识别针对 SARS-CoV-2 Omicron 变异的表位:硅学疫苗设计方法

Manpreet Kaur, Gobind Ram
{"title":"识别针对 SARS-CoV-2 Omicron 变异的表位:硅学疫苗设计方法","authors":"Manpreet Kaur, Gobind Ram","doi":"10.2174/0126667975298936240705064458","DOIUrl":null,"url":null,"abstract":"\n\nSARS-CoV-2, which causes COVID-19, resulted in a global pandemic, and\nthere were millions of confirmed cases and deaths worldwide. The vaccines were developed and\ndistributed to help control the spread of the virus. The numbers and information related to the\nCOVID-19 pandemic have likely evolved. Therefore, rapid immunological epitope identification\nwould be a useful screening technique for vaccine candidates.\n\n\n\nThe aim of this study is to anticipate the protective epitopes for vaccine development\nusing bioinformatics methods and resources\n\n\n\nThe SARS-CoV-2 genome and protein sequences were retrieved. Furthermore, using\nthe ABCpred server, sequential B-cell epitope analysis was carried out. The Ellipro algorithm was\nused to forecast discontinuous B-cell epitopes. Moreover, by utilising the NetCTL server, a sequential\nT-cell epitope analysis was carried out. Furthermore, the 3D structure of the peptide was created\nusing the PEP-FOLD3 server, and the 3D structure of the HLA molecule was identified using the\nhomology modelling tool. The molecular docking was performed by AutoDock Vina.\n\n\n\nThere were 20 B-cell epitopes altogether, of which 11 are highly antigenic. After assessing\nthe antigenicity and toxicity of each resultant epitope, it was determined that the epitope\nSVLYNLAPFFTFKCYG is highly antigenic. Then, out of the 6 T-cell epitopes we had found,\n\"RSYSFRPTY\" was chosen as the epitope most suited for further research. Consequently, 72.42% of\nthe population is covered overall. The structure that was generated was refined and energyminimized.\nRSYSFRPTY's binding affinity to the groove of HLA-B*15:01 was determined by docking\nstudy to be -7.5 kcal/mol. PyMOL's visualisation of the docking result for predicting binding\nsites.\n\n\n\nThe final B-cell and T-cell epitopes are “SVLYNLAPFFTFKCYG” and\n“RSYSFRPTY” in terms of antigenicity score and nonallergenic and nontoxic qualities. An in Silico\nstudy indicated that our hypothesised T cell epitope “RSYSFRPTY” had a greater affinity for binding\nwith its receptor, which might elicit an immune response against the omicron variant.\n","PeriodicalId":10815,"journal":{"name":"Coronaviruses","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Identification of Epitope Against Omicron Variant of SARS-CoV-2:\\nIn Silico Vaccine Design Approach\",\"authors\":\"Manpreet Kaur, Gobind Ram\",\"doi\":\"10.2174/0126667975298936240705064458\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n\\nSARS-CoV-2, which causes COVID-19, resulted in a global pandemic, and\\nthere were millions of confirmed cases and deaths worldwide. The vaccines were developed and\\ndistributed to help control the spread of the virus. The numbers and information related to the\\nCOVID-19 pandemic have likely evolved. Therefore, rapid immunological epitope identification\\nwould be a useful screening technique for vaccine candidates.\\n\\n\\n\\nThe aim of this study is to anticipate the protective epitopes for vaccine development\\nusing bioinformatics methods and resources\\n\\n\\n\\nThe SARS-CoV-2 genome and protein sequences were retrieved. Furthermore, using\\nthe ABCpred server, sequential B-cell epitope analysis was carried out. The Ellipro algorithm was\\nused to forecast discontinuous B-cell epitopes. Moreover, by utilising the NetCTL server, a sequential\\nT-cell epitope analysis was carried out. Furthermore, the 3D structure of the peptide was created\\nusing the PEP-FOLD3 server, and the 3D structure of the HLA molecule was identified using the\\nhomology modelling tool. The molecular docking was performed by AutoDock Vina.\\n\\n\\n\\nThere were 20 B-cell epitopes altogether, of which 11 are highly antigenic. After assessing\\nthe antigenicity and toxicity of each resultant epitope, it was determined that the epitope\\nSVLYNLAPFFTFKCYG is highly antigenic. Then, out of the 6 T-cell epitopes we had found,\\n\\\"RSYSFRPTY\\\" was chosen as the epitope most suited for further research. Consequently, 72.42% of\\nthe population is covered overall. The structure that was generated was refined and energyminimized.\\nRSYSFRPTY's binding affinity to the groove of HLA-B*15:01 was determined by docking\\nstudy to be -7.5 kcal/mol. PyMOL's visualisation of the docking result for predicting binding\\nsites.