GEMfitter: A highly parallel FFT-based 3D density fitting tool with GPU texture memory acceleration

Thai V Hoang,David W Ritchie,Xavier Cavin

doi:10.1016/j.jsb.2013.09.010

Thai V Hoang, David W Ritchie + Show 1 more

Open Access

https://doi.org/10.1016/j.jsb.2013.09.010

Copy DOI

Abstract

Fitting high resolution protein structures into low resolution cryo-electron microscopy (cryo-EM) density maps is an important technique for modeling the atomic structures of very large macromolecular assemblies. This article presents "gEMfitter", a highly parallel fast Fourier transform (FFT) EM density fitting program which can exploit the special hardware properties of modern graphics processor units (GPUs) to accelerate both the translational and rotational parts of the correlation search. In particular, by using the GPU's special texture memory hardware to rotate 3D voxel grids, the cost of rotating large 3D density maps is almost completely eliminated. Compared to performing 3D correlations on one core of a contemporary central processor unit (CPU), running gEMfitter on a modern GPU gives up to 26-fold speed-up. Furthermore, using our parallel processing framework, this speed-up increases linearly with the number of CPUs or GPUs used. Thus, it is now possible to use routinely more robust but more expensive 3D correlation techniques. When tested on low resolution experimental cryo-EM data for the GroEL-GroES complex, we demonstrate the satisfactory fitting results that may be achieved by using a locally normalised cross-correlation with a Laplacian pre-filter, while still being up to three orders of magnitude faster than the well-known COLORES program.

Highlights

Cryo-electron microscopy is an important technique for elucidating the structures of very large macromolecular assemblies
When working with low resolution cryo-electron microscopy (cryo-EM) density maps, we find that the introduction of a small amount of additional “noise” in this way appears to have a negligible effect on the final result
It can be seen that while MKL is always somewhat faster than FFTW, the speed-up obtained by performing the calculation on a graphics processor units (GPUs) is quite dramatic, especially for large target volumes

Summary

Introduction

Cryo-electron microscopy (cryo-EM) is an important technique for elucidating the structures of very large macromolecular assemblies. One way to obtain 3D molecular structures with atomic resolution is to fit high resolution X-ray structures into very large low resolution cryo-EM maps. In this case, the X-ray structure is often converted to the same level of resolution as the cryo-EM map by applying a Gaussian filter (Wriggers, 2010). Some examples of successful and widely used programs include CoAn (Volkmann and Hanein, 1999), EMfit (Rossmann, 2000), DOCKEM (Roseman, 2000), Foldhunter (Jiang et al, 2001), COLORES (Chacón and Wriggers, 2002), 3SOM (Ceulemans and Russell, 2004), MOD-EM (Topf et al, 2005), NORMA (Suhre et al, 2006), EMatch (Lasker et al, 2007), ADP_EM (Garzón et al, 2007), MolMatch (Förster et al, 2010), and PyTom (Hrabe et al, 2012)

Methods

Results

Conclusion