Comparison among Feature Encoding Techniques for HIV-1 Protease Cleavage Specificity

Year 2015, , 62 - 66, 01.04.2015

Murat Gök , Uğur Turhal Aykut Durgut

https://doi.org/10.18201/ijisae.21005

Cited By: 1

Abstract

HIV-1 protease which is responsible for the generation of infectious viral particles by cleaving the virus polypeptides, play an indispensable role in the life cycle of HIV-1. Knowledge of the substrate specificity of HIV-1 protease will pave the way of development of efficacious HIV-1 protease inhibitors. In the prediction of HIV-1 protease cleavage site techniques, many efforts have been devoted. Last decade, several works have approached the prediction of HIV-1 protease cleavage site problem by applying a number of methods from the field of machine learning. However, it is still difficult for researchers to choose the best method due to the lack of an effective and up-to-date comparison. Here, we have made an extensive study on feature encoding techniques for the problem of HIV-1 protease specificity on diverse machine learning algorithms. Also, for the first time, we applied OEDICHO technique, which is a combination of orthonormal encoding and the binary representation of selected 10 best physicochemical properties of amino acids derived from Amino Acid index database, to predict HIV-1 protease cleavage sites.

Keywords

HIV-1 protease specificity, Feature extraction, Peptide classification, Machine learning algorithms, Amino acids

References

• -
• Zachary Q. Beck, Laurence Hervio, Philip E. Dawson, John H. Elder and Edwin L. Madison (2000). Identification of Efficiently Cleaved Substrates for HIV-1 Protease Using a Phage Display Library and Use in Inhibitor Development. Virology. Vol. 274. Pages. 391- 401.
• You-Dong Cai and Kuo-Chen Chou (1998). Artificial Neural Network Model for Predicting HIV Protease Cleavage Sites in Protein. Advances in Engineering Software. Vol. 29. Pages. 119-128.
• Thomas B. Thompson, Kuo-Chen Chou and Chong Zheng (1995). Neural Network Prediction of the HIV-1 Protease Cleavage Sites. Journal of Theoretical Biology. Vol. 177. Pages. 369–379.
• Yu-Dong Cai, Xiao-Jun Liu, Xue-Biao Xu and Kuo-Chen Chou (2002). Support Vector Machines for Predicting HIV Protease Cleavage Sites in Protein. Journal of Computational Chemistry. Vol. 23. Pages. 267-274.
• Thorsteinn Rognvaldsson and Liwen You (2004). Why Neural Networks Should Not Be Used For HIV-1 Protease Cleavage Site Prediction. Bioinformatics. Vol. 20. Pages. 1702-1709.
• Loris Nanni (2006). Comparison among Feature Extraction Methods for HIV-1 Protease Cleavage Site Prediction. Pattern Recognition. Vol. 39. Pages. 711-713. Aleksejs Kontijevskis, Jarl E. S. Wikberg and Jan Komorowski (2007). Computational Proteomics Analysis of HIV-1 Protease Interactome. Proteins: Structure, Function and Bioinformatics. Vol. 68. Pages. 305-312.
• Bing Niu, Lin Lu, Liang Liu, Tian H. Gu, Kai-Yan Feng, Wen-Cong Lu and Yu-Dong Cai (2009). HIV-1 Protease Cleavage Site Prediction Based on Amino Acid Property. Journal of Computational Chemistry. Vol. 30. Pages. 33- 39.
• Shuichi Kawashima and Minoru Kanehisa (2000). AAindex: Amino Acid Index Database. Nucleic Acids Research. Vol. 28. Page. 374. Available online: http://www.genome.jp/dbget/aaindex.html
• Jishou Ruan, Kui Wang, Jie Yang, Lukasz A. Kurgan and Krzysztof Cios (2005). Highly Accurate and Consistent Method for Prediction of Elix and Strand Content from Primary Protein Sequences. Artificial Intelligence in Medicine. Vol. 35. Pages. 19-35.