ArticleComputational and structural biotechnology journal2024
Language models can identify enzymatic binding sites in protein sequences.
Article in Computational and structural biotechnology journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Accurate and task-agnostic modeling of enzymatic reactions through multimodal relational learning.Acta pharmaceutica Sinica. B · 2026Article
- Reaction-aware molecular representation learning: Toward generalizable artificial intelligence for enzymatic catalysis.Acta pharmaceutica Sinica. B · 2026Article
- Protein Language Models: Applications and Perspectives.Journal of proteome research · 2026Review
- Machine Learning in Homogeneous Catalysis: Basic Concepts and Best Practices.ACS catalysis · 2026Article
- BLMPred: Predicting linear B-cell epitopes using pre-trained protein language models and machine learning.Computational and structural biotechnology journal · 2026Article
- Family-Specialized Transformer for L-cystathionine gamma-lyase Engineering and Its Structural Interpretation.Computational and structural biotechnology journal · 2026Article
- Unlocking Enzyme Discovery: Leveraging Multi-Omics, Machine Learning, and De Novo Design.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Integrating protein sequence design and evolutionary sequence conservation to uncover spectral tuning sites in red-light photoreceptors.Structure (London, England : 1993) · 2025Article
- Unified Deep Learning of Molecular and Protein Language Representations with T5ProtChem.Journal of chemical information and modeling · 2025Article
- Self-supervised domain adaptation of protein language model based solely on positive enzyme-reaction pairs.Computational and structural biotechnology journal · 2025Article
- Fine-tuning protein language models unlocks the potential of underrepresented viral proteomes.PeerJ · 2025Article
- Annotation Vocabulary (Might Be) All You Need.bioRxiv : the preprint server for biology · 2024Article
- Leveraging Language Model Multitasking To Predict C-H Borylation Selectivity.Journal of chemical information and modeling · 2024Article
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Authors and funding
6 authors.
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Abstract
Recent advances in language modeling have had a tremendous impact on how we handle sequential data in science. Language architectures have emerged as a hotbed of innovation and creativity in natural language processing over the last decade, and have since gained prominence in modeling proteins and chemical processes, elucidating structural relationships from textual/sequential data. Surprisingly, some of these relationships refer to three-dimensional structural features, raising important questions on the dimensionality of the information encoded within sequential data. Here, we demonstrate that the unsupervised use of a language model architecture to a language representation of bio-catalyzed chemical reactions can capture the signal at the base of the substrate-binding site atomic interactions. This allows us to identify the three-dimensional binding site position in unknown protein sequences. The language representation comprises a reaction-simplified molecular-input line-entry system (SMILES) for substrate and products, and amino acid sequence information for the enzyme. This approach can recover, with no supervision, 52.13% of the binding site when considering co-crystallized substrate-enzyme structures as ground truth, vastly outperforming other attention-based models.
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