Hekstra Doeke R, Wang Harrison K, Klureza Margaret A, Greisman Jack B, Dalton Kevin M
Department of Molecular and Cellular Biology.
School of Engineering and Applied Sciences.
bioRxiv. 2024 Jul 23:2024.07.22.604476. doi: 10.1101/2024.07.22.604476.
Chemical and conformational changes underlie the functional cycles of proteins. Comparative crystallography can reveal these changes over time, over ligands, and over chemical and physical perturbations in atomic detail. A key difficulty, however, is that the resulting observations must be placed on the same scale by correcting for experimental factors. We recently introduced a Bayesian framework for correcting (scaling) X-ray diffraction data by combining deep learning with statistical priors informed by crystallographic theory. To scale comparative crystallography data, we here combine this framework with a multivariate statistical theory of comparative crystallography. By doing so, we find strong improvements in the detection of protein dynamics, element-specific anomalous signal, and the binding of drug fragments.
化学和构象变化是蛋白质功能循环的基础。比较晶体学能够在原子层面详细揭示这些随时间、配体以及化学和物理扰动而发生的变化。然而，一个关键难题在于，必须通过校正实验因素，将所得观测结果置于同一尺度上。我们最近引入了一种贝叶斯框架，通过将深度学习与基于晶体学理论的统计先验知识相结合，来校正（缩放）X射线衍射数据。为了缩放比较晶体学数据，我们在此将此框架与比较晶体学的多元统计理论相结合。通过这样做，我们发现在检测蛋白质动力学、元素特异性反常信号以及药物片段结合方面有了显著改进。
