Quantitative sequence-activity modeling of antimicrobial hexapeptides using a segmented principal component strategy: an approach to describe and predict activities of peptide drugs containing L/D and unnatural residues.

The treatment of infections caused by multi-drugs resistant bacteria and fungi is a particular challenge. Whereas cationic antimicrobial peptides (CAPs) are considered as promising drug candidates for treatment of such superbugs, recent studies have focused on design of those peptides with increased bioavailability and stability against proteases. In between, applications of the quantitative structure-activity relationship (QSAR) studies which provide information on activities of CAPs based on descriptors for each individual amino acid are inevitable. However, the satisfactory results derived from a QSAR model depend highly on the choice of amino acid descriptors and the mathematical strategy used to relate the descriptors to the CAPs' activity. In this study, the quantitative sequence-activity modeling (QSAM) of 60 CAPs derived from O-W-F-I-F-H(1-Bzl)-NH2 sequence which showed excellent activities against a broad range of hazardous microorganisms: e.g., MRSA, MRSE, E. coli and C. albicans, is discussed. The peptides contained natural and non-natural amino acids (AAs) of the both isomers D and L. In this study, a segmented principal component strategy was performed on the structural descriptors of AAs to extract AA's indices. Our results showed that constructed models covered more than 82, 94, 80 and 78 % of the cross-validated variance of C. albicans, MRSA, MRSE and E. coli data sets, respectively. The results were also used to determine the important and significant AAs which are important in CAPs activities. According to the best of our knowledge, it is the first successful attempt in the QSAM studies of peptides containing both natural and non-natural AAs of the both L and D isomers.