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*

*此作品的通信作者

研究成果: 雜誌貢獻期刊論文同行評審

19 引文 斯高帕斯(Scopus)

摘要

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.

原文英語
頁(從 - 到)229-244
頁數16
期刊Journal of Computational Biology
13
發行號2
DOIs
出版狀態已發佈 - 2006 三月
對外發佈

ASJC Scopus subject areas

  • 建模與模擬
  • 分子生物學
  • 遺傳學
  • 計算數學
  • 計算機理論與數學

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