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Bioinformatics 2008 24(13):i205-i213; doi:10.1093/bioinformatics/btn167
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© 2008 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.

Contact replacement for NMR resonance assignment

Fei Xiong 1, Gopal Pandurangan 2,* and Chris Bailey-Kellogg 1,*

1Department of Computer Science, Dartmouth College, Hanover, NH 03755 and 2Department of Computer Science, Purdue University, West Lafayette, IN 47907,USA

*To whom correspondence should be addressed.


  Abstract

Motivation: Complementing its traditional role in structural studies of proteins, nuclear magnetic resonance (NMR) spectroscopy is playing an increasingly important role in functional studies. NMR dynamics experiments characterize motions involved in target recognition, ligand binding, etc., while NMR chemical shift perturbation experiments identify and localize protein–protein and protein–ligand interactions. The key bottleneck in these studies is to determine the backbone resonance assignment, which allows spectral peaks to be mapped to specific atoms. This article develops a novel approach to address that bottleneck, exploiting an available X-ray structure or homology model to assign the entire backbone from a set of relatively fast and cheap NMR experiments.

Results: We formulate contact replacement for resonance assignment as the problem of computing correspondences between a contact graph representing the structure and an NMR graph representing the data; the NMR graph is a significantly corrupted, ambiguous version of the contact graph. We first show that by combining connectivity and amino acid type information, and exploiting the random structure of the noise, one can provably determine unique correspondences in polynomial time with high probability, even in the presence of significant noise (a constant number of noisy edges per vertex). We then detail an efficient randomized algorithm and show that, over a variety of experimental and synthetic datasets, it is robust to typical levels of structural variation (1–2 AA), noise (250–600%) and missings (10–40%). Our algorithm achieves very good overall assignment accuracy, above 80% in {alpha}-helices, 70% in β-sheets and 60% in loop regions.

Availability: Our contact replacement algorithm is implemented in platform-independent Python code. The software can be freely obtained for academic use by request from the authors.

Contact: gopal{at}cs.purdue.edu; cbk{at}cs.dartmouth.edu


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