ArticleProtein science : a publication of the Protein Society2024
Structure-aware deep learning model for peptide toxicity prediction.
Article in Protein science : a publication of the Protein Society, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Peptide-Enabled Nanoplatforms for Malaria and Leishmaniasis: From Intracellular Targeting to Translational Diagnostic Perspectives.ChemMedChem · 2026Review
- UniPept: A Universal Model for Predicting Peptide Properties Based on Atom-Residue Dual-Scale Integration.Interdisciplinary sciences, computational life sciences · 2026Article
- Cyclic Peptides as Modulators of Protein-Protein Interactions: A Survival Guide from Discovery Platforms to AI-Driven Design.International journal of molecular sciences · 2026Review
- Lactoferrin-Derived Peptides in Cancer Therapy: Structural Features, Mechanistic Insights and Clinical Translation Prospects.International journal of molecular sciences · 2026Review
- Efficacy and safety evaluation of artificial intelligence-identified antimicrobial peptides targeting avian pathogenic Escherichia coli in broiler chickens.Journal of animal science and biotechnology · 2026Article
- Structural Evolution and Immunomodulatory Versatility of Fungal Immunomodulatory Proteins: Insights into Mechanisms and Bioproduction Strategies.Journal of agricultural and food chemistry · 2026Review
- Artificial Intelligence-Driven Discovery and Optimization of Antimicrobial Peptides Targeting ESKAPE Pathogens and Multidrug-Resistant Fungi.Microorganisms · 2026Review
- Machine Learning in Preclinical Development of Antiviral Peptide Candidates: A Review of the Current Landscape.Viruses · 2026Review
- AMPSeek: A Workflow for Predicting Antimicrobial Peptide Activity, Three-Dimensional Structure, and Toxicity.Current protocols · 2026Article
- Lipopeptide Engineering: From Natural Origins to Rational Design Against Antimicrobial Resistance.Antibiotics (Basel, Switzerland) · 2026Review
- Peptide-based drug design using generative AI.Chemical communications (Cambridge, England) · 2026Review
- Recent advances in multimodal foundation model-enabled peptide screening and optimization for smart biomaterials and functional tissue engineering.Frontiers in bioengineering and biotechnology · 2026Review
- A framework for the safety evaluation of peptides in cosmetics.Current research in toxicology · 2026Article
- BridgeNet: a high-efficiency framework integrating sequence and structure for protein and enzyme function prediction.Briefings in bioinformatics · 2025Article
- ProToxin, a Predictor of Protein Toxicity.Toxins · 2025Article
- ToxDL 2.0: Protein toxicity prediction using a pretrained language model and graph neural networks.Computational and structural biotechnology journal · 2025Article
- ToxGIN: an In silico prediction model for peptide toxicity via graph isomorphism networks integrating peptide sequence and structure information.Briefings in bioinformatics · 2024Article
- Structure-aware deep learning model for peptide toxicity prediction.Protein science : a publication of the Protein Society · 2024Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Antimicrobial resistance is a critical public health concern, necessitating the exploration of alternative treatments. While antimicrobial peptides (AMPs) show promise, assessing their toxicity using traditional wet lab methods is both time-consuming and costly. We introduce tAMPer, a novel multi-modal deep learning model designed to predict peptide toxicity by integrating the underlying amino acid sequence composition and the three-dimensional structure of peptides. tAMPer adopts a graph-based representation for peptides, encoding ColabFold-predicted structures, where nodes represent amino acids and edges represent spatial interactions. Structural features are extracted using graph neural networks, and recurrent neural networks capture sequential dependencies. tAMPer's performance was assessed on a publicly available protein toxicity benchmark and an AMP hemolysis data we generated. On the latter, tAMPer achieves an F1-score of 68.7%, outperforming the second-best method by 23.4%. On the protein benchmark, tAMPer exhibited an improvement of over 3.0% in the F1-score compared to current state-of-the-art methods. We anticipate tAMPer to accelerate AMP discovery and development by reducing the reliance on laborious toxicity screening experiments.
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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.