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Multi-enzyme Screening Using a High-throughput Genetic Enzyme Screening System
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EnzChemRED, a rich enzyme chemistry relation extraction dataset
Arxiv
|June 21, 2024
Summary
A new dataset, EnzChemRED, aids enzyme curation by training Natural Language Processing (NLP) models to extract enzyme functions and chemical reactions from scientific literature, improving knowledgebase accuracy.
Area of Science:
- Biochemistry
- Bioinformatics
- Computational Biology
Background:
- Expert curation of enzyme functions from scientific literature is crucial for knowledgebases but struggles to keep pace with research output.
- Automated methods are needed to accelerate the extraction of enzyme-catalyzed reaction information.
Purpose of the Study:
- To introduce EnzChemRED, a novel dataset for training and benchmarking Natural Language Processing (NLP) models for enzyme function extraction.
- To develop NLP tools that can assist in the curation of enzyme knowledgebases.
Main Methods:
- EnzChemRED comprises 1,210 expert-curated PubMed abstracts with annotated enzymes and chemical reactions linked to UniProtKB and ChEBI.
- Pre-trained language models were fine-tuned using EnzChemRED for Named Entity Recognition (NER) and Relation Extraction (RE).
- An end-to-end pipeline was developed by combining best-performing models for large-scale knowledge extraction.
Main Results:
- Fine-tuning with EnzChemRED significantly improved NLP model performance.
- Achieved average F1 scores of 86.30% for NER, 86.66% for RE (chemical conversions), and 83.79% for RE (conversions and linked enzymes).
- A draft map of enzyme functions was generated from PubMed abstracts using the developed pipeline.
Conclusions:
- EnzChemRED is an effective resource for developing NLP tools to enhance enzyme curation.
- The developed NLP pipeline can process literature at scale, creating valuable draft knowledge maps for resources like UniProtKB and Rhea.
- Automated extraction of enzyme chemistry relations accelerates the creation and updating of biological knowledgebases.
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