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NMR and computational methods for molecular resolution of allosteric pathways in enzyme complexes

Abstract

Allostery is a ubiquitous biological mechanism in which a distant binding site is coupled to and drastically alters the function of a catalytic site in a protein. Allostery provides a high level of spatial and temporal control of the integrity and activity of biomolecular assembles composed of proteins, nucleic acids, or small molecules. Understanding the physical forces that drive allosteric coupling is critical to harnessing this process for use in bioengineering, de novo protein design, and drug discovery. Current microscopic models of allostery highlight the importance of energetics, structural rearrangements, and conformational fluctuations, and in this review, we discuss the synergistic use of solution NMR spectroscopy and computational methods to probe these phenomena in allosteric systems, particularly protein-nucleic acid complexes. This combination of experimental and theoretical techniques facilitates an unparalleled detection of subtle changes to structural and dynamic equilibria in biomolecules with atomic resolution, and we provide a detailed discussion of specialized NMR experiments as well as the complementary methods that provide valuable insight into allosteric pathways in silico. Lastly, we highlight two case studies to demonstrate the adaptability of this approach to enzymes of varying size and mechanistic complexity.

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Acknowledgments

G.P. acknowledges funding from the National Science Foundation (NSF), as this material is based upon work supported by the National Science Foundation under Grant No. CHE-1905374. GPL acknowledges funding from the COBRE Center for Computational Biology of Human Disease (NIGMS P20GM109035).

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East, K.W., Skeens, E., Cui, J.Y. et al. NMR and computational methods for molecular resolution of allosteric pathways in enzyme complexes. Biophys Rev (2019) doi:10.1007/s12551-019-00609-z

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Keywords

  • Allostery
  • NMR
  • Molecular dynamics
  • Protein dynamics
  • Community network analysis