Abstract
In this paper, VSTPV, was recruited as a novel set of structural and topological descriptors derived from principal component analysis (PCA) on 85 structural and topological variables of 166 coded and non-coded amino acids. By using partial least squares (PLS), we applied VSTPV for the study of quantitative structure-activity models (QSARs) studies on two peptide panels as 101 synthetic cationic Antimicrobial polypeptides (CAMELs), and 28 bovine lactoferricin- (17–31)-pentadecapeptides (LFB). The results of QSARs models were superior to that of the earlier studies, with squared correlative coefficient R2 and cross-validated Q2 of 0.783, 0.656; and 0.864, 0.793, respectively. So, VSTPV descriptors were confirmed to be competent to extract information on 85 structural variables and to relate with biological activities.
Keywords: Antimicrobial peptides (AMPs), VSTPV, Genetic algorithm (GA), Partial least square (PLS), Quantitative structure- activity relationship (QSAR)
Medicinal Chemistry
Title:Predicting the Activity of Antimicrobial Peptides with Amino Acid Topological Information
Volume: 9 Issue: 1
Author(s): Mao Shu, Rui Yu, Yunru Zhang, Juan Wang, Li Yang, Li Wang and Zhihua Lin
Affiliation:
Keywords: Antimicrobial peptides (AMPs), VSTPV, Genetic algorithm (GA), Partial least square (PLS), Quantitative structure- activity relationship (QSAR)
Abstract: In this paper, VSTPV, was recruited as a novel set of structural and topological descriptors derived from principal component analysis (PCA) on 85 structural and topological variables of 166 coded and non-coded amino acids. By using partial least squares (PLS), we applied VSTPV for the study of quantitative structure-activity models (QSARs) studies on two peptide panels as 101 synthetic cationic Antimicrobial polypeptides (CAMELs), and 28 bovine lactoferricin- (17–31)-pentadecapeptides (LFB). The results of QSARs models were superior to that of the earlier studies, with squared correlative coefficient R2 and cross-validated Q2 of 0.783, 0.656; and 0.864, 0.793, respectively. So, VSTPV descriptors were confirmed to be competent to extract information on 85 structural variables and to relate with biological activities.
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Cite this article as:
Shu Mao, Yu Rui, Zhang Yunru, Wang Juan, Yang Li, Wang Li and Lin Zhihua, Predicting the Activity of Antimicrobial Peptides with Amino Acid Topological Information, Medicinal Chemistry 2013; 9 (1) . https://dx.doi.org/10.2174/1573406411309010032
DOI https://dx.doi.org/10.2174/1573406411309010032 |
Print ISSN 1573-4064 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-6638 |
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