Literature DB >> 24333248

Generating NMR chemical shift assignments of intrinsically disordered proteins using carbon-detected NMR methods.

Debashish Sahu1, Monique Bastidas1, Scott A Showalter2.   

Abstract

There is an extraordinary need to describe the structures of intrinsically disordered proteins (IDPs) due to their role in various biological processes involved in signaling and transcription. However, general study of IDPs by NMR spectroscopy is limited by the poor (1)H amide chemical shift dispersion typically observed in their spectra. Recently, (13)C direct-detected NMR spectroscopy has been recognized as enabling broad structural study of IDPs. Most notably, multidimensional experiments based on the (15)N,(13)C CON spectrum make complete chemical shift assignment feasible. Here we document a collection of NMR-based tools that efficiently lead to chemical shift assignment of IDPs, motivated by a case study of the C-terminal disordered region from the human pancreatic transcription factor Pdx1. Our strategy builds on the combination of two three-dimensional (3D) experiments, (HN-flip)N(CA)CON and 3D (HN-flip)N(CA)NCO, that enable daisy chain connections to be built along the IDP backbone, facilitated by acquisition of amino acid-specific (15)N,(13)C CON-detected experiments. Assignments are completed through carbon-detected, total correlation spectroscopy (TOCSY)-based side chain chemical shift measurement. Conducting our study required producing valuable modifications to many previously published pulse sequences, motivating us to announce the creation of a database of our pulse programs, which we make freely available through our website.
Copyright © 2013 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Amino acid-filtered NMR; CAS–NMR; Carbon detect NMR; Intrinsically disordered proteins; Pdx1

Mesh:

Substances:

Year:  2013        PMID: 24333248      PMCID: PMC3944900          DOI: 10.1016/j.ab.2013.12.005

Source DB:  PubMed          Journal:  Anal Biochem        ISSN: 0003-2697            Impact factor:   3.365


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