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Chemical shifts based structural ensemble generation for intrinsically disordered proteins: A case study He, Yi


About a third of the proteome consists of intrinsically disordered proteins (IDPs) that fold, whether fully or partially, upon binding to their partners [1]. IDPs use their inherent flexibility to play key regulatory roles in many biological processes [2]. Such flexibility makes their structural analysis extremely challenging, being nuclear magnetic resonance (NMR) the most suitable high-resolution technique. However, conventional NMR structure determination methods, which seek to determine a single high-resolution structure [3], are inadequate for IDPs. There are several tools available for the structural analysis of IDPs using NMR data and primarily Chemical Shifts (CS) [4-6]. However, a persistent problem is how to effectively sample the extensive, but not random, conformational space of IDPs. We have implemented a novel relational database, termed Glutton, that links all existing CS data with corresponding protein 3D structures with the goal of enabling the conformational analysis of IDPs directly from their experimental CS. Gluttonâ s uniqueness is in its focus on dihedral angle distributions consistent with a given set of CS rather than with unique structures. Such dihedral distributions define how native-like is the ensemble and lead to the effective calculation of large ensembles of structures that efficiently sample the available conformational space. With Glutton, we examined Nuclear Coactivator Binding Domain (NCBD), an IDP with NMR structure obtained using osmolyte stabilizers that is partly disordered in native conditions [7]. As means of comparison, we produced a 60ï ­s long MD simulation of NCBD in explicit solvent starting from the NMR structure and using the CHARMM36m force field with modified TIP3P water which was suggested as a good combination to explore the conformational space of IDPs [8]. The structural ensembles obtained from Glutton are based only on geometric considerations and CS restraints, but they can be further refined using additional computational (force field) and/or experimental (distance restraints) information.

Acknowledgments: This work was supported by grants: the startup fund at the University of New Mexico, the W.M. Keck Foundation, the National Science Foundation [NSF-MCB-161759 and NSF-CREST-1547848] and the European Research Council [ERC-2012-AdG-323059].

[1] H.J. Dyson, P.E. Wright, Chem. Rev., 104, 3607â 3622, 2004

[2] M.M. Babu, Biochem. Soc. Trans., 44, 1185â 1200, 2016

[3] A.M., Gronenborn, G.M. Clore, Anal. Chem., 62, 2â 15, 1990

[4] V. Ozenne, F. Bauer, L. Salmon, J. Huang, M.R. Jensen, S. Segard, P. Bernadó, C. Charavay, M. Blackledge, Bioinformatics, 28, 1463â 1470, 2012

[5] M. Krzeminski, J.A. Marsh, C. Neale, W. Choy, J.D. Forman-Kay, Bioinformatics, 29, 398â 399, 2013

[6] D.H. Brookes, T. Head-Gordon, J. Am. Chem. Soc., 138, 4530â 4538, 2016

[7] A. Naganathan, M. Orozco, J. Am. Chem. Soc., 133, 12154â 12161, 2011

[8] J. Huang, S. Rauscher, G. Nawrocki, T. Ran, M. Feig, B. L de Groot, H. Grubmüller, A.D. MacKerell Jr, Nat Methods, 14, 71â 73, 2017

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