SEPIa, a knowledge-driven algorithm for predicting conformational B-cell epitopes from the amino acid sequence
作者: Georgios A. DalkasMarianne Rooman
作者单位: 1Université Libre de Bruxelles (ULB)
2Heriot-Watt University
3ULB-VUB
刊名: BMC Bioinformatics, 2017, Vol.18 (1)
来源数据库: Springer Journal
DOI: 10.1186/s12859-017-1528-9
关键词: ImmunoinformaticsMachine learningAntigen-antibody complexesB-cell epitopesStatistical potentialsPhysicochemical propertiesBioinformatics predictorΒ2 adrenergic G-protein-coupled receptor
英文摘要: The identification of immunogenic regions on the surface of antigens, which are able to be recognized by antibodies and to trigger an immune response, is a major challenge for the design of new and effective vaccines. The prediction of such regions through computational immunology techniques is a challenging goal, which will ultimately lead to a drastic limitation of the experimental tests required to validate their efficiency. However, current methods are far from being sufficiently reliable and/or applicable on a large scale.
原始语种摘要: The identification of immunogenic regions on the surface of antigens, which are able to be recognized by antibodies and to trigger an immune response, is a major challenge for the design of new and effective vaccines. The prediction of such regions through computational immunology techniques is a challenging goal, which will ultimately lead to a drastic limitation of the experimental tests required to validate their efficiency. However, current methods are far from being sufficiently reliable and/or applicable on a large scale.
全文获取路径: Springer  (合作)
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来源刊物:
影响因子:3.024 (2012)

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关键词翻译
关键词翻译
  • conformational 构造的
  • immunogenic 致免疫的
  • predicting 预测
  • antibody 抗体
  • receptor 接受体
  • immunology 免疫学
  • immune 免疫的
  • vaccines 疫苗
  • predictor 预示
  • protein 蛋白质