Design Methods for Antimicrobial Peptides with Improved Performance

Abstract

The recalcitrance of pathogens to traditional antibiotics has made treating and eradicating bacterial infections more difficult. In this regard, developing new antimicrobial agents to combat antibiotic-resistant strains has become a top priority. Antimicrobial peptides (AMPs), a ubiquitous class of naturally occurring compounds with broad-spectrum antipathogenic activity, hold significant promise as an effective solution to the current antimicrobial resistance (AMR) crisis. Several AMPs have been identified and evaluated for their therapeutic application, with many already in the drug development pipeline. Their distinct properties, such as high target specificity, potency, and ability to bypass microbial resistance mechanisms, make AMPs a promising alternative to traditional antibiotics. Nonetheless, several challenges, such as high toxicity, lability to proteolytic degradation, low stability, poor pharmacokinetics, and high production costs, continue to hamper their clinical applicability. Therefore, recent research has focused on optimizing the properties of AMPs to improve their performance. By understanding the physicochemical properties of AMPs that correspond to their activity, such as amphipathicity, hydrophobicity, structural conformation, amino acid distribution, and composition, researchers can design AMPs with desired and improved performance. In this review, we highlight some of the key strategies used to optimize the performance of AMPs, including rational design and de novo synthesis. We also discuss the growing role of predictive computational tools, utilizing artificial intelligence and machine learning, in the design and synthesis of highly efficacious lead drug candidates.

Rational Design of a Potent Antimicrobial Peptide Based on the Active Region of a Gecko Cathelicidin

“…Due to the fact that vancomycin inhibits only the synthesis of peptidoglycan, it also showed less activity on PI influx and morphological changes than RH-16. Nevertheless, colistin and vancomycin were more effective than RH-16 in mouse models of skin infection induced by A. baumannii and S. aureus, suggesting that future work should focus on the utilization of chemical modification or design strategies 8,50 to improve the stability and effectiveness of RH-16.…”

“…baumannii and S. aureus, suggesting that future work should focus on the utilization of chemical modification or design strategies , to improve the stability and effectiveness of RH-16.…”

Tackling the Antimicrobial Resistance “Pandemic” with Machine Learning Tools: A Summary of Available Evidence

“…Machine learning may find great application in rapid antimicrobial resistance predictions, and it may enhance the discovery of novel antimicrobials [66] (Figure 1). In research on antimicrobial peptides, support vector machines, random forest machine learning algorithms, deep neural networks, and convolutional neural networks have found application [139]. Previously developed machine learning models were used to screen a chemo library and identify potential drug candidates for known therapeutic targets [140].…”

Capping motifs in antimicrobial peptides and their relevance for improved biological activities

“…Among the most commonly employed modifications are N -terminal acetylation and C -terminal amidation strategies, aimed at enhancing the conformational stability and availability of active AMPs, while maintaining or enhancing their antimicrobial potency ( Mura et al, 2016 ; Mwangi et al, 2023 ). These are crucial, especially considering the susceptibility of AMPs to degradation by exopeptidases ( Magana et al, 2020 ).…”