EAGER: Computational Design of Peptide Ligands for the Bioseparation of "Fab" Antibody Fragments

EAGER：用于“Fab”抗体片段生物分离的肽配体的计算设计

• 批准号: 1830272
• 负责人: Carol Hall
• 金额: $ 10万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2020-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1830272&HistoricalAwards=false
• 关键词: EAGER; Computational; Design; Peptide; Ligands

Developing a drug that will cure cancer is the holy grail of the pharmaceutical industry. One strategy is to produce antibodies that mimic, yet enhance, the body's natural ability to bind to, and eliminate, foreign agents (antigens) that lead to chronic and fatal diseases, including cancer. Improving upon the body's natural ability to self-heal requires producing high quantities of highly purified antibodies that are designed to seek out specific antigens while leaving healthy cells unharmed. Producing high purity, targeted synthetic antibodies requires isolation and growth of a culture of cells that produces that antibody, followed by recovery of the antibody from the cell culture. This process leads to monoclonal antibodies, reflecting that one (mono) immune cell able to produce the antibody of interest was "cloned" (reproduced and grown) in large quantity. Recovery and purification of monoclonal antibodies from the complex media that comprises the cell culture is difficult, and thus expensive. Furthermore, monoclonal antibodies are large and complex biological molecules, features that lead to sluggish penetration of the monoclonal antibody into biological tissues. Identifying the particular fragment of the antibody that binds to the targeted antigen and producing only that fragment of the antibody (Fab), thus increases tissue penetration and the potency of the treatment. Fragmentation addresses penetration concerns, but recovery of Fabs from isolated cell cultures remains a challenge. Current methods for Fab recovery are expensive and use potentially-immunogenic proteins. The goal of this project is thus to computationally design and synthesize new peptide ligands (short protein-derived molecules) that will selectively recover targeted Fabs, and can easily be produced in a laboratory rather than derived from cell cultures. This computational project will screen short novel peptides for affinity, selectivity, and reversible binding to FAbs. The screening will use a previously developed search algorithm that identifies short peptide sequences (less than 25 amino acids) that have high affinity for a target Fab at neutral pH, but release the Fab at mildly acidic conditions (4.0 pH 5.0). Moreover, the computations will identify short peptide sequences that distinguish between Fabs with kappa and lambda light chains, and allow fractionation of kappa-lambda mixtures. The project is 'high risk' and exploratory because a good initial guess for the sequence of the peptide binder is currently unavailable. The computational predictions will be validated with laboratory synthesis and testing. The project will train a post doctoral researcher, and enable further efforts to broaden participation of women and under-represented minorities in STEM via mentoring, seminars, and outreach efforts. If successful, this project will lead to the development of short chain (15 amino acids) synthetic peptides for Fab recovery and selective differentiation of kappa and lambda Fabs. These two outcomes represent a new paradigm for antibody purification, with foreseeable extension to fractionation of other antibodies, assay development, investigation of the individual therapeutic powers of antibody subclasses for immune protection, disease diagnosis, and design of novel antibody therapeutics.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

开发一种能治愈癌症的药物是制药业的圣杯。一种策略是产生抗体，模仿并增强人体结合和消除导致慢性和致命疾病（包括癌症）的外来因子（抗原）的自然能力。提高人体自我修复的自然能力需要产生大量的高纯度抗体，这些抗体旨在寻找特定的抗原，同时不伤害健康细胞。生产高纯度的靶向合成抗体需要分离和培养产生该抗体的细胞，然后从细胞培养中恢复抗体。这一过程产生单克隆抗体，反映出能够产生感兴趣抗体的一个（单）免疫细胞被大量“克隆”（繁殖和生长）。从包含细胞培养的复杂培养基中恢复和纯化单克隆抗体是困难的，因此是昂贵的。此外，单克隆抗体是大而复杂的生物分子，这一特性导致单克隆抗体进入生物组织的渗透缓慢。识别与目标抗原结合的抗体的特定片段，并仅产生该抗体片段（Fab），从而增加组织渗透和治疗的效力。碎片化解决了渗透问题，但从分离细胞培养中恢复晶圆片仍然是一个挑战。目前的Fab回收方法是昂贵的，并且使用潜在的免疫原性蛋白质。因此，该项目的目标是通过计算设计和合成新的肽配体（蛋白质衍生的短分子），这些配体将选择性地恢复目标fab，并且可以很容易地在实验室中生产，而不是从细胞培养中获得。这个计算项目将筛选短的新肽的亲和力，选择性和可逆结合的fab。筛选将使用先前开发的搜索算法，识别短肽序列（少于25个氨基酸），这些序列在中性pH下对目标Fab具有高亲和力，但在轻度酸性条件下（4.0 pH 5.0）释放Fab。此外，计算将识别出区分具有kappa和lambda轻链的fab的短肽序列，并允许kappa-lambda混合物的分馏。该项目是“高风险”和探索性的，因为目前还无法对肽结合剂的序列进行良好的初步猜测。计算预测将通过实验室合成和测试进行验证。该项目将培训一名博士后研究员，并通过指导、研讨会和外展工作，进一步努力扩大妇女和代表性不足的少数民族在STEM领域的参与。如果成功，该项目将开发短链（15个氨基酸）合成肽，用于Fab回收和kappa和lambda Fab的选择性分化。这两个结果代表了抗体纯化的新范式，可预见的扩展到其他抗体的分离，检测开发，抗体亚类免疫保护的个体治疗能力的研究，疾病诊断和新型抗体疗法的设计。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。