2018
DOI: 10.1038/s41586-018-0225-9
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A distinct abundant group of microbial rhodopsins discovered using functional metagenomics

Abstract: Many organisms capture or sense sunlight using rhodopsin pigments, which are integral membrane proteins that bind retinal chromophores. Rhodopsins comprise two distinct protein families , type-1 (microbial rhodopsins) and type-2 (animal rhodopsins). The two families share similar topologies and contain seven transmembrane helices that form a pocket in which retinal is linked covalently as a protonated Schiff base to a lysine at the seventh transmembrane helix. Type-1 and type-2 rhodopsins show little or no seq… Show more

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Cited by 209 publications
(505 citation statements)
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“…Binning of contigs assembled from metagenomes, is an error‐prone which can result in spurious sequences containing fragments of multiple unrelated organisms (Sharon and Banfield, ). Contigs deposited in GenBank as diderms were re‐classified after manual validation (Supporting Information Table S5), confirming our previous observation with cultured proteobacteria representatives (Pushkarev et al ., ) that heliorhodopsins are mostly absent in diderms.…”
Section: Resultsmentioning
confidence: 82%
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“…Binning of contigs assembled from metagenomes, is an error‐prone which can result in spurious sequences containing fragments of multiple unrelated organisms (Sharon and Banfield, ). Contigs deposited in GenBank as diderms were re‐classified after manual validation (Supporting Information Table S5), confirming our previous observation with cultured proteobacteria representatives (Pushkarev et al ., ) that heliorhodopsins are mostly absent in diderms.…”
Section: Resultsmentioning
confidence: 82%
“…Based on microbial rhodopsin sequences (Supporting Information Table S1) previously reported (Pushkarev et al, 2018) we created a graftM (Boyd et al, 2018) gene classification package to identify and classify proteins into rhodopsins or heliorhodopsins. GraftM is a tool for fast and accurate phylogenetically informed classification of genes, which employs hidden Markov Models (HMM) (Boyd et al, 2018;Potter et al, 2018).…”
Section: Resultsmentioning
confidence: 99%
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