University of Wisconsin Milwaukee, 3135 N. Maryland Ave, Milwaukee, WI, 53211, USA.
Sci Rep. 2023 Jan 25;13(1):1372. doi: 10.1038/s41598-023-28401-w.
Biomolecules undergo continuous conformational motions, a subset of which are functionally relevant. Understanding, and ultimately controlling biomolecular function are predicated on the ability to map continuous conformational motions, and identify the functionally relevant conformational trajectories. For equilibrium and near-equilibrium processes, function proceeds along minimum-energy pathways on one or more energy landscapes, because higher-energy conformations are only weakly occupied. With the growing interest in identifying functional trajectories, the need for reliable mapping of energy landscapes has become paramount. In response, various data-analytical tools for determining structural variability are emerging. A key question concerns the veracity with which each data-analytical tool can extract functionally relevant conformational trajectories from a collection of single-particle cryo-EM snapshots. Using synthetic data as an independently known ground truth, we benchmark the ability of four leading algorithms to determine biomolecular energy landscapes and identify the functionally relevant conformational paths on these landscapes. Such benchmarking is essential for systematic progress toward atomic-level movies of continuous biomolecular function.
生物分子会发生连续的构象运动，其中一部分与功能相关。理解（并最终控制）生物分子的功能，需要能够描绘连续的构象运动，并识别出与功能相关的构象轨迹。对于平衡和近平衡过程，功能沿着一个或多个能量景观上的最低能量途径进行，因为高能构象仅被弱占据。随着识别功能轨迹的兴趣日益浓厚，对可靠绘制能量景观的需求变得至关重要。作为回应，用于确定结构可变性的各种数据分析工具正在出现。一个关键问题是，每个数据分析工具从一组单颗粒冷冻电镜快照中提取与功能相关的构象轨迹的准确性。我们使用合成数据作为独立的已知基准，来评估四种领先算法确定生物分子能量景观以及识别这些景观上与功能相关的构象路径的能力。这种基准测试对于朝着连续生物分子功能的原子级电影的系统进展是必不可少的。
