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Bioinformatics Advance Access originally published online on September 25, 2007
Bioinformatics 2007 23(21):2829-2835; doi:10.1093/bioinformatics/btm406
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© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Minimizing the overlap problem in protein NMR: a computational framework for precision amino acid labeling

Michael J. Sweredoski 1,3, Kevin J. Donovan 2,3, Bao D. Nguyen 2, A.J. Shaka 2,3 and Pierre Baldi 1,3,*

1Department of Computer Science, 2Department of Chemistry and 3Institute for Genomics and Bioinformatics, University of California, Irvine, USA

*To whom correspondence should be addressed.


  Abstract

Motivation: Recent advances in cell-free protein expression systems allow specific labeling of proteins with amino acids containing stable isotopes (15N, 13 C and 2H), an important feature for protein structure determination by nuclear magnetic resonance (NMR) spectroscopy. Given this labeling ability, we present a mathematical optimization framework for designing a set of protein isotopomers, or labeling schedules, to reduce the congestion in the NMR spectra. The labeling schedules, which are derived by the optimization of a cost function, are tailored to a specific protein and NMR experiment.

Results: For 2D 15N-1H HSQC experiments, we can produce an exact solution using a dynamic programming algorithm in under 2 h on a standard desktop machine. Applying the method to a standard benchmark protein, calmodulin, we are able to reduce the number of overlaps in the 500 MHZ HSQC spectrum from 10 to 1 using four samples with a true cost function, and 10 to 4 if the cost function is derived from statistical estimates. On a set of 448 curated proteins from the BMRB database, we are able to reduce the relative percent congestion by 84.9% in their HSQC spectra using only four samples. Our method can be applied in a high-throughput manner on a proteomic scale using the server we developed. On a 100-node cluster, optimal schedules can be computed for every protein coded for in the human genome in less than a month.

Availability: A server for creating labeling schedules for 15N-1H HSQC experiments as well as results for each of the individual 448 proteins used in the test set is available at http://nmr.proteomics.ics.uci.edu.

Contact: pfbaldi{at}ics.uci.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Burkhard Rost

Received on April 21, 2007; revised on July 6, 2007; accepted on August 6, 2007

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