Minimizing the overlap problem in protein NMR: a computational framework for precision amino acid labeling
Abstract
Motivation: Recent advances in cell-free protein expression systems allow specific labeling of proteins with amino acids containing stable isotopes (¹⁵N, ¹³C 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.
Additional Information
© 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. Received: 21 April 2007; Revision received: 06 July 2007; Accepted: 06 August 2007; Published: 25 September 2007. Work supported by an NIH grant (GM-66763) and a UC Discovery Grant bio05-10533 to A.J.S., and a Laurel Wilkening Faculty Innovation award, a Microsoft Faculty Research Award, an NIH Biomedical Informatics Training grant (LM-07443-01) and an NSF MRI grant (EIA-0321390) to P.B. Conflict of Interest: none declared.Attached Files
Published - btm406.pdf
Supplemental Material - btm406_Supplementary_Data.zip
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Additional details
- Eprint ID
- 103021
- Resolver ID
- CaltechAUTHORS:20200506-083932399
- GM-66763
- NIH
- bio05-10533
- University of California, Irvine
- Laurel Wilkening Faculty Innovation Award
- Microsoft Faculty Research Award
- LM-07443-01
- NIH
- EIA-0321390
- NSF
- Created
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2020-05-06Created from EPrint's datestamp field
- Updated
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2021-11-16Created from EPrint's last_modified field