Classification tutorial
Contents
Classification tutorial¶
This tutorial shows how to convert raw tilt-series from EMPIAR-10304 (E. coli. ribosomes) into a ~4.9A resolution structure and resolve 8 different conformations.
We first use the command below to download and decompress a tbz file containing: 1) a script to download the raw tilt-series from EMPIAR, 2) corresponding metadata with tilt angles and acquisition order, and 3) an initial model:
# cd to a location in the shared file system and run:
wget https://nextpyp.app/files/data/nextpyp_class_tutorial.tbz
tar xfz nextpyp_class_tutorial.tbz
source download_10304.sh
Note
Downloading the raw data from EMPIAR can take several minutes.
Step 1: Create a new project¶
Next, we create an empty folder where all files for the tutorial will be saved:
mkdir EMPIAR-10304
cd EMPIAR-10304
Step 2: Pre-processing¶
The next command does data pre-processing consisting of movie frame alignment, tilt-series alignment, tomogram reconstruction, CTF estimation:
# launch pre-processing
pyp -data_path="/path_to_raw_data/tilt*.mrc" \
-data_mode tomo \
-scope_pixel 2.1 \
-scope_voltage 300 \
-scope_tilt_axis 90.0 \
-movie_no_frames \
-ctf_max_res 5.0 \
-tomo_rec_binning 12 \
-tomo_rec_thickness 3072 \
-no-tomo_rec_format \
-tomo_rec_erase_fiducials \
-slurm_tasks 42 \
-slurm_memory 420
Note
Nominal tilt angles (stored in *.rawtlt files) and acquisition order (stored in *.order files) for each tilt-series are provided with the raw data.
Step 3: Particle detection¶
The next step is to detect ribosome particles using a size-based approach:
pyp -tomo_spk_method "auto" \
-tomo_spk_rad 80 \
-tomo_spk_stdtimes_cont_3d 2.0 \
-tomo_spk_min_size_3d 60 \
-tomo_spk_dilation_3d 100 \
-tomo_spk_radiustimes_3d 2.0 \
-tomo_spk_inhibit_3d \
-tomo_spk_stdtimes_filt_3d 2.0 \
-tomo_spk_detection_width_3d 40.0
Step 4: Reference-based refinement¶
If a 3D reference is available, we use the csp command to align particle projections using constrained refinement:
# launch coarse refinement
csp -refine_parfile=`pwd`/frealign/EMPIAR-10304_original_volumes.txt \
-refine_model="/path_to_raw_data/10304_ref_bin4.mrc" \
-particle_mw 2000 \
-particle_rad 150 \
-extract_box 64 \
-extract_bin 4 \
-refine_skip \
-extract_fmt frealign \
-refine_rhref "22.0" \
-refine_fboost \
-reconstruct_mintilt -50 \
-reconstruct_maxtilt 50 \
-csp_ctf_handedness \
-csp_refine_particles \
-csp_UseImagesForRefinementMin 15 \
-csp_UseImagesForRefinementMax 25 \
-csp_NumberOfRandomIterations 5000000 \
-csp_ToleranceParticlesPhi 180.0 \
-csp_ToleranceParticlesTheta 180.0 \
-csp_ToleranceParticlesPsi 180.0 \
-csp_ToleranceParticlesShifts 50.0
Step 5: Filter particles¶
The next step is to remove particles with low correlation scores:
mv frealign/mapsfrealign/reference_based && mkdir frealign/maps
pcl -clean_parfile=`pwd`/frealign/reference_based/EMPIAR-10304_r01_02.par.bz2 \
-clean_threshold 15.0 \
-clean_dist 20.0 \
-clean_mintilt -7.0 \
-clean_maxtilt 7.0 \
-clean_min_num_projections 1 \
-clean_check_reconstruction
Step 6 (optional): Permanently remove bad particles¶
It is often a good idea to permanently remove any bad particles identified in the previous step:
pcl -clean_discard
Step 7: Fully constrained refinement¶
In this step we do additional refinement using the raw data (without binning):
mv frealign/maps frealign/particle_filter && mkdir frealign/maps
csp -refine_parfile=`pwd`/frealign/particle_filter/EMPIAR-10304_r01_02_clean.par.bz2 \
-refine_model=`pwd`/frealign/particle_filter/EMPIAR-10304_r01_02.mrc \
-extract_box 256 \
-extract_bin 1 \
-refine_skip \
-refine_iter 2 \
-refine_maxiter 3 \
-refine_rhref "18:14" \
-csp_refine_micrographs \
-csp_OptimizerStepLength 100.0 \
-csp_UseImagesForRefinementMin 15 \
-csp_UseImagesForRefinementMax 25 \
-csp_NumberOfRandomIterations 0 \
-csp_ToleranceParticlesPsi 30.0 \
-csp_ToleranceParticlesPhi 30.0 \
-csp_ToleranceParticlesTheta 30.0 \
-csp_ToleranceParticlesShifts 30.0 \
-dose_weighting_enable \
-dose_weighting_fraction 4 \
-dose_weighting_global
All results from 3D refinement are saved in the folder frealign/maps, including png files for visual inspection corresponding to each refinement iteration.
