Journal Article Review Ahead of print
PubMed: 40043838 DOI: 10.1016/j.jmb.2025.169060
The Evolving Landscape of Protein Allostery: From Computational and Experimental Perspectives.
J Mol Biol
Authors: Srinivasan Ekambaram, Grigor Arakelov, Nikolay V Dokholyan
Affiliations
- Department of Neuroscience and Experimental Therapeutics, Penn State College of Medicine, Hershey, PA 17033, USA.
- Department of Neuroscience and Experimental Therapeutics, Penn State College of Medicine, Hershey, PA 17033, USA; Institute of Molecular Biology of the National Academy of Sciences of the Republic of Armenia, Yerevan 0014, Armenia.
- Department of Neuroscience and Experimental Therapeutics, Penn State College of Medicine, Hershey, PA 17033, USA; Department of Biochemistry & Molecular Biology, Penn State College of Medicine, Hershey, PA 17033, USA; Department of Chemistry, Penn State University, University Park, PA 16802, USA; Department of Biomedical Engineering, Penn State University, University Park, PA 16802, USA.
Abstract
Protein allostery is a fundamental biological regulatory mechanism that allows communication between distant locations within a protein, modifying its function in response to signals. Experimental techniques, such as NMR spectroscopy and cryo-electron microscopy (cryo-EM), are critical validation tools for computational predictions and provide valuable insights into dynamic conformational changes. Combining these approaches has greatly improved our understanding of classical conformational allostery and complex dynamic coupling mechanisms. Recent advances in machine learning and enhanced sampling methods have broadened the scope of allostery research, identifying cryptic allosteric sites and directing new drug discovery approaches. Despite progress, bridging static structural data with dynamic functional states remains challenging. This review underscores the importance of combining experimental and computational approaches to comprehensively understand protein allostery and its diverse applications in biology and medicine.
MeSH
Machine Learning, Molecular Dynamics Simulation
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