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Antimicrobial Effectiveness

Antimicrobial Effectiveness

The effectiveness of antimicrobial agents depends on various factors influencing their ability to eliminate microbial populations. Larger microbial populations require more time for complete eradication, emphasizing the importance of population size analysis when evaluating antimicrobial efficacy.Microbial resistance to antimicrobial agents varies significantly. Highly resilient microorganisms include endospores, gram-negative bacteria, and non-enveloped viruses, while prions are exceptionally...

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Antimicrobial Peptides Produced by Selective Pressure Incorporation of Non-canonical Amino Acids

Antimicrobial Peptides Produced by Selective Pressure Incorporation of Non-canonical Amino Acids

Published on: May 4, 2018

Antimicrobial activity predictors benchmarking analysis using shuffled and designed synthetic peptides.

William F Porto¹, Állan S Pires², Octavio L Franco³

¹Centro de Análises Proteômicas e Bioquímicas, Pós-Graduação em Ciências Genômicas e Biotecnologia Universidade Católica de Brasília, Brasília, Distrito Federal, Brazil; Porto Reports, Brasília, Distrito Federal, Brazil.

Journal of Theoretical Biology

|May 25, 2017

Summary

Machine learning tools for antimicrobial peptide (AMP) discovery show low accuracy in predicting shuffled sequences. New methods with high specificity are needed to improve AMP design and discovery.

Keywords:

Amino acid composition Antimicrobial peptides Independent benchmarking Machine learning Physico-chemical properties

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Last Updated: Mar 2, 2026

Antimicrobial Peptides Produced by Selective Pressure Incorporation of Non-canonical Amino Acids

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Published on: May 4, 2018

Quantifying the Antifungal Activity of Peptides Against Candida albicans

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Published on: January 13, 2023

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Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Published on: December 1, 2020

Area of Science:

Computational Biology
Bioinformatics
Drug Discovery

Background:

Antimicrobial peptides (AMPs) are crucial for discovering new antimicrobial agents.
Machine learning (ML) tools are increasingly used for rational design of synthetic peptides.
Predictive models for AMPs are vital for accelerating the discovery of novel therapeutic sequences.

Purpose of the Study:

To evaluate the predictive performance of existing antimicrobial activity prediction tools.
To assess the accuracy of these tools in distinguishing designed AMP sequences from their shuffled counterparts.
To identify limitations in current prediction systems for rational peptide design.

Main Methods:

Utilized four web servers and one standalone software for prediction.
Evaluated 78 sequences: 40 designed and 38 shuffled sequences generated by a linguistic model.
Performed ab initio molecular modeling to assess structural similarities between sequence sets.

Main Results:

Prediction systems showed high sensitivity (100%) but low specificity, leading to overall accuracies below 30% for shuffled sequences.
The tools failed to accurately predict the antimicrobial activity of shuffled peptide sequences.
Molecular modeling indicated that sequence structure did not significantly impact prediction accuracy.

Conclusions:

Current antimicrobial activity prediction tools exhibit limitations, particularly with shuffled peptide sequences.
Low specificity is a major drawback affecting the reliability of these prediction systems.
Development of complementary approaches with high specificity is recommended to enhance AMP discovery and design.