• Title of article

    In Silico Prediction of T and B Cell Epitopes of SAG1-Related Sequence 3 (SRS3) Gene for Developing Toxoplasma gondii Vaccine

  • Author/Authors

    Mirzadeh, Abolfazl Department of Biotechnology - Iranian Research Organization for Science and Technology (IROST), Tehran , Saadatnia, Geita Department of Biotechnology - Iranian Research Organization for Science and Technology (IROST), Tehran , Golkar, Majid Department of Parasitology - Pasteur Institute of Iran, Tehran , Babaie, Jalal Department of Parasitology - Pasteur Institute of Iran, Tehran , Amiri, Samira Department of Parasitology - Pasteur Institute of Iran, Tehran , Yoosefy, Asiyeh Department of Parasitology - Pasteur Institute of Iran, Tehran

  • Pages
    9
  • From page
    1
  • To page
    9
  • Abstract
    Toxoplasmosis is a worldwide infection that can lead to serious problems in immune-compromised individuals and fetuses. A DNA vaccine strategy would be an ideal tool against Toxoplasma gondii. One of the necessary measures to provide an effective vaccine is the selection of proteins with high antigenicity. The SAG1-related sequence 3 (SRS3) protein is a major surface antigen in T. gondii that can be used as a vaccine candidate. In the present study, bioinformatics and computational methods were utilized to predict protein characteristics, as well as secondary and tertiary structures. The in silico approach is highly suited to analyze, design, and evaluate DNA vaccine strategies. Hence, in silico prediction was used to identify B and T cell epitopes and compare the antigenicity of SRS3 and other candidate genes of Toxoplasma previously applied in the production of vaccines. The results of the analysis theoretically showed that SRS3 has multiple epitopes with high antigenicity, proposing that SRS3 is a promising immunogenic candidate for the development of DNA vaccines against toxoplasmosis.
  • Keywords
    Toxoplasma gondii , SRS3 , In Silico , Bioinformatics Analysis
  • Journal title
    Archives of Clinical Infectious Diseases
  • Serial Year
    2020
  • Record number

    2544443