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Mani Subramanian

Researcher at University of Iowa

Publications -  19
Citations -  606

Mani Subramanian is an academic researcher from University of Iowa. The author has contributed to research in topics: Theobromine & Caffeine. The author has an hindex of 12, co-authored 19 publications receiving 500 citations.

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Two Distinct Pathways for Metabolism of Theophylline and Caffeine Are Coexpressed in Pseudomonas putida CBB5

TL;DR: To the authors' knowledge, this is the first report of theophylline N demethylation and coexpression of distinct pathways for caffeine and theophyLLine degradation in bacteria.
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Novel, Highly Specific N-Demethylases Enable Bacteria To Live on Caffeine and Related Purine Alkaloids

TL;DR: This work reports the first report of bacterial N-demethylase genes that enable bacteria to live on caffeine and represent a new class of Rieske oxygenases and have the potential to produce biofuels, animal feed, and pharmaceuticals from coffee and tea waste.
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Genetic characterization of caffeine degradation by bacteria and its potential applications.

TL;DR: Various biotechnological applications of these genes responsible for bacterial caffeine degradation, including bio‐decaffeination, remediation of caffeine‐contaminated environments, production of chemical and fuels and development of diagnostic tests have also been demonstrated.
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Characterization of a broad-specificity non-haem iron N-demethylase from Pseudomonas putida CBB5 capable of utilizing several purine alkaloids as sole carbon and nitrogen source.

TL;DR: Ndm was deduced to be a Rieske [2Fe-2S]-domain-containing non-haem iron oxygenase based on its distinct absorption spectrum and significant identity of the N-terminal sequences of NdmA and NdmB with the gene product of an uncharacterized caffeine demethylase in P. putida IF-3 and a hypothetical protein in Janthinobacterium sp.
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Investigative mining of sequence data for novel enzymes: a case study with nitrilases.

TL;DR: Predictions from sequence analysis and distant superfamily structures yielded enzyme activities with high selectivity for mandelonitrile, suggesting that similar data mining techniques can be used to identify other substrate-specific enzymes from published, unannotated sequences.