ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2024
ProT-Diff: A Modularized and Efficient Strategy for De Novo Generation of Antimicrobial Peptide Sequences by Integrating Protein Language and Diffusion Models.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Emerging antimicrobial peptides in gastrointestinal disorders: Dual role in immunity and therapy.World journal of gastrointestinal pharmacology and therapeutics · 2026Review
- Discovery of potent low-toxicity antimicrobial peptides through diffusion modeling.Nature communications · 2026Article
- Generative AI for controllable protein sequence design: A survey.npj drug discovery · 2026Review
- Artificial intelligence driven protein design and sustainable nanomedicine for advanced theranostics.Bioactive materials · 2026Review
- TargetGAN: A generative AI framework for the design of plant core promoters with targeted activity.Plant communications · 2026Article
- Generative models for antimicrobial peptide design: auto-encoders and beyond.BioData mining · 2026Article
- Contemporary data-driven innovations in peptide-based therapeutic design.Briefings in bioinformatics · 2026Review
- InversePep: Diffusion-driven structure-based inverse folding for functional peptides.Briefings in bioinformatics · 2026Article
- De Novo Multi-Mechanism Antimicrobial Peptide Design via Multimodal Deep Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- The Extreme Environment Microbiome Catalog (EEMC): a global resource for microbial diversity and antimicrobial discovery.Nature communications · 2026Article
- AI-enabled protein design facilitates future plant research and crop breeding.Plant physiology · 2026Review
- The rise and future of peptide-based antimicrobials.Journal of microbiology (Seoul, Korea) · 2026Review
- A PLUM Job: Peptide modeLs for Understanding and engineering antiMicrobial therapeutics.bioRxiv : the preprint server for biology · 2026Article
- Antimicrobial Peptides Against Antimicrobial-Resistant Bacteria: Focus on Machine Learning.Infection and drug resistance · 2026Review
- Artificial intelligence applications in biofilm research: computational approaches for biofilm analysis and antimicrobial prediction.Frontiers in microbiology · 2026Review
- Protein structure prediction powered by artificial intelligence: from biochemical foundations to practical applications.Frontiers in molecular biosciences · 2026Review
- 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
- Combating Gram-negative infections: The role of antimicrobial peptides and nanotechnology in overcoming antibiotic resistance.Materials today. Bio · 2025Review
- Painting Peptides With Antimicrobial Potency Through Deep Reinforcement Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Antimicrobial peptides (AMPs) are a promising solution for treating antibiotic-resistant pathogens. However, efficient generation of diverse AMPs without prior knowledge of peptide structures or sequence alignments remains a challenge. Here, ProT-Diff is introduced, a modularized deep generative approach that combines a pretrained protein language model with a diffusion model for the de novo generation of AMPs sequences. ProT-Diff generates thousands of AMPs with diverse lengths and structures within a few hours. After silico physicochemical screening, 45 peptides are selected for experimental validation. Forty-four peptides showed antimicrobial activity against both gram-positive or gram-negative bacteria. Among broad-spectrum peptides, AMP_2 exhibited potent antimicrobial activity, low hemolysis, and minimal cytotoxicity. An in vivo assessment demonstrated its effectiveness against a drug-resistant E. coli strain in acute peritonitis. This study not only introduces a viable and user-friendly strategy for de novo generation of antimicrobial peptides, but also provides potential antimicrobial drug candidates with excellent activity. It is believed that this study will facilitate the development of other peptide-based drug candidates in the future, as well as proteins with tailored characteristics.
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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.