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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 by Yovani Marrero Ponce; Juan A. Castillo Garit; Delvin Nodarse is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

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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 (q2 = 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.

Who reads 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?

Author

Yovani Marrero Ponce; Juan A. Castillo Garit; Delvin Nodarse

Publisher

Elsevier Science; Elsevier ; Elsevier Ltd.; Elsevier BV (ISSN 0968-0896)

Published

2005

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)