Linear indices of the 'macromolecular graph's nucleotides adjacency matrix as a promising approach or bioinformatics studies.: Part 1:: Prediction of paromomycin's affinity constant with HIV-1 ψ-RNA packaging region

The design of novel anti-HIV compounds has now become a crucial area for scientists around the world. In this paper a new set of macromolecular descriptors (that are calculated from the macromolecular graph's nucleotide adjacency matrix) of relevance to nucleic acid QSAR/QSPR studies, nucleic acids' linear indices. A study of the interaction of the antibiotic Paromomycin with the packaging region of the HIV-1 Psi-RNA has been performed as example of this approach. A multiple linear regression model predicted the local binding affinity constants [Log K (10(-4) M-1)] between a specific nucleotide and the aforementioned antibiotic. The linear model explains more than 87% of the variance of the experimental Log K (R = 0.93 and s = 0. 102 x 10(-4) M-1) and leave-one-out press statistics evidenced its predictive ability (q(2) = 0. 82 and s(cv) = 0. 108 x 10(-4) M-1). The comparison with other approaches (macromolecular quadratic indices, Markovian Negentropies and 'stochastic' spectral moments) reveals a good behavior of our method. (c) 2005 Published by Elsevier Ltd.