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Antimicrobial Peptides Design Using Deep Learning and Rational Modifications: Activity in Bacteria, Candida albicans,

Andrea Mesa1, Andrés Orrego2, John W Branch-Bedoya2

  • 1Departamento de Biociencias, Facultad de Ciencias, Grupo Biología Funcional, Universidad Nacional de Colombia, Sede Medellín, Carrera 65 #59A-110, Medellín, 050034, Colombia. anmesago@unal.edu.co.

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Artificial intelligence optimized antimicrobial peptides to combat rising drug resistance. These novel peptides show high antimicrobial activity and potential anticancer properties, offering a promising alternative to traditional antibiotics.

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Drug Discovery

Background:

  • Antimicrobial resistance is a critical global health threat, necessitating novel therapeutic strategies.
  • Antimicrobial peptides (AMPs) present a promising alternative to conventional antibiotics due to their unique mechanisms of action.
  • Artificial intelligence (AI) accelerates the discovery and optimization of AMPs, reducing development time and costs.

Purpose of the Study:

  • To optimize antimicrobial peptides generated by deep learning algorithms.
  • To evaluate the antimicrobial activity, hemolytic capacity, and toxicity of modified peptides using AI and bioinformatic tools.
  • To synthesize and validate the efficacy of computationally designed peptides against bacterial pathogens and cancer cells.

Main Methods:

  • Utilized two deep learning algorithms for initial peptide generation.
  • Employed bioinformatic and AI tools to predict antimicrobial activity, hemolysis, and toxicity of modified peptides.
  • Synthesized 26 in silico-generated peptides and performed in vitro testing against bacterial strains, Candida albicans, and MCF-7 cancer cells.

Main Results:

  • Identified 26 synthetic peptides with high predicted antimicrobial activity and safety profiles.
  • Nine synthesized peptides exhibited minimum inhibitory concentrations (MICs) below 10 μM against tested pathogens.
  • Several peptides demonstrated potent activity, with some achieving MICs as low as 2 μM, and six showed efficacy against breast cancer cells.

Conclusions:

  • AI-driven optimization successfully generated potent antimicrobial peptides.
  • The optimized synthetic peptides exhibit significant antimicrobial and anticancer potential.
  • These findings highlight the utility of AI in developing next-generation antimicrobial and therapeutic agents.