ARTIFICIAL NEURAL-NETWORK MODEL FOR PREDICTING HIV PROTEASE CLEAVAGE SITES IN PROTEIN

Knowledge of the polyprotein cleavage sites by HIV protease will refin e our understanding of its specificity and be useful for designing spe cific and efficient HIV protease inhibitors. The search for inhibitors of HIV protease will be greatly expedited if an accurate, robust and rapid method is found for predicting the cleavage sites in proteins by HIV protease. In this paper, a back-propagation model, a typical arti ficial neural network, is applied to predict the cleavability of oligo peptides by proteases with multiple and extended specificity subsites. HIV-1 protease was selected as a subject of study; 299 oligopeptides were chosen as a training set, and 63 oligopeptides as a test set. Bec ause of its high correct prediction rate (58/63 = 92.06%) and stronger fault-tolerant ability, the neural network method is expected to be a useful technique for finding effective inhibitors of HIV protease, wh ich is one of the targets in designing potential drugs against AIDS. T he principle of the artificial neural network method can also be appli ed to analyzing the specificity of any multi-subsite enzyme. (C) 1998 Elsevier Science Ltd. All rights reserved.