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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
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3500)

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.

Keywords

Nuclear Magnetic Resonance Spin System Synthetic Dataset Amino Acid Type Backbone Assignment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Kuen-Pin Wu
    • 1
  • Jia-Ming Chang
    • 1
  • Jun-Bo Chen
    • 1
  • Chi-Fon Chang
    • 2
  • Wen-Jin Wu
    • 3
  • Tai-Huang Huang
    • 2
    • 3
  • Ting-Yi Sung
    • 1
  • Wen-Lian Hsu
    • 1
  1. 1.Institute of Information ScienceAcademia SinicaTaiwan
  2. 2.Genomics Research CenterAcademia SinicaTaiwan
  3. 3.Institute of Biomedical SciencesAcademia SinicaTaiwan

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