Liu Zhe, He Bintao, Zhang Tian, Feng Chenjie, Zhang Fa, Yang Zhongjun, Han Renmin
Research Center for Mathematics and Interdisciplinary Sciences, Cheeloo College of Medicine, Qilu Hospital (Qingdao), Shandong University, Qingdao 266237, China.
College of Medical Information and Engineering, Ningxia Medical University, Yinchuan 750004, China.
Bioinformatics. 2025 May 6;41(5). doi: 10.1093/bioinformatics/btaf296.
With the rapid advancements in Cryo-Electron Microscopy (Cryo-EM), an increasing number of high-resolution 3D density maps are being made publicly available, highlighting the urgent need for efficient structure similarity retrieval. Exploring map similarity at various levels is critical for fully utilizing these valuable resources. Our previously proposed CryoAlign can provide more accurate density map alignment while maintaining a low failure rate. However, CryoAlign only offers a method for aligning density maps, with low efficiency in local alignment, and has not yet been applied to the retrieval of Cryo-EM density maps.
We have developed an alignment-based retrieval tool to perform both global and local retrieval. Our approach adopts parallel-accelerated CryoAlign for high-precision 3D alignment and transforms density maps into point clouds for efficient retrieval and storage. Additionally, a multi-dimension scoring function is introduced to accurately assess structural similarities between superimposed density maps. To demonstrate its applicability, we conducted thorough testing across different retrieval tasks, such as global, local or hybrid similarity retrieval. Our tool achieves up to a 7-fold speedup while supporting precise local alignments. Comprehensive experiments demonstrate that even when one density map is entirely contained within another, our tool performs exceptionally well in high-resolution density map retrieval. It provides researchers with an efficient and accurate solution for density map similarity search.
The source code, documentation, and sample data can be downloaded at https://github.com/JokerL2/CryoAlign2.
随着冷冻电子显微镜（Cryo-EM）的迅速发展，越来越多的高分辨率三维密度图被公开，这凸显了对高效结构相似性检索的迫切需求。在各个层面探索图谱相似性对于充分利用这些宝贵资源至关重要。我们之前提出的CryoAlign可以在保持低失败率的同时提供更精确的密度图对齐。然而，CryoAlign仅提供了一种密度图对齐方法，局部对齐效率较低，并且尚未应用于冷冻电镜密度图的检索。
我们开发了一种基于对齐的检索工具，用于执行全局和局部检索。我们的方法采用并行加速的CryoAlign进行高精度三维对齐，并将密度图转换为点云以实现高效检索和存储。此外，引入了多维评分函数来准确评估叠加密度图之间的结构相似性。为了证明其适用性，我们在不同的检索任务（如全局、局部或混合相似性检索）上进行了全面测试。我们的工具在支持精确局部对齐的同时实现了高达7倍的加速。综合实验表明，即使一个密度图完全包含在另一个密度图内，我们的工具在高分辨率密度图检索中也表现出色。它为研究人员提供了一种高效、准确的密度图相似性搜索解决方案。
源代码、文档和示例数据可在https://github.com/JokerL2/CryoAlign2下载。
