| 杨玉花,库鑫,宫雅楠,孟凡亮,卜东波,郭亚慧,魏销玥,龙丽瑾,范佳铭,张茂俊,张建中,闫笑梅.金黄色葡萄球菌类肠毒素W的超抗原活性位点预测与克隆表达[J].Chinese journal of Epidemiology,2023,44(4):629-635 |
| 金黄色葡萄球菌类肠毒素W的超抗原活性位点预测与克隆表达 |
| Prediction of superantigen active sites and clonal expression of staphylococcal enterotoxin-like W |
| Received:August 22, 2022 |
| DOI：10.3760/cma.j.cn112338-20220822-00725 |
| KeyWord: 金黄色葡萄球菌肠毒素 分子对接 超抗原活性位点 克隆表达 |
| English Key Word: Staphylococcal enterotoxins Molecular docking Superantigen active site Clonal expression |
| FundProject:国家自然科学基金面上项目（81873959） |
| Author Name | Affiliation | E-mail | | Yang Yuhua | State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China | | | Ku Xin | Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
University of Chinese Academy of Sciences, Beijing 100049, China | | | Gong Yanan | State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China | | | Meng Fanliang | State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China | | | Bu Dongbo | Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
University of Chinese Academy of Sciences, Beijing 100049, China
Big Data Academy, Zhongke, Zhengzhou 450046, China | | | Guo Yahui | State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China
Baotou Medical College, Inner Mongolia University of Science and Technology, Baotou 014040, China | | | Wei Xiaoyue | State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China | | | Long Lijin | State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China
China Medical University, Shenyang 110122, China | | | Fan Jiaming | State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China | | | Zhang Maojun | State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China | | | Zhang Jianzhong | State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China | | | Yan Xiaomei | State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China | yanxiaomei@icdc.cn |
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| Abstract: |
| 目的 预测金黄色葡萄球菌类肠毒素W（SElW）与T细胞受体（TCR）的对接和超抗原活性位点，并对SElW进行克隆表达和纯化。方法 利用AlphaFold对SElW蛋白单体进行三维结构预测，借助SAVES在线服务器使用ERRAT、拉氏图和Verify_3D等软件对蛋白模型进行评估。用ZDOCK服务器模拟SElW与TCR的对接构象，并对SElW与其他肠毒素的氨基酸序列进行比对。设计引物扩增selw基因，将该片段重组于pMD18-T载体中并进行测序；经限制性内切酶BamHⅠ和Hind Ⅲ双酶切后将目的片段重组于表达质粒pET-28a（+）中，重组质粒鉴定后用IPTG诱导表达；用亲和层析法对表达于上清中的SElW蛋白进行纯化，用BCA法对蛋白进行定量。结果 三维结构预测结果显示，SElW蛋白由氨基末端和羧基末端两个结构域组成，其中氨基末端结构域由3个α-螺旋和6个β-片层组成，羧基末端结构域由2个α-螺旋和7个反向平行的β-片层组成。SElW蛋白模型的整体质量因素得分为98.08，其中93.24％的氨基酸Verify_3D得分≥0.2，且无氨基酸位于不允许区。模拟对接预测选择评分最高的对接构象（评分值为1 521.328）作为分析对象，采用PyMOL软件分析SElW与TCR之间形成的19个氢键。结合序列比对和既往研究结果，本研究预测发现了SElW蛋白的5个重要超抗原活性位点，分别是Y18、N19、W55、C88和C98。通过克隆表达和蛋白纯化，获得了高纯度的可溶性重组蛋白SElW。结论 研究预测了SElW蛋白中5个可能的超抗原活性位点，成功构建并表达了SElW蛋白，为进一步探索SElW免疫识别机制奠定了基础。 |
| English Abstract: |
| Objective The docking and superantigen activity sites of staphylococcal enterotoxin-like W (SElW) and T cell receptor (TCR) were predicted, and its SElW was cloned, expressed and purified. Methods AlphaFold was used to predict the 3D structure of SElW protein monomers, and the protein models were evaluated with the help of the SAVES online server from ERRAT, Ramachandran plot, and Verify_3D. The ZDOCK server simulates the docking conformation of SElW and TCR, and the amino acid sequences of SElW and other serotype enterotoxins were aligned. The primers were designed to amplify selw, and the fragment was recombined into the pMD18-T vector and sequenced. Then recombinant plasmid pMD18-T was digested with BamHⅠand Hind Ⅲ. The target fragment was recombined into the expression plasmid pET-28a(+). After identification of the recombinant plasmid, the protein expression was induced by isopropyl-beta-D- thiogalactopyranoside. The SElW expressed in the supernatant was purified by affinity chromatography and quantified by the BCA method. Results The predicted three-dimensional structure showed that the SElW protein was composed of two domains, the amino-terminal and the carboxy-terminal. The amino-terminal domain was composed of 3 α-helices and 6 β-sheets, and the carboxy-terminal domain included 2 α-helices and 7 antiparallel β-sheets composition. The overall quality factor score of the SElW protein model was 98.08, with 93.24% of the amino acids having a Verify_3D score ≥0.2 and no amino acids located in disallowed regions. The docking conformation with the highest score (1 521.328) was selected as the analysis object, and the 19 hydrogen bonds between the corresponding amino acid residues of SElW and TCR were analyzed by PyMOL. Combined with sequence alignment and the published data, this study predicted and found five important superantigen active sites, namely Y18, N19, W55, C88, and C98. The highly purified soluble recombinant protein SElW was obtained with cloning, expression, and protein purification.Conclusions The study found five superantigen active sites in SElW protein that need special attention and successfully constructed and expressed the SElW protein, which laid the foundation for further exploration of the immune recognition mechanism of SElW. |
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