More effective antimicrobial mastoparan derivatives, generated by 3D-QSAR-Almond and computational mutagenesis

Abstract

Antimicrobial peptides are drugs used against a wide range of pathogens which present a great advantage: in contrast with antibiotics they do not develop resistance. The wide spectrum of antimicrobial peptides advertises them in the research and pharmaceutical industry as attractive starting points for obtaining new, more effective analogs. Here we predict the antimicrobial activity against Bacillus subtilis (expressed as minimal inhibitory concentration values) for 33 mastoparan analogs and their new derivatives by a non-aligned 3D-QSAR (quantitative structure-activity relationship) method. We establish the contribution to antimicrobial activity of molecular descriptors (hydrophobicity, hydrogen bond donor and steric), correlated with contributions from the membrane environment (sodium, potassium, chloride). Our best QSAR models show significant cross-validated correlation q(2) (0.55-0.75), fitted correlation r(2) (greater than 0.90) coefficients and standard error of prediction SDEP (less than 0.250). Moreover, based on our most accurate 3D-QSAR models, we propose nine new mastoparan analogs, obtained by computational mutagenesis, some of them predicted to have significantly improved antimicrobial activity compared to the parent compound.