RIBRA–an error-tolerant algorithm for the NMR backbone assignment problem

Authors:
Kuen-Pin Wu;Jia-Ming Chang;Jun-Bo Chen;Chi-Fon Chang;Wen-Jin Wu;Tai-Huang Huang;Ting-Yi Sung;Wen-Lian Hsu
Affiliations:
Institute of Information Science, Academia Sinica, Taiwan;Institute of Information Science, Academia Sinica, Taiwan;Institute of Information Science, Academia Sinica, Taiwan;Genomics Research Center, Academia Sinica, Taiwan;Institute of Biomedical Sciences, Academia Sinica, Taiwan;Genomics Research Center, Academia Sinica, Taiwan;Institute of Information Science, Academia Sinica, Taiwan;Institute of Information Science, Academia Sinica, Taiwan
Venue:
RECOMB'05 Proceedings of the 9th Annual international conference on Research in Computational Molecular Biology
Year:
2005

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Abstract

We develop an iterative relaxation algorithm, called RIBRA, for NMR protein backbone assignment. RIBRA applies nearest neighbor and weighted maximum independent set algorithms to solve the problem. To deal with noisy NMR spectral data, RIBRA is executed in an iterative fashion based on the quality of spectral peaks. We first produce spin system pairs using the spectral data without missing peaks, then the data group with one missing peak, and finally, the data group with two missing peaks. We test RIBRA on two real NMR datasets: hb-SBD and hbLBD, and perfect BMRB data (with 902 proteins) and four synthetic BMRB data which simulate four kinds of errors. The accuracy of RIBRA on hbSBD and hbLBD are 91.4% and 83.6%, respectively. The average accuracy of RIBRA on perfect BMRB datasets is 98.28%, and 98.28%, 95.61%, 98.16% and 96.28% on four kinds of synthetic datasets, respectively.