Linear Indices of the “Macromolecular Graph’s Nucleotides Adjacency Matrix” as a Novel Approach in Bioinformatics Studies. 1. Prediction of Paromomycin’s Affinity Constant with HIV-1 Y-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 ?-RNA has been performed as example of this approach. A multiple linear regression model predicted the local binding affinity constants [log K (10-4M-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 = 0.102x10- 4M-1) and leave-one-out press statistics evidenced its predictive ability (q2 = 0.82 and scv = 0.108x10-4M-1). The comparison with other approaches (macromolecular quadratic indices, Markovian Negentropies and ´stochastic´ spectral moments) reveals a good behavior of our method.