Finding the Dark Matter: Large Language Model-based Enzyme Kinetic Data Extractor and Its Validation

Despite the vast number of enzymatic kinetic measurements reported across decades of biochemical literature, the majority of relational enzyme kinetic data—linking amino acid sequence, substrate identity, kinetic parameters, and assay conditions—remains uncollected and inaccessible in structured form. This constitutes a significant portion of the “dark matter” of enzymology. Unlocking these hidden data through automated extraction offers an opportunity to expand enzyme dataset diversity and size, critical for building accurate, generalizable models that drive predictive enzyme engineering. To address this limitation, we built EnzyExtract, a large language model-powered pipeline that automates the extraction, verification, and structuring of enzyme kinetics data from scientific literature. By processing 137,892 full-text publications (PDF/XML), EnzyExtract curated more than 218,810 enzyme–substrate–kinetics entries, including 218,770 kcat and 169,459 KM values. EnzyExtract identified 94,576 unique kinetic entries (kcat and KM combined) absent from BRENDA, significantly expanding the known enzymology dataset. The newly curated dataset was compiled into a database named EnzyExtractDB. EnzyExtract demonstrates high accuracy when benchmarked against manually curated datasets and strong consistency with BRENDA-derived data. To create model-ready datasets, enzyme and substrate sequences were aligned to UniProt and PubChem, yielding 85,980 high-confidence, sequence-mapped kinetic entries. To assess the practical utility of our dataset, we retrained several state-of-the-art kcat predictors (including MESI, DLKcat, and TurnUp) using EnzyExtractDB. Across held-out test sets, all models demonstrate improved predictive performance in terms of RMSE, MAE, and R², highlighting the value of high-quality, large-scale, literature-derived EnzyExtractDB for enhancing predictive modeling of enzyme kinetics.