ArticleJournal of chemical information and modeling2023
AggBERT: Best in Class Prediction of Hexapeptide Amyloidogenesis with a Semi-Supervised ProtBERT Model.
Article in Journal of chemical information and modeling, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Predicting Peptide Aggregation with Protein Language Model Embeddings.Journal of chemical information and modeling · 2026Article
- PLM-eXplain: divide and conquer the protein embedding space.Bioinformatics (Oxford, England) · 2026Article
- Application and Prospects of Large Language Models in Small-Molecule Drug Discovery.Analytical chemistry · 2025Review
- Proteins need extra attention: improving the predictive power of protein language models on mutational datasets with hint tokens.NAR genomics and bioinformatics · 2025Article
- iAmyP: A Multi-view Learning for Amyloidogenic Hexapeptides Identification Based on Sequence Least Squares Programming.Interdisciplinary sciences, computational life sciences · 2025Article
- Identifying the DNA methylation preference of transcription factors using ProtBERT and SVM.PLoS computational biology · 2025Article
- Chain of Thought Utilization in Large Language Models and Application in Nephrology.Medicina (Kaunas, Lithuania) · 2024Review
- PatchProt: hydrophobic patch prediction using protein foundation models.Bioinformatics advances · 2024Article
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Authors and funding
4 authors.
Funding
Abstract
The prediction of peptide amyloidogenesis is a challenging problem in the field of protein folding. Large language models, such as the ProtBERT model, have recently emerged as powerful tools in analyzing protein sequences for applications, such as predicting protein structure and function. In this article, we describe the use of a semisupervised and fine-tuned ProtBERT model to predict peptide amyloidogenesis from sequences alone. Our approach, which we call AggBERT, achieved state-of-the-art performance, demonstrating the potential for large language models to improve the accuracy and speed of amyloid fibril prediction over simple heuristics or structure-based approaches. This work highlights the transformative potential of machine learning and large language models in the fields of chemical biology and biomedicine.
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Registered trials
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