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.

背景与影响：越来越多的人认为聚集是通过蛋白质结构的部分展开而发生的，从而暴露出更容易自我相互作用的位点。总的来说，蛋白质的自结合受到一系列表面特性的影响，这些表面特性决定了胶体稳定性和表面相互作用的倾向，整体和局部展开的程度和动力学，以及局部序列的溶剂可及性和聚集倾向。所有这些都是由配方提供的物理环境调节的，因此可以改变动力学和聚集的主要途径。确定配方对特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，而很少有普遍性可以可靠地用于设计新蛋白质的配方。最近在一系列配方中对蛋白质聚集机制的实验表征有了相当大的增长，并且计算分子动力学模拟的作用越来越大，以提供对导致聚集的分子事件的见解。在此基础上，计算方法有很大的潜力开始预测配方对蛋白质稳定性和聚集的影响。目的和目标：该项目的目的将是开发建模和模拟方法的工作流程，可以提供分子水平的见解，以了解单克隆抗体在一系列条件下的实验聚集行为，并为其在配方稳定性预测中的应用提供基础。研究方法：该项目将与CSL合作，探索在配方条件下和与表面接触的单抗的全原子和粗粒（CG）分子动力学模拟的使用，以研究模拟识别与已知稳定性特征相关的单抗行为（蛋白质-蛋白质和蛋白质-表面相互作用）的能力。统计方法将用于将模拟特征（例如RMSD， RMSF， APR暴露）与已知的实验特性相关联，以阐明潜在的聚集启动机制。A.混合方法也将被探索，其中全原子模拟被聚集以提供CG模拟的替代构象。因此，该项目还将培训学生掌握最新的数字技能，包括机器学习。预计研究结果和方法可用于预测（和理解）历史CSL单抗数据集。预期结果：洞察单克隆抗体制剂中分子动力学的预测和机制力量。进一步MDS平台开发和未来实现到CSL工作流程的路线图。一些同行评议的出版物。该项目与EPSRC“制造未来”的主题直接一致。