Unlocking Enzyme Discovery: Leveraging Multi-Omics, Machine Learning, and De Novo Design.

Hongming Xia, Chunxiu Zhou, Baotong Fu, Huawen Han

Abstract read

In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Hongming XiaCenter for Grassland Microbiome, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, Gansu, China.

Chunxiu ZhouCenter for Grassland Microbiome, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, Gansu, China.

Baotong FuCenter for Grassland Microbiome, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, Gansu, China.

Huawen HanCenter for Grassland Microbiome, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, Gansu, China. hanhuawen@lzu.edu.cn.ORCID https://orcid.org/0000-0002-6284-110X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Enzymes are fundamental protein catalysts essential to life processes and widely applied in industrial and healthcare sectors. However, the broader application of natural enzymes is constrained by their inherent catalytic limitations, and traditional discovery methods such as microbial enrichment are often slow and low-throughput. Driven by advances in multi-omics and artificial intelligence, a range of novel screening strategies has been developed, enabling significant enhancements in both catalytic efficiency and stability of enzymes. This chapter assesses high-throughput approaches, such as metagenomics, metaproteomics, machine learning, and de novo design, comparing their respective advantages and limitations for enzyme discovery. Furthermore, we discuss the application potential of lignocellulose-degrading and plastic-degrading enzymes in biomass conversion and plastic waste recycling.

Indexed as

EnzymesMachine LearningProteomicsLigninMetagenomicsMultiomicsEnzymesLigninDe novo enzymeEnzyme discoveryMachine learningMetagenomicsMetaproteomics

Identifiers

PMID42108294

DOI10.1038/s41467-025-58521-y

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.