ArticleNature chemistry2015
Exploring the sequence space for (tri-)peptide self-assembly to design and discover new hydrogels.
Article in Nature chemistry, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 242 papers.
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Who cites it
242 citing papers in PubMed.
- Artificial intelligence-assisted design of self-assembling peptide hydrogels for neural regeneration: Principles and opportunities.Bioactive materials · 2027Review
- DeepSAP: an integrated generative and predictive deep-learning framework for controllable design of self-assembling peptides.Bioinformatics (Oxford, England) · 2026Article
- Predicting Supramolecular Self-Assembly of Peptide Structures with AlphaFold3.bioRxiv : the preprint server for biology · 2026Article
- In Silicon Deciphering Atomic-Scale Structural Units in Peptide Glass.Angewandte Chemie (International ed. in English) · 2026Article
- Amphipathic β‑Sheet-Forming Octapeptide Self-Assembly Using the Martini Potential Family.ACS omega · 2026Article
- Validation ofACS central science · 2026Article
- Lithium-selective supramolecular assembly and capture by tripeptide gelators.Chemical science · 2026Article
- Chirality Effect on Physical and Biological Properties of Peptide-Based Hydrogels.Gels (Basel, Switzerland) · 2026Review
- Article
- Computational Characterization of the Role of LEM2/LaminA Interactions on the Stability of BAF-Dimer Using Molecular Simulations.Proteins · 2026Article
- Formation of S- and Z-twist supramolecular micro-ropes by peptide stereoisomers.Nature communications · 2026Article
- Secondary Structure Bead-Encoded Amphiphilicity Biases Peptide Self-Assembly Prediction in MARTINI Coarse-Grained Simulations.ACS applied materials & interfaces · 2026Article
- Article
- Mechanism-Guided Nanoengineered Therapeutic Peptides for Bone Healing.International journal of nanomedicine · 2026Review
- Bridging Scales: Coarse-Grained Protein Models in Computational Biology.Advances in experimental medicine and biology · 2026Review
- Peptide Electrostatic Modulation Directs Human Neural Cell Fate.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Hydrophobic and Electrostatic Sequence Patterning Directs Hierarchical Assembly of Phenylalanine-Rich Oligopeptides.The journal of physical chemistry letters · 2025Article
- Computational Discovery of Aggregation-Mediated Dipeptide Inhibitors Targeting PHLDA1 for Cardiovascular Therapy.ACS omega · 2025Article
- Article
- Discrete Actuation of Water-Responsive Crystalline Metal-Peptide Frameworks.Angewandte Chemie (International ed. in English) · 2025Article
182 more citing papers are in PubMed but not listed here.
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Peptides that self-assemble into nanostructures are of tremendous interest for biological, medical, photonic and nanotechnological applications. The enormous sequence space that is available from 20 amino acids probably harbours many interesting candidates, but it is currently not possible to predict supramolecular behaviour from sequence alone. Here, we demonstrate computational tools to screen for the aqueous self-assembly propensity in all of the 8,000 possible tripeptides and evaluate these by comparison with known examples. We applied filters to select for candidates that simultaneously optimize the apparently contradicting requirements of aggregation propensity and hydrophilicity, which resulted in a set of design rules for self-assembling sequences. A number of peptides were subsequently synthesized and characterized, including the first reported tripeptides that are able to form a hydrogel at neutral pH. These tools, which enable the peptide sequence space to be searched for supramolecular properties, enable minimalistic peptide nanotechnology to deliver on its promise.
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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.