Center for Computational Science, RIKEN, Kobe, Hyogo, 650-0047, Japan.
Center for Computational Science, RIKEN, Kobe, Hyogo, 650-0047, Japan; Department of Physics, Graduate School of Science, Nagoya University, Aichi, 464-8602, Japan; Institute of Transformative Bio-Molecules (WPI-ITbM), Nagoya University, Aichi, 464-8601, Japan.
Biochim Biophys Acta Gen Subj. 2020 Feb;1864(2):129420. doi: 10.1016/j.bbagen.2019.129420. Epub 2019 Aug 28.
Atomic Force Microscopy (AFM) is an experimental technique to study structure-function relationship of biomolecules. AFM provides images of biomolecules at nanometer resolution. High-speed AFM experiments produce a series of images following dynamics of biomolecules. To further understand biomolecular functions, information on three-dimensional (3D) structures is beneficial.
We aim to recover 3D information from an AFM image by computational modeling. The AFM image includes only low-resolution representation of a molecule; therefore we represent the structures by a coarse grained model (Gaussian mixture model). Using Monte-Carlo sampling, candidate models are generated to increase similarity between AFM images simulated from the models and target AFM image.
The algorithm was tested on two proteins to model their conformational transitions. Using a simulated AFM image as reference, the algorithm can produce a low-resolution 3D model of the target molecule. Effect of molecular orientations captured in AFM images on the 3D modeling performance was also examined and it is shown that similar accuracy can be obtained for many orientations.
The proposed algorithm can generate 3D low-resolution protein models, from which conformational transitions observed in AFM images can be interpreted in more detail.
High-speed AFM experiments allow us to directly observe biomolecules in action, which provides insights on biomolecular function through dynamics. However, as only partial structural information can be obtained from AFM data, this new AFM based hybrid modeling method would be useful to retrieve 3D information of the entire biomolecule.
原子力显微镜（AFM）是一种用于研究生物分子结构-功能关系的实验技术。AFM 以纳米分辨率提供生物分子的图像。高速 AFM 实验产生一系列随生物分子动力学变化的图像。为了进一步了解生物分子功能，获取三维（3D）结构信息是有益的。
我们旨在通过计算建模从 AFM 图像中恢复 3D 信息。AFM 图像仅包含分子的低分辨率表示；因此，我们使用粗粒度模型（高斯混合模型）表示结构。通过蒙特卡罗采样，生成候选模型以增加从模型模拟的 AFM 图像与目标 AFM 图像之间的相似性。
该算法已在两种蛋白质上进行了测试，以模拟它们的构象转变。使用模拟的 AFM 图像作为参考，该算法可以生成目标分子的低分辨率 3D 模型。还检查了 AFM 图像中捕获的分子取向对 3D 建模性能的影响，结果表明可以为许多取向获得相似的准确性。
所提出的算法可以生成 3D 低分辨率蛋白质模型，从中可以更详细地解释在 AFM 图像中观察到的构象转变。
高速 AFM 实验使我们能够直接观察生物分子的活动，通过动力学提供对生物分子功能的深入了解。然而，由于从 AFM 数据只能获得部分结构信息，这种新的基于 AFM 的混合建模方法将有助于检索整个生物分子的 3D 信息。
