Evidence map›Paper›PMID 29181632›Full record

ReviewWorld journal of microbiology & biotechnology2017

Microbial lipolytic fusion enzymes: current state and future perspectives.

Renata Gudiukaite, Alisa Gricajeva

Abstract readReview
PubMed Publisher
In one paragraph

Review in World journal of microbiology & biotechnology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Renata GudiukaiteInstitute of Biosciences, Life Sciences Center, Vilnius University, Sauletekio Avenue 7, 10257, Vilnius, Lithuania. renata.gudiukaite@gf.vu.lt.
Alisa GricajevaInstitute of Biosciences, Life Sciences Center, Vilnius University, Sauletekio Avenue 7, 10257, Vilnius, Lithuania.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Genetic fusion of coding ORFs or connection of proteins in a post translational process are rather novel techniques to build products called fusion proteins that possess combined characteristics of their parental biomolecules. This attractive strategy used to create new enzymes not only diversifies their functionality by improving thermostability, thermo- and catalytic activity, substrate specificity, regio- or enantio-selectivity but also facilitates their purification and increases their yield. Many examples of microbial synthetic fusion biocatalysts are associated with fused enzymes that are involved in biomass degradation. However, one of the leading production segments is occupied by microbial lipolytic enzymes (lipases and esterases). As powerful biocatalysts these enzymes found their application in detergent, food, oil and fat, pulp and paper, leather, textile, cosmetics, biodiesel production industries. Moreover, lipolytic enzymes market is predicted to maintain leadership up to the year of 2024 and exceed millions of dollars. Recently, creation of lipolytic fusion biocatalysts for industrial applications gained more attention since it is not only a way of achievement of enzymes with improved properties but also a way to reduce industrial energy costs and ensure other economic benefits. This paper provides a comprehensive review on current state of microbial lipolytic fusion enzymes and their future potential.

Indexed as

Bacterial ProteinsBiotechnologyEsterasesLipaseOpen Reading FramesProtein EngineeringRecombinant Fusion ProteinsSubstrate SpecificityBacterial ProteinsEsterasesLipaseRecombinant Fusion ProteinsChimeric enzymesEnd-to-end fusionFusion enzymesLinkerLipases

Identifiers

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.