讲座主题：De novo antibody discovery: From gene to Medicine
主讲人：杨平，博士、CEO，苏州泓讯生物科技有限公司
时间：2021年11月11日（周四），13：30-14：30
地点：18新利体育 独墅湖校区二期1003幢3504
个人简介：
杨平，博士，苏州泓讯生物科技有限公司创始人，基因技术专家；江苏省双创专家；江苏省产业教授；苏州工业园区领军；姑苏领军；18新利体育 特聘产业教授。
杨平博士于中国药科大学获得药理学学位，美国范德堡大学博士后。此后，他加入了生物行业并担任了多个管理角色。他拥有广泛的管理经验，曾在多家全球生物技术和制药公司担任研发和管理工作。
杨平博士于2013年创立了苏州泓迅生物科技有限公司，目前为泓迅科技的首席执行官和董事长。泓迅科技专注于下一代DNA合成技术、合成生物学及其应用。泓迅科技已经建立了包括下一代DNA合成、抗体大分子从头设计、AI辅助分子优化及设计、DNA数字信息存储技术等在内的专有技术平台。
杨平博士曾获得多项专业奖项，发表了30余篇专业论文，总影响因子>100，引用次数超过800。拥有30余项以上发明专利，软件著作，出席和主持过许多国际科学研讨会。
内容简介：
In a combination of breakthroughs in DNA engineering, structure biology machine learning, artificial intelligence and high throughput DNA synthesizing, De novo antibody discovery may replace time intensive and cost intensive traditional antibody discovery process. Traditionally, antibodies are discovered by in vivo immunization procedures or in vitro antibody library screening approaches. By contrast, De novo antibody discovery is a new generation in silico-based antibody discovery methods to facilitate antibody discovery.
Syno®Ab is an algorithm that predicts antibody structure and sequences by simulating interactions between antigen and antibody molecules, select high-probability candidates, evaluate their properties, and then synthesize antibody sequences to screen for any specific antigen target. This structure-based antibody prediction processing includes: 1) Extract potential epitopes based on the interface between target and ligand protein; 2) Construct antibody library based on 3D structure; 3) Construct antibody library based on 3D structure; 4) Refine “hitting” antibody-target to increase its stability according to AA bias at different locations in antibody; 5) Select the best antibody hits according to evaluation, optimize DNA sequence and then synthesize gene/library. Our POC (proof of concept) study indicated that SynoAb powering advantages to design antibodies for difficult epitopes; to Computational simulate featured function, to humanize antibody with minimized or no immunogenicity. In addition, it is a fast and cost-effective selection that is animal free as well. In summary, de novo antibody design provides a novel approach for both time and cost-effective antibody drug discovery and development.