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An integrated transcriptomic and proteomic approach to antibody sequencing and repertoire characterization

用于抗体测序和库表征的集成转录组学和蛋白质组学方法

基本信息

批准号：
9254618
负责人：
Natalie Castellana
金额：
$ 38.84万
依托单位：
DIGITAL PROTEOMICS LLC
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-02-16 至 2019-01-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9254618
关键词：
Address Algorithms Animals Antibiotic Resistance Antibodies Antibody Diversity Antibody Repertoire Antibody Response Antigens Area Attention Autoimmune Diseases B-Lymphocytes Base Sequence Cells Clone Cells Data Databases Development Disease Drug Industry Drug resistance Genes High-Throughput Nucleotide Sequencing Human Hybridomas Immune response Immunization Immunize Infection Informatics Injectable Learning Legal patent Life Cycle Stages Longitudinal Studies Malignant Neoplasms Mass Spectrum Analysis Mature B-Lymphocyte Measurement Measures Memory B-Lymphocyte Methods Monoclonal Antibodies Mutation Nucleotides Organism Outcome Patients Peptide Sequence Determination Peptides Phage Display Pharmaceutical Preparations Phase II Clinical Trials Process Proteins Proteomics Proxy Reporting Research Resources Sampling Savings Somatic Mutation Specificity Survivors Technology Therapeutic Monoclonal Antibodies Therapeutic Polyclonal Antibodies Time TimeLine Work bacterial resistance base candidate identification candidate selection cost design digital drug candidate drug development drug discovery drug market falls improved in vivo infancy insight interest molecular sequence database next generation sequencing novel novel strategies novel therapeutics polyclonal antibody proteogenomics response screening therapeutic candidate therapeutic development tool transcriptomics vaccine trial

项目摘要

Antibodies are ideal drug candidates due to their high specificity for target molecules. Monoclonal antibodies represent one of the fastest growing segments of the drug market, however, recent attention has focused on polyclonal antibodies and monoclonal mixtures to reduce the opportunity for a disease to become drug resistant. Polyclonal antibodies sampled from disease survivors or immunized hosts offer a wealth of new drug candidates. Current pipelines for investigating the immune response rely on hybridoma technology, which is time-‐consuming and does not come close to mimicking the diversity of antibodies present in the organism. Next generation sequencing of B-‐cells can deeply interrogate the immune response, however, this technology falls short of providing insight into the best antibody drug candidates. We propose the development of Valens-‐Poly, which will integrate mass spectrometry-‐based proteomics data with next generation sequencing of B-‐cells, an emerging field called immunoproteogenomics. By interrogating the immune response at the protein-‐level, Valens-‐Poly will be able to rank antibody sequences based on their abundance, which is a proxy for specificity to the antigen of interest. It is impossible to sequence all memory B-‐cells in a host organism, therefore the antibody sequences reported would only represent a small fraction of the antibodies that could be present. Using our patented spectral network approach, pioneered in our monoclonal antibody sequencing tool, Valens, we will be able to recover complementarity-‐defining regions (CDRs) of antibodies even when the B-‐cell was not captured for next generation sequencing. Finally, we will characterize the broad spectrum of antibodies produced as part of the immune response, called the antibody repertoire. Identifying changes in germline gene usage and tracking clone abundance and lineage in response to immunization or across patients is an important component of characterizing diseases and guiding drug discovery.

抗体因其对靶标的高特异性而成为理想的候选药物