Towards Automated Structure-Based NMR Resonance Assignment View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2010

AUTHORS

Richard Jang , Xin Gao , Ming Li

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. More... »

PAGES

189-207

References to SciGraph publications

Book

TITLE

Research in Computational Molecular Biology

ISBN

978-3-642-12682-6
978-3-642-12683-3

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-12683-3_13

DOI

http://dx.doi.org/10.1007/978-3-642-12683-3_13

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1006848820


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