美国洛杉矶字节跳动研究中心Quanquan Gu团队近期取得重要工作进展。他们研究开发了CryoSTAR，利用结构先验和约束条件进行冷冻电镜数据集的异质重建。相关研究成果2024年10月29日在线发表于《自然—方法学》杂志上。

据介绍，解决冷冻电镜数据集中的构象异质性仍然是结构生物学的一个重要挑战。以前的方法通常仅限于研究体积密度，忽视了将任何预先存在的结构知识作为先验或约束的可能性。

研究人员提出了cryoSTAR，它利用原子模型信息作为结构正则化来阐明这种异质性。这一方法独特地输出粗粒度模型和密度图，展示了不同水平的分子构象变化。

研究人员针对四个不同的实验数据集进行验证，这些数据集涵盖了大型复合物、膜蛋白和小型单链蛋白，结果证明了一种高效有效的解决方案，可以在最小的人为偏差下解决构象异质性问题。通过将原子模型见解与冷冻电镜数据相结合，cryoSTAR代表了向前迈出的有意义的一步，为更深入地理解动态生物过程奠定了基础。

附：英文原文

Title: CryoSTAR: leveraging structural priors and constraints for cryo-EM heterogeneous reconstruction

Author: Li, Yilai, Zhou, Yi, Yuan, Jing, Ye, Fei, Gu, Quanquan

Issue&Volume: 2024-10-29

Abstract: Resolving conformational heterogeneity in cryogenic electron microscopy datasets remains an important challenge in structural biology. Previous methods have often been restricted to working exclusively on volumetric densities, neglecting the potential of incorporating any preexisting structural knowledge as prior or constraints. Here we present cryoSTAR, which harnesses atomic model information as structural regularization to elucidate such heterogeneity. Our method uniquely outputs both coarse-grained models and density maps, showcasing the molecular conformational changes at different levels. Validated against four diverse experimental datasets, spanning large complexes, a membrane protein and a small single-chain protein, our results consistently demonstrate an efficient and effective solution to conformational heterogeneity with minimal human bias. By integrating atomic model insights with cryogenic electron microscopy data, cryoSTAR represents a meaningful step forward, paving the way for a deeper understanding of dynamic biological processes.

DOI: 10.1038/s41592-024-02486-1

Source: https://www.nature.com/articles/s41592-024-02486-1