Software for the complete characterization of antibody repertoires: from germline and mRNA sequence assembly to deep learning predictions of their protein structures and targets

用于完整表征抗体库的软件：从种系和 mRNA 序列组装到其蛋白质结构和靶标的深度学习预测

• 批准号: 10699546
• 负责人: FREDERICK R BLATTNER
• 金额: $ 64.88万
• 依托单位: DNASTAR, INC.
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-29 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10699546
• 关键词: 3-Dimensional; Acceleration; Adaptive Immune System; Affinity; Allergens; Antibodies; Antibody Affinity; Antibody Formation; Antibody Repertoire; Antigen Targeting; Antigens; Autoimmune Diseases; B-Lymphocytes; B-cell receptor repertoire sequencing; Binding; Biology; Biotechnology; Cells; Characteristics; Clonal Expansion; Code; Communities; Complement; Complex; Computer Hardware; Computer software; Computers; DNA; Data; Development; Diagnostic; Distant; Emerging Technologies; Epitopes; Future; Genes; Genetic; Genetic Recombination; Genomics; Goals; Grouping; Haplotypes; Health; Healthcare; Hour; Human; Immune system; Immunoglobulin Somatic Hypermutation; Individual; Infection; Libraries; Light; Malignant Neoplasms; Messenger RNA; Modeling; Molecular Conformation; Molecular Immunology; Nature; Performance; Phase; Play; Population; Process; Proteins; Recording of previous events; Research; Resources; Role; Running; SARS-CoV-2 spike protein; Specialist; Specificity; Structure; System; Technology; Therapeutic; Translating; Vaccination; Validation; Variant; antigen binding; clinical care; clinical decision-making; contig; cost effective; deep learning; environmental agent; improved; in silico; interest; member; neutralizing antibody; novel diagnostics; pathogen; personalized medicine; programs; protein purification; protein structure; response; screening; software development; structural biology; therapeutic development; three dimensional structure; tool; transcriptome sequencing; virtual; 3-Dimensional; Acceleration; Adaptive Immune System; Affinity; Allergens; Antibodies; Antibody Affinity; Antibody Formation; Antibody Repertoire; Antigen Targeting; Antigens; Autoimmune Diseases; B-Lymphocytes; B-cell receptor repertoire sequencing; Binding; Biology; Biotechnology; Cells; Characteristics; Clonal Expansion; Code; Communities; Complement; Complex; Computer Hardware; Computer software; Computers; DNA; Data; Development; Diagnostic; Distant; Emerging Technologies; Epitopes; Future; Genes; Genetic; Genetic Recombination; Genomics; Goals; Grouping; Haplotypes; Health; Healthcare; Hour; Human; Immune system; Immunoglobulin Somatic Hypermutation; Individual; Infection; Libraries; Light; Malignant Neoplasms; Messenger RNA; Modeling; Molecular Conformation; Molecular Immunology; Nature; Performance; Phase; Play; Population; Process; Proteins; Recording of previous events; Research; Resources; Role; Running; SARS-CoV-2 spike protein; Specialist; Specificity; Structure; System; Technology; Therapeutic; Translating; Vaccination; Validation; Variant; antigen binding; clinical care; clinical decision-making; contig; cost effective; deep learning; environmental agent; improved; in silico; interest; member; neutralizing antibody; novel diagnostics; pathogen; personalized medicine; programs; protein purification; protein structure; response; screening; software development; structural biology; therapeutic development; three dimensional structure; tool; transcriptome sequencing; virtual

