Analysis of protegrin structure–activity relationships: the structural characteristics important for antimicrobial activity using smoothed amino acid sequence descriptors

Structure-activity relationships study on biological activity of peptides as dipeptidyl peptidase IV inhibitors by chemometric modeling

The aim of this study is to identify the potential descriptors affecting the inhibitory activity of the peptides inhibiting dipeptidyl peptidase IV (DPP-IV). This study provides important information for assessing the biological activity of the new peptide sequences of food origin or making structural modifications to the current inhibitors to improve their performance. For this purpose, the chemometric method describing the relationship between the structure of food peptides and their biological activity (structure-activity relationship [SAR]) was used to theoretically predict the potential of bioactivity of peptides. Data on the physicochemical properties of amino acids in the dipeptides acting as inhibitors of DPP-IV were collected and analyzed for using these properties as descriptors in further analysis. A total of 252 dipeptide sequences with confirmed DPP-IV inhibitory activity available in the BIOPEP-UWM database were included in the analysis, and 16 descriptors defining individual amino acids (such as molecular weight, polarity, hydropathicity, bulkiness, buried residue, and acceptable and normalized frequency of alpha-helix and beta-sheet) were identified. Based on this information, a data matrix was constructed and used in the chemometric analysis (principal component analysis and multiple linear regression). From the SAR model created, a multiple regression equation was derived to predict the biological activity of the dipeptide DPP-IV inhibitors.

Descriptors for antimicrobial peptides

INTRODUCTION

A frightening increase in the number of isolated multidrug resistant bacterial strains linked to the decline in novel antimicrobial drugs entering the market is a great cause for concern. Cationic antimicrobial peptides (AMPs) have lately been introduced as a potential new class of antimicrobial drugs, and computational methods utilizing molecular descriptors can significantly accelerate the development of new peptide drug candidates.

AREAS COVERED

This paper gives a broad overview of peptide and amino-acid scale descriptors available for AMP modeling and highlights which of these are currently being used in quantitative structure-activity relationship (QSAR) studies for AMP optimization. Additionally, some key commercial computational tools are discussed, and both successful and less successful studies are referenced, illustrating some of the challenges facing AMP scientists. Through examples of different peptide QSAR studies, this review highlights some of the missing links and illuminates some of the questions that would be interesting to challenge in a more systematic fashion.

EXPERT OPINION

Computer-aided peptide QSAR using molecular descriptors may provide the necessary edge to peptide drug discovery, enabling successful design of a new generation anti-infective drug molecules. However, if this wonderful scenario is to play out, computational chemists and peptide microbiologists would need to start playing together and not just side by side.

Computational studies of protegrin antimicrobial peptides: a review

Antimicrobial peptides (AMPs) are small, naturally occurring peptides that exhibit strong antibacterial properties generally believed to be a result of selective bacterial membrane disruption. As a result, there has been significant interest in the development of therapeutic antibiotics based on AMPs; however, the poor understanding of the fundamental mechanism of action of these peptides has largely hampered such efforts. We present a summary of computational and theoretical investigations of protegrin, a particularly potent peptide that is both an excellent model for the mechanism of action of AMPs and a promising therapeutic candidate. Experimental investigations have shed light on many of the key steps in the action of protegrin: protegrin monomers are known to dimerize in various lipid environments; protegrin peptides interact strongly with lipid bilayer membranes, particularly anionic lipids; protegrins have been shown to form pores in lipid bilayers, which results in uncontrolled ion transport and may be a key factor in bacterial death. In this work, we present a comprehensive review of the computational and theoretical studies that have complemented and extended the information obtained from experimental work with protegrins, as well as a brief survey of the experimental biophysical studies that are most pertinent to such computational work. We show that a consistent, mechanistic description of the bactericidal mechanism of action of protegrins is emerging, and briefly outline areas where the current understanding is deficient. We hope that the research reviewed herein offers compelling evidence of the benefits of computational investigations of protegrins and other AMPs, as well as providing a useful guide to future work in this area.