A Click-Type Enzymatic Method for Antigen-Adjuvant Conjugation
SMALL METHODS(2024)
Beijing Inst Biotechnol
Abstract
The Toll-like receptor 9 (TLR9) stimulator, CpG oligodeoxynucleotide, has emerged as a potent enhancer of protein subunit vaccines. Incorporating the protein antigen directly with the CpG adjuvant presents a novel strategy to significantly reduce the required dosage of CpG compared to traditional methods that use separate components. In contrast to existing chemical conjugation methods, this study introduces an enzymatic approach for antigen-adjuvant coupling using a recombinant endonuclease DCV fused with SpyTag. This fusion protein catalyzes the covalent linkage between itself and the CpG adjuvant under mild conditions. These conjugates can be further linked with target protein antigens containing the SpyCatcher sequence, yielding stable, covalently-linked antigen-adjuvant complexes. The corresponding complex utilizing the receptor-binding domain (RBD) of SARS-CoV-2 spike protein as the model antigen, elicits high-titer, specific antibody production in mice via both subcutaneous administration and intratracheal inoculation. Notably, the tumor vaccine candidate fabricated by this method has also shown significant inhibition of cancer progression after intratracheal administration. The technique ensures precise, site-specific coupling and preserves the antigen's structural integrity due to the post-purification coupling strategy that simplifies manufacturing and aids in developing inhalable vaccines. In this study, an enzymatic method is introduced that employs a recombinant endonuclease DCV fused with SpyTag to conjugate target protein antigens containing the SpyCatcher sequence with the CpG adjuvant under mild conditions. The target covalently-linked antigen-adjuvant complexes could induce strong and specific antibody production when administered via subcutaneous and intratracheal routes. image
MoreTranslated text
Key words
antigen-adjuvant complex,CpG adjuvant,HUH endonuclease,inhalable vaccine
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined