Predicting the aggregation propensity of mAb formulations from molecular dynamics simulations

通过分子动力学模拟预测 mAb 制剂的聚集倾向

• 批准号: 2585856
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2585856
• 关键词: Predicting; aggregation; propensity; mAb; formulations

Context & impact: Aggregation is increasingly thought to occur through the partial unfolding of protein structure to expose sites that are more prone to self-interaction. Overall, the self-association of proteins is influenced by a combination of surface properties that determine colloidal stability and propensity for surface interactions, the extent and kinetics of global and local unfolding, and the solvent accessibility and aggregation-propensity of local sequences. All of these are modulated by the physical environment provided by the formulation, which can thus alter both the kinetics and dominant pathways of aggregation. Identifying the specific influence of formulations on aggregation kinetics and mechanism for a give protein, requires considerable experimental characterisation, while few generalities can be reliably used in the design of formulations for new proteins of interest. There has been considerable recent growth of experimental characterisation of protein aggregation mechanisms in a range of formulations, and an increased role of computational molecular dynamics simulations to provide insights into the molecular events that lead to aggregation. Building on this, there is now significant potential for computational approaches to begin to predict the impact of formulations on protein stability and aggregation.Aims and objectives: The aim of this project will be to develop a workflow of modelling and simulation approaches, that can provide molecular-level insights into the experimental aggregation behaviour of mAbs under a range of conditions, and provide a basis for their use in prediction of formulation stability.Research methodology:The project will collaborate with CSL to explore the use of all-atom and course-grain (CG) molecular dynamics simulations of mAbs under formulation conditions, and in contact with surfaces, to investigate ability of simulations to identify mAb behaviours (protein-protein and protein-surface interactions) that correlate to known stability characteristics. Statistical approaches will be used to correlate simulation features (e.g. RMSD, RMSF, APR exposure) to known experimental properties, to elucidate potential aggregation initiation mechanisms. A. hybrid approach will also be explored in which all-atom simulations are clustered to provide alternative conformations for CG simulations. Thus, the project will also train the student in the latest digital skills, including machine learning. It is anticipated that the findings and approaches could be used to predict (& understand) historical CSL mAb datasets. Anticipated outcomes: Insights into the predictive & mechanistic power of molecular dynamics in mAb formulations. A road map for further MDS platform development & future implementation into CSL workflows. Several peer-reviewed publications. The project is aligned directly to the EPSRC Manufacturing the Future theme.

背景和影响：聚集越来越多地被认为是通过蛋白质结构的部分解折叠来暴露更倾向于自我相互作用的位点而发生的。总的来说，蛋白质的自缔合是由表面性质的组合，确定胶体稳定性和倾向的表面相互作用，全球和本地展开的程度和动力学，以及溶剂的可及性和聚集倾向的本地序列的影响。所有这些都受到制剂提供的物理环境的调节，因此可以改变聚集的动力学和主要途径。确定制剂对给定蛋白质的聚集动力学和机制的具体影响需要大量的实验表征，而很少有一般性可以可靠地用于设计新的感兴趣蛋白质的制剂。最近有相当大的增长，在一系列配方的蛋白质聚集机制的实验表征，并增加了计算分子动力学模拟的作用，以提供深入了解导致聚集的分子事件。在此基础上，现在有很大的潜力，计算方法开始预测制剂对蛋白质稳定性和聚集的影响。该项目的目的是开发一个建模和模拟方法的工作流程，可以在分子水平上深入了解单克隆抗体在一系列条件下的实验聚集行为，研究方法：该项目将与CSL合作，探索在制剂条件下以及与表面接触时使用全原子和粗粒（CG）分子动力学模拟mAb，以研究模拟识别与已知稳定性特征相关的mAb行为（蛋白质-蛋白质和蛋白质-表面相互作用）的能力。将使用统计方法将模拟特征（例如RMSD、RMSF、APR暴露）与已知实验性质相关联，以阐明潜在的聚集引发机制。A.混合方法也将被探讨，其中所有原子模拟集群提供替代构象的CG模拟。因此，该项目还将培训学生最新的数字技能，包括机器学习。预计这些发现和方法可用于预测（和理解）历史CSL mAb数据集。预期成果：深入了解mAb制剂中分子动力学的预测和机制能力。MDS平台进一步开发的路线图以及CSL工作流程的未来实施。若干同行评审出版物。该项目直接与EPSRC未来制造主题保持一致。