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bioRxiv · 10.1101/2024.07.22.604620

FuncFetch: An LLM-assisted workflow enables mining thousands of enzyme-substrate interactions from published manuscripts

Smith, N.·Yuan, X.·Melissinos, C.·Moghe, G.
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Abstract

MotivationThousands of genomes are publicly available, however, most genes in those genomes have poorly defined functions. This is partly due to a gap between previously published, experimentally-characterized protein activities and activities deposited in databases. This activity deposition is bottlenecked by the time-consuming biocuration process. The emergence of large language models (LLMs) presents an opportunity to speed up text-mining of protein activities for biocuration. ResultsWe developed FuncFetch -- a workflow that integrates NCBI E-Utilities, OpenAIs GPT-4 and Zotero -- to screen thousands of manuscripts and extract enzyme activities. Extensive validation revealed high precision and recall of GPT-4 in determining whether the abstract of a given paper indicates presence of a characterized enzyme activity in that paper. Provided the manuscript, FuncFetch extracted data such as species information, enzyme names, sequence identifiers, substrates and products, which were subjected to extensive quality analyses. Comparison of this workflow against a manually curated dataset of BAHD acyltransferase activities demonstrated a precision/recall of 0.86/0.64 in extracting substrates. We further deployed FuncFetch on nine large plant enzyme families. Screening 27,120 papers, FuncFetch retrieved 32,605 entries from 5547 selected papers. We also identified multiple extraction errors including incorrect associations, non-target enzymes, and hallucinations, which highlight the need for further manual curation. The BAHD activities were verified, resulting in a comprehensive functional fingerprint of this family and revealing that [~]70% of the experimentally characterized enzymes are uncurated in the public domain. FuncFetch represents an advance in biocuration and lays the groundwork for predicting functions of uncharacterized enzymes. Availability and ImplementationCode and minimally-curated activities available at: https://github.com/moghelab/funcfetch and https://tools.moghelab.org/funczymedb

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Smith, N., Yuan, X., Melissinos, C., Moghe, G.. 2024-07-23. FuncFetch: An LLM-assisted workflow enables mining thousands of enzyme-substrate interactions from published manuscripts. https://doi.org/10.1101/2024.07.22.604620

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