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

Kuen Pin Wu, Jia Ming Chang, Jun Bo Chen, Chi Fon Chang, Wen Jin Wu, Tai Huang Huang, Ting Yi Sung, Wen Lian Hsu

Research output: Contribution to journalConference article

1 Citation (Scopus)

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.

Original languageEnglish
Pages (from-to)103-117
Number of pages15
JournalLecture Notes in Bioinformatics (Subseries of Lecture Notes in Computer Science)
Volume3500
DOIs
Publication statusPublished - 2005 Jan 1
Event9th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2005 - Cambridge, MA, United States
Duration: 2005 May 142005 May 18

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'RIBRA-an error-tolerant algorithm for the NMR backbone assignment problem'. Together they form a unique fingerprint.

  • Cite this