论文标题
使用可扩展的高分辨率深高斯混合物模型将分子模型整合到冷冻异质性分析中
Integrating molecular models into CryoEM heterogeneity analysis using scalable high-resolution deep Gaussian mixture models
论文作者
论文摘要
解决蛋白质的结构变异通常是了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）结合机器学习算法的单个粒子分析，提供了一种从嘈杂的显微照片中揭示蛋白质系统中动力学的方法。在这里，我们介绍了一种改进的计算方法，该方法使用高斯混合模型用于蛋白质结构表示和深层神经网络，以嵌入构象空间。通过将分子模型的信息整合到异质性分析中，我们可以在接近原子分辨率下解决复杂的蛋白质构象变化，并以更容易解释的形式呈现结果。
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 resolve complex protein conformational changes at near atomic resolution and present the results in a more interpretable form.
