A novel low-parameter computational model to aid in-silico glycoengineering

Philipp N. Spahn, Anders Holmgaard Hansen, Henning Gram Hansen, Johnny Arnsdorf, Helene Faustrup Kildegaard, Nathan E. Lewis

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Abstract

Glycosylation is a key post-translational modification that can affect critical properties of proteins produced in biopharmaceutical manufacturing, such as stability, therapeutic efficacy or immunogenicity. However, unlike a protein's amino acid sequence, glycosylation is hard to engineer since it does not follow any direct equivalent of a genetic code. Instead, its complex biogenesis in the Golgi apparatus (Figure 1A) integrates a variety of influencing factors most of which are only incompletely understood. Various attempts have been undertaken so far to computationally model the process of glycosylation, but due to the high parametric demand of most of these models, it has been challenging to leverage these models for glycoengineering purposes. Consequently, industrial glycoengineering is still largely carried out using costly and time-consuming trial-and-error strategies and could greatly benefit from computational models that would better meet the requirements for industrial utilization. Here, we introduce a novel approach combining constraints-based and stochastic techniques to derive a computational model that can predict the effects of gene knockouts on protein glycoprofiles while requiring only minimal a-priori parameter input.
Original languageEnglish
Article numberP26
JournalBMC Proceedings
Volume9
Issue numberSuppl. 9
Number of pages3
ISSN1753-6561
DOIs
Publication statusPublished - 2015
EventThe 24th European Society for Animal Cell Technology - Barcelona, Spain
Duration: 31 May 20153 Jun 2015
Conference number: 24

Conference

ConferenceThe 24th European Society for Animal Cell Technology
Number24
CountrySpain
CityBarcelona
Period31/05/201503/06/2015

Bibliographical note

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the
original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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