# Initial model generation ```{image} ini-model.assets/hiv-initial-positions.png :width: 250px ``` Now that the Dynamo catalogue contains initial estimates for the positions and orientations of our particles, we need to obtain an initial model of our lattice structure which will later allow us to find more accurate estimates of the positions and orientations of our particles for the whole dataset. For this, we will work on a small subset of the VLPs. ````{margin} ```{tip} Working on a small subset initially allows you to move quickly, testing lots of different approaches and optimising your approach. How small this subset should be will depend on the quality of your data, your initial particle picking and your object of interest. ``` ```` With data of such good quality and so many particles per VLP, two of our vesicle models should be more than enough for generating a good initial model of the underlying lattice structure. When choosing an initial subset, it can be a good idea to choose VLPs with different defoci. This ensures that nodes of the CTF are at different spatial frequencies within the subset and thus no region of Fourier space should be particularly undersampled. The RELION team put together a useful [web app](https://3dem.github.io/relion/ctf.html) which can be used to visualising the effect of changing CTF model parameters. Choosing a subset which contains all available views of the particle is also important, ensuring maximum coverage of Fourier space in a resulting reconstruction From the `Overview` tab in Warp, we quickly see that `TS_01` and `TS_03` have significantly different defoci, so we choose to use one vescicle from each of these tomograms. (hiv:extract-subset)= ## Extracting a subset of particles ````{margin} ```{note} A box sidelength of 32 corresponds to a 320$Å$. While this is much larger than the expected 75$Å$ inter-particle distance, signal from neighbouring particles in lattices can help to drive correct alignments at the beginning of a project. When working at smaller pixel sizes a bigger box can also help to avoid CTF aliasing. ``` ```` Extract particles from your `Dynamo` catalogue with a sidelength of `32` following [this mini-tutorial](../../mini-tutorials/dynamo/extract-from-catalogue). To make things faster, for now we can extract particles from one vesicle in `TS_01` and one in `TS_03`. We call this volume list file created at this stage `inimodel.vll`) and extract a data folder called `inimodelData.Boxes`. ```{tip} Later, we will perform an alignment on particles from every VLP. You can save some time by launching a separate particle extraction for the whole dataset at this stage. ``` ## Aligning the particles Once the particle volumes are extracted for our two VLPs, we want to perform a first rough alignment to see how the particles align and average. We run `dcp` from MATLAB to open the "Dynamo current project" GUI. This GUI is designed to be used in a sequential way: we will need to provide a few parameters before launching the project. For a full tutorial on how to setting up a simple subtomogram averaging project in Dynamo, click [here](../../mini-tutorials/dynamo/simple-alignment). In our case, we set it up as follows: `project` : we call our project `inimodel` `particles` : the `inimodelData.Boxes` Dynamo data folder. `table` : the `inimodelData.Boxes/crop.tbl` particle metadata (table) file. When we cropped particles in the Dynamo catalogue, it created this metadata file for us and puts it inside the data folder. When selecting your table file, click `look inside data folder` and pick the `crop.tbl` file. ````{margin} ```{tip} While we don't expect to see any detailed structure in this map, but it's worth checking that it matches what we expect to see. If you right click on the file path at the top of the template GUI, you can choose to `[view]` the volume. Our initial picking contained no knowledge about the specific positions of each particle within the lattice. Because of this, we expect to see a curved, membrane like density and blurred density for the lattice proteins in the average. ``` ```` `template` : since we don't have one, we generate one from a subset of our particles. Using 500 particles at this step is a good starting point. This will generate a reconstruction from 500 particles using our initial alignments from the vesicle model stored in the `crop.tbl` file. ```{image} ini-model.assets/first-template-all.png :width: 400px ``` `masks` : `Use default masks` will give us appropriate masks at this stage that cover the full extent of our 32px box. At this stage, we aren't very sure of our particle positions and orientations as they haven't undergone any alignment. We want our mask to include the whole box so that signal from multiple neighbouring particles in the lattice can help to drive initial alignments. ````{margin} ```{note} Press `Alt` when a parameter is selected for a detailed description. ``` ```` `numerical parameters` : set the numerical parameters for the alignment procedure as shown below. Check out the [mini-tutorial](../../mini-tutorials/dynamo/simple-alignment.md) for a more in-depth explanation. At this stage, we don't enforce symmetry during refinement. We hope that any symmetry present should appear after aligning our