IPASS: error tolerant NMR backbone resonance assignment by linear programming

IPASS: error tolerant NMR backbone resonance assignment by linear programming (2009)

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by Babak Alipanahi , Xin Gao , Emre Karakoc , Frank Balbach , Logan Donaldson , Ming Li

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Datum	Value	Source
TITLE	IPASS: error tolerant NMR backbone resonance assignment by linear programming	INFERENCE
AUTHOR NAME	Babak Alipanahi	SVM HeaderParse 0.2
AUTHOR AFFIL	1 David R. Cheriton School of Computer Science,; University of Waterloo, Waterloo	SVM HeaderParse 0.2
AUTHOR ADDR	ON, Canada N2L 6P7	SVM HeaderParse 0.2
AUTHOR NAME	Xin Gao	SVM HeaderParse 0.2
AUTHOR NAME	Emre Karakoc	SVM HeaderParse 0.2
AUTHOR NAME	Frank Balbach	SVM HeaderParse 0.2
AUTHOR NAME	Logan Donaldson	SVM HeaderParse 0.2
AUTHOR NAME	Ming Li	SVM HeaderParse 0.2
AUTHOR AFFIL	2 Department of Biology, York University, Toronto	SVM HeaderParse 0.2
AUTHOR ADDR	ON, Canada M3J 1P3	SVM HeaderParse 0.2
ABSTRACT	Abstract. The automation of the entire NMR protein structure determination process requires a superior error tolerant backbone resonance assignment method. Although a variety of assignment approaches have been developed, none works well on noisy automatically picked peaks. IPASS is proposed as a novel integer linear programming (ILP) based assignment method. In order to reduce size of the problem, IPASS employs probabilistic spin system typing based on chemical shifts and secondary structure predictions. Furthermore, IPASS extracts connectivity information from the inter-residue information and the 15 N-edited NOESY peaks which are then used to fix reliable fragments. The experimental results demonstrate that IPASS significantly outperforms the previous assignment methods on the synthetic data sets. It achieves an average of 99 % precision and 96 % recall on the synthesized spin systems, and an average of 96 % precision and 90 % recall on the synthesized peak lists. When applied on automatically picked peaks from experimentally derived data sets, it achieves an average precision and recall of 78 % and 67%, respectively. In contrast, the next best method, MARS, achieved an average precision and recall of 50 % and 40%, respectively. Availability: IPASS is available upon request, and the web server for IPASS is under construction.	SVM HeaderParse 0.2
YEAR	2009	INFERENCE
VENUE TYPE	TECHREPORT	INFERENCE
PAGES	2009	INFERENCE
TECH	Technical Report CS-2009-16	INFERENCE
CITATIONS	21 found	ParsCit 1.0