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Remote Homology Detection Tools and Algorithms -A Survey

Dr. D. Ramyachitra, M. Khaviya, R. Ranjani Rani

Abstract


Remote homology detection is an important challenge for computational biology systems. Protein structures and protein functions are aiming to detect the distantly evolutionary interaction with proteins using computational methods. There are many computational approaches have been planned to explain this essential task. These methods have made an important role to protein remote homology detection. This computational approach has alignment methods, discriminative methods and ranking methods. The advantage and disadvantage of remote homology detection are discussed below.


Keywords


Protein Remote Homology Detection; Protein Structure and Function; Alignment Methods; Discriminative Methods; Ranking Methods

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References


P. Bork and E. V. Koonin, “Predicting functions from protein sequences—where are the bottlenecks?” Nature Genetics, vol.18, no.4, pp.313–318, 1998.

B. Liu, J. Chen, and X. Wang, “Application of learning to rank to protein remote homology detection,” Bioinformatics, vol.31, no.21, pp.3492–3498, 2015.

B. Rost, “Twilight zone of protein sequence alignments, ”Protein Engineering,vol.12,no.2,pp.85–94,1999.

B. Liu, X. Wang, L. Lin, Q. Dong, and X. Wang, “A discriminative method for protein remote homology detection and fold recognition combining Top-n-grams and latent semantic analysis,”BMCBioinformatics,vol.9,article510,2008.

L. Liao and W. S. Noble, “Combining pair wise sequence similarity and support vector machines for detecting remote protein evolutionary and structural relationships,” Journal of ComputationalBiology, vol.10, no.6,pp.857–868,2003.

M. Gribskov, A.D. McLachlan and D. Eisenberg. Profile analysis: Detection of distantly related proteins. Proceedings of the National Academy of Sciences U S A, 84: 4355 – 4358, 1987.

A. Krogh, M. Brown, I. Mian, K. Sjolander and D. Haussler. Hidden Markov models in computational biology: Applications to protein modeling. Journal of Molecular Biology, 235: 1501 – 1531, 1994.

P. Baldi, Y. Chauvin, T. Hunkapiller and M. McClure. Hidden Markov models of biological primary sequence information. Proceedings of the National Academy of Sciences U S A, 91: 1053 – 1063, 1994.

S.F. Altschul, L.T. Madden, A.A. Sch à d’ offer, J. Zhang, Z. Zhang, W. Miller, and D.J. Lipmann. Gapped blast and psi - blast: A new generation of protein database search programs. Nucleic Acids Research, 25 (17): 3389 – 402, 1997.

K. Karplus, C. Barrett, and R. Hughey. Hidden Markov models for detecting remote protein homologies. Bioinformatics, 14 (10): 846 – 856, 1998. 6. V . V apnik. Statistical Learning Theory. New York: John Wiley, 1998.

T. Jaakkola, M. Diekhans, and D. Hassler. A discriminative framework for detecting remote protein homologies. Journal of Computational Biology, 7 (1/2): 95 – 114, 2000.

L. Liao and W.S. Noble. Combining pair wise sequence similarity and support vector machines for detecting remote protein evolutionary and structural relationships. In Proceedings of the International Conference on Research in Computational Molecular Biology, Washington, DC: ACM, 2002, pp. 225 – 232.

C. Leslie, E. Eskin, and W.S. Noble. The spectrum kernel: A string kernel for svm protein classifi cation. In Proceedings of the Pacifi c Symposium on Biocomputing, Stanford, CA: World Scientific Press, 2002, pp. 564 – 575. c08.indd 189 c08.indd 189 8/20/2010

C. Leslie, E. Eskin, W.S. Noble, and J. Weston. Mismatch string kernels for svm protein classifi cation. Advances in Neural Information Processing Systems, 20 (4): 467 – 476, 2003.

Y. Hou, W. Hsu, M. L. Lee, and C. Bystroff. Effi cient remote homology detection using local structure. Bioinformatics, 19 (17): 2294 – 2301, 2003.

Y. Hou, W. Hsu, M. L. Lee, and C. Bystroff. Remote homology detection using local sequence - structure correlations. Proteins: Structure, Function, and Bioinformatics, 57: 518 – 530, 2004.

H. Saigo, J.P. Vert, N. Ueda, and T.Akutsu. Protein homology detection using string alignment kernels. Bioinformatics, 20 (11): 1682 – 1689, 2004.

R. Kuang, E. I e, K. Wang, K. Wang, M. Siddiqi, Y. Freund, and C. Leslie. Profile based string kernels for remote homology detection and motif extraction. Computational Systems Bioinformatics, 3: 152 – 160, 2004.

V. N. Vapnik, Statistical Learning Theory, New York, 1998

C. Bystroff and D. Baker. Prediction of local structure in proteins using a library of sequence-structure motifs. Journal of Molecular Biology, 281:565–577, 1998.


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