Nucleic Acid Quadratic Indices of the “Macromolecular Graph’s Nucleotides Adjacency Matrix”. Modeling of Footprints after the Interaction of Paromomycin with the HIV-1 Ψ-RNA Packaging Region

This report describes a new set of macromolecular descriptors of relevance to nucleic acid QSAR/QSPR studies, nucleic acids’ quadratic indices. These descriptors are calculated from the macromolecular graph’s nucleotide adjacency matrix. A study of the interaction of the antibiotic Paromomycin with the packaging region of the RNA present in type-1 HIV illustrates this approach. A linear discriminant function gave rise to excellent discrimination between 90.10% (91/101) and 81.82% (9/11) of interacting/noninteracting sites of nucleotides in training and test set, respectively. The LOO crossvalidation procedure was used to assess the stability and predictability of the model. Int. J. Mol. Sci. 2004, 5 277 Using this approach, the classification model has shown a LOO global good classification of 91.09%. In addition, the model’s overall predictability oscillates from 89.11% until 87.13%, when n varies from 2 to 3 in leave-n-out jackknife method. This value stabilizes around 88.12% when n was > 3. On the other hand, a linear regression model predicted the local binding affinity constants [log K (10M)] between a specific nucleotide and the aforementioned antibiotic. The linear model explains almost 92% of the variance of the experimental log K (R = 0.96 and s = 0.07) and LOO press statistics evidenced its predictive ability (q = 0.85 and scv = 0.09). These models also permit the interpretation of the driving forces of the interaction process. In this sense, developed equations involve short-reaching (k ≤ 3), middle-reaching (4 < k < 9) and far-reaching (k = 10 or greater) nucleotide’s quadratic indices. This situation points to electronic and topologic nucleotide’s backbone interactions control of the stability profile of Paromomycin-RNA complexes. Consequently, the present approach represents a novel and rather promising way to chem & bioinformatics research.