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Predicting the Activity of Antimicrobial Peptides with Amino Acid Topological Information

[ Vol. 9 , Issue. 1 ]


Mao Shu, Rui Yu, Yunru Zhang, Juan Wang, Li Yang, Li Wang and Zhihua Lin   Pages 32 - 44 ( 13 )


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.


Antimicrobial peptides (AMPs), VSTPV, Genetic algorithm (GA), Partial least square (PLS), Quantitative structure- activity relationship (QSAR)


School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing, 400054, PR China

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