ArticleScientific reports2024
Protein embeddings predict binding residues in disordered regions.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Mechanistic Interpretability of Protein Language Models Reveals Encoded Structural and Functional Properties of Intrinsically Disordered Proteins.bioRxiv : the preprint server for biology · 2026Article
- Protein Language Model Embeddings Recover Evolutionary and Functional Organization Across the Ras Superfamily.Journal of molecular evolution · 2026Article
- Predicting molecular recognition features in protein sequences with MoRFchibi 2.0.BMC bioinformatics · 2026Article
- Dynamic yet well-defined organization of the FUS RGG3 dense phase.Communications chemistry · 2026Article
- Scalable embedding fusion with protein language models: insights from benchmarking text-integrated representations.Briefings in bioinformatics · 2026Article
- Critical Assessment of Protein Intrinsic Disorder Round 3 - Predicting Disorder in the Era of Protein Language Models.Proteins · 2026Article
- Predicting Protein-Protein Interactions from Machine-Learned Representations.Advances in experimental medicine and biology · 2026Review
- The Human Omnibus of Targetable Pockets.Journal of cheminformatics · 2025Article
- Scalable embedding fusion with protein language models: insights from benchmarking text-integrated representations.bioRxiv : the preprint server for biology · 2025Article
- A deep learning method for predicting interactions for intrinsically disordered regions of proteins.bioRxiv : the preprint server for biology · 2025Article
- Predicting Protein Function in the AI and Big Data Era.Biochemistry · 2025Review
- PLM-DBPs: enhancing plant DNA-binding protein prediction by integrating sequence-based and structure-aware protein language models.Briefings in bioinformatics · 2025Article
- CaLMPhosKAN: prediction of general phosphorylation sites in proteins via fusion of codon aware embeddings with amino acid aware embeddings and wavelet-based Kolmogorov-Arnold network.Bioinformatics (Oxford, England) · 2025Article
- Multimeric protein interaction and complex prediction: Structure, dynamics and function.Computational and structural biotechnology journal · 2025Review
- Frontiers in integrative structural modeling of macromolecular assemblies.QRB discovery · 2025Review
- Advancements in one-dimensional protein structure prediction using machine learning and deep learning.Computational and structural biotechnology journal · 2025Review
- bindNode24: Competitive binding residue prediction with 60 % smaller model.Computational and structural biotechnology journal · 2025Article
- StrIDR: a database of intrinsically disordered regions of proteins with experimentally resolved structures.bioRxiv : the preprint server for biology · 2024Article
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Authors and funding
4 authors.
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Abstract
The identification of protein binding residues helps to understand their biological processes as protein function is often defined through ligand binding, such as to other proteins, small molecules, ions, or nucleotides. Methods predicting binding residues often err for intrinsically disordered proteins or regions (IDPs/IDPRs), often also referred to as molecular recognition features (MoRFs). Here, we presented a novel machine learning (ML) model trained to specifically predict binding regions in IDPRs. The proposed model, IDBindT5, leveraged embeddings from the protein language model (pLM) ProtT5 to reach a balanced accuracy of 57.2 ± 3.6% (95% confidence interval). Assessed on the same data set, this did not differ at the 95% CI from the state-of-the-art (SOTA) methods ANCHOR2 and DeepDISOBind that rely on expert-crafted features and evolutionary information from multiple sequence alignments (MSAs). Assessed on other data, methods such as SPOT-MoRF reached higher MCCs. IDBindT5's SOTA predictions are much faster than other methods, easily enabling full-proteome analyses. Our findings emphasize the potential of pLMs as a promising approach for exploring and predicting features of disordered proteins. The model and a comprehensive manual are publicly available at https://github.com/jahnl/binding_in_disorder .
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