CryoFold 2.0: Cryo-EM Structure Determination with MELD
CryoFold 2.0: Cryo-EM Structure Determination with MELD
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DOI：
10.1021/acs.jpca.3c01731
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发表时间：
2023-04-21
影响因子：
2.9
通讯作者：
Perez, Alberto
中科院分区：
文献类型：
--
作者：
Chang, Liwei;Mondal, Arup;Perez, Alberto
低温电子显微镜数据正变得越来越普遍，并且可以在更高的分辨率水平上访问，从而导致新的计算工具的发展，以确定大分子的原子结构。然而，虽然现有的x射线晶体学工具适用于最高分辨率的地图，但需要新的工具来处理低分辨率的地图，并考虑到地图的异质性。在本文中，我们介绍了CryoFold 2.0，这是一种基于物理的综合方法，将贝叶斯推理和处理多个数据源的能力与分子动力学灵活拟合（MDFF）方法相结合，通过使用cryo-EM数据来确定大分子的结构。CryoFold 2.0被整合到MELD（使用有限数据建模）插件中，从而产生比单独运行MELD或MDFF更具计算效率和准确性的管道。与原来的CryoFold相比，该方法需要更少的计算资源和更短的模拟时间，并且可以最大限度地减少人工干预。我们在八个不同的系统上展示了该方法的有效性，突出了它的各种好处。
Cryo-electron microscopy data are becoming more prevalent and accessible at higher resolution levels, leading to the development of new computational tools to determine the atomic structure of macromolecules. However, while existing tools adapted from X-ray crystallography are suitable for the highest-resolution maps, new tools are needed for lower-resolution levels and to account for map heterogeneity. In this article, we introduce CryoFold 2.0, an integrative physics-based approach that combines Bayesian inference and the ability to handle multiple data sources with the molecular dynamics flexible fitting (MDFF) approach to determine the structures of macromolecules by using cryo-EM data. CryoFold 2.0 is incorporated into the MELD (modeling employing limited data) plugin, resulting in a pipeline that is more computationally efficient and accurate than running MELD or MDFF alone. The approach requires fewer computational resources and shorter simulation times than the original CryoFold, and it minimizes manual intervention. We demonstrate the effectiveness of the approach on eight different systems, highlighting its various benefits.