DocumentCode :
2362036
Title :
Cleavage knowledge extraction in HIV-1 protease using hidden Markov model
Author :
Jayavardhana Rama, G.L. ; Palaniswami, Marimuthu
Author_Institution :
Dept. of Electr. & Electron. Eng., Melbourne Univ., Parkville, Vic., Australia
fYear :
2005
fDate :
4-7 Jan. 2005
Firstpage :
469
Lastpage :
473
Abstract :
Inactive HIV is a poly protein precursor. This protein chain has to be cleaved at 9 specific positions to produce individual functional mature proteins responsible for making up a new active virus. Cleavage knowledge extraction in HIV protease will assist in designing effective inhibitors used in the treatment of AIDS. Although much progress has been made in sequencing the viral protease, little progress has been made in understanding the specificity. Several machine learning techniques have been used in understanding the specificity of HIV-1 protease with the highest prediction rate being 92%. In this paper the hidden Markov model is used for analyzing the specificity of HIV-1 protease. The objective is to learn the lock and key mechanism of the protease and protein precursor using the hidden Markov model (HMM) from a set of experimental observations. A good self consistency rate of 96% and recognition accuracy of 95.24% on unseen data is achieved. The HIV protease specificity to cleave between a phenylalanine and tyrosine or proline is also validated by our experiments indicating that the HMM is successful in learning the complex lock and key rule between protease and precursor protein. Used with other techniques, HMM can be used as an effective tool for designing new drugs.
Keywords :
enzymes; hidden Markov models; knowledge acquisition; learning (artificial intelligence); medical computing; microorganisms; AIDS treatment; HIV-1 protease; HMM; cleavage knowledge extraction; drug design tool; hidden Markov model; inactive HIV; machine learning technique; phenylalanine; poly protein precursor; proline; tyrosine; viral protease; Acquired immune deficiency syndrome; Amino acids; DNA; Drugs; Hidden Markov models; Human immunodeficiency virus; Inhibitors; Protein engineering; RNA; Tiles;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Sensing and Information Processing, 2005. Proceedings of 2005 International Conference on
Print_ISBN :
0-7803-8840-2
Type :
conf
DOI :
10.1109/ICISIP.2005.1529500
Filename :
1529500
Link To Document :
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