RIBRA--an error-tolerant algorithm for the NMR backbone assignment problem.
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.
Wu
Kun-Pin
KP
Institute of Information Science, Nankang, Taipei, Taiwan.
Chang
Jia-Ming
JM
Chen
Jun-Bo
JB
Chang
Chi-Fon
CF
Wu
Wen-Jin
WJ
Huang
Tai-Huang
TH
Sung
Ting-Yi
TY
Hsu
Wen-Lian
WL
D000465:Algorithms; D000596:Amino Acids / Q000737:chemistry*; D003198:Computer Simulation; D030562:Databases, Protein; D006801:Humans; D008957:Models, Genetic; D019906:Nuclear Magnetic Resonance, Biomolecular; D011506:Proteins / Q000737:chemistry* / Q000235:genetics
D000596:Amino Acids; D011506:Proteins