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Towards Automated Structure-Based NMR Resonance Assignment

  • Conference paper
Book cover Research in Computational Molecular Biology (RECOMB 2010)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 6044))

Abstract

We propose a general framework for solving the structure-based NMR backbone resonance assignment problem. The core is a novel 0-1 integer programming model that can start from a complete or partial assignment, generate multiple assignments, and model not only the assignment of spins to residues, but also pairwise dependencies consisting of pairs of spins to pairs of residues. It is still a challenge for automated resonance assignment systems to perform the assignment directly from spectra without any manual intervention. To test the feasibility of this for structure-based assignment, we integrated our system with our automated peak picking and sequence-based resonance assignment system to obtain an assignment for the protein TM1112 with 91% recall and 99% precision without manual intervention. Since using a known structure has the potential to allow one to use only N-labeled NMR data and avoid the added expense of using C-labeled data, we work towards the goal of automated structure-based assignment using only such labeled data. Our system reduced the assignment error of Xiong-Pandurangan-Bailey-Kellogg’s contact replacement (CR) method, which to our knowledge is the most error-tolerant method for this problem, by 5 folds on average. By using an iterative algorithm, our system has the added capability of using the NOESY data to correct assignment errors due to errors in predicting the amino acid and secondary structure type of each spin system. On a publicly available data set for Ubiquitin, where the type prediction accuracy is 83%, we achieved 91% assignment accuracy, compared to the 59% accuracy that was obtained without correcting for typing errors.

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Authors and Affiliations

  1. David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada, N2L 6P7

    Richard Jang, Xin Gao & Ming Li

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  1. Richard Jang
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  2. Xin Gao
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  1. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 77 Massachusetts Avenue, 02139, Cambridge, MA, USA

    Bonnie Berger

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Jang, R., Gao, X., Li, M. (2010). Towards Automated Structure-Based NMR Resonance Assignment. In: Berger, B. (eds) Research in Computational Molecular Biology. RECOMB 2010. Lecture Notes in Computer Science(), vol 6044. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12683-3_13

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  • DOI: https://doi.org/10.1007/978-3-642-12683-3_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12682-6

  • Online ISBN: 978-3-642-12683-3

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