Artificial neural network method for predicting HIV protease cleavage sites in protein |
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Authors: | Yu -Dong Cai Hanry Yu Kuo -Chen Chou |
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Institution: | (1) Shanghai Research Centre of Biotechnology, Chinese Academy of Sciences, 200233 Shanghai, China;(2) EMBL, 69012 Heidelberg, Germany;(3) Computer-Aided Drug Discovery, Upjohn Laboratories, 49001-4940 Kalamazoo, Michigan |
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Abstract: | Knowledge of the polyprotein cleavage sites by HIV protease will refine our understanding of its specificity, and the information
thus acquired will be useful for designing specific and efficient HIV protease inhibitors. The search for inhibitors of HIV
protease will be greatly expedited if one can find and accurate, robust, and rapid method for predicting the cleavage sites
in proteins by HIV protease. In this paper, Kohonen’s self-organization model, which uses typical artificial neural networks,
is applied to predict the cleavability of oligopeptides by proteases with multiple and extended specificity subsites. We selected
HIV-1 protease as the subject of study. We chose 299 oligopeptides for the training set, and another 63 oligopeptides for
the test set. Because of its high rate of correct prediction (58/63=92.06%) and stronger fault-tolerant ability, the neural
network method should be a useful technique for finding effective inhibitors of HIV protease, which is one of the targets
in designing potential drugs against AIDS. The principle of the artificial neural network method can also be applied to analyzing
the specificity of any multisubsite enzyme. |
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Keywords: | HIV protease artificial neural network T Kohonen’ s self-organization model cleavage sites |
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