Atom Filtering Algorithm and GPU-Accelerated Calculation of Simulation Atomic Force Microscopy Images

Abstract

Simulation of atomic force microscopy (AFM) computationally emulates experimental scanning of a biomolecular structure to produce topographic images that can be correlated with measured images. Its application to the enormous amount of available high-resolution structures, as well as to molecular dynamics modelling data, facilitates the quantitative interpretation of experimental observations by inferring atomistic information from resolution-limited measured topographies. The computation required to generate a simulated AFM image generally includes the calculation of contacts between the scanning tip and all atoms from the biomolecular structure. However, since only contacts with surface atoms are relevant, a filtering method shall highly improve the efficiency of simulated AFM computations. In this report, we address this issue and present an elegant solution based on graphics processing unit (GPU) computations that significantly accelerates the computation of simulated AFM images. This method not only allows for the visualization of biomolecular structures combined with ultra-fast synchronized calculation and graphical representation of corresponding simulated AFM images (live simulation AFM), but, as we demonstrate, it can also reduce the computational effort during the automatized fitting of atomistic structures into measured AFM topographies by orders of magnitude. Hence, the developed method will play an important role in post-experimental computational analysis involving simulated AFM, including expected applications in machine learning approaches. The implementation is realized in our BioAFMviewer software (ver. 3) package for simulated AFM of biomolecular structures and dynamics.