Computational Analysis of Antimicrobial Peptides Targeting Agglutinin-Like Protein 3 (Als3) and β-Glucan Synthase in...

Candida albicans adhesion and biofilm formation on prosthetic surfaces drive denture-associated candidiasis and are unaffected by traditional fungicidal treatments. Pre-emptive prevention of fungal attachment to prosthetic devices is a rational strategy to avoid recurrent infections.

We utilized a mechanism-based computational pipeline to screen peptides with activity centred on inhibiting fungal adhesion instead of viability. We chose two colonization-relevant targets: agglutinin-like protein 3 (Als3), involved in surface attachment, and 1,3-β-glucan synthase (GS), involved in glucan-dependent biofilm-support processes. Peptides were ranked using protein-peptide docking, 100-ns molecular dynamics simulations, and MM/PBSA binding free energy calculations to determine metrics of interface coverage, interaction persistence, and dynamic energetic favourability.

We found that tachystatin consistently and stably occupied the highest proportion of adhesion-related interfaces on both Als3 and GS. Tachystatin dynamics demonstrated lower residue-level fluctuations in functionally relevant residues and preservation of hydrogen bonds throughout the simulation. MM/PBSA assessments revealed favourable and consistent binding energetics facilitated by van der Waals forces. These findings suggest that the peptide may comparatively maintain interface occupancy consistent with a potential steric hindrance effect at adhesion-relevant regions. The selected control peptides failed to occupy the predicted interfaces and demonstrated inadequate stabilities.

Targeting Als3-mediated adhesion together with GS-associated biofilm-support processes represent a promising preventive strategy for denture-associated candidiasis. Tachystatin emerged as the most promising peptide within this comparative computational screen and may warrant further evaluation as a candidate for reducing biofilm formation on dental prosthetics Overall, this interaction- and simulation-based framework may support antimicrobial peptide selection focused on biofilm prevention.