Schrodinger Inc, 1540 Broadway 24th Floor, New York, NY 10036, United States.
Schrodinger Inc, 1540 Broadway 24th Floor, New York, NY 10036, United States.
J Mol Biol. 2022 Jan 30;434(2):167375. doi: 10.1016/j.jmb.2021.167375. Epub 2021 Nov 23.
This work describes the application of a physics-based computational approach to predict the relative thermodynamic stability of protein variants, and evaluates the quantitative accuracy of those predictions compared to experimental data obtained from a diverse set of protein systems assayed at variable pH conditions. Physical stability is a key determinant of the clinical and commercial success of biological therapeutics, vaccines, diagnostics, enzymes and other protein-based products. Although experimental techniques for measuring the impact of amino acid residue mutation on the stability of proteins exist, they tend to be time consuming and costly, hence the need for accurate prediction methods. In contrast to many of the commonly available computational methods for stability prediction, the Free Energy Perturbation approach applied in this paper explicitly accounts for solvent effects and samples conformational dynamics using a rigorous molecular dynamics simulation process. On the entire validation dataset, consisting of 328 single point mutations spread across 14 distinct protein structures, our results show good overall correlation with experiment with an R of 0.65 and a low mean unsigned error of 0.95 kcal/mol. Application of the FEP approach in conjunction with experimental assessment techniques offers opportunities to lower the time and expense of product development and reduce the risk of costly late-stage failures.
这项工作描述了一种基于物理的计算方法在预测蛋白质变体的相对热力学稳定性方面的应用,并评估了这些预测与在不同 pH 条件下测定的各种蛋白质系统的实验数据相比的定量准确性。物理稳定性是生物治疗药物、疫苗、诊断试剂、酶和其他蛋白质产品的临床和商业成功的关键决定因素。尽管存在测量氨基酸残基突变对蛋白质稳定性影响的实验技术,但这些技术往往耗时且昂贵,因此需要准确的预测方法。与许多常用的稳定性预测计算方法不同,本文应用的自由能微扰方法明确考虑了溶剂效应,并使用严格的分子动力学模拟过程来采样构象动力学。在整个验证数据集上,包括分布在 14 个不同蛋白质结构中的 328 个单点突变,我们的结果与实验具有很好的整体相关性,R 为 0.65,平均未签名误差为 0.95 kcal/mol。结合实验评估技术应用 FEP 方法为降低产品开发的时间和成本以及降低昂贵的后期失败风险提供了机会。