ArticleMolecular therapy. Nucleic acids2020
Deep-AmPEP30: Improve Short Antimicrobial Peptides Prediction with Deep Learning.
Article in Molecular therapy. Nucleic acids, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 122 papers, 1 of them a synthesis that pooled it.
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Who cites it
122 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Antimicrobial resistance crisis: could artificial intelligence be the solution?Military Medical Research · 2024Pooled it
- Artificial intelligence catalyzes antimicrobial peptide design.Synthetic and systems biotechnology · 2027Review
- Interpretable machine learning framework for peptide lead optimization in ultralarge design spaces.npj drug discovery · 2026Article
- Nano-antimicrobial peptides (Nano-AMPs) to combat resistant gram-negative bacteria.Drug delivery and translational research · 2026Review
- Towards Safer Antimicrobial Peptide Therapeutics: A Predictive-Generative Framework Targeting ESKAPE Pathogens.Probiotics and antimicrobial proteins · 2026Article
- An Interpretable Multi-Objective Machine Learning Framework for In Silico Prioritization of Anti-Diagnostics (Basel, Switzerland) · 2026Article
- DeepACPred: an integrated multistage framework for anticancer peptide discovery and activity prediction.Briefings in bioinformatics · 2026Article
- Fung-AI: An AI/ML-driven pipeline for antifungal peptide discovery.PLoS computational biology · 2026Article
- MAPLE: interpretable deep learning identifies selective antimicrobial peptides using joint evolutionary-physicochemical analysis.Briefings in bioinformatics · 2026Article
- Molecular fingerprints are strong models for peptide function prediction.Bioinformatics (Oxford, England) · 2026Article
- Plant-associated fungi co-opt ancient antimicrobials for host manipulation.Science advances · 2026Article
- Integrative Peptide Drug Development: Chemical Engineering, AI-Driven Design, and Cell-Penetrating Peptides.Pharmaceutics · 2026Review
- Antimicrobial peptides: An important link in the game theory between plants and pathogens.Journal of advanced research · 2026Review
- A generative explainable model for antimicrobial peptide prediction using bidirectional temporal convolutional neural network.Scientific reports · 2026Article
- Mechanistic Insights into Human Defensin Antimicrobial Activity from Membrane Simulations.The Journal of membrane biology · 2026Article
- SGAC: a graph neural network framework for imbalanced and structure-aware AMP classification.Briefings in bioinformatics · 2026Article
- ANIA: an inception-attention network for predicting minimum inhibitory concentration of antimicrobial peptides.Briefings in bioinformatics · 2026Article
- PepGraphormer: an ESM-GAT hybrid deep learning framework for antimicrobial peptide prediction.Journal of cheminformatics · 2026Article
- IntelligentFrontiers in cellular and infection microbiology · 2026Article
- Article
62 more citing papers are in PubMed but not listed here.
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Antimicrobial peptides (AMPs) are a valuable source of antimicrobial agents and a potential solution to the multi-drug resistance problem. In particular, short-length AMPs have been shown to have enhanced antimicrobial activities, higher stability, and lower toxicity to human cells. We present a short-length (≤30 aa) AMP prediction method, Deep-AmPEP30, developed based on an optimal feature set of PseKRAAC reduced amino acids composition and convolutional neural network. On a balanced benchmark dataset of 188 samples, Deep-AmPEP30 yields an improved performance of 77% in accuracy, 85% in the area under the receiver operating characteristic curve (AUC-ROC), and 85% in area under the precision-recall curve (AUC-PR) over existing machine learning-based methods. To demonstrate its power, we screened the genome sequence of Candida glabrata-a gut commensal fungus expected to interact with and/or inhibit other microbes in the gut-for potential AMPs and identified a peptide of 20 aa (P3, FWELWKFLKSLWSIFPRRRP) with strong anti-bacteria activity against Bacillus subtilis and Vibrio parahaemolyticus. The potency of the peptide is remarkably comparable to that of ampicillin. Therefore, Deep-AmPEP30 is a promising prediction tool to identify short-length AMPs from genomic sequences for drug discovery. Our method is available at https://cbbio.cis.um.edu.mo/AxPEP for both individual sequence prediction and genome screening for AMPs.
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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.