QSAR modeling of the antimicrobial activity of peptides as a mathematical function of a sequence of amino acids. (December 2015)

• Record Type: Journal Article
• Authors: Toropova, Mariya A.; Veselinović, Aleksandar M.; Veselinović, Jovana B.; Stojanović, Dušica B.; Toropov, Andrey A.
• Is Part Of: Computational biology and chemistry. Volume 59:Part A(2015)
• Page Start: 126
• Page End: 130
• Publication Date: 2015-12
• Subjects: Monte Carlo method -- Mastoparan analogs -- CORAL software -- QSAR -- Optimal descriptor -- Antimicrobial activity; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2015.09.009
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: QSAR models for mastoparan analogs as antibacterial agents are developed. Mathematical function of a sequence of amino acids was used for QSAR models building. The Monte Carlo method was used as a computational technique for QSAR calculations. Reasonably good prediction for the antibacterial activity of peptides is obtained. Abstract: Antimicrobial peptides have emerged as new therapeutic agents for fighting multi-drug-resistant bacteria. However, the process of optimizing peptide antimicrobial activity and specificity using large peptide libraries is both tedious and expensive. Therefore, computational techniques had to be applied for process optimization. In this work, the representation of the molecular structure of peptides (mastoparan analogs) by a sequence of amino acids has been used to establish quantitative structure—activity relationships (QSARs) for their antibacterial activity. The data for the studied peptides were split three times into the training, calibration and test sets. The Monte Carlo method was used as a computational technique for QSAR models calculation. The statistical quality of QSAR for the antibacterial activity of peptides for the external validation set was: n = 7, r 2 = 0.8067, s = 0.248 (split 1); n = 6, r 2 = 0.8319, s = 0.169 (split 2); and n = 6, r 2 = 0.6996, s = 0.297 (split 3). The stated statistical parameters favor the presented QSAR models in comparison to 2D and 3D descriptor based ones. The Monte Carlo method gave a reasonably good prediction for the antibacterial activity of peptides. The statistical quality of the prediction is different for three random splits. However, the predictive potential is reasonably well for all cases. The presented QSAR modeling approach can be an attractive alternative of 3D QSAR at least for the described peptides.