Stöckelmaier Johannes, Capraz Tümay, Oostenbrink Chris
Institute of Molecular Modeling and Simulation (MMS), BOKU University, 1190 Vienna, Austria.
European Molecular Biology Laboratory (EMBL), 69117 Heidelberg, Germany.
Molecules. 2025 Jun 3;30(11):2449. doi: 10.3390/molecules30112449.
The elucidation of protein dynamics, especially in the context of intrinsically disordered proteins, is challenging and requires cooperation between experimental studies and computational analysis. Molecular dynamics simulations are an essential investigation tool but often struggle to accurately quantify the conformational preferences of flexible proteins. To create a quantitatively validated conformational ensemble, such simulations may be refined with experimental data using Bayesian and maximum entropy methods. In this study, we present a method to optimize a conformational ensemble using Bayes' theorem in connection with a methodology derived from Umbrella Sampling. The resulting method, called the Umbrella Refinement of Ensembles (URE), reduces the number of parameters to be optimized in comparison to the classical Bayesian Ensemble Refinement and remains methodologically suitable for use with the forward formulated Kullback-Leibler divergence. The method is validated using two established systems, an alanine-alanine zwitterion and the chignolin peptide, using nuclear magnetic resonance data from the literature.
蛋白质动力学的阐明，尤其是在内在无序蛋白质的背景下，具有挑战性，需要实验研究和计算分析之间的合作。分子动力学模拟是一种重要的研究工具，但往往难以准确量化柔性蛋白质的构象偏好。为了创建一个经过定量验证的构象集合，此类模拟可以使用贝叶斯和最大熵方法结合实验数据进行优化。在本研究中，我们提出了一种结合源于伞形采样的方法，利用贝叶斯定理优化构象集合的方法。由此产生的方法称为集合的伞形优化（URE），与经典的贝叶斯集合优化相比，减少了需要优化的参数数量，并且在方法上仍然适用于正向公式化的库尔贝克-莱布勒散度。该方法使用文献中的核磁共振数据，通过两个成熟的系统——丙氨酸-丙氨酸两性离子和chignolin肽进行了验证。
