Computational tools for T- and B-cell epitope prediction

T 细胞和 B 细胞表位预测的计算工具

• 批准号: 7012338
• 负责人: WERNER A BRAUN
• 金额: $ 25.8万
• 依托单位: UNIVERSITY OF TEXAS MED BR GALVESTON
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-02-01 至 2010-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7012338
• 关键词: B lymphocyte; T lymphocyte; allergens; aminoacid; antigens; artificial intelligence; bioinformatics; chemical property; chemical structure; computational biology; computer program /software; computer system design /evaluation; dengue virus; immunoglobulin E; major histocompatibility complex; mathematical model; molecular biology information system; peptides; physical property; protein binding; stereochemistry; structural biology

DESCRIPTION (provided by applicant): In the proposed work, we will develop software tools to predict T- and B-cell epitopes of allergenic and viral proteins. The approach is based on novel quantitative descriptors of the physical-chemical properties of amino acids developed recently by our group. The primary goal of the new approach is to use a minimal number of variables to establish the classification procedures and QSAR models. The novel descriptors of physical-chemical properties of amino acids will be used in combination with a partial least squares approach to reduce the number of variables in the discriminant analysis and in artificial neural networks. Algorithms based on multivariate classification, K-nearest-neighbor methods, support vector machines and neural networks will be developed and assessed by cross-validation for their ability to predict T- and B-cell epitopes in proteins. The resulting QSAR models/database approach can then be used to identify immunogenic epitopes in the proteins of pathogens for vaccine development and drug design. IgE epitopes, archived in our web-based, relational Structural Database of Allergenic Proteins (SDAP), will be used to develop the Bcell epitope prediction methods. Stereochemical variability plots will also be used to predict functional and immunological determinants on proteins from Dengue virus (DV). This information can aid in the design of vaccines that better stimulate neutralizing T- and B-cell responses to diverse variants of DV. The validated suite of software tools to identify and classify immunogenic peptides will be made available to the scientific community as a Web server, similar to SDAP. Collaborations with experimental groups will enable the practical applications of the tools, which include predicting the allergenicity of novel foods and drugs, improving specific immunotherapies for allergy and asthma, and vaccine design.

描述（由申请人提供）：在拟议的工作中，我们将开发软件工具来预测过敏性和病毒蛋白的 T 细胞和 B 细胞表位。该方法基于我们小组最近开发的氨基酸物理化学性质的新颖定量描述符。新方法的主要目标是使用最少数量的变量来建立分类程序和 QSAR 模型。氨基酸物理化学性质的新描述符将与偏最小二乘法结合使用，以减少判别分析和人工神经网络中的变量数量。将开发基于多元分类、K 最近邻方法、支持向量机和神经网络的算法，并通过交叉验证评估其预测蛋白质中 T 细胞和 B 细胞表位的能力。由此产生的 QSAR 模型/数据库方法可用于识别病原体蛋白质中的免疫原性表位，用于疫苗开发和药物设计。 IgE 表位存档在我们基于网络的过敏蛋白关系结构数据库 (SDAP) 中，将用于开发 Bcell 表位预测方法。立体化学变异图也将用于预测登革热病毒 (DV) 蛋白质的功能和免疫决定因素。这些信息有助于设计疫苗，更好地刺激中和 T 细胞和 B 细胞对不同 DV 变体的反应。经过验证的用于识别和分类免疫原性肽的软件工具套件将作为网络服务器提供给科学界，类似于 SDAP。与实验小组的合作将使这些工具的实际应用成为可能，其中包括预测新型食品和药物的过敏性、改进过敏和哮喘的特异性免疫疗法以及疫苗设计。