ArticleDrug development research2020
Novel antimicrobial peptide discovery using machine learning and biophysical selection of minimal bacteriocin domains.
Article in Drug development research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
27 citing papers in PubMed, 65 citations in OpenAlex.
- XGBPred-ACSM: A Hybrid Descriptor-Driven XGBoost Framework for Anticancer Small Molecule Prediction.Pharmaceuticals (Basel, Switzerland) · 2026Article
- MoleculeFormer is a GCN-transformer architecture for molecular property prediction.Communications biology · 2025Article
- Novel antimicrobial peptide HFIAP-1 mutant as a β-lactamase inhibitor against extended-spectrum β-lactamases of Escherichia coli: a comprehensive in-silico approach.Archives of microbiology · 2025Article
- Predicting Antimicrobial Peptide Activity: A Machine Learning-Based Quantitative Structure-Activity Relationship Approach.Pharmaceutics · 2025Article
- Bacteriocin prediction through cross-validation-based and hypergraph-based feature evaluation approaches.Frontiers in bioinformatics · 2025Article
- Recent Advances in Peptide Drug Discovery: Novel Strategies and Targeted Protein Degradation.Pharmaceutics · 2024Review
- Recent Applications of Artificial Intelligence in Discovery of New Antibacterial Agents.Advances and applications in bioinformatics and chemistry : AABC · 2024Review
- Deep learning neural network development for the classification of bacteriocin sequences produced by lactic acid bacteria.F1000Research · 2024Article
- BaPreS: a software tool for predicting bacteriocins using an optimal set of features.BMC bioinformatics · 2023Article
- Diagnosis, Treatment, and Management of Otitis Media with Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2023Review
- Combined charge and hydrophobicity-guided screening of antibacterial peptides: two-level approach to predict antibacterial activity and efficacy.Amino acids · 2023Article
- Evolutionary andCurrent research in microbial sciences · 2023Article
- Beneficial modulation of human health in the oral cavity and beyond using bacteriocin-like inhibitory substance-producing streptococcal probiotics.Frontiers in microbiology · 2023Review
- 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
- Rationally Designed Minimal Bioactive Domains of AS-48 Bacteriocin Homologs Possess Potent Antileishmanial Properties.Microbiology spectrum · 2022Article
- Design, characterization and structure-function analysis of novel antimicrobial peptides based on the N-terminal CATH-2 fragment.Scientific reports · 2022Article
- Plant Antimicrobial Peptides (PAMPs): Features, Applications, Production, Expression, and Challenges.Molecules (Basel, Switzerland) · 2022Review
- Artificial Intelligence and Antibiotic Discovery.Antibiotics (Basel, Switzerland) · 2021Review
- Biomedical Relevance of Novel Anticancer Peptides in the Sensitive Treatment of Cancer.Biomolecules · 2021Review
- Mining and unearthing hidden biosynthetic potential.Nature communications · 2021Review
Corrections and comments
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Authors and funding
11 authors at 2 institutions in 1 country.
Funding
Abstract
Bacteriocins, the ribosomally produced antimicrobial peptides of bacteria, represent an untapped source of promising antibiotic alternatives. However, bacteriocins display diverse mechanisms of action, a narrow spectrum of activity, and inherent challenges in natural product isolation making in vitro verification of putative bacteriocins difficult. A subset of bacteriocins exert their antimicrobial effects through favorable biophysical interactions with the bacterial membrane mediated by the charge, hydrophobicity, and conformation of the peptide. We have developed a pipeline for bacteriocin-derived compound design and testing that combines sequence-free prediction of bacteriocins using machine learning and a simple biophysical trait filter to generate 20 amino acid peptides that can be synthesized and evaluated for activity. We generated 28,895 total 20-mer candidate peptides and scored them for charge, α-helicity, and hydrophobic moment. Of those, we selected 16 sequences for synthesis and evaluated their antimicrobial, cytotoxicity, and hemolytic activities. Peptides with the overall highest scores for our biophysical parameters exhibited significant antimicrobial activity against Escherichia coli and Pseudomonas aeruginosa. Our combined method incorporates machine learning and biophysical-based minimal region determination to create an original approach to swiftly discover bacteriocin candidates amenable to rapid synthesis and evaluation for therapeutic use.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.