Elucidate Glycosyltransferase Specificities And Interactions for Rational Glycoengineering

The emergent market of new therapeutic glycoprofiles demands cost‐and‐labor efficient N‐linked glycoengineering, such as rational glycoengineering. However, rational glycoengineering remains challenging due to our limited understanding of the complex glycosyltransferase (GT) interactions. Predicting glycosylation outcomes of GT knockins/knockouts requires comprehensive and context‐specific knowledge of GT activities. To combat this issue, we proposed an improved low‐parameter Markov chain model to learn GT activities and used the learnt information to predict glycoprofiles impacted by complex glycoengineering. Specifically, we have quantified the impact of GT isozyme specificities and interactions by fitting the model with a compilation of single‐GT‐knockout erythropoietin (EPO) glycoprofiles for CHO cells. We then showed that the quantified GT impact can be generalized by our model framework to predict complex multiple‐GT‐knockout glycoprofiles of four diverse therapeutic drugs, including EPO, Rituximab, Enbrel, and alpha‐1 antitrypsin. Therefore, the proposed methodology allowed us to systematically unravel the complicated GT interactions beyond simple enzyme rules and with little a priori information. Such approach will further promote progress in rational glycoengineering by broadening our scope on the genetic basis of glycosylation, expediting product development for biopharmaceutical research.Support or Funding InformationThis work was conducted with support from the Novo Nordisk Foundation provided to the Center for Biosustainability at the Technical University of Denmark (NNF10CC1016517: A.L., A.W.T.C., A.H.H., B.G.V.) and NIGMS (R35 GM119850: N.E.L.)Figure 1