\\n\\n\\n\\nThe final B-cell and T-cell epitopes are “SVLYNLAPFFTFKCYG” and\\n“RSYSFRPTY” in terms of antigenicity score and nonallergenic and nontoxic qualities. An in Silico\\nstudy indicated that our hypothesised T cell epitope “RSYSFRPTY” had a greater affinity for binding\\nwith its receptor, which might elicit an immune response against the omicron variant.\\n\",\"PeriodicalId\":10815,\"journal\":{\"name\":\"Coronaviruses\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Coronaviruses\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2174/0126667975298936240705064458\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Coronaviruses","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2174/0126667975298936240705064458","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

引起 COVID-19 的 SARS-CoV-2 导致了全球大流行,全世界有数百万确诊病例和死亡。疫苗的开发和分发有助于控制病毒的传播。与 COVID-19 大流行相关的数字和信息很可能已经发生了演变。本研究的目的是利用生物信息学方法和资源预测疫苗开发所需的保护性表位。此外,利用 ABCpred 服务器进行了 B 细胞表位序列分析。使用 Ellipro 算法预测不连续的 B 细胞表位。此外,还利用 NetCTL 服务器进行了连续 T 细胞表位分析。此外,还利用 PEP-FOLD3 服务器创建了多肽的三维结构,并利用同源性建模工具确定了 HLA 分子的三维结构。分子对接由 AutoDock Vina 完成。共有 20 个 B 细胞表位,其中 11 个具有高度抗原性。在评估了每个表位的抗原性和毒性后,确定表位 SVLYNLAPFFTFKCYG 具有高抗原性。然后,在我们发现的 6 个 T 细胞表位中,"RSYSFRPTY "被选为最适合进一步研究的表位。因此,总体上覆盖了 72.42% 的人群。通过对接研究确定 RSYSFRPTY 与 HLA-B*15:01 沟槽的结合亲和力为 -7.5 kcal/mol。PyMOL 预测结合位点的对接结果可视化。最终的 B 细胞和 T 细胞表位分别为 "SVLYNLAPFFTFKCYG "和 "RSYSFRPTY"。一项硅胶研究表明,我们假定的 T 细胞表位 "RSYSFRPTY "与其受体的结合亲和力更强,可能会引起针对欧米茄变体的免疫反应。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Identification of Epitope Against Omicron Variant of SARS-CoV-2: In Silico Vaccine Design Approach
SARS-CoV-2, which causes COVID-19, resulted in a global pandemic, and there were millions of confirmed cases and deaths worldwide. The vaccines were developed and distributed to help control the spread of the virus. The numbers and information related to the COVID-19 pandemic have likely evolved. Therefore, rapid immunological epitope identification would be a useful screening technique for vaccine candidates. The aim of this study is to anticipate the protective epitopes for vaccine development using bioinformatics methods and resources The SARS-CoV-2 genome and protein sequences were retrieved. Furthermore, using the ABCpred server, sequential B-cell epitope analysis was carried out. The Ellipro algorithm was used to forecast discontinuous B-cell epitopes. Moreover, by utilising the NetCTL server, a sequential T-cell epitope analysis was carried out. Furthermore, the 3D structure of the peptide was created using the PEP-FOLD3 server, and the 3D structure of the HLA molecule was identified using the homology modelling tool. The molecular docking was performed by AutoDock Vina. There were 20 B-cell epitopes altogether, of which 11 are highly antigenic. After assessing the antigenicity and toxicity of each resultant epitope, it was determined that the epitope SVLYNLAPFFTFKCYG is highly antigenic. Then, out of the 6 T-cell epitopes we had found, "RSYSFRPTY" was chosen as the epitope most suited for further research. Consequently, 72.42% of the population is covered overall. The structure that was generated was refined and energyminimized. RSYSFRPTY's binding affinity to the groove of HLA-B*15:01 was determined by docking study to be -7.5 kcal/mol. PyMOL's visualisation of the docking result for predicting binding sites. The final B-cell and T-cell epitopes are “SVLYNLAPFFTFKCYG” and “RSYSFRPTY” in terms of antigenicity score and nonallergenic and nontoxic qualities. An in Silico study indicated that our hypothesised T cell epitope “RSYSFRPTY” had a greater affinity for binding with its receptor, which might elicit an immune response against the omicron variant.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
1.50
自引率
0.00%
发文量
0
期刊最新文献
COVID-19 Vaccine Effectiveness on the Severity of Disease: A Case-control Study in Southeastern Iran Evaluation of Influenza Vaccination Coverage Among Healthcare Workers During the COVID-19 Pandemic and Analysis of Motivating and Hindering Factors Novel Coronavirus: An Overview of the History, Pathophysiology, Diagnosis, and Management of the Pandemic COVID-19 Catastrophe, with Special Emphasis on Herbal Treatment Alternatives Spectrum of Long COVID Symptoms and Management Approaches in Europe and Latin America Identification of Epitope Against Omicron Variant of SARS-CoV-2: In Silico Vaccine Design Approach
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1