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

每个个体中的B细胞群体产生估计1010种不同的抗体，统称为 抗体库。这种非凡的多样性对于应对非洲独特的历史至关重要。 感染，疫苗接种和癌症在一个人的一生中遇到。相反，监管失误 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， 重链和轻链，各自含有可变区VH和VL，其一起赋予抗原结合 的特异性通过在三个V区编码基因座处的差异重组来启动多样性，以产生 天真的剧目。在抗原暴露后，表达对该抗原特异性的抗体的B细胞经历 克隆扩增和集中体细胞超突变（SHM）的V区序列的编码， 抗原识别结构域。这些克隆衍生的B细胞（克隆型）各自表达不同的序列， 从而产生初始未突变抗体的结构变体。选择表达更高亲和力变体的细胞 这一过程被称为亲和力成熟。这样，成熟的剧目是从历史上建立起来的， 与抗原的接触有效破译这段历史有助于改善 从更好的临床决策到改进的诊断和治疗，人类健康的许多方面。 为了实现这一目标，DNA/RNA测序、蛋白质结构建模 软件和高性能可扩展计算机硬件正在制造虚拟库规模抗体 结构和抗原筛选在不久的将来可以实现。 在这个直接进入第二阶段的应用程序中，我们建议构建一个软件套件， 基因组学和结构生物学使抗体库能够被破译和挖掘的精致细节。 为此，我们首先利用我们高度可扩展的序列组装器XNG来产生单倍型， 可以模拟幼稚库的种系IG基因座的注释序列（目的1）。接下来， XNG用于组装和注释批量BCR-seq数据，产生线性VH和VL编码 成熟库的序列（Aim 2）。然后，将翻译的库序列用作我们的 蛋白质建模软件NovaFold-Ab和NovaFold-AI，其中高精度3D抗体结构 预测（目标3）。这些抗体结构文库然后用于虚拟筛选以鉴定 用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 针对感兴趣的特异性表位，例如，已知引发中和抗体的那些。如果实现， 这些能力将为补充现有技术提供重要的商业机会， 改善临床护理和个性化医疗，并帮助开发更快、更低成本的 有效的诊断和治疗。

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.