Integrative computational-experimental framework accelerates antifungal peptide discovery for multidrug-resistant fungi

Multidrug-resistant fungal infections, particularly those caused by Candida and Aspergillus species, pose a significant global health threat, especially in immunocompromised individuals. The limited arsenal of available antifungals and the rapid emergence of resistance to classes like azoles, echinocandins, and polyenes necessitate novel therapeutic strategies with distinct mechanisms. Antifungal peptides (AFPs) are emerging as promising candidates due to their broad-spectrum activity, multimodal mechanisms of action, and lower propensity for resistance development, addressing a critical gap in current treatment paradigms.

This review provides a holistic summary of research on antifungal peptides (AFPs) targeting clinically significant drug-resistant fungi, including Candida auris, azole-resistant Candida albicans, and triazole-resistant Aspergillus fumigatus. The authors reviewed the structural and physicochemical properties of AFPs and their diverse antifungal mechanisms, such as membrane disruption, oxidative stress induction, disruption of intracellular homeostasis, and biofilm inhibition. They highlighted an emerging computational-experimental pipeline combining sequence mining, machine learning-based screening, molecular docking, molecular dynamics simulations, and in vitro and in vivo validation. Furthermore, the review explored major translational challenges like hemolytic toxicity, proteolytic instability, pharmacokinetic constraints, and manufacturing complexity, alongside advanced delivery systems (e.g., liposomes, PLGA nanoparticles, hydrogels) to enhance efficacy.

Antifungal peptides (AFPs) exhibit broad-spectrum activity against multidrug-resistant fungi through multiple mechanisms, including direct membrane disruption, induction of oxidative stress, and perturbation of intracellular homeostasis, alongside potent biofilm inhibition. The review details a powerful computational-experimental pipeline that integrates sequence mining and machine learning for initial screening, followed by molecular docking and molecular dynamics simulations to predict interactions, culminating in rigorous in vitro and in vivo validation. This integrated approach significantly streamlines the discovery and optimization process for novel AFPs. Translational challenges, such as hemolytic toxicity and proteolytic instability, are systematically addressed, with advanced delivery systems like liposomes and hydrogels identified as key strategies to improve therapeutic efficacy and stability. The synthesis of these findings underscores the potential of AFPs as a next-generation antifungal strategy.

The review proposes an integrated translational development framework that connects computational design, experimental validation, and delivery engineering, positioning AFPs as a promising next-generation strategy in the fight against drug-resistant fungal infections.