Blending deep learning with probabilistic mechanistic models to predict and understand the evolution and function of adaptive immune receptors

将深度学习与概率机制模型相结合，以预测和理解适应性免疫受体的进化和功能

• 批准号: 10159730
• 负责人: Frederick Albert Matsen
• 金额: --
• 依托单位: FRED HUTCHINSON CANCER RESEARCH CENTER
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-06-01 至 2022-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10159730
• 关键词: 3-Dimensional; Algorithms; Animal Model; Antibodies; Antibody Affinity; Antigens; Architecture; Autoimmune Diseases; Automobile Driving; Big Data; Binding; Biochemical; Biochemical Process; Categories; Cell Line; Characteristics; Collection; Complement; Complex; Data; Data Set; Dependence; Diagnosis; Disease; Entropy; Evolution; Exposure to; Foundations; Gene Conversion; Generations; Goals; High-Throughput DNA Sequencing; High-Throughput Nucleotide Sequencing; Human; Hybrids; Immune; Immune response; Immune system; Immunoglobulin Somatic Hypermutation; Immunologic Memory; Immunologic Receptors; Immunological Models; Immunologics; Immunologist; Immunology; Immunotherapy; In Vitro; Individual; Knock-out; Knowledge; Laboratories; Machine Learning; Medical; Methods; Modeling; Modification; Mutation; Pathway interactions; Population; Procedures; Process; Property; Prophylactic treatment; Resolution; Sampling; Science; Statistical Distributions; Statistical Methods; Statistical Models; Structure; T-Cell Receptor; T-Lymphocyte; T-cell receptor repertoire; Techniques; Technology; Testing; Time; Training; Update; V(D)J Recombination; Vaccination; Vaccine Design; Vaccines; Validation; Work; algorithm training; analytical tool; base; biochemical model; cancer immunotherapy; cancer therapy; complex data; data complexity; deep learning; deep neural network; deep sequencing; design; experimental study; fighting; functional group; in vivo; insertion/deletion mutation; large datasets; machine learning method; markov model; pathogen; progenitor; receptor; repaired; response; success; three dimensional structure; tool

Project Summary Scientific understanding of adaptive immune receptors (i.e. antibodies and T cell receptors) has the potential to revolutionize prophylaxis, diagnosis, and treatment of disease. High‐throughput DNA sequencing and functional experiments have now brought the study of adaptive immune receptors into the big‐data era. To realize this potential of these data they must be matched with appropriately powerful analytical techniques. Existing probabilistic and mechanistic models are insufficient to capture the complexities of these data, while a naïve application of machine learning cannot leverage our profound existing knowledge of the immune system. The goal of this project is to blend deep learning with mechanistic modeling in order to predict and understand the evolution and function of adaptive immune receptors. Aim 1: Develop generative models of immune receptor sequences that capture the complexity of real adaptive immune receptor repertoires. These will combine deep learning along with our knowledge of VDJ recombination, and provide a rigorous platform for detailed repertoire comparison. Aim 2: Develop quantitative mechanistic models of antibody somatic hypermutation that incorporate the underlying biochemical processes. Estimate intractable likelihoods using deep learning to infer important latent variables, and validate models using knock‐out experiments in cell lines. Aim 3: Develop hybrid deep learning models to predict binding properties from sequence data, combining large experimentally‐derived binding data with even larger sets of immune sequences from human immune memory samples. Incorporate structural information via 3D convolution or distance‐based penalties. These tools will reveal the full power of immune repertoire data for medical applications. We will obtain more rigorous comparisons of repertoires via their distribution in a relevant space. These will reveal the effects of immune perturbations such as vaccination and disease, allowing us to pick out sequences that are impacted by these perturbations. We will have a greater quantitative understanding of somatic hypermutation in vivo, and statistical models that appropriately capture long‐range effects of collections of mutations. We will also have algorithms that will be able to combine repertoire data and sparse binding data to predict binding properties. Put together, these advances will enable rational vaccine design, treatment for autoimmune disease, and identification of T cells that are promising candidates for cancer immunotherapy.

项目概要 对适应性免疫受体（即抗体和 T 细胞受体）的科学理解有可能 彻底改变疾病的预防、诊断和治疗。 功能实验现已将适应性免疫受体的研究带入大数据时代。 要认识到这些数据的潜力，必须将它们与适当强大的分析技术相匹配。 现有的概率和机械模型不足以捕捉这些数据的复杂性，而 机器学习的天真应用无法利用我们对免疫系统的深厚现有知识 系统。 该项目的目标是将深度学习与机械建模相结合，以便预测和 了解适应性免疫受体的进化和功能 目标 1：开发适应性免疫受体的生成模型。 捕捉真实适应性免疫受体库的复杂性的免疫受体序列。 将深度学习与我们的 VDJ 重组知识相结合，并提供严格的平台 目标 2：开发抗体体细胞的定量机制模型。 使用潜在的生化过程来估计棘手的可能性。 深度学习推断重要的潜在变量，并使用细胞中的敲除实验验证模型 目标 3：开发混合深度学习模型以预测序列数据的结合特性， 将大量实验得出的结合数据与更大的人类免疫序列集相结合 通过 3D 卷积或基于距离的惩罚合并结构信息。 这些工具将揭示免疫组库数据在医疗应用中的全部威力，我们将获得更多。 通过曲目在相关空间中的分布进行严格比较，这将揭示其效果。 免疫扰动，例如疫苗接种和疾病，使我们能够挑选出受免疫扰动影响的序列 我们将对体内体细胞超突变有更深入的定量了解，并且 我们还将拥有适当捕捉突变集合的长期影响的统计模型。 能够结合库数据和稀疏结合数据来预测结合特性的算法。 总而言之，这些进步将使合理的疫苗设计、自身免疫性疾病的治疗以及 鉴定有希望用于癌症免疫治疗的 T 细胞。