ProtoPure: Literature Mining and Analysis of Protein Purification Protocols at Proteome Scale

Protein purification is a fundamental step for analyzing individual proteins and protein complexes and identifying interactions with other proteins, DNA, or RNA. A variety of protein purification strategies exist to address desired scale, throughput, and downstream applications. However, selecting an effective experimental procedure remains a largely empirical process that a quick literature review can help guide. Researchers typically survey on the order of a dozen publications for proteins with similar biochemical properties, assessing conditions on a case-by-case basis, leaving no systematic framework for comparison and decision-making at the scale of entire protein families. As protein engineering and design evolve, identifying effective purification conditions remains critical for obtaining sufficient quantities of properly folded and functional proteins. To address this gap, we developed ProtoPure, an LLM-based workflow and database that automates the extraction, standardization and analysis of protein purification conditions from peer-reviewed literature at scale. Given a user-defined UniProt table-enabling flexible selection of proteins by family, organism, or proteome. ProtoPure retrieves associated publications, classifies them based on the presence of purification protocols, and extracts a standardized set of 12 purification parameters (e.g., buffer composition, elution conditions). By transforming dispersed experimental descriptions into a structured resource, ProtoPure makes it possible to compare purification strategies across thousands of proteins. The resulting dataset revealed quantitative convergence toward a limited set of recombinant expression practices, with BL21(DE3)-based systems dominating protein production and recurrent combinations of expression host, inducer, growth medium, and plasmid occurring substantially more frequently than expected by chance. In contrast, downstream purification parameters exhibited greater diversity, revealing a workflow architecture that combines standardized expression infrastructure with more flexible biochemical components.