Abstract
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.
Highlights
The atomic force microscopy (AFM) is a powerful tool for imaging the structures of molecules bound on surface at atomic resolution (Ando et al, 2001; Kodera et al, 2006; Kodera et al, 2010; Uchihashi et al, 2011; Casuso et al, 2012; Ando et al, 2013; Ando et al, 2014; Dufrene et al, 2017)
Before performing the flexible-fitting molecular dynamics (MD) simulations, we quantify the relation between the AFM probe tip radius in the collision detection method and the σ parameter used in the smoothed method
We investigated the statistical inference of the AFM probe tip radius via flexible-fitting MD simulations
Summary
The atomic force microscopy (AFM) is a powerful tool for imaging the structures of molecules bound on surface at atomic resolution (Ando et al, 2001; Kodera et al, 2006; Kodera et al, 2010; Uchihashi et al, 2011; Casuso et al, 2012; Ando et al, 2013; Ando et al, 2014; Dufrene et al, 2017). On the other hand, when the target molecules are flexible, as are often the case for biomolecules, one needs to allow a structural change of the model upon superimposing. This so-called flexible-fitting has been successfully applied in the modeling based on the cryo-electron microscopy data by various methods, for example, the molecular dynamics flexible fitting (MDFF) method and its extension (Trabuco et al, 2008; McGreevy et al, 2016; Singharoy et al, 2016), the correlation-coefficient-based method (Orzechowski and Tama, 2008), and CryoFold (Shekhar et al, 2020). We developed a flexible-fitting MD method for finding molecular structures that fit the AFM image (Niina et al, 2020)
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