Linear indices of the 'macromolecular graph's nucleotides adjacency matrix' as a promising approach for 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 Ψ-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 × 10^-4 M^-1) and leave-one-out press statistics evidenced its predictive ability (q² = 0.82 and s_cv = 0.108 × 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.