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Evolutionary couplings of amino acid residues reveal structure and function of bacterial signaling proteins.
Szurmant, Hendrik.
Affiliation
  • Szurmant H; Basic Medical Science, College of Osteopathic Medicine of the Pacific, Western University of Health Sciences, Pomona, CA, USA.
Mol Microbiol ; 112(2): 432-437, 2019 08.
Article in En | MEDLINE | ID: mdl-31102561
ABSTRACT
The genomic era along with major advances in high-throughput sequencing technology has led to a rapid expansion of the genomic and consequently the protein sequence space. Bacterial extracytoplasmic function sigma factors have emerged as an important group of signaling proteins in bacteria involved in many regulatory decisions, most notably the adaptation to cell envelope stress. Their wide prevalence and amplification among bacterial genomes has led to sub-group classification and the realization of diverse signaling mechanisms. Mathematical frameworks have been developed to utilize extensive protein sequence alignments to extract co-evolutionary signals of interaction. This has proven useful in a number of different biological fields, including de novo structure prediction, protein-protein partner identification and the elucidation of alternative protein conformations for signal proteins, to name a few. The mathematical tools, commonly referred to under the name 'Direct Coupling Analysis' have now been applied to deduce molecular mechanisms of activation for sub-groups of extracytoplasmic sigma factors adding to previous successes on bacterial two-component signaling proteins. The amplification of signal transduction protein genes in bacterial genomes made them the first to be amenable to this approach but the sequences are available now to aid the molecular microbiologist, no matter their protein pathway of interest.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Sigma Factor / Bacteria / Bacterial Proteins / Evolution, Molecular Type of study: Prognostic_studies / Risk_factors_studies Language: En Journal: Mol Microbiol Journal subject: BIOLOGIA MOLECULAR / MICROBIOLOGIA Year: 2019 Document type: Article Affiliation country: Estados Unidos

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Sigma Factor / Bacteria / Bacterial Proteins / Evolution, Molecular Type of study: Prognostic_studies / Risk_factors_studies Language: En Journal: Mol Microbiol Journal subject: BIOLOGIA MOLECULAR / MICROBIOLOGIA Year: 2019 Document type: Article Affiliation country: Estados Unidos