Generative Deep Learning toward Antibody Discovery for the Prevention of Food-Borne Illnesses

据加拿大公共卫生局统计，禽肉中最常见的污染物是肠杆菌、空肠弯曲杆菌和产气荚膜梭菌。由肠炎沙门氏菌引起的沙门氏菌病是人类最常见的人畜共患疾病，每年在美国造成130万人食源性疾病和500多人死亡。然而，尽管这种流行和定期暴发，可用于告知这些病原体的控制方法的分子相互作用的研究仍处于初级阶段。AbCelex的工作和专业知识在于开发创新的单域**抗体片段(也称为sdAbs或AbiBody)，用于消除或显著减少**人畜共患病原体对禽肉的定植。为了找到这项任务的最佳抗体，使用下一代测序平台对抗体库**进行了测序，并对选定的抗体进行了**各种生化和体外功能筛选，以选择先导抗体。这种**当前工作流程的一个限制是，这项测试可能需要几个月的时间才能完成；相反，这项工作是通过利用最先进的机器学习算法在**Silico库中进行设计，这些算法可以生成特定于所选目标的各种**序列。这一过程将把生成图书馆的时间**缩短到2周。考虑到不需要对文库进行排序(因为它们将以电子计算机生成)，但**只对它们进行筛选，这也将节省1.5个月的时间。这笔Engage项目资金将**用于建立一个全面的纳米体抑制剂目录，用于控制**安大略省家禽业的细菌负荷，该行业的年市场价值为7.5亿美元。

According to statistics accumulated by the Public Health Agency of Canada, S.enterica, Campylobacter jejuni,**and Clostridium perfringens are the most prevalent contaminants in poultry meat. Salmonellosis caused by**S.enterica is the most common zoonotic diseases in humans, responsible for 1.3 million human food-borne**illnesses and more than 500 deaths each year in the US. However, despite this prevalence and regular**outbreaks, studies of the molecular interactions that can be used to inform control methods of these pathogens**are in their infancy. AbCelex's work and expertise lies in the development of innovative single-domain**antibody fragments (also known as sdAbs or AbiBodies) utilized for elimination or significant reduction of**poultry meat colonization by zoonotic pathogens. In order to find the optimal antibodies for this task, a library**of antibodies is sequenced using next generation sequencing platforms, and selected antibodies are subjected to**various biochemical and ex vivo functional activity screens to select lead antibodies. A limitation of this**current workflow is that this testing can take months to accomplish; instead, this work sets out to design in**silico libraries by taking advantage of state-of-the-art machine learning algorithms that can generate varied**sequences that are specific to the selected targets. This process would shorten the timeline of library generation**to 2 weeks. Considering that there is no need to sequence the libraries (as they will be generated in silico), but**only screen them, this would also provide a time saving of 1.5 months. This Engage project funding will be**used to build a comprehensive catalogue of nanobody inhibitors that will be used to control bacterial load in**Ontario's poultry industry, which has a thriving annual market value of $750 million.