Computer-aided design, structural dynamics analysis, and in vitro susceptibility test of antibacterial peptides incorporating unnatural amino acids against microbial infections. Issue 134 (October 2016)

• Record Type: Journal Article
• Authors: Wang, Yan; Yang, Yong-Jian; Chen, Ya-Na; Zhao, Hong-Yu; Zhang, Shuai
• Is Part Of: Computer methods and programs in biomedicine. Issue 134(2016)
• Page Start: 215
• Page End: 223
• Publication Date: 2016-10
• Subjects: Antibacterial peptide -- Rational peptide design -- Machine learning -- Bioinformatics -- Infectious disease; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2016.06.005
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: An integrated protocol is described to design antibacterial peptides containing unnatural amino acids. A variety of machine learning predictors are developed, optimized, and validated through a synthetic approach. The predictors are used to guide computer-aided peptide design. Four designed peptides are measured in vitro to have potent antibacterial activity. The potent peptides can spontaneously embed into an artificial lipid bilayer. Graphical Abstract: Abstract: Background and objective: Antibacterial peptides (ABPs) are essential components of host defense against microbial infections present in all domains of life. The AMPs incorporating unnatural amino acids ( u ABPs) exhibit several advantages over naturally occurring AMPs based on factors such as bioavailability, metabolic stability and overall toxicity. Methods: Computer-aided modeling and in vitro susceptibility test were combined to rationally design short u ABPs with potent antimicrobial activity. In the procedure, peptide characterization and machine learning modeling were used to develop statistical regression predictors, which were then employed to guide the molecular design and structural optimization of u ABPs, to which a number of commercially available unnatural amino acids were introduced. Results: An improved u ABP population was obtained, from which several promising candidates were successfully prepared and their antibacterial potencies against three bacterial strains Staphylococcus aureus, Pseudomonas aeruginosa and Escherichia coli were measured using broth microdilution assay. Consequently, four u ABPs with hybrid structure property were determined to have high potency against the tested strains with minimum inhibitory concentration (MIC) of <50 µg/ml. Conclusions: Molecular dynamics (MD) simulations revealed that the designed u ABPs are amphipathic helix in solution but they would largely unfold when spontaneously embedding into an artificial lipid bilayer that mimics microbial membrane.