ArticleScientific reports2021
Protein embeddings and deep learning predict binding residues for various ligand classes.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers, 2 of them syntheses that pooled it.
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Who cites it
73 citing papers in PubMed, 2 syntheses or guidelines pooled it, 126 citations in OpenAlex.
- Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models.Database : the journal of biological databases and curation · 2025Pooled it
- Advances in the Application of Protein Language Modeling for Nucleic Acid Protein Binding Site Prediction.Genes · 2024Pooled it
- EmmaEmb: A quantitative framework for analyzing embedding spaces in molecular biology.Patterns (New York, N.Y.) · 2026Article
- COQ8 chaperones coenzyme Q lipid intermediates through ATP-driven structural gating.Science advances · 2026Article
- SPPIDER-seq: sequence-based partner-aware predictor of protein-protein interaction sites.Bioinformatics (Oxford, England) · 2026Article
- Beyond the canonical: The role of post-transcriptional regulation in drug-target interaction prediction.PLoS computational biology · 2026Article
- KmalPred: a deep learning framework for lysine malonylation site prediction using protein language model representations.BMC biology · 2026Article
- SPPIDER-seq: Sequence-based partner-aware predictor of protein-protein interaction sites.bioRxiv : the preprint server for biology · 2026Article
- LiBRe: A Ligand-Aware Sequence-Based Binding Residue Prediction Model for Virtual Screening.Journal of chemical information and modeling · 2026Article
- Compressing the collective knowledge of ESM into a single protein language model.Nature methods · 2026Article
- Predictive modeling of gene expression and localization of DNA binding site using deep convolutional neural networks.PLoS computational biology · 2026Article
- Modern resources for intrinsic disorder predictions: protein language models, deep learning, meta-servers, and databases.Cellular and molecular life sciences : CMLS · 2026Review
- Protein Language Models: Applications and Perspectives.Journal of proteome research · 2026Review
- Prediction of Intrinsically Disordered Regions That Bind RNA with DisoRDPbind.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Critical Assessment of Protein Intrinsic Disorder Round 3 - Predicting Disorder in the Era of Protein Language Models.Proteins · 2026Article
- Comparative assessment of binding residue predictions in intrinsically disordered regions.Protein science : a publication of the Protein Society · 2025Article
- PEGASUS: Prediction of MD-derived protein flexibility from sequence.Protein science : a publication of the Protein Society · 2025Article
- BertADP: a fine-tuned protein language model for anti-diabetic peptide prediction.BMC biology · 2025Article
- Predicting Protein Function in the AI and Big Data Era.Biochemistry · 2025Review
- TransBind allows precise detection of DNA-binding proteins and residues using language models and deep learning.Communications biology · 2025Article
13 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
One important aspect of protein function is the binding of proteins to ligands, including small molecules, metal ions, and macromolecules such as DNA or RNA. Despite decades of experimental progress many binding sites remain obscure. Here, we proposed bindEmbed21, a method predicting whether a protein residue binds to metal ions, nucleic acids, or small molecules. The Artificial Intelligence (AI)-based method exclusively uses embeddings from the Transformer-based protein Language Model (pLM) ProtT5 as input. Using only single sequences without creating multiple sequence alignments (MSAs), bindEmbed21DL outperformed MSA-based predictions. Combination with homology-based inference increased performance to F1 = 48 ± 3% (95% CI) and MCC = 0.46 ± 0.04 when merging all three ligand classes into one. All results were confirmed by three independent data sets. Focusing on very reliably predicted residues could complement experimental evidence: For the 25% most strongly predicted binding residues, at least 73% were correctly predicted even when ignoring the problem of missing experimental annotations. The new method bindEmbed21 is fast, simple, and broadly applicable-neither using structure nor MSAs. Thereby, it found binding residues in over 42% of all human proteins not otherwise implied in binding and predicted about 6% of all residues as binding to metal ions, nucleic acids, or small molecules.
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