Instituto de Matemática Aplicada San Luis, CONICET-UNSL, San Luis, Argentina.
J Comput Chem. 2021 Aug 5;42(21):1466-1474. doi: 10.1002/jcc.26556. Epub 2021 May 14.
We explore how ideas and practices common in Bayesian modeling can be applied to help assess the quality of 3D protein structural models. The basic premise of our approach is that the evaluation of a Bayesian statistical model's fit may reveal aspects of the quality of a structure when the fitted data is related to protein structural properties. Therefore, we fit a Bayesian hierarchical linear regression model to experimental and theoretical C chemical shifts. Then, we propose two complementary approaches for the evaluation of such fitting: (a) in terms of the expected differences between experimental and posterior predicted values; (b) in terms of the leave-one-out cross-validation point-wise predictive accuracy. Finally, we present visualizations that can help interpret these evaluations. The analyses presented in this article are aimed to aid in detecting problematic residues in protein structures. The code developed for this work is available on: https://github.com/BIOS-IMASL/Hierarchical-Bayes-NMR-Validation.
我们探讨了在贝叶斯建模中常见的思想和实践如何应用于帮助评估 3D 蛋白质结构模型的质量。我们方法的基本前提是，当拟合数据与蛋白质结构特性相关时，评估贝叶斯统计模型的拟合度可能会揭示结构质量的某些方面。因此，我们拟合了一个贝叶斯层次线性回归模型，以实验和理论 C 化学位移。然后，我们提出了两种互补的方法来评估这种拟合：（a）根据实验值和后验预测值之间的预期差异；（b）根据留一法交叉验证的逐点预测准确性。最后，我们提出了有助于解释这些评估的可视化。本文中提出的分析旨在帮助检测蛋白质结构中的问题残基。为此工作开发的代码可在：https://github.com/BIOS-IMASL/Hierarchical-Bayes-NMR-Validation 上获得。
