Identifying Structural Properties of Proteins from X-ray Free Electron Laser Diffraction Patterns
Identifying Structural Properties of Proteins from X-ray Free Electron Laser Diffraction Patterns
复制标题
从 X 射线自由电子激光衍射图识别蛋白质的结构特性
DOI：
10.1109/escience55777.2022.00017
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Taufer, Michela
中科院分区：
文献类型：
--
作者：
Olaya, Paula;Caino-Lores, Silvina;Lama, Vanessa;Patel, Ria;Rorabaugh, Ariel Keller;Miyashita, Osamu;Tama, Florence;Taufer, Michela
获取生物分子的结构信息对于确定其功能和理解其机制至关重要。x射线自由电子激光器（XFEL）是一种用于创建可以揭示结构信息的衍射图案（图像）的实验方法。在这项工作中，我们设计、实现和评估了XPSI（基于x射线自由电子激光的蛋白质结构标识符），这是一个能够从分子的衍射模式预测分子的三种结构特性（即取向、构象和蛋白质类型）的框架。XPSI在具有挑战性的情况下可以高精度地预测这些特性，例如在衍射模式中识别对称的方向，即使结构相似也能区分构象，以及在不同噪声条件下识别蛋白质类型。与其他机器学习方法（如随机森林和神经网络）相比，我们的框架具有低计算成本和高预测精度。
Capturing structural information of a biological molecule is crucial to determine its function and understand its mechanics. X-ray Free Electron Lasers (XFEL) are an experimental method used to create diffraction patterns (images) that can reveal structural information. In this work we design, implement, and evaluate XPSI (X-ray Free Electron Laser-based Protein Structure Identifier), a framework capable of predicting three structural properties in molecules (i.e., orientation, conformation, and protein type) from their diffraction patterns. XPSI predicts these properties with high accuracy in challenging scenarios, such as recognizing orientations despite symmetries in diffraction patterns, distinguishing conformations even when they have similar structures, and identifying protein types under different noise conditions. Our framework shows low computational cost and high prediction accuracy compared to other machine learning methods such as random forest and neural networks.
DOI：
10.1109/tpds.2022.3140681
发表时间：
2022-11-01
影响因子：
5.3
作者：
Keller Rorabaugh, Ariel;Caino-Lores, Silvina;Taufer, Michela
通讯作者：
Taufer, Michela
影响因子：
3
作者：
de la Rosa-Trevin, J. M.;Oton, J.;Sorzano, C. O. S.
通讯作者：
Sorzano, C. O. S.