Predicting protein refolding from a knowledge of protein interactions under renaturation conditions

根据复性条件下蛋白质相互作用的知识预测蛋白质重折叠

• 批准号: BB/I016848/1
• 金额: $ 11.71万
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Training Grant
• 财政年份: 2011
• 资助国家: 英国
• 起止时间: 2011 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FI016848%2F1
• 关键词: Predicting; protein; refolding; knowledge; interactions

Recombinant proteins are often produced as inclusion bodies, which are densely packed aggregates of mis-folded proteins. Isolating the native protein requires dissolving the aggregates under highly denaturing conditions followed by a denaturant removal step to allow for protein refolding. During renaturation, aggregation of partially folded intermediates irreversibly traps inactive proteins. At the industrial level, overcoming this problem requires refolding proteins at low concentrations requiring large tanks. Yields can be improved by using certain additives, which enhance refolding or prevent aggregation. The scaled-up process can only be run in a batch or fed-batch mode, in which protein feed concentration and feed flowrate can be optimized. Often refolding yields can still be low (on the order of 10 percent) and in some instances, proteins do not refold at all, whereas in other cases refolding takes much longer than permitted for a scaled-up process. Thus the overall aims of the proposed project are 1) Understand how solvent influences refolding behaviour so as to make informed decisions about choosing renaturation conditions 2) Provide models of protein aggregation for designing large-scale refolding operations 3) Incorporate knowledge of protein-protein interactions into predictions of protein intrinsic refoldability. 4) Develop robust methods for quantifying refolding yield. Meeting these aims requires determining how refolding yield depends upon protein structural properties and the renaturation conditions (variables including denaturants, salts, additives, pH, protein concentration). We will make this connection by determining the molecular origin of protein intra and intermolecular interactions and then either correlating or fitting the interactions to protein refolding behaviour. The intramolecular interactions will be measured in terms of a protein stability fluoresence assay and an osmotic second virial coefficient (SVC) will be used to characterize the protein-protein interactions. An advantage of our methods is their ability to rapidly screen a range of solvent conditions and proteins. Studies will include therapeutically relevant proteins, which might include recombinant growth colony stimulating factor (rGCSF), human growth hormone (HGH), erythropoietin (EPO), insulin, and insulin like growth factor (ILGF). The protein systems are chosen to encompass a broad range of behaviour so as to isolate structure-interaction relationships. Screening solvent conditions (pH, ionic strength, denaturant concentration, oxidation conditions) will allow us to build up simplified interaction models based upon protein physicochemical properties (protein charge, shape and size, hydrophobicity). This knowledge would then allow us to improve upon current methods for predicting intrinsic refoldability from stability. Also, we will determine how solvent components (denaturants, additives) interact with the protein and/or modulate protein-protein interactions. Understanding the mechanism of interaction will guide the choice of renaturation conditions, with emphasis on finding solvents compatible with the downstream operations. As part of the work we will develop methods for quantifying the refolding yield. The current choice is to use RP-HPLC to separate proteins after the refolding step; natively folded proteins are expected to have different hydrophobicities from incorrectly or aggregated proteins. This method will be assessed in our work by characterizing the monomer peak from the RP-HPLC step using a combination of bioactivity assays, alternative HPLC steps, spectroscopic approaches (FTIR, CD), DSC, and light scattering. For a select few cases, we will also use NMR, which is especially sensitive to any incorrectly folded proteins. The results of the characterization will guide us in choosing a good strategy for assessing refolding yield using a small number of experimentally accessible techniques.

重组蛋白通常以包涵体的形式产生，包涵体是错误折叠的蛋白质的紧密堆积的聚集体。分离天然蛋白质需要在高度变性的条件下溶解聚集体，然后进行变性去除步骤，以允许蛋白质重新折叠。在复性过程中，部分折叠的中间产物的聚集不可逆转地捕获不活跃的蛋白质。在工业层面上，克服这一问题需要在低浓度下重新折叠蛋白质，这需要大型储罐。可以通过使用某些添加剂来提高产量，这些添加剂可以增强复性或防止聚集。放大的过程只能以间歇或补料间歇模式运行，在这种模式下可以优化蛋白质饲料浓度和饲料流量。通常情况下，复性产率仍然很低(大约10%)，在某些情况下，蛋白质根本不会复性，而在其他情况下，复性所需的时间比放大过程所允许的时间要长得多。因此，拟议项目的总体目标是：1)了解溶剂如何影响复性行为，以便就复性条件的选择做出明智的决定；2)为设计大规模复性操作提供蛋白质聚集模型；3)将蛋白质-蛋白质相互作用的知识纳入蛋白质内在复性能力的预测中。4)发展稳健的方法来量化复性产率。要达到这些目标，需要确定复性产率如何取决于蛋白质的结构性质和复性条件(变量包括变性剂、盐、添加剂、pH、蛋白质浓度)。我们将通过确定蛋白质分子内和分子间相互作用的分子起源，然后将这些相互作用与蛋白质的折叠行为相关联或匹配来建立这种联系。分子内相互作用将通过蛋白质稳定性荧光分析来测量，渗透第二维里系数(SVC)将被用来表征蛋白质-蛋白质相互作用。我们方法的一个优点是能够快速筛选一系列的溶剂条件和蛋白质。研究将包括与治疗相关的蛋白质，可能包括重组生长集落刺激因子(RGCSF)、人生长激素(HGH)、促红细胞生成素(EPO)、胰岛素和胰岛素样生长因子(ILGF)。蛋白质系统被选择为包含广泛的行为范围，以便隔离结构-相互作用关系。筛选溶剂条件(pH、离子强度、变性剂浓度、氧化条件)将使我们能够根据蛋白质的物理化学性质(蛋白质的电荷、形状和大小、疏水性)建立简化的相互作用模型。这一知识将使我们能够改进目前从稳定性预测内在可折叠性的方法。此外，我们还将确定溶剂成分(变性剂、添加剂)如何与蛋白质相互作用和/或调节蛋白质-蛋白质相互作用。了解相互作用的机理将指导复性条件的选择，重点是寻找与下游操作兼容的溶剂。作为这项工作的一部分，我们将开发计算复性产率的方法。目前的选择是在复性步骤后使用反相高效液相色谱分离蛋白质；自然折叠的蛋白质与错误或聚集的蛋白质具有不同的疏水性。在我们的工作中，我们将通过结合生物活性分析、替代的高效液相步骤、光谱方法(FTIR、CD)、DSC和光散射来表征来自RP-HPLC步骤的单体峰，从而对该方法进行评估。对于少数几种情况，我们还将使用核磁共振，它对任何错误折叠的蛋白质特别敏感。表征的结果将指导我们选择一种好的策略，使用少量的实验可获得的技术来评估复性产率。