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

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)


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, hbSBD 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)229-244
Number of pages16
JournalJournal of Computational Biology
Issue number2
Publication statusPublished - 2006 Mar
Externally publishedYes


  • Iterative relaxation algorithm
  • Nearest neighbor
  • NMR resonance assignment
  • Weighted maximum independent set

ASJC Scopus subject areas

  • Modelling and Simulation
  • Molecular Biology
  • Genetics
  • Computational Mathematics
  • Computational Theory and Mathematics


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