Rapid Response Antimicrobial Peptide Design Strategy Driven by Meta-Learning for Emerging Drug-Resistant Pathogens

Area of Science:

• Computational biology and drug discovery
• Infectious disease research
• Antimicrobial peptide design

Background:

• Antimicrobial resistance (AMR) is a major global health crisis.
• Developing new treatments for drug-resistant bacteria is urgently needed.
• Limited pathogen-specific data hinders rapid response to emerging threats.

Purpose of the Study:

• To develop a Rapid Response Antimicrobial Peptide design strategy (RR-ADS).
• To create a framework for designing effective antimicrobial peptides (AMPs) using minimal data.
• To optimize AMPs for biocompatibility and efficacy against drug-resistant pathogens.

Main Methods:

• Utilized meta-learning and reinforcement learning for robust generalization from minimal samples.
• Developed a computational framework (RR-ADS) for rapid AMP design.
• Employed machine learning to optimize AMPs for efficacy and biocompatibility.

Main Results:

• The RR-ADS model accurately identified and generated AMPs against drug-resistant bacteria with minimal sample sizes.
• Successfully designed and verified AMPs against multidrug-resistant *Acinetobacter baumannii* within two weeks.
• Achieved a 93.3% positive verification rate for designed AMPs.

Conclusions:

• RR-ADS demonstrates the potential of meta-learning in bioactive peptide design.
• The strategy shows promise for rapidly addressing infectious disease public health emergencies.
• Provides an effective alternative measure for combating drug-resistant bacterial infections.