Particle orientation refinement using GMM representation

Most programs are available in EMAN2 builds after 2023-03, but some are still under continuous development. Newer versions are typically better.
It is recommended to add the "examples/" folder in EMAN2 binary to $PATH, as some new programs have not been moved to "bin/" yet.
The tutorial is only tested on Linux with Nvidia GPU and CUDA.

Here we use particles of SARS-COV-2 from EMPIAR-10492 as an example. Starting from particles with assigned orientation, i.e. the Polished folder (13.5GB) from EMPIAR, as well as job096_run_data.star.

Import existing refinement

Here we will need a .lst file with the location of all particles and their initial orientation assignment. Since here we start from a Relion star file, run

e2convertrelion.py job096_run_data.star --voltage 300 --cs 2.7 --apix 1.098 --amp 10 --skipheader 26 --onestack particles/particles_all.hdf --make3d --sym c3

Note that we need to phase flip the particles before the refinement, so this may take a while. Also make sure to provide the correct CTF related information to the program, including voltage, cs, amp, apix, since the program does not read those from the star file automatically. Check --help for more details. After importing the particles, with the --make3d option, the program will create a r3d_00 folder and reconstruct the 3D maps. You should see the structure of Covid spike with FSC at ~3.9Å at this point. Note the resolution number here is different from the one reported, because the pixel size used for processing is 1.098, which is then calibrated to 1.061. We still use the pixel size of 1.098 here, since otherwise the CTF information from the star file would be incorrect.

To start from other formats:

From classical EMAN2 refinement (e2refine_easy), run e2evalrefine.py refine_XX --extractorientptcl particles.lst
From the new EMAN2 refinement (e2spa_refine), simply use the ptcls_XX.lst file from the last iteration.
From CryoSPARC or others, convert it to a relion star file using pyem, then follow the relion conversion.

Global orientation refinement

We first need to determine the number of Gaussian to represent the volume. Often it is convenient to just use the number of non-H atoms in the molecule. Alternatively, we can guess the number given an existing map, isosurface threshold, and target resolution.

e2gmm_guess_n.py r3d_00/threed_00.hdf --thr 4 --maxres 3.5 --startn 10000

Here the number we get is 18000, and the program should also generate a file called threed_seg.pdb which can be used to visualize the coordinates of the Gaussian in the density map, and also used to initialize the GMM for refinement. Now we can run the GMM based global refinement.

e2gmm_refine_iter.py r3d_00/threed_00.hdf --startres 3.9 --initpts threed_seg.pdb --sym c3

The program will start from the initial orientation assignment and run five iterations of refinement using GMMs as references. After the refinement, the resolution should reach ~3.4Å

Focused refinement

Starting from a finished global refinement, run

e2gmm_refine_new.py gmm_XX/threed_XX.hdf --startres X --npt N --mask mask.hdf --masksigma

Here mask.hdf is a mask focusing on the target region. It is recommended to create this using Filtertool.

Refine from a GMM heterogeneity analysis

e2gmm_heter_refine.py gmm_XX/threed_XX.hdf --maxres X --mask mask.hdf

Here we also start from the global refinement. --maxres defines the resolution for the heterogeneity analysis, and it is typically safer to use a lower resolution (7Å by default), since the flexible parts are often not well resolved in the first place. The target region is specified with mask.hdf.

Patch-by-patch refinement

Starting from a finished global refinement, run

e2gmm_refine_patch.py gmm_XX/threed_XX.hdf --startres X --npatch N