The B cell population in each individual produces an estimated 1010 different antibodies, collectively known as the antibody repertoire. This extraordinary diversity is essential for responding to the unique history of infections, vaccinations and cancer encountered over an individual’s lifetime. Conversely, regulatory errors in the system play a pivotal role in a host of auto-immune diseases. Antibodies are composed of two proteins, a heavy and light chain, each containing a variable region, VH and VL, which together confer antigen binding specificity. Diversity is initiated through differential recombination at the three V region encoding loci to produce the naïve repertoire. Upon antigen exposure, B cells expressing an antibody specific to that antigen undergo clonal expansion and concentrated somatic hypermutation (SHM) of V region sequences that code for the antigen recognition domain. Those clonally derived B cells (clonotypes) each express a different sequence and thereby structural variant of the initial unmutated antibody. Cells expressing higher affinity variants are selected for in a process known as affinity maturation. In this way, the mature repertoire is built from the history of antigenic encounters by that individual. Efficient deciphering of that history could contribute to improving human health in numerous ways from better clinical decision making to improved diagnostics and therapeutics. Toward that goal, ongoing technological advances in both DNA/RNA sequencing, protein structure modeling software and high-performance scalable computer hardware are making virtual repertoire scale antibody structure and antigen screening attainable in the not-too-distant future. In this Direct to Phase II application, we propose to build a software suite that bridges the gap between genomics and structural biology enabling antibody repertoires to be deciphered and mined in exquisite detail. To do so, we first leverage our highly extensible sequence assembler, XNG, to produce haplotype phased and annotated sequences of the germline IG loci from which the naïve repertoire can be simulated (Aim 1). Next, XNG is used to assemble and annotate bulk BCR-seq data producing the linear VH and VL encoding sequences of the mature repertoire (Aim 2). Translated repertoire sequences are then used as input for our protein modeling software, NovaFold-Ab and NovaFold-AI, where high accuracy 3D antibody structures are predicted (Aim 3). Those antibody structure libraries are then used in virtual screens to identify members that bind to a target antigen with our protein interaction modeling program, NovaDock (Aim 4). Screens can also be refined to specific epitopes of interest, for example, those known to elicit neutralizing antibodies. If realized, these capabilities will have significant commercial opportunities for complementing existing technology in improving clinical care and personalized medicine as well as aiding in the development of faster, more cost effective diagnostics and therapeutics.

每个个体中的B细胞群估计有1010种不同的抗体，统称为 抗体库。这种非凡的多样性对于回应独特的历史至关重要 在一个人的一生中遇到的感染，疫苗接种和癌症。相反，监管错误 该系统在许多自身免疫性疾病中起关键作用。抗体由两种蛋白质组成，A 重和轻链，每个链都包含一个可变区域的VH和VL，它们将抗原结合在一起 特异性。多样性是通过编码本地的三个V区域的差分重组来启动的 抗原暴露后，表达特异性抗体经历的抗体的B细胞。 V区域序列的克隆膨胀和集中的体细胞超成名（SHM） 抗原识别域。那些克隆衍生的B细胞（clonotypes）每个序列表达了不同的序列， 因此，初始未成型抗体的结构变体。选择表达较高亲和力变体的单元格 在一个称为亲和力成熟的过程中。这样，成熟的曲目是从历史上构建的 抗原遇到该人。该历史的有效解密可能有助于改善 人类健康以许多方式从更好的临床决策到改进的诊断和治疗。 为了实现这一目标，DNA/RNA测序，蛋白质结构建模的持续技术进步 软件和高性能可扩展的计算机硬件正在制造虚拟库量表抗体 在不远的未来可以实现结构和抗原筛查。 在此直接到II阶段应用程序中，我们建议建立一个软件套件，以弥合 基因组学和结构生物学使抗体曲目可以独家详细地确定和开采。 为此，我们首先利用高度可扩展的序列组件XNG来产生单倍型分阶段和 可以模拟幼稚曲目的种系Ig基因座的注释序列（AIM 1）。下一个， XNG用于组装和注释批量BCR-seq数据，生成线性VH和VL编码 成熟曲目的序列（AIM 2）。然后将翻译的曲目序列用作我们的输入 蛋白质建模软件，novafold-ab和novafold-ai，高精度3D抗体结构是 预测（目标3）。然后在虚拟屏幕中使用这些抗体结构库来识别成员 通过我们的蛋白质相互作用建模程序Novadock（AIM 4）结合靶抗原。屏幕也可以是 例如，特定的感兴趣的表位，例如，已知引起中和抗体的表位。如果实现， 这些功能将具有完成现有技术的巨大商业机会 改善临床护理和个性化医学，并有助于更快的发展，更高的成本 有效的诊断和治疗。