Evidence map›Paper›PMID 35254498›Full record

ReviewApplied microbiology and biotechnology2022

Bioprospecting of microbial enzymes: current trends in industry and healthcare.

Eswar Rao Tatta, Madangchanok Imchen, Jamseel Moopantakath, Ranjith Kumavath

Abstract readReview
PubMed Publisher
In one paragraph

Review in Applied microbiology and biotechnology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Review
  2. Review
  3. Special Issue: "Microbial Enzymes for Biotechnological Applications".International journal of molecular sciences · 2025
    Article
  4. Review
  5. Review
  6. Review
  7. Fungal pectinases: an insight into production, innovations and applications.World journal of microbiology & biotechnology · 2023
    Review
  8. Article
  9. 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

4 authors.

Eswar Rao TattaDepartment of Genomic Science, School of Biological Sciences, Central University of Kerala, Tejaswini Hills, Periya (PO.), Kasaragod, Kerala, 671320, India.
Madangchanok ImchenDepartment of Genomic Science, School of Biological Sciences, Central University of Kerala, Tejaswini Hills, Periya (PO.), Kasaragod, Kerala, 671320, India.
Jamseel MoopantakathDepartment of Genomic Science, School of Biological Sciences, Central University of Kerala, Tejaswini Hills, Periya (PO.), Kasaragod, Kerala, 671320, India.
Ranjith KumavathDepartment of Genomic Science, School of Biological Sciences, Central University of Kerala, Tejaswini Hills, Periya (PO.), Kasaragod, Kerala, 671320, India. RNKumavath@gmail.com.ORCID http://orcid.org/0000-0002-0751-5491

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Microbial enzymes have an indispensable role in producing foods, pharmaceuticals, and other commercial goods. Many novel enzymes have been reported from all domains of life, such as plants, microbes, and animals. Nonetheless, industrially desirable enzymes of microbial origin are limited. This review article discusses the classifications, applications, sources, and challenges of most demanded industrial enzymes such as pectinases, cellulase, lipase, and protease. In addition, the production of novel enzymes through protein engineering technologies such as directed evolution, rational, and de novo design, for the improvement of existing industrial enzymes is also explored. We have also explored the role of metagenomics, nanotechnology, OMICs, and machine learning approaches in the bioprospecting of novel enzymes. Overall, this review covers the basics of biocatalysts in industrial and healthcare applications and provides an overview of existing microbial enzyme optimization tools. KEY POINTS: • Microbial bioactive molecules are vital for therapeutic and industrial applications. • High-throughput OMIC is the most proficient approach for novel enzyme discovery. • Comprehensive databases and efficient machine learning models are the need of the hour to fast forward de novo enzyme design and discovery.

Indexed as

BacteriaBioprospectingEnzymesFungiProtein EngineeringAnimalsBiotechnologyHealth Care SectorIndustryMetagenomicsEnzymesBio-prospectionEnzyme designIndustrial biotechnologyIndustrial enzymesMicrobial enzymes

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.