Step 8: Create shape mask¶
The next step is to create a shape mask:
mv frealign/maps frealign/fully_constrained && mkdir frealign/maps
pmk -mask_model=`pwd`/frealign/fully_constrained/EMPIAR-10304_r01_03.mrc \
-mask_threshold 0.4 \
-mask_normalized \
-mask_edge_width 8
Step 9: Region-based local refinement¶
The following command performs region-based constrained alignment:
mv frealign/maps frealign/mask && mkdir frealign/maps
csp -refine_parfile=`pwd`/frealign/fully_constrained/EMPIAR-10304_r01_03.par.bz2 \
-refine_model=`pwd`/frealign/fully_constrained/EMPIAR-10304_r01_03.mrc \
-refine_maskth=`pwd`/frealign/mask/mask.mrc" \
-refine_iter 2 \
-refine_maxiter 6 \
-refine_rhref "12:10:8:6:5" \
-csp_UseImagesForRefinementMin 18 \
-csp_UseImagesForRefinementMax 22 \
-csp_ToleranceMicrographTiltAngles 5.0 \
-csp_ToleranceMicrographTiltAxisAngles 5.0 \
-csp_ToleranceParticlesPsi 5.0 \
-csp_ToleranceParticlesPhi 5.0 \
-csp_ToleranceParticlesTheta 5.0 \
-csp_ToleranceParticlesShifts 20.0 \
-csp_Grid "8,8,2"
Step 10: Particle-based CTF refinement¶
In this step we refine the CTF parameters on a per-particle basis:
mv frealign/maps frealign/region_based && mkdir frealign/maps
csp -refine_parfile=`pwd`/frealign/region_based/EMPIAR-10304_r01_06.par.bz2 \
-refine_model=`pwd`/frealign/region_based/EMPIAR-10304_r01_06.mrc \
-refine_iter 2 \
-refine_maxiter 2 \
-refine_rhref "4.5" \
-no-csp_refine_particles \
-no-csp_refine_micrographs \
-csp_refine_ctf \
-csp_UseImagesForRefinementMin 15 \
-csp_UseImagesForRefinementMax 25 \
-csp_ToleranceMicrographDefocus1 2000 \
-csp_ToleranceMicrographDefocus2 2000
Step 11: Additional region-based refinement after CTF refinement¶
The following command does additional region-based refinement:
mv frealign/maps frealign/ctf_refine && mkdir frealign/maps
csp -refine_parfile=`pwd`/frealign/ctf_refine/EMPIAR-10304_r01_02.par.bz2 \
-refine_model=`pwd`/frealign/ctf_refine/EMPIAR-10304_r01_02.mrc \
-refine_iter 2 \
-refine_maxiter 4 \
-refine_rhref "6:5:4.5" \
-csp_refine_particles \
-csp_refine_micrographs \
-no-csp_refine_ctf \
-csp_OptimizerStepLength 20.0 \
-csp_UseImagesForRefinementMin 18 \
-csp_UseImagesForRefinementMax 22 \
-csp_ToleranceMicrographShifts 20.0 \
-csp_Grid "16,16,4" \
-dose_weighting_fraction 2
Step 12: 3D classification¶
In the last step we perform 3D classification into 8 classes:
mv frealign/maps frealign/region_based_2 && mkdir frealign/maps
csp -refine_parfile=`pwd`/frealign/region_based_2/EMPIAR-10304_r01_04.par.bz2 \
-refine_model=`pwd`/frealign/region_based_2/EMPIAR-10304_r01_04.mrc \
-refine_iter 2 \
-refine_maxiter 20 \
-no-refine_skip \
-refine_fboost \
-refine_rhref "8" \
-no-csp_refine_particles \
-no-csp_refine_micrographs \
-class_num 8 \
-class_rhcls 8.0 \
-dose_weighting_weights=`pwd`/frealign/weights/global_weight.txt"
All results will be saved in the frealign/maps folder.