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首页> 外文期刊>British Biotechnology Journal >Insilico Prediction of T-cell Epitopes to Therapeutic Interferon -Beta (IFN-β) Protein
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Insilico Prediction of T-cell Epitopes to Therapeutic Interferon -Beta (IFN-β) Protein

机译:T细胞抗原决定簇对治疗性干扰素-β(IFN-β)蛋白的预测

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Aims: Several studies have reported the existence for T helper cell epitopes with the persistence of unwanted immune reactions for several protein drugs. T-cell epitope is an amino acid or set of amino acids that are capable of being recognized form one or more T-cell receptors. There is also an indication that T helper cells are involved in the anti-drug antibodies development to therapeutic interferon beta-1a. Protein drugs containing Major histocompatibility complex class II T cell epitopes are likely to elicit anti-drug antibodies. Binding specificity between T-cell epitopes and major histocompatibility molecules are the most important determinant step in finding the T-cellular immune responses. The data obtained from the present study provides new insights into prediction of therapeutic Interferon beta T helper cells epitopes using T cell epitope prediction tools, mapping of clusters of predicted epitopes. Study Design: Insilico analysis by bioinformatics tools was to predict T-cell epitopes of Interferon beta-1a. Methodology: Several Insilico prediction tools (immunoinformatics tools) including Proped, NetMHCIIpan3.0 and Immune Epitope Database Analysis Resource (IEDB-AR) are available to map the potential major histocompatibility class II T cell epitopes. After predicting potential T-cell epitopes, epitopes were mapped on interferon beta-1a using MIMOX2 server. Results: The potential MHC class II immunogenic sequence of 50 amino acids “TRGKLMSSLHLKRYYGRILHYLKAKEYSHCAWTIVRVEILRNFYFINRLTG” With IFN-β-1a (position 111-161) were identified. This study can provide the understanding the relevance to T-cell activation for prediction and assessment of unwanted immune responses. Conclusions: Insilico prediction by using the available tools helps in reducing the time and cost for the immunologists during the vaccine design. By predicting them we will come to know, which peptides play major role and synthesize them using invitro technologies.
机译:目的:数项研究报告了T辅助细胞表位的存在以及对几种蛋白质药物的有害免疫反应的持续存在。 T细胞表位是能够被一个或多个T细胞受体识别的一种氨基酸或一组氨基酸。也有迹象表明,T辅助细胞参与了针对治疗性干扰素β-1a的抗药物抗体的开发。包含主要组织相容性复合物II类T细胞表位的蛋白药物可能会引发抗药物抗体。 T细胞表位和主要组织相容性分子之间的结合特异性是发现T细胞免疫反应最重要的决定性步骤。从本研究获得的数据为使用T细胞表位预测工具预测治疗性干扰素βT辅助细胞表位提供了新的见识,并绘制了预测表位的簇图。研究设计:通过生物信息学工具进行的计算机电子分析可预测干扰素beta-1a的T细胞表位。方法:包括Proped,NetMHCIIpan3.0和免疫表位数据库分析资源(IEDB-AR)在内的几种Insilico预测工具(免疫信息学工具)可用于绘制潜在的主要组织相容性II类T细胞表位。预测潜在的T细胞表位后,使用MIMOX2服务器将表位定位在干扰素beta-1a上。结果:鉴定了具有IFN-β-1a(111-161位)的50个氨基酸的潜在MHC II类免疫原性序列“ TRGKLMSSLHLKRYYGRILHYLKAKEYSHCAWTIVRVEILRNFYFINRLTG”。这项研究可以提供与T细胞活化的相关性,以预测和评估有害的免疫反应。结论:通过使用可用的工具进行的计算机病学预测有助于减少免疫学家在疫苗设计过程中的时间和成本。通过预测它们,我们将了解哪些肽起主要作用,并使用体外技术合成它们。

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