Predicting the aggregation propensity of mAb formulations from molecular dynamics simulations

背景与影响：聚集越来越被认为是通过蛋白质结构的部分展开来暴露更容易自我相互作用的部位而发生的。总体而言，蛋白质的自结合受到多种表面性质的影响，这些表面性质决定了胶体稳定性和表面相互作用的倾向，全局和局部去折叠的程度和动力学，以及局部序列的溶剂可及性和聚集倾向。所有这些都受到配方提供的物理环境的调节，从而可以改变聚集的动力学和主导路径。确定配方对给定蛋白质的聚集动力学和机理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的蛋白质的配方。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，计算分子动力学模拟的作用也越来越大，以提供对导致聚集的分子事件的洞察。在此基础上，现在有很大的潜力开始计算方法来预测配方对蛋白质稳定性和聚集性的影响。目的和目标：本项目的目的是开发一套建模和模拟方法的工作流程，能够在分子水平上深入了解单抗在一系列条件下的实验聚集行为，并为它们在配方稳定性预测中的使用提供基础。研究方法：该项目将与CSL合作，探索在配方条件下以及在与表面接触的情况下对单抗进行全原子和颗粒(CG)分子动力学模拟的使用，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。统计方法将被用来将模拟特征(例如RMSD、RMSF、APR暴露)与已知的实验性质相关联，以阐明潜在的聚集启动机制。答：还将探索混合方法，其中将所有原子模拟聚集在一起，为CG模拟提供替代构象。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)CSL单抗的历史数据集。预期结果：对单抗配方中分子动力学的预测力和机械力的洞察。进一步开发MDS平台和未来在CSL工作流程中实施的路线图。几份同行评议的出版物。该项目直接与EPSRC制造未来的主题保持一致。

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.