Nature Machine Intelligence ( IF 23.8 ) Pub Date : 2021-02-04 , DOI: 10.1038/s42256-020-00290-y Shigeyuki Matsumoto , Shoichi Ishida , Mitsugu Araki , Takayuki Kato , Kei Terayama , Yasushi Okuno
Elucidation of both the three-dimensional structure and the dynamics of a protein is essential to understand its function. Technical breakthroughs in single-particle analysis based on cryo-electron microscopy (cryo-EM) have enabled the three-dimensional structures of numerous proteins to be solved at atomic or near-atomic resolution. However, the analysis of the dynamics of protein targets using cryo-EM is often challenging because of their large sizes and complex structural assemblies. Here, we describe DEFMap, a deep learning-based approach to directly extract the dynamics associated with the atomic fluctuations that are hidden in cryo-EM density maps. Using only cryo-EM density data, DEFMap provides dynamics that correlate well with data obtained from molecular dynamics simulations and experimental approaches. Furthermore, DEFMap successfully detects changes in dynamics that are associated with molecular recognition. This strategy combines deep learning, experimental data and molecular dynamics simulations, and may reveal a new multidisciplinary approach for protein science.
中文翻译:
使用深度学习从冷冻电镜图中提取蛋白质动力学信息
阐明蛋白质的三维结构和动力学对于了解其功能至关重要。基于低温电子显微镜 (cryo-EM) 的单粒子分析技术突破使众多蛋白质的三维结构能够以原子或近原子分辨率解析。然而,由于其大尺寸和复杂的结构组件,使用冷冻电镜分析蛋白质靶标的动力学通常具有挑战性。在这里,我们描述了 DEFMap,这是一种基于深度学习的方法,可直接提取与隐藏在低温电磁密度图中的原子波动相关的动力学。仅使用低温电磁密度数据,DEFMap 提供的动力学与从分子动力学模拟和实验方法获得的数据具有很好的相关性。此外,DEFMap 成功检测到与分子识别相关的动力学变化。该策略结合了深度学习、实验数据和分子动力学模拟,并可能为蛋白质科学揭示一种新的多学科方法。