(Below N is a link to NCBI taxonomic web page and E link to ESTHER at designed phylum.) > cellular organisms: NE > Bacteria: NE > Proteobacteria: NE > Alphaproteobacteria: NE > Rhizobiales: NE > Rhizobiaceae: NE > Rhizobium/Agrobacterium group: NE > Rhizobium: NE > Rhizobium leguminosarum: NE
Warning: This entry is a compilation of different species or line or strain with more than 90% amino acide identity. You can retrieve all strain data
(Below N is a link to NCBI taxonomic web page and E link to ESTHER at designed phylum.) Rhizobium leguminosarum bv. viciae: N, E.
Rhizobium leguminosarum bv. trifolii: N, E.
Rhizobium leguminosarum bv. viciae WSM1455: N, E.
Rhizobium leguminosarum bv. trifolii WSM2297: N, E.
Rhizobium leguminosarum bv. trifolii WSM2012: N, E.
Rhizobium leguminosarum bv. trifolii WSM1689: N, E.
Rhizobium leguminosarum bv. phaseoli CCGM1: N, E.
Rhizobium leguminosarum bv. viciae 3841: N, E.
Rhizobium leguminosarum bv. trifolii WSM1325: N, E.
Rhizobium leguminosarum bv. trifolii WSM2304: N, E.
Rhizobium leguminosarum bv. trifolii WU95: N, E.
Rhizobium leguminosarum bv. trifolii WSM597: N, E.
Rhizobium leguminosarum bv. trifolii CB782: N, E.
LegendThis sequence has been compared to family alignement (MSA) red => minority aminoacid blue => majority aminoacid color intensity => conservation rate title => sequence position(MSA position)aminoacid rate Catalytic site Catalytic site in the MSA MPTIDILTSFISYEELGEGDPIVFLHGNPTSSHLWRNIMPVIGPGRCLAP DLIGMGRSGKPDIGYRYGDHIAYLDAWFDALDLDDVVLVGHDWGGALAFD WASRHAERVRGIAFMETVLRPMSWQDLPGGGKARYELLRGTGTGEAKVLD ENFFIEQALRATTLKGLSDADWDVYRAPYPDRDSRRPLLEWPRAMPINGE PADVVARIEAYDRWLAASPQTPKLLLTFDGPAETLLIGSEMISWCRDTIA GLEIRGCGPARHIAPEDQPEAIAAEIKNWIDRRGLRTA
https://www.researchsquare.com/article/rs-1027271/v1
Next-generation sequencing doubles genomic databases every 2.5 years. The accumulation of sequence data raises the need to speed up functional analysis. Herein, we present a pipeline integrating bioinformatics and microfluidics and its application for high-throughput mining of novel haloalkane dehalogenases. We employed bioinformatics to identify 2,905 putative dehalogenases and selected 45 representative enzymes, of which 24 were produced in soluble form. Droplet-based microfluidics accelerates subsequent experimental testing up to 20,000 reactions per day while achieving 1,000-fold lower protein consumption. This resulted in doubling the dehalogenation 'toolbox' characterized over three decades, yielding biocatalysts surpassing the efficiency of currently available enzymes. Combining microfluidics with modern global data analysis provided precious mechanistic information related to the high catalytic efficiency of new variants. This pipeline applied to other enzyme families can accelerate the identification of biocatalysts for industrial applications as well as the collection of high-quality data for machine learning.
