Evidence map›Paper›PMID 38254030›Full record

ArticleBMC bioinformatics2024

Tpgen: a language model for stable protein design with a specific topology structure.

Xiaoping Min, Chongzhou Yang, Jun Xie, Yang Huang, Nan Liu, Xiaocheng Jin, Tianshu Wang, Zhibo Kong, Xiaoli Lu, Shengxiang Ge and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC bioinformatics, 2024. 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. 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

12 authors.

Xiaoping Min *School of Informatics, Institute of Artificial Intelligence, Xiamen University, No. 422 Siming South Rd, Xiamen, 361005, China.
Chongzhou Yang *School of Informatics, Institute of Artificial Intelligence, Xiamen University, No. 422 Siming South Rd, Xiamen, 361005, China.
Jun XieSchool of Informatics, Institute of Artificial Intelligence, Xiamen University, No. 422 Siming South Rd, Xiamen, 361005, China.
Yang HuangNational Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Collaborative Innovation Centers of Biologic Products, Xiamen University, No. 422 Siming South Rd, Xiamen, 361005, China.
Nan LiuNational Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Collaborative Innovation Centers of Biologic Products, Xiamen University, No. 422 Siming South Rd, Xiamen, 361005, China.
Xiaocheng JinNational Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Collaborative Innovation Centers of Biologic Products, Xiamen University, No. 422 Siming South Rd, Xiamen, 361005, China.
Tianshu WangSchool of Informatics, Institute of Artificial Intelligence, Xiamen University, No. 422 Siming South Rd, Xiamen, 361005, China.
Zhibo KongNational Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Collaborative Innovation Centers of Biologic Products, Xiamen University, No. 422 Siming South Rd, Xiamen, 361005, China.
Xiaoli LuInformation and Networking Center, Xiamen University, No. 422 Siming South Rd, Xiamen, 361005, China.
Shengxiang GeNational Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Collaborative Innovation Centers of Biologic Products, Xiamen University, No. 422 Siming South Rd, Xiamen, 361005, China. sxge@xmu.edu.cn.
Jun ZhangNational Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Collaborative Innovation Centers of Biologic Products, Xiamen University, No. 422 Siming South Rd, Xiamen, 361005, China.
Ningshao XiaNational Institute of Diagnostics and Vaccine Development in Infectious Diseases, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Collaborative Innovation Centers of Biologic Products, Xiamen University, No. 422 Siming South Rd, Xiamen, 361005, China.

Funding

Chinese Academy of Medical Sciences Research Unit 2019RU022Fundamental Research Funds for the Central Universities 20720220005 and 20720220006National Natural Science Foundation of China 62272399the Key Program Foundation of Fujian Province of China 2021J02006
6 · The paper itself

Abstract

backgroundNatural proteins occupy a small portion of the protein sequence space, whereas artificial proteins can explore a wider range of possibilities within the sequence space. However, specific requirements may not be met when generating sequences blindly. Research indicates that small proteins have notable advantages, including high stability, accurate resolution prediction, and facile specificity modification.

resultsThis study involves the construction of a neural network model named TopoProGenerator(TPGen) using a transformer decoder. The model is trained with sequences consisting of a maximum of 65 amino acids. The training process of TopoProGenerator incorporates reinforcement learning and adversarial learning, for fine-tuning. Additionally, it encompasses a stability predictive model trained with a dataset comprising over 200,000 sequences. The results demonstrate that TopoProGenerator is capable of designing stable small protein sequences with specified topology structures.

conclusionTPGen has the ability to generate protein sequences that fold into the specified topology, and the pretraining and fine-tuning methods proposed in this study can serve as a framework for designing various types of proteins.

Indexed as

Amino AcidsElectric Power SuppliesAmino Acid SequenceLanguageLearningAmino AcidsDe novo protein designLSTMNeural networkProtein topologiesTransformer

Identifiers

PMID38254030
PMCPMC10804651

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.