Evidence map›Paper›PMID 40316666›Full record

ArticleScientific reports2025

Computational identification of plastic-degrading enzymes in ocean microbiomes.

Sophie Li, Wencai Zhang

Abstract read
In one paragraph

Article in Scientific reports, 2025. 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. Exploring theFrontiers in microbiology · 2026
    Article
  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.

Sophie LiWinter Springs High School, 130 Tuskawilla Rd, Winter Springs, FL, 32708, USA. lis26_wshs@outlook.com.
Wencai ZhangBurnett School of Biomedical Sciences, College of Medicine, University of Central Florida, 6900 Lake Nona Blvd, Orlando, FL, 32827, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With about 7 billion metric tons of plastic waste already in our environment and over 20 million metric tons of plastic produced annually, plastic waste has become a major global problem. Current methods to address this problem, such as incineration and landfills, are unsustainable and environmentally harmful. More trending approaches, such as plastic degradation using microbial enzymes, are rarely efficient enough to be applied industrially. To fill this gap in our knowledge, we developed a computational method called IPDE (Identification of Plastic Degrading Enzymes) to systematically identify promising enzymes, enzyme combinations, and microbial species for effective plastic waste degradation. Using IPDE, we discovered 50 and 86 enzymes in ocean and topsoil microbiomes, respectively, with at least 46% of these 136 enzymes highly likely to have a role in plastic degradation. Additionally, we identified 43 ocean enzyme combinations and 12 topsoil enzyme combinations, 20% of which contain enzymes that co-occur in the same metabolic pathways. Furthermore, we found 72 operational taxonomic units, with genus-level information available for 20 of them and 18 (25%) of them suggested in literature to be associated with plastic degradation. Our study identified promising plastic-degrading enzyme candidates for future experimental validation, functional studies, protein engineering, and industrial applications. The IPDE tool, which can be applied to other samples for further validation, is freely accessible at https://github.com/SophieL8/Plastic-degrading-enzymes .

Indexed as

BacteriaComputational BiologyEnzymesMicrobiotaPlasticsSeawaterBiodegradation, EnvironmentalOceans and SeasEnzymesPlastics

Identifiers

PMID40316666
PMCPMC12048482

What OpenQuestion holds

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Registered trials

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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.