Literature DB >> 29196969

Rapid and reliable protein structure determination via chemical shift threading.

Noor E Hafsa1, Mark V Berjanskii2, David Arndt2, David S Wishart3,4.   

Abstract

Protein structure determination using nuclear magnetic resonance (NMR) spectroscopy can be both time-consuming and labor intensive. Here we demonstrate how chemical shift threading can permit rapid, robust, and accurate protein structure determination using only chemical shift data. Threading is a relatively old bioinformatics technique that uses a combination of sequence information and predicted (or experimentally acquired) low-resolution structural data to generate high-resolution 3D protein structures. The key motivations behind using NMR chemical shifts for protein threading lie in the fact that they are easy to measure, they are available prior to 3D structure determination, and they contain vital structural information. The method we have developed uses not only sequence and chemical shift similarity but also chemical shift-derived secondary structure, shift-derived super-secondary structure, and shift-derived accessible surface area to generate a high quality protein structure regardless of the sequence similarity (or lack thereof) to a known structure already in the PDB. The method (called E-Thrifty) was found to be very fast (often < 10 min/structure) and to significantly outperform other shift-based or threading-based structure determination methods (in terms of top template model accuracy)-with an average TM-score performance of 0.68 (vs. 0.50-0.62 for other methods). Coupled with recent developments in chemical shift refinement, these results suggest that protein structure determination, using only NMR chemical shifts, is becoming increasingly practical and reliable. E-Thrifty is available as a web server at http://ethrifty.ca .

Keywords:  Chemical shifts; Fold recognition; Nuclear magnetic resonance; Structure prediction; Threading

Mesh:

Substances:

Year:  2017        PMID: 29196969     DOI: 10.1007/s10858-017-0154-1

Source DB:  PubMed          Journal:  J Biomol NMR        ISSN: 0925-2738            Impact factor:   2.835


  68 in total

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Journal:  J Biomol NMR       Date:  2000-10       Impact factor: 2.835

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Authors:  David S Wishart; David Arndt; Mark Berjanskii; Peter Tang; Jianjun Zhou; Guohui Lin
Journal:  Nucleic Acids Res       Date:  2008-05-30       Impact factor: 16.971

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