue Information

Dear Colleagues,

The fundamental biological importance and complexity of allosterically regulated proteins stems from their central role in signal transduction and a variety of cellular processes. Although significance of allosteric phenomenon for biology has been recognized for a long time, the molecular mechanisms of protein allostery remain elusive due to a complex interplay of thermodynamic and dynamic changes that are difficult to observe and simulate. The quantitative elucidation of these highly dynamic processes require a multi-faceted platform of innovative computational and experimental approaches that can dissect fundamental principles of allosteric regulation and advance atomistic characterization of allosteric states, interactions and mechanisms. This Special Issue will cover recent computational and experimental advances in studies of conformational dynamics and protein allostery, and mechanisms of allosteric regulation. The Special Issue will a involve a broad range of methodological and applicational topics including multiscale simulation approaches and AI-augmented enhanced sampling methods for exploring protein conformational dynamics and allostery; dynamic network methods for atomistic modeling of protein allostery, computational and experimental approaches for detection of allosteric states, interactions, communications and allosteric regulatory hotspots; integration of computational approaches with NMR methods for interrogation of allosteric protein functions and identification of functional allosteric networks; high-throughput deep mutational scanning of allosteric protein functions; applications of Alpha-fold structural prediction methods for studies of protein dynamics and allostery; new frontiers in integrating machine learning and enhanced sampling techniques for characterization of allostery; and recent advances in structural biology approaches for studies of allosteric systems; allostery and the function of molecular machines and protein complexes; directed evolution and engineering of allostery employing enhanced sampling, Markov state models and machine learning. This Special Issue will explore the most recent advances in elucidating the molecular mechanisms underlying allostery as well as a broad range of applications involving molecular systems.

Prof. Dr. Gennady Verkhivker
Guest Editor

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  • protein structure
  • conformational dynamics
  • modeling of protein allostery
  • allosteric regulation
  • allosteric protein landscapes
  • network biology
  • enhanced sampling approaches
  • AI-augmented protein modeling
  • Markov state models
  • machine learning and protein dynamics
  • structural characterization of allosteric systems with cryo-EM, NMR, smFRET
  • integrative biophysical approaches and allostery
  • allosteric drug discovery

Published Papers (1 paper)

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18 pages, 7626 KiB  
Novel Allosteric Effectors Targeting Human Transcription Factor TEAD
by Mayar Tarek Ibrahim, Gennady M. Verkhivker, Jyoti Misra and Peng Tao
Int. J. Mol. Sci. 2023, 24(10), 9009; https://doi.org/10.3390/ijms24109009 - 19 May 2023
Cited by 2 | Viewed by 1773
The Hippo pathway is an evolutionary conserved signaling network involved in several cellular regulatory processes. Dephosphorylation and overexpression of Yes-associated proteins (YAPs) in the Hippo-off state are common in several types of solid tumors. YAP overexpression results in its nuclear translocation and interaction [...] Read more.
The Hippo pathway is an evolutionary conserved signaling network involved in several cellular regulatory processes. Dephosphorylation and overexpression of Yes-associated proteins (YAPs) in the Hippo-off state are common in several types of solid tumors. YAP overexpression results in its nuclear translocation and interaction with transcriptional enhanced associate domain 1-4 (TEAD1-4) transcription factors. Covalent and non-covalent inhibitors have been developed to target several interaction sites between TEAD and YAP. The most targeted and effective site for these developed inhibitors is the palmitate-binding pocket in the TEAD1-4 proteins. Screening of a DNA-encoded library against the TEAD central pocket was performed experimentally to identify six new allosteric inhibitors. Inspired by the structure of the TED-347 inhibitor, chemical modification was performed on the original inhibitors by replacing secondary methyl amide with a chloromethyl ketone moiety. Various computational tools, including molecular dynamics, free energy perturbation, and Markov state model analysis, were employed to study the effect of ligand binding on the protein conformational space. Four of the six modified ligands were associated with enhanced allosteric communication between the TEAD4 and YAP1 domains indicated by the relative free energy perturbation to original molecules. Phe229, Thr332, Ile374, and Ile395 residues were revealed to be essential for the effective binding of the inhibitors. Full article
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