ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2023
Deep Learning Empowers the Discovery of Self-Assembling Peptides with Over 10 Trillion Sequences.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Artificial intelligence-assisted design of self-assembling peptide hydrogels for neural regeneration: Principles and opportunities.Bioactive materials · 2027Review
- Amphipathic β‑Sheet-Forming Octapeptide Self-Assembly Using the Martini Potential Family.ACS omega · 2026Article
- Article
- Catalytic Amyloids: Turning Fibrils Into Biocatalysts.Chemistry (Weinheim an der Bergstrasse, Germany) · 2026Review
- Uniform design-embedded predictions of (tetra-)peptide physicochemical properties.Bioinformatics (Oxford, England) · 2026Article
- Computational Discovery of Aggregation-Mediated Dipeptide Inhibitors Targeting PHLDA1 for Cardiovascular Therapy.ACS omega · 2025Article
- Discovery of unconventional and nonintuitive self-assembling peptide materials using experiment-driven machine learning.Science advances · 2025Article
- Interplay of Hydrophobicity, Charge, and Sequence Length in Oligopeptide Coassembly.The journal of physical chemistry. B · 2025Article
- Learning the rules of peptide self-assembly through data mining with large language models.Science advances · 2025Article
- Safety and effects of scorpion-derived antimicrobial peptides as an alternative to antibiotic growth promoters in broilers: growth performance, immune function, and intestinal development.Frontiers in veterinary science · 2025Article
- Integrating biophysical modeling, quantum computing, and AI to discover plastic-binding peptides that combat microplastic pollution.PNAS nexus · 2025Article
- Aggregation Rules of Short Peptides.JACS Au · 2024Article
- HydrogelFinder: A Foundation Model for Efficient Self-Assembling Peptide Discovery Guided by Non-Peptidal Small Molecules.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Challenges and opportunities of poly(amino acid) nanomedicines in cancer therapy.Nanomedicine (London, England) · 2024Review
- Deep Learning Empowers the Discovery of Self-Assembling Peptides with Over 10 Trillion Sequences.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Self-assembling of peptides is essential for a variety of biological and medical applications. However, it is challenging to investigate the self-assembling properties of peptides within the complete sequence space due to the enormous sequence quantities. Here, it is demonstrated that a transformer-based deep learning model is effective in predicting the aggregation propensity (AP) of peptide systems, even for decapeptide and mixed-pentapeptide systems with over 10 trillion sequence quantities. Based on the predicted AP values, not only the aggregation laws for designing self-assembling peptides are derived, but the transferability relation among the APs of pentapeptides, decapeptides, and mixed pentapeptides is also revealed, leading to discoveries of self-assembling peptides by concatenating or mixing, as consolidated by experiments. This deep learning approach enables speedy, accurate, and thorough search and design of self-assembling peptides within the complete sequence space of oligopeptides, advancing peptide science by inspiring new biological and medical applications.
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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.