Mining association rules for HIV-1 protease cleavage site prediction

Sandro da Silva CamargoPaulo Martins Engel

Several machine learning techniques, like neural networks, nonlinear support vector machines and decision trees, have been used to model the specificity of HIV-1 protease and to extract specific patterns from peptides cleaved by this protease. Despite many studies, no perfect rules are already known to determine the cleavage of a peptide by HIV-1 protease. These rules are useful for designing specific and efficient HIV inhibitors. Our results show that the technique of mining association rules can find several specificity rules of HIV-1 protease which presents 100% of cleavage probability. Recent papers on this subject show results in which the best rules present cleavage probability ranging from 16% to 91%.

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