免疫信息学:免疫学与生物信息学的结合

Naveen Kumar, S. Bhatia, R. Sood, A. Pateriya, Y. Malik
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引用次数: 1

摘要

生物学中最具挑战性的研究领域之一是了解复杂的免疫系统,计算工具在提高免疫学领域的研究速度方面发挥了关键作用。因此,应用计算方法和工具来揭示复杂的免疫系统,并由此转化对解决免疫问题的理解,已经产生了一个新的领域,免疫信息学。在不同的研究领域中,预测b细胞和t细胞表位被认为是免疫信息学的主要和潜在的翻译应用之一。现在可以根据全基因组测序来识别和描述个体的MHC异型,这是设计有效疫苗和治疗方法的基本和初步组成部分。潜在的硅表位预测方法已经开发出来,通过减少潜在的表位候选列表进行实验测试,使表位定位成为一项容易的任务。在这里,我们回顾了一系列免疫信息学工具,并在网上免费提供给公众使用,重点是b细胞和t细胞表位预测。我们还强调了构成重要免疫信息学工具基础的各种算法和方法,并讨论了它们的优缺点。
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Immunoinformatics: Where Immunology Meets Bioinformatics
One of the most challenging research areas in biology is to understand the complex immune system and the computational tools have played a pivotal role in increasing the pace of research in the field of immunology. Thus, applications of computational methods and tools in unravelling the complex immune system and thereof translating the understanding in solving the immunological problems have given rise of a new field, immunoinformatics. Among the diverse areas of research, prediction of B-and T-cell epitopes is being considered one of the major and potential translational applications of immunoinformatics. It is now possible to identify and characterise an individual's MHC allotype based on the whole genome sequencing, which is an essential and a preliminary component in designing effective vaccines and therapeutics. The potential in silico epitopes prediction methods have been developed that have made epitope mapping an easy task by decreasing the list of potential epitope candidates for experimental testing. Here, we review a range of immunoinformatics tools developed and available online for public use freely, with an emphasis on B-and T-cell epitopes prediction. We also highlight the various algorithms and methods that formed the basis of important immunoinformatics tools and discuss their strengths and weakness.
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