Artificial neural network method for predicting HIV protease cleavage sites in protein.
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 an 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.
Cai
Y D
YD
Shanghai Research Centre of Biotechnology, Chinese Academy of Sciences.
Yu
H
H
Chou
K C
KC
D000595:Amino Acid Sequence; D019380:Anti-HIV Agents / Q000138:chemical synthesis*; D001665:Binding Sites; D015195:Drug Design; D016333:HIV Protease / Q000737:chemistry* / Q000378:metabolism; D017320:HIV Protease Inhibitors / Q000138:chemical synthesis*; D008956:Models, Chemical; D008969:Molecular Sequence Data; D016571:Neural Networks, Computer; D009842:Oligopeptides / Q000737:chemistry / Q000378:metabolism; D012680:Sensitivity and Specificity; D013329:Structure-Activity Relationship
D019380:Anti-HIV Agents; D017320:HIV Protease Inhibitors; D009842:Oligopeptides; D016333:HIV Protease