Hidden Markov Modeling of Molecular Orientations and Structures from High-Speed Atomic Force Microscopy Time-Series Images

Abstract

AbstractHigh-speed atomic force microscopy (HS-AFM) is a powerful technique for capturing the time-resolved behavior of biomolecules. However, structural information in HS-AFM images is limited to the surface geometry of a sample molecule. Inferring latent three-dimensional structures from the surface geometry is thus important for getting more insights into conformational dynamics of a target biomolecule. Existing methods for estimating the structures are based on the rigid-body fitting of candidate structures to each frame of HS-AFM images. Here, we extend the existing frame-by-frame rigid-body fitting analysis to multiple frames to exploit orientational correlations of a sample molecule between adjacent frames in HS-AFM data due to the interaction with the stage. In the method, we treat HS-AFM data as time-series data, and they are analyzed with the hidden Markov modeling. Using simulated HS-AFM images of the taste receptor type 1 as a test case, the proposed method shows a more robust estimation of molecular orientations than the frame-by-frame analysis. The method is widely applicable in integrative modeling of conformational dynamics using real experimental HS-AFM data.Author summaryBiomolecules dynamically change their structures in cells and perform various functions that are important for life. It is difficult to observe such dynamic structural changes using conventional experimental techniques, such as X-ray crystallography, that mainly observe static atomic structures. Alternative approach is the atomic force microscopy (AFM), a technique to measure the surface geometry of a sample molecule by scanning with an acute tip. Recently, by increasing the imaging rate of the AFM, it has become possible to observe the dynamics of biomolecules at work. However, the information measured with AFM is limited to the surface geometry of a molecule, so it is important to extract three-dimensional structural information from those data. In this study, we develop a method to estimate structural information with higher accuracy.