Refine a structure to high-resolution

Filling in the GUI

For 3D refinements, select the run-type of 3D auto-refine from the drop-down menu at the top of the GUI. (This is a new feature of version 1.1.) This procedure implements so-called gold-standard FSC calculations, where two models are refined independently for two random halves of the data to prevent overfitting. Thereby, reliable resolution estimates and clean reconstructions are obtained without compromising reconstruction quality, see (Scheres & Chen, Nature Methods, in press) for more details. Note that for cyclic point group symmetries (i.e. C<n>), the two half-reconstructions are averaged up to 40 Angstrom resolution to prevent diverging orientations.

I/O tab

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

• Also 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.

• Provide the correct symmetry point group. 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

• The pixel size (in Angstrom) should be the same as the one used to estimate the CTF parameters (unless you rescaled the images afterwards, in which case the same scale factor should be applied to the pixel size).

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

• If the particles have been phase flipped, tell the program about this.

• Some data sets have very-low resolution features that are not accounted for in the linear CTF model (with ~10% amplitude contrast). This will sometimes lead to too strong low-resolution features in the reconstructed maps. Separation based on these very low-resolution features may then hamper separation of distinct structural states. Therefore, it may be useful to ignore the CTFs (i.e. set them to one) until their first maximum. In several cases, this has led to successful classification of structurally heterogeneous data that could not be classified using the full CTF-correction. If desired, full CTF-correction can then be applied subsequently during separate refinements of the distinct classes.

Optimisation tab

• To prevent model bias it is recommended to start refinement from a very strongly low-pass filtered map. If your input map is not low-pass filtered, it may be filtered internally using the Initial low-pass filter option. Typically, one filters as much as possible, i.e. before the reference becomes a feature-less blob that can no longer be refined. For example, we use 80 Angstroms for ribosomes and 60 Angstroms for GroEL.

• The particle diameter (in Angstroms) serves to define a soft spherical mask that will be applied to the references to reduce their background noise. Note that a (preferably soft) user-provided mask (1=protein, 0=solvent) may also be used for highly non-spherical particles. Be careful though not to mask away any unexpected signal and always use a soft mask, i.e. one with values between 0 and 1 at the protein/solvent boundary.

Sampling tab

• The initial angular and translational sampling rates given here will be automatically increased to their optimal values by the auto-refine procedure. We tend to use 7.5 degrees angular sampling for non-icosahedral cases and 3.7 degrees for icosahedral viruses. Most of the times using 6 pixels for the initial translational searches is enough, although this ultimately depends somewhat on how well-centered the particles were picked. However, 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 one uses multi-core nodes, the use of myltiple threads (as many threads as cores on a machine) is recommended because the shared-memory parallelisation increases the amount of memory available per process. MPI is typically used for more scalable parallelisation over the different nodes. (In terms of CPU usage, MPI parallelisation is a bit more efficient than threads.)

Analyzing results

The program will write a out_it???_half?_model.star and a out_it???_half?_class001.mrc file for each of the two independent data set halves at every iteration. Only upon convergence the program will write one out_model.star and out_class001.mrc file with the results from the joined halves of the data. Note that the joined map may no longer be used ifor refinement to prevent overfitting.

Sharpening the map

Although the map that is output by RELION is arleady optimally filtered from a signal-to-noise point of view, it still needs to be sharpened to account for the envelope function in the combined imaging and image processing procedures. For maps higher than 10 Angstrom resolution, the recommended way of filtering your maps is as explained in Rosenthal & Henderson (2003). Easy-to-use implementations of this procedure have been made in the program EMBfactor and in the XMIPP package (xmipp_correct_bfactor). Please do note that weighting the map with the FSC-curve is NOT necessary anymore: this has been taken care of already inside the RELION reconstruction. The only remaining thing to do is to apply the negative B-factor as determined from the plateau in the Guinier plot beyond 10 Angstrom. If RELION indicates the resolution of the map is for example 7 Angstrom, then one may typically apply the B-factor correction until a somewhat higher resolution, say 7 Angstrom. For example, the corresponding commands in XMIPP (v2.4) would be:

xmipp_convert_spi22ccp4 -i run1_class001.mrc -o run1_class001.spi
xmipp_correct_bfactor -i run1_class001.spi -o run1_class001_autob.spi -auto -sampling 1.77 -maxres 7    

The sharpened map can then be used for fitting atomic models, displaying in Chimera, etc.