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

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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.