Department of Applied Mathematics, National Sun Yat-sen University, Kaohsiung 804, Taiwan.
Bioinformatics. 2024 Jun 3;40(6). doi: 10.1093/bioinformatics/btae345.
Cryo-electron microscopy (cryo-EM) is a powerful technique for studying macromolecules and holds the potential for identifying kinetically preferred transition sequences between conformational states. Typically, these sequences are explored within two-dimensional energy landscapes. However, due to the complexity of biomolecules, representing conformational changes in two dimensions can be challenging. Recent advancements in reconstruction models have successfully extracted structural heterogeneity from cryo-EM images using higher-dimension latent space. Nonetheless, creating high-dimensional conformational landscapes in the latent space and then searching for preferred paths continues to be a formidable task.
This study introduces an innovative framework for identifying preferred trajectories within high-dimensional conformational landscapes. Our method encompasses the search for the minimum energy path in the graph, where edge weights are determined based on the energy estimation at each node using local density. The effectiveness of this approach is demonstrated by identifying accurate transition states in both synthetic and real-world datasets featuring continuous conformational changes.
The CLEAPA package is available at https://github.com/tengyulin/energy_aware_pathfinding/.
低温电子显微镜（cryo-EM）是一种研究大分子的强大技术，有可能识别构象状态之间的动力学优先转变序列。通常，这些序列在二维能量景观中进行探索。然而，由于生物分子的复杂性，在二维中表示构象变化可能具有挑战性。最近的重建模型进展成功地使用更高维的潜在空间从 cryo-EM 图像中提取结构异质性。尽管如此，在潜在空间中创建高维构象景观并搜索首选路径仍然是一项艰巨的任务。
本研究提出了一种在高维构象景观中识别首选轨迹的创新框架。我们的方法包括在图中搜索最小能量路径，其中边的权重基于使用局部密度在每个节点处的能量估计来确定。通过在具有连续构象变化的合成和真实数据集上识别准确的过渡状态，证明了该方法的有效性。
CLEAPA 包可在 https://github.com/tengyulin/energy_aware_pathfinding/ 获得。
