Antimicrobial Peptides

Antimicrobial peptides (AMPs) are a promising alternative to traditional antibiotics, as they possess diverse mechanisms of action and a low potential for inducing resistance. However, their use in a clinical setting is limited by issues such as enzymatic degradation, short half-life, and poor tissue penetration.

To address these challenges, our research project is focused on developing novel antimicrobial peptides. We use machine and deep learning tools to identify and design optimal AMPs with enhanced antimicrobial activity and minimal toxicity. This computational approach allows for the efficient screening of peptide sequences, which circumvents the time-consuming processes of traditional experimental methods. Our goal is to engineer AMPs that are more stable, have a longer half-life, and possess improved tissue penetration capabilities, thereby overcoming the limitations that have historically hindered their clinical use.

We are also working on discovering novel antimicrobial agents from natural sources. This involves two methods: the direct extraction of antimicrobial compounds from various organisms and the use of genome sequencing to identify the genetic code for new antimicrobial agents. By exploring the natural world, we aim to uncover previously unknown antimicrobial molecules that may offer unique mechanisms of action. Our strategy combines computational design with natural discovery, allowing us to build a comprehensive pipeline for the development of effective, next-generation antimicrobial treatments. The ultimate objective is to contribute to the global effort to combat antimicrobial resistance (AMR) by creating a new class of powerful and safe antimicrobial agents.