Fuchigami Sotaro, Niina Toru, Takada Shoji
Department of Biophysics, Graduate School of Science, Kyoto University, Kyoto, Japan.
Front Mol Biosci. 2021 Mar 10;8:636940. doi: 10.3389/fmolb.2021.636940. eCollection 2021.
The atomic force microscopy (AFM) is a powerful tool for imaging structures of molecules bound on surfaces. To gain high-resolution structural information, one often superimposes structure models on the measured images. Motivated by high flexibility of biomolecules, we previously developed a flexible-fitting molecular dynamics (MD) method that allows protein structural changes upon superimposing. Since the AFM image largely depends on the AFM probe tip geometry, the fitting process requires accurate estimation of the parameters related to the tip geometry. Here, we performed a Bayesian statistical inference to estimate a tip radius of the AFM probe from a given AFM image via flexible-fitting molecular dynamics (MD) simulations. We first sampled conformations of the nucleosome that fit well the reference AFM image by the flexible-fitting with various tip radii. We then estimated an optimal tip parameter by maximizing the conditional probability density of the AFM image produced from the fitted structure.
原子力显微镜（AFM）是用于对结合在表面的分子结构进行成像的强大工具。为了获得高分辨率的结构信息，人们常常将结构模型叠加到测量图像上。受生物分子高柔韧性的启发，我们之前开发了一种灵活拟合分子动力学（MD）方法，该方法允许在叠加时蛋白质结构发生变化。由于AFM图像很大程度上取决于AFM探针尖端的几何形状，拟合过程需要准确估计与尖端几何形状相关的参数。在这里，我们通过灵活拟合分子动力学（MD）模拟，从给定的AFM图像中进行贝叶斯统计推断以估计AFM探针的尖端半径。我们首先通过使用各种尖端半径进行灵活拟合，对与参考AFM图像拟合良好的核小体构象进行采样。然后，我们通过最大化由拟合结构产生的AFM图像的条件概率密度来估计最佳尖端参数。
