Artificial Intelligence-Driven Discovery and Optimization of Antimicrobial Peptides Targeting ESKAPE Pathogens and...

Area of Science:

• Biochemistry and Molecular Biology
• Computational Biology and Bioinformatics
• Infectious Diseases and Microbiology

Background:

• Antimicrobial resistance (AMR) is a critical global health challenge, exacerbated by multidrug-resistant ESKAPE pathogens and fungi like Candida auris.
• Antimicrobial peptides (AMPs) offer a novel therapeutic avenue due to their unique membrane-targeting mechanisms and lower resistance potential, but face challenges in clinical translation (toxicity, stability, manufacturing).

Purpose of the Study:

• To explore the transformative role of artificial intelligence (AI) in overcoming the limitations of traditional antimicrobial peptide (AMP) discovery and development.
• To highlight how AI, including machine learning and generative models, is enabling the design and optimization of novel AMPs with improved efficacy, safety, and stability against resistant pathogens.

Main Methods:

• Leveraging machine learning (ML), deep learning (DL), and transformer-based protein language models for predicting AMP activity, selectivity, stability, and toxicity.
• Employing generative AI approaches (variational autoencoders, diffusion models, reinforcement learning) for de novo multi-objective peptide design and pathogen-directed optimization.
• Utilizing integrated design-test-learn pipelines to accelerate iterative peptide engineering through computational prediction and experimental validation.

Main Results:

• AI models demonstrate enhanced prediction of crucial AMP properties, including antimicrobial activity, selectivity, protease stability, and host toxicity.
• Generative AI facilitates the design of novel AMPs tailored for specific resistant bacteria and multidrug-resistant fungi.
• Integrated computational and experimental pipelines are significantly speeding up the engineering and optimization of promising peptide candidates.

Conclusions:

• AI is revolutionizing antimicrobial peptide discovery, offering solutions to long-standing challenges in clinical translation.
• The convergence of AI-driven design, integrated pipelines, and experimental validation is accelerating the development of next-generation AMPs.
• Future efforts should focus on scalable, surveillance-informed AI frameworks to expedite the clinical translation of multi-objective AMPs against high-priority resistant pathogens.