Toropov Andrey A, Toropova Alla P, Leszczynska Danuta, Leszczynski Jerzy
Department of Environmental Health Science, Laboratory of Environmental Chemistry and Toxicology, Istituto di RicercheFarmacologiche Mario Negri IRCCS, Via La Masa 19, 20156 Milan, Italy.
Department of Environmental Health Science, Laboratory of Environmental Chemistry and Toxicology, Istituto di RicercheFarmacologiche Mario Negri IRCCS, Via La Masa 19, 20156 Milan, Italy.
Biosystems. 2019 Jul;181:51-57. doi: 10.1016/j.biosystems.2019.04.008. Epub 2019 Apr 25.
Sequences of one-symbol abbreviations of amino acids are applied as the basis to build up predictive model of Angiotensin converting enzyme (ACE) inhibitory activity of dipeptides and antibacterial activity of group of polypeptides. The developed models are one-variable correlations between biological activity and descriptors calculated with so-called correlation weights of amino acids. The numerical data on the correlation weights are obtained by the Monte Carlo method. The Index of Ideality of Correlation (IIC) is a mathematical function of (i) the determination coefficient; and (ii) sums of positive and negative values of "observed minus predicted" endpoints values. The obtained results confirm that IIC can be applied to improve predictive potential of models for ACE inhibitor activity of dipeptides and antibacterial activity of polypeptides.
氨基酸单符号缩写序列被用作构建二肽血管紧张素转换酶（ACE）抑制活性和一组多肽抗菌活性预测模型的基础。所开发的模型是生物活性与用所谓氨基酸相关权重计算的描述符之间的单变量相关性。相关权重的数值数据通过蒙特卡罗方法获得。相关性理想指数（IIC）是（i）决定系数；以及（ii）“观察值减去预测值”端点值的正负值之和的数学函数。所得结果证实，IIC可用于提高二肽ACE抑制剂活性和多肽抗菌活性模型的预测潜力。
