“大分子图的核苷酸邻接矩阵”的线性指标作为生物信息学研究的一种有前景的方法。第1部分：巴龙霉素与HIV-1 ψ-RNA包装区域亲和力常数的预测。

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 psi-RNA packaging region.

作者信息

Marrero Ponce Yovani, Castillo Garit Juan A, Nodarse Delvin

机构信息

Department of Pharmacy, Faculty of Chemical-Pharmacy, Chemical Bioactive Center, Central University of Las Villas, Santa Clara 54830, Villa Clara, Cuba.

出版信息

Bioorg Med Chem. 2005 May 16;13(10):3397-404. doi: 10.1016/j.bmc.2005.03.010.

DOI:10.1016/j.bmc.2005.03.010

PMID:15848751

Abstract

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.

摘要

新型抗HIV化合物的设计现已成为全球科学家关注的关键领域。本文介绍了一组新的与核酸QSAR/QSPR研究相关的大分子描述符（由大分子图的核苷酸邻接矩阵计算得出），即核酸线性指数。作为该方法的示例，对抗生素巴龙霉素与HIV-1 ψ-RNA包装区域的相互作用进行了研究。一个多元线性回归模型预测了特定核苷酸与上述抗生素之间的局部结合亲和常数[Log K(10⁻⁴ M⁻¹)]。该线性模型解释了实验Log K方差的87%以上（R = 0.93，s = 0.102×10⁻⁴ M⁻¹），留一法交叉验证统计证明了其预测能力（q² = 0.82，s(cv) = 0.108×10⁻⁴ M⁻¹）。与其他方法（大分子二次指数、马尔可夫负熵和“随机”谱矩）的比较表明我们的方法表现良好。