Department of Biochemical Engineering, University College London, London WC1E 6BT, U.K.
Biopharmaceutical Product Development, CSL Ltd., 45 Poplar Road, Parkville 3052, Australia.
Mol Pharm. 2024 Nov 4;21(11):5827-5841. doi: 10.1021/acs.molpharmaceut.4c00859. Epub 2024 Sep 30.
Computational methods including machine learning and molecular dynamics simulations have strong potential to characterize, understand, and ultimately predict the properties of proteins relevant to their stability and function as therapeutics. Such methods would streamline the development pathway by minimizing the current experimental testing required for many protein variants and formulations. The molecular understanding of thermostability and aggregation propensity has advanced significantly along with predictive algorithms based on the sequence-level or structural-level information on a protein. However, these approaches focus largely on a comparison of protein sequence variations to correlate the properties of proteins to their stability, solubility, and aggregation propensity. For therapeutic protein development, it is of equal importance to take into account the impact of the formulation conditions to elucidate and predict the stability of the antibody drugs. At the macroscopic level, changing temperature, pH, ionic strength, and the addition of excipients can significantly alter the kinetics of protein aggregation. The mechanisms controlling aggregation kinetics have been traced back to a combination of molecular features, including conformational stability, partial unfolding to aggregation-prone states, and the colloidal stability governed by surface charges and hydrophobicity. However, very little has been done to evaluate these features in the context of protein dynamics in different formulations. In this work, we have combined a range of molecular features calculated from the Fab A33 protein sequence and molecular dynamics simulations. Using the power of advanced, yet interpretable, statistical tools, it has been possible to uncover greater insights into the mechanisms behind protein stability, validating previous findings, and also develop models that can predict the aggregation kinetics within a range of 49 different solution conditions.
计算方法，包括机器学习和分子动力学模拟，具有很强的潜力，可以对蛋白质的特性进行描述、理解，并最终预测其稳定性和功能（作为治疗药物）。这些方法将通过最小化目前对许多蛋白质变体和配方所需的实验测试，简化开发途径。随着基于蛋白质序列水平或结构水平信息的预测算法的发展，对热稳定性和聚集倾向的分子理解已经有了显著的进步。然而，这些方法主要集中在比较蛋白质序列变异，以将蛋白质的特性与其稳定性、溶解度和聚集倾向联系起来。对于治疗性蛋白质的开发，同样重要的是要考虑制剂条件的影响，以阐明和预测抗体药物的稳定性。在宏观层面上，改变温度、pH 值、离子强度和添加赋形剂可以显著改变蛋白质聚集的动力学。控制聚集动力学的机制可以追溯到分子特征的组合，包括构象稳定性、部分展开到聚集倾向状态，以及由表面电荷和疏水性控制的胶体稳定性。然而，在不同配方中评估蛋白质动力学的这些特征方面，几乎没有什么工作。在这项工作中，我们结合了从 Fab A33 蛋白序列和分子动力学模拟中计算出的一系列分子特征。利用先进的、可解释的统计工具的强大功能，我们能够更深入地了解蛋白质稳定性背后的机制，验证先前的发现，并开发能够预测在 49 种不同溶液条件范围内的聚集动力学的模型。
