ArticleScience advances2025
Discovery of unconventional and nonintuitive self-assembling peptide materials using experiment-driven machine learning.
Article in Science advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
8 citing papers in PubMed.
- Automated Extraction of Multicomponent Alloy Data Using Large Language Models for Sustainable Design.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Article
- In vivo membrane engineering traps Gd-based MRI contrast agents for detecting microhepatocellular carcinoma.Science advances · 2026Article
- 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
- Emerging Technologies and Integrated Interdisciplinary Strategies for Mitigating Protein Aggregation in Therapeutic Formulations.Pharmaceutical research · 2026Review
- Biomolecular Condensates as Emerging Biomaterials: Functional Mechanisms and Advances in Computational and Experimental Approaches.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Computational modeling of drug-eluting balloons in peripheral artery disease: Mechanisms, optimization, and translational insights.Computational and structural biotechnology journal · 2025Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prediction of peptide secondary structure is challenging because of complex molecular interactions, sequence-specific behavior, and environmental factors. Traditional design strategies, based on hydrophobicity and structural propensity, can be biased and could indeed prevent discovery of interesting, diverse, and unconventional peptides with desired nanostructure assembly. Using β sheet formation in pentapeptides as a case study, we used an integrated high-throughput experimental workflow and an artificial intelligence-driven active learning framework to improve prediction accuracy of self-assembly. By focusing on sequences where machine learning (ML) predictions deviate from conventional design strategies, we synthesized and tested 268 pentapeptides, successfully finding 96 forming β sheet assemblies, including unconventional sequences (e.g., ILFSM, LMISI, MITIY, MISIW, and WKIYI) not predicted by traditional methods. Our ML models outperformed conventional β sheet propensity tables, revealing useful chemical design rules. A web interface is provided to facilitate community access to these models. This work highlights the value of ML-driven approaches in overcoming the limitations of current peptide design strategies.
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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.