Unraveling the Complexities of Fibril-Forming Antimicrobial Peptides: A Computational Exploration
My research aims to explore and evaluate peptides capable of forming cross-α fibrillar structures. It introduces a grid-based energy estimation method that utilizes existing cross-structures as templates. A library of antimicrobial peptides, selected for high helical propensity and significant orthogonal hydrophobic moments, was screened. Interaction energies between peptides and the generated grids resulted in a dataset containing 23,304 entries, which is then refined through rigorous energy-based filters. Peptides are ranked by their energy profiles, and the top ranked candidates were selected for further experimental analysis. This study presents a novel systematic method for identifying and characterizing cross-α fibril-forming peptides, potentially advancing our understanding of these intriguing molecules for useful applications.
