Marzieh Asadi, Younes Ghasemi, Navid Nezafat, Bahador Sarkari, Maryam Baneshi, Zohreh Mostafavi-Pour, Mohammad Hossein Anbardar, Amir Savardashtaki
{"title":"设计用于诊断人类巨细胞病毒感染的新型多表位抗原:免疫信息学方法","authors":"Marzieh Asadi, Younes Ghasemi, Navid Nezafat, Bahador Sarkari, Maryam Baneshi, Zohreh Mostafavi-Pour, Mohammad Hossein Anbardar, Amir Savardashtaki","doi":"10.1002/bab.2677","DOIUrl":null,"url":null,"abstract":"<p><p>Human cytomegalovirus (HCMV) infection can lead to congenital infections and severe complications, particularly in immunocompromised individuals. Current serological tests for diagnosing HCMV infection often face limitations in sensitivity and specificity. Developing multi-epitope antigens for serological assays offers the potential for enhancing diagnostic accuracy. This study aimed to design a novel multi-epitope antigen for HCMV infection diagnosis using immunoinformatic approaches. Five tegument proteins (universal protein resource [UniProt] ID: Po8318, Po6725, F5HC97, Q6RX10, and F5HC05) were selected based on their antigenic properties and literature review. Six linear B-cell epitopes were predicted within conserved regions of each antigen sequence and linked with appropriate linkers. The designed multi-epitope antigen underwent thorough evaluation for physicochemical properties, solubility, antigenicity, and cross-reactivity. Additionally, the three-dimensional structure of the antigen was predicted, refined, and validated. The nucleotide sequence of the designed antigen was optimized for successful expression in Escherichia coli and inserted into a pET23a (+) vector. Immunoinformatic analysis revealed that the multi-epitope antigen exhibits stability, antigenicity, and lacks cross-reactivity. Our findings suggest that this multi-epitope antigen is a promising candidate for diagnosing HCMV infection. However, further validation through laboratory testing is required to confirm its diagnostic efficacy.</p>","PeriodicalId":9274,"journal":{"name":"Biotechnology and applied biochemistry","volume":" ","pages":""},"PeriodicalIF":3.2000,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Designing a novel multi-epitope antigen for diagnosing human cytomegalovirus infection: An immunoinformatics approach.\",\"authors\":\"Marzieh Asadi, Younes Ghasemi, Navid Nezafat, Bahador Sarkari, Maryam Baneshi, Zohreh Mostafavi-Pour, Mohammad Hossein Anbardar, Amir Savardashtaki\",\"doi\":\"10.1002/bab.2677\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Human cytomegalovirus (HCMV) infection can lead to congenital infections and severe complications, particularly in immunocompromised individuals. Current serological tests for diagnosing HCMV infection often face limitations in sensitivity and specificity. Developing multi-epitope antigens for serological assays offers the potential for enhancing diagnostic accuracy. This study aimed to design a novel multi-epitope antigen for HCMV infection diagnosis using immunoinformatic approaches. Five tegument proteins (universal protein resource [UniProt] ID: Po8318, Po6725, F5HC97, Q6RX10, and F5HC05) were selected based on their antigenic properties and literature review. Six linear B-cell epitopes were predicted within conserved regions of each antigen sequence and linked with appropriate linkers. The designed multi-epitope antigen underwent thorough evaluation for physicochemical properties, solubility, antigenicity, and cross-reactivity. Additionally, the three-dimensional structure of the antigen was predicted, refined, and validated. The nucleotide sequence of the designed antigen was optimized for successful expression in Escherichia coli and inserted into a pET23a (+) vector. Immunoinformatic analysis revealed that the multi-epitope antigen exhibits stability, antigenicity, and lacks cross-reactivity. Our findings suggest that this multi-epitope antigen is a promising candidate for diagnosing HCMV infection. 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Designing a novel multi-epitope antigen for diagnosing human cytomegalovirus infection: An immunoinformatics approach.
Human cytomegalovirus (HCMV) infection can lead to congenital infections and severe complications, particularly in immunocompromised individuals. Current serological tests for diagnosing HCMV infection often face limitations in sensitivity and specificity. Developing multi-epitope antigens for serological assays offers the potential for enhancing diagnostic accuracy. This study aimed to design a novel multi-epitope antigen for HCMV infection diagnosis using immunoinformatic approaches. Five tegument proteins (universal protein resource [UniProt] ID: Po8318, Po6725, F5HC97, Q6RX10, and F5HC05) were selected based on their antigenic properties and literature review. Six linear B-cell epitopes were predicted within conserved regions of each antigen sequence and linked with appropriate linkers. The designed multi-epitope antigen underwent thorough evaluation for physicochemical properties, solubility, antigenicity, and cross-reactivity. Additionally, the three-dimensional structure of the antigen was predicted, refined, and validated. The nucleotide sequence of the designed antigen was optimized for successful expression in Escherichia coli and inserted into a pET23a (+) vector. Immunoinformatic analysis revealed that the multi-epitope antigen exhibits stability, antigenicity, and lacks cross-reactivity. Our findings suggest that this multi-epitope antigen is a promising candidate for diagnosing HCMV infection. However, further validation through laboratory testing is required to confirm its diagnostic efficacy.
期刊介绍:
Published since 1979, Biotechnology and Applied Biochemistry is dedicated to the rapid publication of high quality, significant research at the interface between life sciences and their technological exploitation.
The Editors will consider papers for publication based on their novelty and impact as well as their contribution to the advancement of medical biotechnology and industrial biotechnology, covering cutting-edge research in synthetic biology, systems biology, metabolic engineering, bioengineering, biomaterials, biosensing, and nano-biotechnology.