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

机译:一种新颖的低参数计算模型,用于辅助计算机内的糖工程

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摘要

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.
机译:糖基化是关键的翻译后修饰,其可以影响在生物制药生产中产生的蛋白质的关键特性,例如稳定性,治疗功效或免疫原性。但是,与蛋白质的氨基酸序列不同,糖基化很难进行工程设计,因为它不遵循遗传密码的任何直接等价物。取而代之的是,它在高尔基体中复杂的生物发生过程(图1A)整合了多种影响因素,其中大多数只是不完整的理解。迄今为止,已经进行了各种尝试来对糖基化过程进行计算建模,但是由于大多数这些模型的高参数需求,将这些模型用于糖工程目的一直是挑战性的。因此,工业糖工程学仍然主要使用昂贵且费时的反复试验策略来进行,并且可以从能够更好地满足工业利用要求的计算模型中受益匪浅。在这里,我们介绍了一种新颖的方法,结合了基于约束的技术和随机技术,可以得出一个计算模型,该模型可以预测基因敲除对蛋白质糖谱的影响,而只需要最少的先验参数输入。

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