groovDB in 2026: A community-editable database of small molecule biosensors
Love, J. D.; Rafferty, B. M.; Thomas, M.; Springer, M.; Silver, P. A.; d'Oelsnitz, S.
Show abstract
The groovDB database (https://groov.bio) was launched in 2022 with the goal of organizing information on prokaryotic ligand-inducible transcription factors (TFs). This class of proteins is important in fundamental areas of microbiology research and for biotechnological applications that develop biosensors for diagnostics, enzyme screening, and real-time metabolite tracking. Uniquely, groovDB contains stringently curated, literature-referenced data on both TF:DNA and TF:ligand interactions. Here, we describe a major technical update to groovDB, making the database community-editable and adding several advanced features. Users can now add new TF entries and update existing entries using a simple online form. New user interface elements display interactive protein structures and DNA-binding motifs. Updated query methods enable database searches via text, chemical similarity, and attribute-filtering. A new data architecture reduces page load time by five-fold. Finally, the number of TF entries has more than doubled and all source code is now open-access. Graphic abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/670880v3_ufig1.gif" ALT="Figure 1"> View larger version (18K): org.highwire.dtl.DTLVardef@17842beorg.highwire.dtl.DTLVardef@65d608org.highwire.dtl.DTLVardef@1c6e2eborg.highwire.dtl.DTLVardef@5bd824_HPS_FORMAT_FIGEXP M_FIG C_FIG
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Domainator, a flexible software suite for domain-based annotation and neighborhood analysis, identifies proteins involved in antiviral systems 95%
- Datanator: an integrated database of molecular data for quantitatively modeling cellular behavior 94%
- Design, optimization, and analysis of large DNA and RNA nanostructures through interactive visualization, editing, and molecular simulation 92%
Similar papers in this journal
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.