We have created a structure-selectivity database (AMPad) of frog-derived, helical antimicrobial peptides (AMPs), in which the selectivity was determined as a therapeutic index (TI), and then used the novel concept of sequence moments to study the lengthwise asymmetry of physicochemical peptide properties. We found that the cosine of the angle between two sequence moments obtained with different hydrophobicity scales, defined as the D-descriptor, identifies highly selective peptide antibiotics. We could then use this descriptor to predict TI changes after point mutations in known AMPs, and to aid the prediction of TI for de noVo designed AMPs. In combination with an amino acid selectivity index, a motif regularity index and other statistical rules extracted from AMPad, the D-descriptor enabled construction of the AMP-Designer algorithm. A 23 residue, glycine-rich, peptide suggested by the algorithm was synthesized and the activity and selectivity tested. This peptide, adepantin 1, is less than 50% identical to any other AMP, has a potent antibacterial activity against the reference organism, E. coli, and has a significantly greater selectivity (TI > 200) than the best AMP present in the AMPad database (TI ) 125).
Antimicrobial peptides often show broad-spectrum activity due to a mechanism based on bacterial membrane disruption, which also reduces development of permanent resistance, a desirable characteristic in view of the escalating multidrug resistance problem. Host cell toxicity however requires design of artificial variants of natural AMPs to increase selectivity and reduce side effects. Kiadins were designed using rules obtained from natural peptides active against E. coli and a validated computational algorithm based on a training set of such peptides, followed by rational conformational alterations. In vitro activity, tested against ESKAPE strains (ATCC and clinical isolates), revealed a varied activity spectrum and cytotoxicity that only in part correlated with conformational flexibility. Peptides with a higher proportion of Gly were generally less potent and caused less bacterial membrane alteration, as observed by flow cytometry and AFM, which correlate to structural characteristics as observed by circular dichroism spectroscopy and predicted by molecular dynamics calculations.
We describe computational approaches for identifying promising lead candidates for the development of peptide antibiotics, in the context of quantitative structure-activity relationships (QSAR) studies for this type of molecule. A first approach deals with predicting the selectivity properties of generated antimicrobial peptide sequences in terms of measured therapeutic indices (TI) for known antimicrobial peptides (AMPs). Based on a training set of anuran AMPs, the concept of sequence moments was used to construct algorithms that could predict TIs for a second test set of natural AMPs and could also predict the effect of point mutations on TI values. This approach was then used to design peptide antibiotics (adepantins) not homologous to known natural or synthetic AMPs. In a second approach, many novel putative AMPs were identified from DNA sequences in EST databases, using the observation that, as a rule, specific subclasses of highly conserved signal peptides are associated exclusively with AMPs. Both anuran and teleost sequences were used to elucidate this observation and its implications. The predicted therapeutic indices of identified sequences could then be used to identify new types of selective putative AMPs for future experimental verification.
A challenge when designing membrane-active peptide antibiotics with therapeutic potential is how to ensure a useful antibacterial activity whilst avoiding unacceptable cytotoxicity for host cells. Understanding their mode of interaction with membranes and the reasons underlying their ability to distinguish between bacterial and eukaryotic cytoplasmic cells is crucial for any rational attempt to improve this selectivity. We have approached this problem by analysing natural helical antimicrobial peptides of anuran origin, using a structure-activity database to determine an antimicrobial selectivity index (SI) relating the minimal inhibitory concentration against Escherichia coli to the haemolytic activity (SI=HC(50)/MIC). A parameter that correlated strongly with SI, derived from the lengthwise asymmetry of the peptides' hydrophobicity (sequence moment), was then used in the "Designer" algorithm to propose novel, highly selective peptides. Amongst these are the 'adepantins', peptides rich in glycines and lysines that are highly selective for Gram-negative bacteria, have an exceptionally low haemolytic activity, and are less than 50% homologous to any other natural or synthetic antimicrobial peptide. In particular, they showed a very high SI for E. coli (up to 400) whilst maintaining an antimicrobial activity in the 0.5-4μM range. Experiments with monomeric, dimeric and fluorescently labelled versions of the adepantins, using different bacterial strains, host cells and model membrane systems provided insight into their mechanism of action.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.