# Multi-particle refinement¶

Now that our particle poses are mostly correct and we have a good estimate of the reconstructed density, there are several parameters that can be tweaked a posteriori to further refine our result. We will perform these optimisations in M.

## Create Population and Species¶

A project in M is referred to as a population. To create a new one press the big + button in the center of the window, and provide a directory. Here, we put it in root/M/1.6Apx/.

We then need to add a data source. Click on Manage data sources to add a local source, and select the *.settings file inside the frames directory. Make sure to Include items outside of filter ranges (since we did not use the filters for this project), give it a name such as HIV and press CREATE. We also only want to Use only first 15 frames/tilts in order to refine only based on the high resolution information.

We can then create a Species by pressing the next big ‘+’, selecting from scratch and filling in the parameters as follows:

Afterwards, select the two last halfmaps generated by the RELION refinements at 1.6Å/px. For the mask, we will generate a binary mask around the central hexamer (160 Å diameter).

Mask one of the halfmaps to include only the central hexamer in Dynamo then use your favourite program and determine an appropriate threshold for binarisation. Use this as the --ini_threshold parameter in the following relion_mask_create command, replacing <latest_halfmap> as appropriate:

relion_mask_create --i <latest_halfmap>.mrc --angpix 1.6 --extend_inimask 5 --o mask_1.6Apx.mrc --ini_threshold 0.05


Provide this mask to M, and then select the final particle positions (e.g: run_it019_data.star) as the input for particle poses. Click FINISH to generate the new species.

## Refinement Parameters¶

To start the refinement, click on the REFINE button and fill in the parameters as follows.

Note

We will do a few iterations of refinements: first some with only spatial parameter optimizations, and then with most parameters. We chose to first optimise a smaller number of parameters to avoid falling into a local minimum, there may be a better way to do this!

First, run four iterations with these parameters:

Afterwards, three iterations as follows:

## End Result!¶

If everything went well, after the second round of refinements you should be able to get to something like this:

Here’s a closer look to the map compared to the available structures: