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Bioinformatics Advance Access originally published online on November 15, 2005
Bioinformatics 2006 22(2):172-180; doi:10.1093/bioinformatics/bti786
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© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oxfordjournals.org

An efficient randomized algorithm for contact-based NMR backbone resonance assignment

Hetunandan Kamisetty 1,3, Chris Bailey-Kellogg 2,* and Gopal Pandurangan 1

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

*To whom correspondence should be addressed.

Motivation: Backbone resonance assignment is a critical bottleneck in studies of protein structure, dynamics and interactions by nuclear magnetic resonance (NMR) spectroscopy. A minimalist approach to assignment, which we call ‘contact-based’, seeks to dramatically reduce experimental time and expense by replacing the standard suite of through-bond experiments with the through-space (nuclear Overhauser enhancement spectroscopy, NOESY) experiment. In the contact-based approach, spectral data are represented in a graph with vertices for putative residues (of unknown relation to the primary sequence) and edges for hypothesized NOESY interactions, such that observed spectral peaks could be explained if the residues were ‘close enough’. Due to experimental ambiguity, several incorrect edges can be hypothesized for each spectral peak. An assignment is derived by identifying consistent patterns of edges (e.g. for {alpha}-helices and ß-sheets) within a graph and by mapping the vertices to the primary sequence. The key algorithmic challenge is to be able to uncover these patterns even when they are obscured by significant noise.

Results: This paper develops, analyzes and applies a novel algorithm for the identification of polytopes representing consistent patterns of edges in a corrupted NOESY graph. Our randomized algorithm aggregates simplices into polytopes and fixes inconsistencies with simple local modifications, called rotations, that maintain most of the structure already uncovered. In characterizing the effects of experimental noise, we employ an NMR-specific random graph model in proving that our algorithm gives optimal performance in expected polynomial time, even when the input graph is significantly corrupted. We confirm this analysis in simulation studies with graphs corrupted by up to 500% noise. Finally, we demonstrate the practical application of the algorithm on several experimental ß-sheet datasets. Our approach is able to eliminate a large majority of noise edges and to uncover large consistent sets of interactions.

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

Contact: cbk{at}cs.dartmouth.edu

Received on October 12, 2005; revised on November 14, 2005; accepted on November 14, 2005

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