ArticleThe journal of physical chemistry. B2024
IDP-Bert: Predicting Properties of Intrinsically Disordered Proteins Using Large Language Models.
Article in The journal of physical chemistry. B, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Hierarchical multi-timescale structural dynamics of the disordered N-terminal of p53.Nature communications · 2026Article
- Large Language Model Agent for Modular Task Execution in Drug Discovery.Journal of chemical information and modeling · 2026Article
- Protein Structure-Function Relationship: A Kernel-PCA Approach for Reaction Coordinate Identification.Journal of chemical theory and computation · 2025Article
- Multi-Peptide: Multimodality Leveraged Language-Graph Learning of Peptide Properties.Journal of chemical information and modeling · 2025Article
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4 authors.
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Abstract
Intrinsically disordered Proteins (IDPs) constitute a large and structureless class of proteins with significant functions. The existence of IDPs challenges the conventional notion that the biological functions of proteins rely on their three-dimensional structures. Despite lacking well-defined spatial arrangements, they exhibit diverse biological functions, influencing cellular processes and shedding light on disease mechanisms. However, it is expensive to run experiments or simulations to characterize this class of proteins. Consequently, we designed an ML model that relies solely on amino acid sequences. In this study, we introduce the IDP-Bert model, a deep-learning architecture leveraging Transformers and Protein Language Models to map sequences directly to IDP properties. Our experiments demonstrate accurate predictions of IDP properties, including Radius of Gyration, end-to-end Decorrelation Time, and Heat Capacity.
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Registered trials
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