Design Methods for Antimicrobial Peptides with Improved Performance
James Mwangi, Peter Muiruri Kamau, Rebecca Caroline Thuku, Ren Lai
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 anti-pathogen activity, hold significant promise as one of the most effective solutions 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 the ability to bypass microbial resistance mechanisms, make them 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, much recent research has focused on optimizing antimicrobial peptides’ properties to improve their performance. By understanding the physiochemical properties of AMPs, such as amphipathicity, hydrophobicity, structural conformation, amino acid distribution, and composition, that correspond to their activity, researchers can design AMPs with desired and improved performance. In this review, we highlight some of the key strategies, rational design and de novo design, used to optimize the performance of AMPs. We also discuss how the evolution of predictive computational tools and technologies that utilize artificial intelligence and machine learning are increasingly being used to design and synthesize lead drug candidates with high efficacy.