Cai Y D, Yu H, Chou K C
Shanghai Research Centre of Biotechnology, Chinese Academy of Sciences.
J Protein Chem. 1998 Oct;17(7):607-15. doi: 10.1007/BF02780962.
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.
了解HIV蛋白酶的多蛋白切割位点将增进我们对其特异性的理解，而由此获得的信息将有助于设计特异性强且高效的HIV蛋白酶抑制剂。如果能够找到一种准确、可靠且快速的方法来预测HIV蛋白酶在蛋白质中的切割位点，那么HIV蛋白酶抑制剂的研发进程将大大加快。在本文中，运用典型人工神经网络的Kohonen自组织模型被用于预测具有多个和扩展特异性亚位点的蛋白酶对寡肽的可切割性。我们选择HIV-1蛋白酶作为研究对象。我们选取了299个寡肽作为训练集，另外63个寡肽作为测试集。由于其较高的正确预测率（58/63 = 92.06%）以及更强的容错能力，神经网络方法应该是寻找HIV蛋白酶有效抑制剂的一种有用技术，而HIV蛋白酶是设计抗艾滋病潜在药物的靶点之一。人工神经网络方法的原理也可应用于分析任何多亚位点酶的特异性。