particles. ```{image} ini-model.assets/numerical-params.png :width: 400px ``` We perform initial alignments with only local out-of-plane searches (defined by the `cone range` parameter), making use of the initial estimates from our geometrical models. ```{image} ini-model.assets/scanning-angles.png :width: 400px ``` `computing environment` : this will depend on your computing environment, including number of available CPUs/GPUs. We typically run projects in the `gpu_standalone` modus on 4 GPUs. Running in the standalone modus allows you to continue with other work in the matlab shell while the alignment project runs. In this modus, the number of CPU cores should be 1. The number of CPU cores used during averaging can be changed to the number of logical CPU cores on your machine. Specific GPUs can be selected by their index as seen in `nvidia-smi`. `check` and `unfold` : run a sanity `check` to make sure the project seems correctly setup, then `unfold` to prepare an executable for the alignment project. If running a project on a remote machine without graphical access, a project can be set up locally and sent to the remote machine as a tarball. For information on this, please see [this wiki page](https://wiki.dynamo.biozentrum.unibas.ch/w/index.php/Tarring_projects). ````{margin} ```{tip} You can track the progress of the project from the `dcp` GUI by clicking on the `progress` button. ``` ```` We can now run the executable. To do so, open the `dynamo` directory in a terminal and run: ```bash source $DYNAMO_ROOT/dynamo_activate_linux_shipped_MCR.sh ``` to prepare the environment. Then, simply execute: ```bash ./inimodel.exe ``` ## Assessing the results We can look at the result of our first alignment project using tools from the `dcp` GUI under show. Alternatively, the file containing the average can be found at `dynamo/inimodel/results/ite_0008/averages/average_ref_001_ite_0008.em` and visualised with any volume visualisation tool. In contrast to our previous reconstruction from particles with initial estimates for positions and orientations, in this map we clearly see a hexagonal lattice starting to take shape, containing 2-, 3- and 6-fold symmetry axes. ```{image} ini-model.assets/first-aligned-all.png :width: 400px ``` --- ## Aligning and centering the 6-fold symmetry axis ````{margin} ```{note} By convention, rotational symmetry axes for $C_n$ symmetries in electron microscopy software are aligned along the Z axis of the volume. ``` ```` In order to take advantage of the symmetry present in the structure during refinement we first need to recenter the average on its 6-fold symmetry axis and align that axis along the z axis of our volume. To center and align the 6-fold symmetry axis, we will use a script provided with this tutorial, [`align_symmetry_axis.m`](https://github.com/teamtomo/teamtomo.github.io/blob/master/walkthroughs/EMPIAR-10164/scripts/align_symmetry_axis.m). The script generates a synthetic template of a lattice with a 6-fold symmetry axis centered and aligned along the z-axis. The volume from our first subtomogram averaging experiment is then aligned to this synthetic template and C6 symmetry is applied. This produces an initial model with the symmetry axis correctly aligned for further experiments. `````{tab-set} ````{tab-item} command ```matlab align_symmetry_axis ``` ```` ````{tab-item} source code ```matlab v = dread('average_ref_001_ite_0008.em'); rod_radius = 2; rod_height = 4; membrane_thickness = 15; box_size = 32; % Create fake membrane mr = dpktomo.examples.motiveTypes.Membrane(); % create membrane object mr.thickness = membrane_thickness; % choose thickness of membrane mr.sidelength = box_size; % choose sidelength of box mr.fillData(); mem = mr.getData().*-1; % Create cylinder in center to represent hole in center of structure to align to cyl = dynamo_cylinder([rod_radius, floor(rod_height / 2)], 32, [16, 16, 21]); cyl = dynamo_sym(cyl, 9); cyl_shift = dynamo_shift_rot(cyl, [8, 0, 0], [0,0,0]); cyl_shift(isnan(cyl_shift)) = 0; cyl_shift_sym = dynamo_sym(cyl_shift, 6) .* 6; % Combine membrane and hole template = mem - cyl - cyl_shift_sym + 1; % normalise both volumes template = dynamo_normalize_roi(template); v = dynamo_normalize_roi(v); % Align average to synthetic template sal = dalign(v, template ,'cr',60,'cs',20,'ir',90, 'is', 30, 'rf', 5, 'dim', box_size,'limm',1,'lim',[4,4,4]); v_aligned = sal.aligned_particle; v_aligned_c6 = dynamo_sym(v_aligned, 'c6'); dmapview({v, template, v_aligned, v_aligned_c6}) % write out averages dwrite(template, 'synthetic_template.em'); dwrite(v_aligned, 'average_aligned_along_z.em'); dwrite(v_aligned_c6, 'average_aligned_along_z_c6.em'); ``` ```` ````` To run the script, open `dynamo/inimodel/results/ite_0008/averages` in Matlab. Copy the script into this directory, and execute it by running `align_symmetry_axis`. Once the script has finished, compare the initial average, the template, the aligned average and the symmetrised aligned average to check that the alignment worked as intended. ```{image} ini-model.assets/aligned-to-z.png :width: 300px ``` We now have an initial model with the 6-fold symmetry axis aligned to the center of the volume. We can use this initial model to obtain good estimates for particle positions and orientations of HIV-1 CA-SP1 hexamers for the whole dataset.