Division of CryoEM and Bioimaging, SSRL, SLAC National Accelerator Laboratory, Stanford University, Menlo Park, CA, USA.
Department of Statistics and Data Science, Yale University, New Haven, CT, USA.
J Mol Biol. 2023 May 1;435(9):168014. doi: 10.1016/j.jmb.2023.168014. Epub 2023 Feb 16.
Resolving the structural variability of proteins is often key to understanding the structure-function relationship of those macromolecular machines. Single particle analysis using Cryogenic electron microscopy (CryoEM), combined with machine learning algorithms, provides a way to reveal the dynamics within the protein system from noisy micrographs. Here, we introduce an improved computational method that uses Gaussian mixture models for protein structure representation and deep neural networks for conformation space embedding. By integrating information from molecular models into the heterogeneity analysis, we can analyze continuous protein conformational changes using structural information at the frequency of 1/3 Å, and present the results in a more interpretable form.
解析蛋白质的结构可变性通常是理解这些大分子机器的结构-功能关系的关键。使用低温电子显微镜（CryoEM）的单颗粒分析，结合机器学习算法，为从嘈杂的显微照片中揭示蛋白质系统内的动力学提供了一种方法。在这里，我们引入了一种改进的计算方法，该方法使用高斯混合模型表示蛋白质结构，并使用深度神经网络对构象空间进行嵌入。通过将分子模型的信息集成到异质性分析中，我们可以使用每 1/3 Å 的结构信息分析连续的蛋白质构象变化，并以更具可解释性的形式呈现结果。
