From Bytes to Bench: In Silico Prediction of Modified Peptide Interactions For Antifungal Drug Design

The rise of antifungal resistance necessitates the development of new treatment strategies that work with existing antifungal drugs, such as caspofungin. Antifungal peptides offer a promising avenue for combination therapy, given their potential synergism with commercial antifungal agents. This study investigated the peptide 2Me2-AW9MA, modified with methylated lysines, based on the “magic methyl effect,” which suggests that strategic methylation boosts binding affinity. Molecular docking simulations using AutoDock and RosettaDock were conducted to predict binding interactions. As part of the in silico screening, nineteen peptide candidates were evaluated for their potential binding affinity to the fungal target. Results indicated poor binding affinity for 2Me2-AW9MA, with the lowest AutoDock binding energy recorded at – 4.01 kcal/mol and a RosettaDock-5.0 score of 98.22. Experimental validation further confirmed limited antifungal activity, with a minimum inhibitory concentration (MIC) of 128 μg/mL. Overall, these findings show that methylation did not enhance peptide efficacy and highlight the value of computational screening for early elimination of ineffective candidates. Integrating docking simulations into the peptide development process supports a green chemistry approach, reducing hazardous reagents and laboratory waste during solid-phase peptide synthesis. This study underscores the role of in silico methods for advancing antifungal peptide development while promoting sustainability research practices.