CAREER: Generation of Highly Selective Inhibitory Antibodies by Novel Paratope Design, Function-Based Screening, and Deep Sequencing

这项研究将极大地促进我们对酶抑制和病毒中和机制的理解，从而将导致一系列新方法的发展，以产生高效的抑制性抗体，在制药和生物技术行业有广泛的应用。具体来说，这项工作旨在开发干扰和抑制蛋白质切割酶或蛋白酶结合的抗体。该项目将(1)提高美国的技术竞争力；（ii）培养具有全球竞争力的STEM劳动力；增加妇女和代表性不足的少数民族的参与；（iv）为本科和研究生STEM教育做出贡献。加州大学河滨分校是加州大学所有校区中西班牙裔学生人数最多的少数族裔服务机构。更具体地说，该项目计划首先通过与两所中国大学的合作，为UCR的STEM本科生准备全球市场和充满活力的科学界；第二，通过讲座、研讨会和互动活动激发他们对科学和工程项目的兴趣，增加河滨县和圣贝纳迪诺县高中毕业生的大学/学院入学率，特别是那些来自少数民族和其他社会经济背景不利的高中毕业生。作为极其重要的信号分子，蛋白酶精确控制着多种生理过程，因此是潜在药物靶点的最大家族之一。考虑到估计约2%的人类基因组编码蛋白酶，任何蛋白酶抑制疗法都高度需要特异性。然而，蛋白酶在同一类蛋白酶中具有较高的氨基酸相似性，其活性位点广泛保守。开发能够提供所需特异性的小分子抑制剂一直是一项具有挑战性的任务。因此，抗体正在成为一种非常有吸引力的高选择性抑制替代方案。迄今为止，至少有三个障碍使得常规发现蛋白酶抑制单克隆抗体（mab）相当困难：(i)蛋白水解活性位点的低抗原性，（ii）缺乏基于功能的选择方法，以及（iii）在选择过程中失去有益的克隆。该职业奖的长期目标是开发用于生物医学应用的抑制特定蛋白酶的治疗性单克隆抗体（mab）或生物制剂。本研究的目的是克服技术障碍，建立促进抑制性抗体鉴定的一般方法。假设是凸抗原结合位点（paratopes）易于抑制。这一中心假设将通过以下三种具体方法得到验证：(1)通过设计、构建和优化富含凸面结构的合成人抗体文库，明确验证抑制机制；(2)建立基于功能的高通量筛选方法，高效鉴定抑制性抗体；(3)通过深度测序和数据挖掘系统地了解序列抑制景观。

1453645Ge, Xin This research will significantly advance our understanding on the mechanisms of enzymatic inhibition and virus neutralization, and thus will lead to development of a panel of novel methods for the generation of highly potent inhibitory antibodies for a broad range of applications in pharmaceutical and biotechnological industries. Specifically, the work aims to develop antibodies that interfere and inhibit the binding of protein cleaving enzymes or proteases. The project will (i) increase US technological competitiveness; (ii) develop a globally competitive STEM workforce; (iii) increase participation of women and underrepresented minorities; and (iv) contribute to undergraduate and graduate STEM education. UC Riverside is the minority serving institution with the largest Hispanic student population among all UC campuses. More specifically, the PI plans to first prepare UCR STEM undergraduates for a global marketplace and dynamic scientific communities through collaboration with two Chinese universities; and second increase university/college enrollment of high school graduates in Riverside and San Bernardino Counties, especially those from minority and other disadvantaged socio-economic backgrounds, by inspiring their interests in science and engineering programs through lectures, workshops and interacting events. As extremely important signaling molecules, proteases precisely control a wide variety of physiological processes, and thus represent one of the largest families of potential pharmaceutical targets. Considering that ~2% of the human genome is estimated to encode proteases, specificity is highly desired for any protease inhibition therapy. However, proteases share high amino acid similarity among the same class of proteases and their active sites are extensively conserved. It has been a challenging task to develop small molecule inhibitors that can deliver required specificities. Therefore, antibodies are emerging as a very attractive alternative for highly selective inhibition. To date, at least three obstacles make the routine discovery of protease-inhibiting monoclonal antibodies (mAbs) considerably difficult: (i) low antigenicity of the proteolytic active sites, (ii) lack of a function-based selection method, and (iii) loss of beneficial clones during the selection. The long-term goal of this CAREER award is to develop therapeutic monoclonal antibodies (mAbs) or biologics that inhibit specific proteases for biomedical applications. The objective of this research is to overcome the technical hurdles and establish general methodologies that facilitate the identification of inhibitory antibodies. The hypothesis is that convex antigen-binding sites (paratopes) are inhibition-prone. This central hypothesis will be tested by the following three specific approaches: (1) Clearly verify inhibition mechanisms by design, construction and optimization of synthetic human antibody libraries enriched with convex paratopes; (2) Efficiently identify inhibitory antibodies by developing a function-based high-throughput screening method; (3) Systematically understand sequence-inhibition landscapes by deep sequencing and data mining.