Nucleotideʹs bilinear indices: Novel bio-macromolecular descriptors for bioinformatics studies of nucleic acids. I. Prediction of paromomycinʹs affinity constant with HIV-1 Ψ-RNA packaging region

A new set of nucleotide-based bio-macromolecular descriptors are presented. This novel approach to bio-macromolecular design from a linear algebra point of view is relevant to nucleic acids quantitative structure-activity relationship (QSAR) studies. These bio-macromolecular indices are based on the calculus of bilinear maps on ℜ n [ b mk ( x ¯ m , y ¯ m ) : ℜ n × ℜ n → ℜ ] in canonical basis. Nucleic acidʹs bilinear indices are calculated from kth power of non-stochastic and stochastic nucleotideʹs graph-theoretic electronic-contact matrices, M m k and s M m k , respectively. That is to say, the kth non-stochastic and stochastic nucleic acidʹs bilinear indices are calculated using M m k and s M m k as matrix operators of bilinear transformations. Moreover, biochemical information is codified by using different pair combinations of nucleotide-base properties as weightings (experimental molar absorption coefficient ε 260 at 260 nm and pH=7.0, first ( Δ E 1 ) and second ( Δ E 2 ) single excitation energies in eV, and first (f1) and second (f2) oscillator strength values (of the first singlet excitation energies) of the nucleotide DNA–RNA bases. As example of this approach, an interaction study of the antibiotic paromomycin with the packaging region of the HIV-1 Ψ-RNA have been performed and it have been obtained several linear models in order to predict the interaction strength. The best linear model obtained by using non-stochastic bilinear indices explains about 91% of the variance of the experimental Log K (R=0.95 and s=0.08×10−4 M−1) as long as the best stochastic bilinear indices-based equation account for 93% of the Log K variance (R=0.97 and s=0.07×10−4 M−1). The leave-one-out (LOO) press statistics, evidenced high predictive ability of both models (q2=0.86 and scv=0.09×10−4 M−1 for non-stochastic and q2=0.91 and scv=0.08×10−4 M−1 for stochastic bilinear indices). The nucleic acidʹs bilinear indices-based models compared favorably with other nucleic acidʹs indices-based approaches reported nowadays. 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) nucleotideʹs bilinear indices. This situation points to electronic and topologic nucleotideʹs backbone interactions control of the stability profile of paromomycin–RNA complexes. Consequently, the present approach represents a novel and rather promising way to theoretical-biology studies.