Software for sequencing human antibody proteins from polyclonal immune responses

用于对多克隆免疫反应中的人类抗体蛋白进行测序的软件

• 批准号: 10352420
• 负责人: Anand Patel
• 金额: $ 74.85万
• 依托单位: ABTERRA BIOSCIENCES, INC.
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-03-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10352420
• 关键词: 2019-nCoV; Address; Affinity; Algorithms; Amino Acid Sequence; Antibodies; Antibody Affinity; Antibody Repertoire; Antibody Response; Antigens; Autoantibodies; Autoimmune Diseases; B-Lymphocytes; Biocompatible Materials; Bioinformatics; Biological Response Modifier Therapy; Biomedical Research; Blood; Blood Cells; Blood Circulation; COVID-19 pandemic; Cell Death; Cells; Clinical; Clinical Research; Color; Communicable Diseases; Complementarity Determining Regions; Complex; Computer software; Coronavirus; Coronavirus Infections; Data; Databases; Decision Making; Ebola; Exposure to; Fingerprint; Genetic; Genomics; Graph; HIV; HIV Infections; Human; Hybrids; Immune response; Immunologic Memory; Individual; Infection; Infectious Diseases Research; Length; Machine Learning; Mass Spectrum Analysis; Methods; Monoclonal Antibodies; Mus; Oncology; Oryctolagus cuniculus; Pathogenicity; Patients; Peptide Sequence Determination; Peptides; Population; Poxviridae; Proteins; Proteomics; Rattus; Reagent; Reproducibility; Respiratory syncytial virus; Salvelinus; Sampling; Serum; Services; Specificity; Survivors; Testing; Therapeutic; Therapeutic antibodies; Transcript; Vaccine Design; Viral; adaptive immune response; analytical method; communicable disease diagnosis; cost; digital; experimental study; human pathogen; improved; innovation; instrumentation; interest; machine learning model; member; neoplastic cell; next generation sequencing; novel; pathogen; polyclonal antibody; proteogenomics; response; sample collection; screening; transcriptomics

The natural immune response to foreign pathogens involves a complex coordination of cells, including an adaptive response to select and secrete antibodies into circulation. Individuals who have recovered from a pathogenic infection retain immune memory and continue to circulate pathogen-specific antibodies. For many infectious diseases like respiratory syncytial virus, Ebola, and poxviruses, antibodies with neutralizing activity to multiple viral strains have been discovered from human survivors. The discovered antibodies are highly valuable as potential biologic therapeutics for the broader population, as the antibodies have been naturally optimized to defend against human pathogens. Efforts to discover antibodies from humans recovering from coronavirus infection are underway, SARS-CoV-2 in particular, but are hampered by the limitations of existing antibody discovery platforms. Current approaches require screening for live B cells actively producing pathogen-specific antibodies, which are sensitive to cell death and rarely found in blood. In contrast, antibody protein is stable and pathogen-reactive antibodies are abundant in serum. While protein is the ideal material to start with, characterization of polyclonal antibody (pAb) protein presents new challenges. Recent advances in mass spectrometry and analysis have shown individual antibody candidates can be derived from affinity-purified pAb proteins when a sufficiently matched B-cell genetic antibody repertoire is provided. We aim to develop algorithms to supplant the need of a genetic antibody repertoire, and de novo identify antibody candidates from limited complexity pAb samples. This is achieved by improvements to de novo peptide sequencing using machine learning, and targeted assembly of specificity determining regions (CDR3s) and antibody frameworks using de Bruijn graphs. The proposed software will provide estimates of clonal diversity and candidate sequences that can be synthesized and tested for reactivity. In addition to addressing needs for infectious diseases, as demonstrated with an urgent unmet need to stop the COVID-19 pandemic, the software also applies to clinical and biomedical research needs in autoimmune disease, and commercial interests in replacing polyclonal antibody reagents with highly reproducible monoclonal antibody equivalents.

对异物病原体的自然免疫反应涉及复杂的协调 细胞，包括对选择并将抗体分泌为循环的自适应反应。 从致病感染中恢复的个体保留免疫记忆和 继续循环病原体特异性抗体。对于许多传染病，例如呼吸道 合成病毒，埃博拉病毒和痘病毒，具有中和活性的抗体 从人类幸存者那里发现了菌株。发现的抗体高度高 作为对更广泛人群的潜在生物治疗的价值，因为抗体具有 自然优化以防御人类病原体。 从冠状病毒感染中恢复的人类中发现抗体的努力是 正在进行中，特别是SARS-COV-2，但受到现有抗体的局限性的阻碍 发现平台。当前的方法需要筛选活跃产生的活细胞 病原体特异性抗体，对细胞死亡敏感，在血液中很少发现。在 对比度，抗体蛋白是稳定的，病原体反应性抗体在血清中丰富。 蛋白质是理想的材料，但表征多克隆抗体（PAB） 蛋白质提出了新的挑战。 质谱和分析的最新进展显示了单个抗体 当足够匹配的B细胞时，可以从亲和纯化的PAB蛋白得出候选者 提供了遗传抗体库。我们旨在开发算法，以取代 遗传抗体库，从头鉴定有限复杂性的抗体候选物 PAB样品。这是通过使用机器对从头肽测序进行改进来实现的 学习和针对特异性确定区域（CDR3）和抗体的目标组装 使用de bruijn图的框架。提出的软件将提供克隆的估计值 可以合成并测试反应性的多样性和候选序列。此外 满足感染性疾病的需求，如有紧急未满足 停止COVID-19大流行，该软件也适用于临床和生物医学研究 自身免疫性疾病的需求以及替代多克隆抗体的商业利益 具有高度可重现的单克隆抗体当量的试剂。