Refine a structure to high-resolution

Typical refinement strategy

High-resolution refinement will typically require multiple runs, which are continuations of each other (see Running RELION#Continuing an old run). For reasons of computational efficiency, one often performs initial runs at relatively coarse angular sampling rates and relatively large offset searches. Then, once the resolution of the model(s) no longer improves, one continues the previous run using a finer angular sampling (and with a smaller range and step size for the translations). Also, to speed up calculations with very fine angular samplings, after a while (i.e. when most of the images will have found orientations close to their correct ones) local angular searches may be performed to speed up the calculations.

Note that in the standard output to the screen (stdout), the program will print estimated accuracies of angular and translational assignments. These estimations are based on a detailed comparison of the signal in the current reconstruction(s) and the noise in the data. Do not use finer angular or translational sampling rates than these estimates.

Filling in the GUI

For 3D refinements, select the run-type of 3D reconstruction from the drop-down menu at the top of the GUI.

I/O tab

• See the Prepare input files page on how to prepare your input.

• The pixel size (in Angstrom) should be the same as the one used to estimate the CTF parameters.

• See the notes of the reference map on the Prepare input files page.

• If the reference was not reconstructed from the input images in either XMIPP or RELION, you may assume it is not on the absolute greyscale.

• Note there are various settings for icosahedral symmetry, also see the Conventions. Make sure your input map is in the one you provide here.

CTF tab

• CTF-correction is recommended, especially for cryo-EM data. If no CTF correction is to be performed, make sure you phase-flipped your data during preprocessing. See the Prepare input files page.

• Normalisation correction is robust and therefore recommended in the general case.

• Intensity correction corrects for distinct grey-scale intensities among the signal in the data, e.g. because due to distinct SNRs among the micrographs. This option is only effective if the data is provided in a STAR file that contains multiple unique strings for the rlnMicrographName label (see the Prepare input files page.

Optimisation tab

• One typically starts refinement from a medium-low resolution filtered map to minimise model bias. If your input map is not low-pass filtered, it may be filtered internally using the Initial low-pass filter option.

• Often 25-50 iterations are necessary before the refinement converges to a stable solution, but high-resolution refinement may take even more iterations. A fundamental difference with conventional refinement schemes is that iterating does not only serve to find the optimal orientations, iteration also serves to progressively increase the resolution of the model. Note there is currently no convergence criterion implemented, so the user is responsible for monitoring the convergence, e.g. when the resolution where the rlnSsnrMap drops below one does no longer change.

• The regularisation parameter determines the relative weight between the experimental data and the prior. Bayes' law dictates it should be 1, but better results are often obtained using slightly higher values (e.g. 2-4), especially when dealing with cryo-data.

• The particle diameter (in Angstroms) serves to define a soft spherical mask that will be applied to the experimental images to reduce their background noise. If solvent flattening is set to Yes, then also the references will be masked using the same spherical mask (or using a user-provided one under the solvent mask option).

Sampling tab

• As mentioned in the #General refinement strategy, initial runs are typically performed with relatively coarse angular sampling rates, and the angular sampling rate is gradually decreased during subsequent continuation runs.

• After multiple (e.g. 10-20) iterations of exhaustive angular searches, local angular searches may be performed. This will considerably speed up the calculations, especially for very fine angular samplings.

• In some cases tilt angle searches may be restricted (e.g. because one knows there are only side views in the data set).

• Translational search ranges may depend on how well-centered the particles were picked, but often 5-10 pixel search ranges with a step size of 1 or 2 pixels during the initial (say 10) iterations will do the job. Translational searches in subsequent iterations are centered at the optimal translation in the previous one, so that particles may "move" much more than the original search range during the course of multiple iterations. In continuation runs, typically finer step sizes (e.g. of 0.5 pixels) and smaller search ranges are used. Note that pre-centering prior to RELION refinement is not necessary, and also not recommended (it often messes up the Gaussian distribution of origin offsets).

Running tab

• If available memory becomes a limitation (the main need for memory comes from storing the oversampled Fourier transforms of the references) then running multiple threads on multi-core machines are a good option. Otherwise, MPI parallelisation is probably more efficient.