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A combined bioinformatics and functional metagenomics approach to discovering lipolytic biocatalysts

Overview of attention for article published in Frontiers in Microbiology, October 2015
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Title
A combined bioinformatics and functional metagenomics approach to discovering lipolytic biocatalysts
Published in
Frontiers in Microbiology, October 2015
DOI 10.3389/fmicb.2015.01110
Pubmed ID
Authors

Thorsten Masuch, Anna Kusnezowa, Sebastian Nilewski, José T. Bautista, Robert Kourist, Lars I. Leichert

Abstract

The majority of protein sequence data published today is of metagenomic origin. However, our ability to assign functions to these sequences is often hampered by our general inability to cultivate the larger part of microbial species and the sheer amount of sequence data generated in these projects. Here we present a combination of bioinformatics, synthetic biology, and Escherichia coli genetics to discover biocatalysts in metagenomic datasets. We created a subset of the Global Ocean Sampling dataset, the largest metagenomic project published to date, by removing all proteins that matched Hidden Markov Models of known protein families from PFAM and TIGRFAM with high confidence (E-value > 10(-5)). This essentially left us with proteins with low or no homology to known protein families, still encompassing ~1.7 million different sequences. In this subset, we then identified protein families de novo with a Markov clustering algorithm. For each protein family, we defined a single representative based on its phylogenetic relationship to all other members in that family. This reduced the dataset to ~17,000 representatives of protein families with more than 10 members. Based on conserved regions typical for lipases and esterases, we selected a representative gene from a family of 27 members for synthesis. This protein, when expressed in E. coli, showed lipolytic activity toward para-nitrophenyl (pNP) esters. The K m-value of the enzyme was 66.68 μM for pNP-butyrate and 68.08 μM for pNP-palmitate with k cat/K m values at 3.4 × 10(6) and 6.6 × 10(5) M(-1)s(-1), respectively. Hydrolysis of model substrates showed enantiopreference for the R-form. Reactions yielded 43 and 61% enantiomeric excess of products with ibuprofen methyl ester and 2-phenylpropanoic acid ethyl ester, respectively. The enzyme retains 50% of its maximum activity at temperatures as low as 10°C, its activity is enhanced in artificial seawater and buffers with higher salt concentrations with an optimum osmolarity of 3,890 mosmol/l.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 70 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 2 3%
India 1 1%
Unknown 67 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 23%
Student > Master 13 19%
Researcher 6 9%
Student > Postgraduate 6 9%
Student > Bachelor 5 7%
Other 10 14%
Unknown 14 20%
Readers by discipline Count As %
Agricultural and Biological Sciences 25 36%
Biochemistry, Genetics and Molecular Biology 18 26%
Chemistry 3 4%
Environmental Science 2 3%
Business, Management and Accounting 1 1%
Other 6 9%
Unknown 15 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 04 November 2015.
All research outputs
#14,680,831
of 23,498,099 outputs
Outputs from Frontiers in Microbiology
#12,924
of 25,939 outputs
Outputs of similar age
#146,772
of 280,675 outputs
Outputs of similar age from Frontiers in Microbiology
#211
of 440 outputs
Altmetric has tracked 23,498,099 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 25,939 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.3. This one is in the 45th percentile – i.e., 45% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 280,675 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 440 others from the same source and published within six weeks on either side of this one. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.