Abstract
Mapping allosteric sites is emerging as one of the central challenges in physiology, pathology, and pharmacology. Nuclear Magnetic Resonance (NMR) spectroscopy is ideally suited to map allosteric sites, given its ability to sense at atomic resolution the dynamics underlying allostery. Here, we focus specifically on the NMR CHEmical Shift Covariance Analysis (CHESCA), in which allosteric systems are interrogated through a targeted library of perturbations (e.g., mutations and/or analogs of the allosteric effector ligand). The atomic resolution readout for the response to such perturbation library is provided by NMR chemical shifts. These are then subject to statistical correlation and covariance analyses resulting in clusters of allosterically coupled residues that exhibit concerted responses to the common set of perturbations. This chapter provides a description of how each step in the CHESCA is implemented, starting from the selection of the perturbation library and ending with an overview of different clustering options.
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References
Nussinov R, Tsai C (2013) Allostery in disease and in drug discovery. Cell 153(2):293–305
Boulton S, Melacini G (2016) Advances in NMR methods to map allosteric sites: from models to translation. Chem Rev 116(11):6267–6304
Hilser VJ, Wrabl JO, Motlagh HN (2012) Structural and energetic basis of allostery. Annu Rev Biophys 41:585–609
Kuriyan J, Eisenberg D (2007) The origin of protein interactions and allostery in colocalization. Nature 450(7172):983–990
Smock RG, Gierasch LM (2009) Sending signals dynamically. Science 324(5924):198–203
Nussinov R, Tsai C (2012) The different ways through which specificity works in orthosteric and allosteric drugs. Curr Pharm Des 18(9):1311–1316
Wenthur CJ, Gentry PR, Mathews TP et al (2014) Drugs for allosteric sites on receptors. Annu Rev Pharmacol Toxicol 54:165–184
Monod J, Wyman J, Changeux J (1965) On the nature of allosteric transitions: a plausible model. J Mol Biol 12(1):88–118
Boulton S, Akimoto M, Selvaratnam R et al (2014) A tool set to map allosteric networks through the NMR chemical shift covariance analysis. Sci Rep 4:7306
Akimoto M, Selvaratnam R, McNicholl ET et al (2013) Signaling through dynamic linkers as revealed by PKA. Proc Natl Acad Sci U S A 110(35):14231–14236
Selvaratnam R, Chowdhury S, VanSchouwen B et al (2011) Mapping allostery through the covariance analysis of NMR chemical shifts. Proc Natl Acad Sci U S A 108(15):6133–6138
Selvaratnam R, Mazhab-Jafari MT, Das R et al (2012) The auto-inhibitory role of the EPAC hinge helix as mapped by NMR. PLoS One 7(11):e48707
Cembran A, Kim J, Gao J et al (2014) NMR mapping of protein conformational landscapes using coordinated behavior of chemical shifts upon ligand binding. Phys Chem Chem Phys 16(14):6508–6518
Axe JM, Yezdimer EM, O'Rourke KF et al (2014) Amino acid networks in a (β/α)8 barrel enzyme change during catalytic turnover. J Am Chem Soc 136(19):6818–6821
Williamson MP (2013) Using chemical shift perturbation to characterise ligand binding. Prog Nucl Magn Reson Spectrosc 73:1–16
Selvaratnam R (2013) Probing allostery in the exchange protein activated by cAMP (EPAC) using NMR spectroscopy. Unpublished doctoral thesis, McMaster University, Hamilton, ON, Canada
Selvaratnam R, VanSchouwen B, Fogolari F et al (2012) The projection analysis of NMR chemical shifts reveals extended EPAC autoinhibition determinants. Biophys J 102(3):630–639
Chatterjee S, Firat A (2007) Generating data with identical statistics but dissimilar graphics. Am Stat 61(3):248–254
Axe JM, O'Rourke KF, Kerstetter NE et al (2015) Severing of a hydrogen bond disrupts amino acid networks in the catalytically active state of the alpha subunit of tryptophan synthase. Protein Sci 24(4):484–494
Axe JM, Boehr DD (2013) Long-range interactions in the alpha subunit of tryptophan synthase help to coordinate ligand binding, catalysis, and substrate channeling. J Mol Biol 425(9):1527–1545
de Hoon MJ, Imoto S, Nolan J et al (2004) Open source clustering software. Bioinformatics 20(9):1453–1454
Saldanha AJ (2004) Java treeview–extensible visualization of microarray data. Bioinformatics 20(17):3246–3248
Tomaselli S, Pagano K, Boulton S et al (2015) Lipid binding protein response to a bile acid library: a combined NMR and statistical approach. FEBS J 282(21):4094–4113
Acknowledgments
We thank Amir Bashiri (McMaster U.), Dr. M. Akimoto (Keio U.), Professor G. Veglia (U. Minnesota), and L.E. Kay (U. Toronto) for helpful discussions. This study received funding from Canadian Institutes of Health Research (Grant MOP-68897) to G.M. and Natural Sciences and Engineering Research Council of Canada (Grant RGPIN-2014−04514) to G.M.
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Boulton, S., Selvaratnam, R., Ahmed, R., Melacini, G. (2018). Implementation of the NMR CHEmical Shift Covariance Analysis (CHESCA): A Chemical Biologist’s Approach to Allostery. In: Ghose, R. (eds) Protein NMR. Methods in Molecular Biology, vol 1688. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7386-6_18
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DOI: https://doi.org/10.1007/978-1-4939-7386-6_18
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