ArticleBioinformatics (Oxford, England)2017
Empirical comparison of web-based antimicrobial peptide prediction tools.
Article in Bioinformatics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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Who cites it
46 citing papers in PubMed.
- Towards Safer Antimicrobial Peptide Therapeutics: A Predictive-Generative Framework Targeting ESKAPE Pathogens.Probiotics and antimicrobial proteins · 2026Article
- Identification and Structure-Activity Relationship of a Novel Dermatoxin-like Antimicrobial Peptide with Partial LPS-Mediated Membrane Interaction.Molecules (Basel, Switzerland) · 2026Article
- Discovery of potent low-toxicity antimicrobial peptides through diffusion modeling.Nature communications · 2026Article
- Artificial Intelligence-Driven Discovery and Optimization of Antimicrobial Peptides Targeting ESKAPE Pathogens and Multidrug-Resistant Fungi.Microorganisms · 2026Review
- Bacteriocins fromFrontiers in microbiology · 2026Review
- Leveraging Different Distance Functions to Predict Antiviral Peptides with Geometric Deep Learning from ESMFold-Predicted Tertiary Structures.Antibiotics (Basel, Switzerland) · 2026Article
- Lab to codes: multimodel advanced AI-driven pipelines to drive antimicrobial peptide design for the post-antibiotic era.Frontiers in cellular and infection microbiology · 2026Review
- Harnessing Machine Learning Approaches for the Identification, Characterization, and Optimization of Novel Antimicrobial Peptides.Antibiotics (Basel, Switzerland) · 2025Review
- Designing of a novel chimeric antimicrobial peptide againstResearch in pharmaceutical sciences · 2025Article
- Discovery of antimicrobial peptides with notable antibacterial potency by an LLM-based foundation model.Science advances · 2025Article
- Exploring Tenebrio molitor as a source of low-molecular-weight antimicrobial peptides using a n in silico approach: correlation of molecular features and molecular docking.Journal of the science of food and agriculture · 2025Article
- Review
- Bioactive Molecules from the Innate Immunity of Ascidians and Innovative Methods of Drug Discovery: A Computational Approach Based on Artificial Intelligence.Marine drugs · 2023Review
- iAMPCN: a deep-learning approach for identifying antimicrobial peptides and their functional activities.Briefings in bioinformatics · 2023Article
- Combined charge and hydrophobicity-guided screening of antibacterial peptides: two-level approach to predict antibacterial activity and efficacy.Amino acids · 2023Article
- Discovery and Mechanism of Action of a Novel Antimicrobial Peptide from an Earthworm.Microbiology spectrum · 2023Article
- Thinking on the Construction of Antimicrobial Peptide Databases: Powerful Tools for the Molecular Design and Screening.International journal of molecular sciences · 2023Review
- Hierarchical machine learning model predicts antimicrobial peptide activity againstFrontiers in molecular biosciences · 2023Article
- Article
- BPAGS: a web application for bacteriocin prediction via feature evaluation using alternating decision tree, genetic algorithm, and linear support vector classifier.Frontiers in bioinformatics · 2023Article
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2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
motivationAntimicrobial peptides (AMPs) are innate immune molecules that exhibit activities against a range of microbes, including bacteria, fungi, viruses and protozoa. Recent increases in microbial resistance against current drugs has led to a concomitant increase in the need for novel antimicrobial agents. Over the last decade, a number of AMP prediction tools have been designed and made freely available online. These AMP prediction tools show potential to discriminate AMPs from non-AMPs, but the relative quality of the predictions produced by the various tools is difficult to quantify.
resultsWe compiled two sets of AMP and non-AMP peptides, separated into three categories-antimicrobial, antibacterial and bacteriocins. Using these benchmark data sets, we carried out a systematic evaluation of ten publicly available AMP prediction methods. Among the six general AMP prediction tools-ADAM, CAMPR3(RF), CAMPR3(SVM), MLAMP, DBAASP and MLAMP-we find that CAMPR3(RF) provides a statistically significant improvement in performance, as measured by the area under the receiver operating characteristic (ROC) curve, relative to the other five methods. Surprisingly, for antibacterial prediction, the original AntiBP method significantly outperforms its successor, AntiBP2 based on one benchmark dataset. The two bacteriocin prediction tools, BAGEL3 and BACTIBASE, both provide very good performance and BAGEL3 outperforms its predecessor, BACTIBASE, on the larger of the two benchmarks. CONTACT: gaberemu@ngha.med.sa or william-noble@uw.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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