Cryo-EM is a powerful method for determining protein structures.But it requires computational assistance. Physics-based computations have the power to give low-free-energy structures and ensembles of populations, but have been computationally limited to only small soluble proteins. Here, we introduce CryoFold. By integrating data of varying sparsity from electron density maps of 3-5 A resolution with coarse-grained physical knowledge of secondary and tertiary interactions, CryoFold determines ensembles of protein structures directly from sequence. We give six examples showing its broad capabilities, over proteins ranging from 72 to 2000 residues,including membrane and multi-domain proteins, and including results from two EMDB competitions. The ensembles CryoFold predicts starting from the density data of a single known protein conformation encompass multiple low-energy conformations, all of which are experimentally validated and biologically relevant.

中文翻译：

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CryoFold：使用分子动力学从电子密度图确定从头算结构

Cryo-EM是确定蛋白质结构的强大方法，但需要计算辅助。基于物理的计算具有给出低自由能结构和种群聚集的能力，但是在计算上仅限于小的可溶性蛋白质。在这里，我们介绍CryoFold。通过将3-5 A分辨率的电子密度图的稀疏性数据与二级和三级相互作用的粗粒度物理知识相结合，CryoFold可直接从序列确定蛋白质结构的集合。我们提供了六个示例，显示了其广泛的功能，可处理72至2000个残基范围内的蛋白质，包括膜和多域蛋白质，还包括两次EMDB竞争的结果。