Evidence map›Paper›PMID 40372653›Full record

ReviewApplied biochemistry and biotechnology2025

Advances in Microbial Alkaline Proteases: Addressing Industrial Bottlenecks Through Genetic and Enzyme Engineering.

Nitin Srivastava, Sunil Kumar Khare

Abstract readReview
PubMed Publisher
In one paragraph

Review in Applied biochemistry and biotechnology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. 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.

Nitin SrivastavaEnzyme and Microbial Biochemistry Laboratory, Department of Chemistry, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, 110016, India.
Sunil Kumar KhareEnzyme and Microbial Biochemistry Laboratory, Department of Chemistry, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, 110016, India. skkhare@chemistry.iitd.ac.in.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Microbial alkaline proteases are versatile enzymes chiefly employed in various industrial sectors, viz., food processing, detergents, leather, textile, pharmaceutical industries. However, the existing bottlenecks, such as lower enzyme yields, stability, purification, specificity, and catalytic rates, bring resistance toward their industrial suitability. The robust microbes are prominent sources of stable enzymes. However, further challenges may exist, such as low yield, difficult purification, and lesser enzymatic efficiency. With the advent of advanced genomic and enzyme engineering approaches, such bottlenecks can be overcome. Initially, the microbial genomes can be used as novel repositories for stable enzyme sequences for further heterologous production with higher enzymatic yields and an easier purification process. Moreover, enzyme improvement through directed evolution and rational engineering could enhance enzyme stability and efficiency. Currently, conventional enzyme improvement methods are increasingly replaced by Artificial Intelligence-Machine Learning (AI-ML) and computational data-driven tools that provide precise information for tailoring enzymes for industrial endeavors. Hence, the current review encompasses a deliberate study of microbial alkaline proteases, their major industrial applications, and the bottlenecks in their commercial implementations. Further, it presents in-detailed solutions, including genetic and enzyme engineering, and insights toward incorporating advanced tools like AI-ML and de novo enzyme engineering to subside the existing challenges.

Indexed as

Bacterial ProteinsEndopeptidasesProtein EngineeringEnzyme Stabilityalkaline proteaseBacterial ProteinsEndopeptidasesAI-ML toolsAlkaline proteasesBottlenecksDe novo enzyme engineeringGenetic engineeringIndustrial applications

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.