AMPSeek: A Workflow for Predicting Antimicrobial Peptide Activity, Three-Dimensional Structure, and Toxicity.

Area of Science:

• Computational Biology
• Drug Discovery
• Proteomics

Background:

• Antimicrobial resistance (AMR) is a critical global health threat, necessitating novel therapeutic strategies.
• Antimicrobial peptides (AMPs) show potential as alternatives to conventional antibiotics.
• Traditional wet lab screening for AMPs is resource-intensive; in silico methods are increasingly vital for peptide property prediction.

Purpose of the Study:

• To introduce AMPSeek, a novel computational protocol for screening peptides.
• To predict antimicrobial activity, toxicity, and three-dimensional structure of candidate peptides using a single command.
• To provide a scalable and efficient in silico tool for accelerating the discovery of antimicrobial peptides.

Main Methods:

• Development of the AMPSeek framework, integrating AMPlify, tAMPer, and LocalColabFold.
• Implementation of a streamlined protocol for predicting peptide physicochemical, structural, and biological properties.
• Testing and validation using datasets of varying sizes (n=10, 20, and 100) to demonstrate scalability.

Main Results:

• AMPSeek enables rapid prediction of antimicrobial activity, toxicity, and 3D structure.
• The protocol demonstrates near-linear scalability with increasing sample size.
• An interactive HTML report is generated for user-friendly interpretation of results.

Conclusions:

• AMPSeek provides an efficient and scalable in silico solution for identifying potential antimicrobial peptides.
• This protocol facilitates faster drug discovery by reducing reliance on costly and time-consuming experimental screening.
• AMPSeek is publicly available on GitHub, promoting wider adoption in antimicrobial research.