Evidence map›Paper›PMID 42278656›Full record

ArticleInternational journal of molecular sciences2026

Deep Learning-Guided Reverse Translation Enhances Soluble Expression of Recombinant Proteins in

Dong Yu, Nan Geng, Lin Fan, Yanmei Qin, Shangshang Sun, Hao Chen, Ruoyu Wang, Xiaoping Liao, Chun You

Abstract read
In one paragraph

Article in International journal of molecular sciences, 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

9 authors.

Dong YuCollege of Biotechnology, Tianjin University of Science and Technology, Tianjin 300457, China.
Nan GengCollege of Biotechnology, Tianjin University of Science and Technology, Tianjin 300457, China.
Lin FanSino-Danish College, University of Chinese Academy of Sciences, Beijing 100049, China.
Yanmei QinTianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China.
Shangshang SunTianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China.
Hao ChenCollege of Biotechnology, Tianjin University of Science and Technology, Tianjin 300457, China.
Ruoyu WangTianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China.
Xiaoping LiaoTianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China.ORCID 0000-0002-9852-910X
Chun YouTianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China.ORCID 0000-0003-2305-336X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Enhancing the soluble expression of heterologous proteins in chassis microorganisms is critical for fundamental biological research and synthetic biology-driven industrial applications. Current methods for designing DNA sequences to ensure high soluble expression often rely excessively on high-frequency codons while overlooking optimal codon context, leading to suboptimal outcomes. To address these limitations, we developed an integrated deep learning framework combining a synonymous codon generation (SCG) model and a gene expression level prediction (GELP) model. The SCG model captures codon usage patterns in

Indexed as

Deep LearningEscherichia coliProtein BiosynthesisRecombinant ProteinsCodonCodon UsageSolubilityCodonRecombinant Proteinscodon optimizationdeep learningEscherichia colirecombinant proteinsoluble expression

Identifiers

PMID42278656
PMCPMC13258643

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.