Biology subjects

Roethler, N.

Publications and source records attributed to Roethler, N..

3 recordsLinked to original sources

bioRxiv2026

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.

biophysics↗

bioRxiv2026

Alkamines reveal a hidden layer of steroid and drug metabolism

Biomedical research overlooks most genes in favor of a well-studied minority, yet whether analogous blind spots exist in metabolomics remains unknown. We show that reductive amination, forming secondary amines from aldehydes or ketones and amines, generates a previously hidden class of metabolites we term alkamines. Multiplexed synthesis of 8,475 alkamines combined with MS/MS searches across 1.7 billion spectra identified 1,626 candidates across multiple species and organs. Of these, 56 were confirmed in biological samples, including 27 steroid- and 12 drug-derived alkamines matching prescription patterns. Notably, 77% of synthesized alkamines are absent from PubChem. This combinatorial logic likely explains why alkamines have evaded detection and suggests drug metabolism frameworks substantially underestimate drug-derived metabolite diversity. Reductive amination is an overlooked route modifying steroids, bile acids, and xenobiotics.

biochemistry↗

bioRxiv2024

The Protein Language Visualizer: Sequence Similarity Networks for the Era of Language Models

AO_SCPLOWBSTRACTC_SCPLOWThe era of modern AI-driven representations of proteins is here, and moving fast, yet tools for their intuitive visualization and exploration lag behind. Sequence Similarity Networks (SSNs) have long filled this role for alignment-based methods, providing simple but widely adopted platforms for grouping proteins by homology. Building on this foundation, we present the Protein Language Visualizer (PLVis), a modular framework that applies existing pre-trained protein language model (pLM) embeddings, dimensionality reduction, and clustering to generate interactive maps of protein relationships. The central contribution is the PLVis Repository, an online resource where thousands of reference proteomes can be compared and annotated through an accessible, interactive interface, much like SSNs became impactful not for their technical novelty but for their broad usability. We first validate that well-separated clusters in PLVis reliably capture homology information, while emphasizing caution when interpreting central "fuzzy" regions. We then illustrate the value of PLVis through case studies spanning individual protein families to full proteome comparisons across Mycobacterium and Plasmodium species. By combining methodological clarity with broad accessibility, the PLVis Repository provides a low-barrier platform for exploring proteomes through the lens of language models.

bioinformatics↗