Enhancing Density Maps by Removing the Majority of Particles in Single Particle Cryogenic Electron Microscopy Final Stacks

Cryogenic electron microscopy is essential for determining near atomic structures or biological micromolecules. Despite its reliance on averaging numerous low signal to noise images, the optimum number of particles needed for specific resolution remains unknown. This limitation hinders progress in sample analysis and preparation methods.

To tackle this, we introduce an iterative sorting method. Standard progress selection includes two dimensional and three dimensional classification. Other progress sorting criteria such as the normalized cross correlation method, the angular graph consistency approach, and the non-alignment classification are currently in use.

Extensive experiments demonstrate that CryoSieve outperforms other cryo EM, particle salting algorithms, revealing that most particles are unnecessary in final stacks. The minority of particles remaining in the final stacks yields very high resolution amplitude in reconstructed density maps. For some datasets, the size of the final subset approaches the theoretical limit.

In cryo EM sample preparation, in this workflow, due to the lack of standard metrics for protocol comparison, the ratio of selected to collected particles could serve as a quality metric, examining their spatial and temporal distribution may also highlight key, physical factors in preparation effectiveness. To configure the GPU acceleration environment, open the terminal and enter the command, nvidia.smi. Verify that the command successfully displays all information about the GPU card, and that the CUDA version is higher than 10.2.

Then execute the command conda V to confirm the installation of Conda. Enter the following command to set up the virtual environment and wait for a few minutes until the environment is successfully configured. Execute the command conda activate CRYOSIEVE_ENV, to activate the newly created virtual environment.

To install CryoSieve, run pip install cryosieve command. After installation, enter the command cryosieve h to ensure that the help information is displayed correctly. From NPR, download the NPR 10097 final stack dataset.

Open GitHub, then download the star file and mask. mrc, and the initial mrc file. Place all these files in a folder together.

Open the terminal and use the command CD file path to navigate to the folder containing the dataset. Then enter the conda activate CRYOSIEVE_ENV command to activate the Conda environment. Use the following command to start particle sieving.

Monitor the terminal display for the output logs for each iteration. Enter the indicated command to print resolution results for the 10 iterations of sieving. The particle stack filtered in the seventh iteration has the highest resolution, with the fewest particles showing the optimal result.

To import sieved particles, open the CryoSPARC web interface. Enter a workspace, and click on the builder button at the top right of the panel. Then select and click on the import particle stack option.

From the parameter section of the particle stack import panel, specify the particle meta path as the _. nstar file from the output folder, and the particle data path to the folder containing the MRCS file. Click the Q job button, followed by the Q button to initiate the process.

In the top right panel of CryoSPARC, click on the builder button, then select and click on the import 3D volumes option. Specify the volume data path as the initial MRC file. Click the Q job button, followed by the Q button to initiate the process.

Again, click on the builder button and select the homogenous refinement option. In the main panel on the left, open the job for importing the particle stack of the fourth iteration. Drag the imported particles module from the right side of the main panel and drop it into the particle stacks section of the builder.

Click on the red X to close the import particle stack job. Open the job for importing 3D volumes. Drag the imported volumes module from the right side of the main panel and drop it into the initial volume section of the builder.

Under the parameters folder, locate the symmetry option and set it to C3.Find and disable the force redo GS split option. Click the Q job button, followed by the queue button to initiate the homogenous refinement. Once all the jobs are finished, review the results and confirm that the particle stack filtered in the sixth iteration provided the optimal result.

The model to map and half maps for your shell correlation curves of the reconstructed density maps for the influenza hemagglutinin and trimer dataset before and after the method are shown. Raw and sharp density maps were also compared with the equivalent contour level applied. The enhancement of reconstructed density maps is evident from the comparison of side chains in sharp maps.

After removing the majority of particles in the final stack, the Rosenthal Henderson B factor raised from 226.9 Angstrom square to 146.2 Angstrom square Parameters such as local resolution, local B factor, and res log indicated that CryoSieve enhances both the quality of the density maps and the particle.