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Related Concept Videos

Antimicrobial Proteins01:23

Antimicrobial Proteins

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Antimicrobial proteins are important components of the immune system. They aid the body in combating pathogens by either killing them directly or hindering their replication processes. Four main types of antimicrobial substances are interferons, the complement system, iron-binding proteins, and antimicrobial proteins.
Interferons
Interferons (IFNs) are proteins produced by lymphocytes, macrophages, and fibroblasts infected with viruses. While IFNs cannot prevent viruses from entering and...
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Emerging Computational Approaches for Antimicrobial Peptide Discovery.

Guillermin Agüero-Chapin1,2, Deborah Galpert-Cañizares3, Dany Domínguez-Pérez1,4

  • 1CIIMAR-Centro Interdisciplinar de Investigação Marinha e Ambiental, Universidade do Porto, Terminal de Cruzeiros do Porto de Leixões, Av. General Norton de Matos, s/n, 4450-208 Porto, Portugal.

Antibiotics (Basel, Switzerland)
|July 27, 2022
PubMed
Summary

Artificial intelligence (AI) and machine learning (ML) accelerate antimicrobial peptide (AMP) discovery using novel features and algorithms. Emerging computational tools and proteogenomic analyses expand the search for new AMPs.

Keywords:
AMPsartificial intelligencecomplex networksevolutionary algorithmsmachine learningmolecular descriptorsproteogenomics

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Area of Science:

  • Computational chemistry and bioinformatics
  • Drug discovery and development
  • Antimicrobial research

Background:

  • The application of artificial intelligence (AI) and machine learning (ML) in antimicrobial peptide (AMP) discovery has been widely reported over the last two decades.
  • Current ML models primarily utilize sequence-based features for identifying novel AMP scaffolds.
  • Existing literature has limited focus on non-conventional in silico approaches for AMP search and design.

Purpose of the Study:

  • To highlight non-standard peptide features employed in classical ML predictive models for AMPs.
  • To review emerging ML algorithms and computational tools for predicting and designing AMPs, including exploration of their chemical space.
  • To discuss recent advancements in evolutionary algorithms for generating diverse peptide libraries and optimizing hit peptides.
  • To incorporate new considerations in proteogenomic analyses for uncovering AMPs from natural sources.

Main Methods:

  • Utilizing non-standard peptide features for developing machine learning (ML) predictive models.
  • Applying emerging ML algorithms and alternative computational tools for AMP prediction and design.
  • Employing evolutionary algorithms that simulate sequence evolution for peptide library generation and optimization.
  • Integrating proteogenomic analyses into computational workflows.

Main Results:

  • Identification of non-standard peptide features that enhance the predictive power of ML models for AMPs.
  • Demonstration of novel computational tools and algorithms for efficient exploration of AMP chemical space.
  • Successful application of evolutionary algorithms for generating diverse and optimized peptide libraries.
  • Integration of proteogenomic data provides new avenues for discovering AMPs from natural sources.

Conclusions:

  • Advanced computational strategies, including non-standard features and emerging algorithms, are crucial for efficient antimicrobial peptide (AMP) discovery and design.
  • The integration of evolutionary algorithms and proteogenomic analyses offers powerful approaches to expand the chemical space and identify novel AMPs.
  • These in silico methods significantly contribute to the ongoing search for new therapeutic agents against microbial infections.