Joosten, K. | bioRxiv Science

bioRxiv2024

LogoMotif: a comprehensive database of transcription factor binding site profiles in Actinobacteria

Actinobacteria undergo a complex multicellular life cycle and produce a wide range of specialized metabolites, including the majority of the antibiotics. These biological processes are controlled by intricate regulatory pathways, and to better understand how they are controlled we need to augment our insights into the transcription factor binding sites. Here, we present LogoMotif (https://logomotif.bioinformatics.nl), an open-source database for characterized and predicted transcription factor binding sites in Actinobacteria, along with their cognate position weight matrices and hidden Markov models. Genome-wide predictions of binding site locations in Streptomyces model organisms are supplied and visualized in interactive regulatory networks. In the web interface, users can freely access, download and investigate the underlying data. With this curated collection of actinobacterial regulatory interactions, LogoMotif serves as a basis for binding site predictions, thus providing users with clues on how to elicit the expression of genes of interest and guide genome mining efforts. HighlightsO_LIActinobacterial regulatory networks are key for compound discovery, including antibiotics. C_LIO_LIContains [~]400 validated and [~]12,100 predicted interactions, presented in interactive networks. C_LIO_LIServes as foundation for regulatory predictions in the gene cluster detection tool, antiSMASH. C_LIO_LILogoMotifs data and algorithms provide knowledge on expression and functional inference of genes. C_LIO_LILogoMotif aids in the discovery of novel chemistry within Actinobacteria and beyond. C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=74 SRC="FIGDIR/small/582527v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@cfb7cdorg.highwire.dtl.DTLVardef@12c017borg.highwire.dtl.DTLVardef@155096org.highwire.dtl.DTLVardef@15dcbcc_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2018

Homology-based loop modelling yields more complete crystallographic protein structures

Inherent protein flexibility, poor or low-resolution diffraction data, or poor electron density maps, often inhibit building complete structural models during X-ray structure determination. However, advances in crystallographic refinement and model building nowadays often allow to complete previously missing parts. Here, we present algorithms that identify regions missing in a certain model but present in homologous structures in the Protein Data Bank (PDB), and \"graft\" these regions of interest. These new regions are refined and validated in a fully automated procedure. Including these developments in our PDB-REDO pipeline, allowed to build 24,962 missing loops in the PDB. The models and the automated procedures are publically available through the PDB-REDO databank and web server (https://pdb-redo.eu). More complete protein structure models enable a higher quality public archive, but also a better understanding of protein function, better comparison between homologous structures, and more complete data mining in structural bioinformatics projects.\n\nSynopsisThousands of missing regions in existing protein structure models are completed using new methods based on homology.

bioinformatics↗