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eman2:e2tomo_atpsyn [2026/06/05 18:10] – muyuancheneman2:e2tomo_atpsyn [2026/06/06 03:10] (current) – muyuanchen
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 ====== EMAN2 tomography - ATP synthase in mitochondria (2026) ====== ====== EMAN2 tomography - ATP synthase in mitochondria (2026) ======
  
-This tutorial uses a public in situ CryoET dataset ([[https://www.ebi.ac.uk/empiar/EMPIAR-11830/ | EMPIAR-11830]]) of Chlamydomonas reinhardtii prepared using cryo-plasmaFIB milling. Here, we use [[https://drive.google.com/file/d/18llt4TLnAbDn5MajfAZA-zumwf4w4LGp/view?usp=sharing | 5 tilt series]] and target the structure and dynamics of ATP synthase inside mitochondria. +This tutorial uses a public in situ CryoET dataset ([[https://www.ebi.ac.uk/empiar/EMPIAR-11830/ | EMPIAR-11830]]) of Chlamydomonas reinhardtii prepared using cryo-plasmaFIB milling. Here, we use [[https://drive.google.com/file/d/18llt4TLnAbDn5MajfAZA-zumwf4w4LGp/view?usp=sharing | 5 tilt series]] and target the structure and dynamics of ATP synthase inside mitochondria. In the end, from this small dataset, we will produce monomer structure at (slightly) sub-nanometer resolution, and characterize the rotary movement of the F1 head domain. Using a larger dataset, it is possible to reach ~5Å resolution and solve the full rotation of the central stalk as well.
  
 It is recommended to cross reference with previous tutorials of [[https://blake.bcm.edu/emanwiki/EMAN2/e2TomoSmall | ribosomes ]] and [[https://blake.bcm.edu/emanwiki/EMAN2/e2tomo_p22 | viruses ]].  It is recommended to cross reference with previous tutorials of [[https://blake.bcm.edu/emanwiki/EMAN2/e2TomoSmall | ribosomes ]] and [[https://blake.bcm.edu/emanwiki/EMAN2/e2tomo_p22 | viruses ]]. 
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 Unzip the dataset, and you should have a folder called "tiltseries", with four hdf image stacks in it, inside the project folder. To view the tilt series, run **e2display.py**, locate the file in the browser, and click **Show2D**. Unzip the dataset, and you should have a folder called "tiltseries", with four hdf image stacks in it, inside the project folder. To view the tilt series, run **e2display.py**, locate the file in the browser, and click **Show2D**.
  
-{{http://blake.bcm.edu/dl/EMAN2/atpsyn_tilt_series.png|tiltseries|width="600"}}+{{:eman2:atpsyn_tilt_series.jpg|tiltseries|width="600"}}
  
 ===== Initial tomogram reconstruction ===== ===== Initial tomogram reconstruction =====
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 </code> </code>
  
-The handedness of the tilt series should be correct. +The handedness of the tilt series should be correct. Just use the reported --tltax for the reconstruction of all tomograms. 
  
 ===== All tomogram reconstruction ===== ===== All tomogram reconstruction =====
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 </code> </code>
  
-{{http://blake.bcm.edu/dl/EMAN2/atpsyn_eval_tomo.png|Evaluate tomogram|width="600"}}+{{:eman2:atpsyn_eval_tomo.png|Evaluate tomogram|width="600"}}
  
 Here we pick a few particles manually. In this dataset, we just need ~70 particles to make a good initial model. Here we label them as **atpsyn_init**.  Here we pick a few particles manually. In this dataset, we just need ~70 particles to make a good initial model. Here we label them as **atpsyn_init**. 
  
-{{http://blake.bcm.edu/dl/EMAN2/atpsyn_pick_ptcls.png| Pick particles |width="600"}}+{{:eman2:atpsyn_pick_ptcls.png| Pick particles |width="600"}}
  
 ===== Initial model generation ===== ===== Initial model generation =====
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 </code> </code>
  
-{{http://blake.bcm.edu/dl/EMAN2/atpsyn_init_model.png| initial model |width="600"}}+{{:eman2:atpsyn_init_model.png| initial model |width="600"}}
  
 Note the structure should be c2 symmetrical. At this point, it is recommended to rotate the initial model to the symmetry axis to take advantage of the symmetry in later steps. Sometimes, this can be done automatically.  Note the structure should be c2 symmetrical. At this point, it is recommended to rotate the initial model to the symmetry axis to take advantage of the symmetry in later steps. Sometimes, this can be done automatically. 
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 The template matching should work fine inside mitochondria but leave false positives outside. Since we only have 5 tomograms here, it is easy to take a look and manually clean the particles. Lauch the manual boxer and use the Eraser tool to remove particles outside the mitochondria. Uncheck "Limit Side Boxes" will show all boxes along one axis in each view so particles on the edge across all depth can be removed with one click.  The template matching should work fine inside mitochondria but leave false positives outside. Since we only have 5 tomograms here, it is easy to take a look and manually clean the particles. Lauch the manual boxer and use the Eraser tool to remove particles outside the mitochondria. Uncheck "Limit Side Boxes" will show all boxes along one axis in each view so particles on the edge across all depth can be removed with one click. 
  
-{{http://blake.bcm.edu/dl/EMAN2/atpsyn_temp_pick.png| Template based particle picking |width="600"}}+{{:eman2:atpsyn_temp_pick.png| Template based particle picking |width="600"}}
  
 After the clean up, I got ~5000 particles total.  After the clean up, I got ~5000 particles total. 
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 This should bring the resolution to about 12Å. This should bring the resolution to about 12Å.
  
-{{http://blake.bcm.edu/dl/EMAN2/atpsyn_c2_refine.png| C2 refinement |width="600"}}+{{:eman2:atpsyn_c2_refine.png| C2 refinement |width="600"}}
  
 ===== Refinement of ATP synthase monomers ===== ===== Refinement of ATP synthase monomers =====
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 To make sure the operations on particle lists are done properly, compare the Euler angles of the lists by clicking "Plot2D".  To make sure the operations on particle lists are done properly, compare the Euler angles of the lists by clicking "Plot2D". 
  
-{{http://blake.bcm.edu/dl/EMAN2/atpsyn_euler_view.png| Euler angle comparison |width="600"}}+{{:eman2:atpsyn_euler_view.png| Euler angle comparison |width="600"}}
  
 Finally we can refine the monomer particles. Here we also need to make a customized mask for the monomer, keeping only one ATP synthase inside the mask. This is also done in FilterTool using mask.zeroedge3d followed by mask.auto3d.thresh. Call this mask mask_01.hdf. Finally we can refine the monomer particles. Here we also need to make a customized mask for the monomer, keeping only one ATP synthase inside the mask. This is also done in FilterTool using mask.zeroedge3d followed by mask.auto3d.thresh. Call this mask mask_01.hdf.
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 </code> </code>
  
-{{http://blake.bcm.edu/dl/EMAN2/atpsyn_bad_ptcls.png| Bad particle removal | width="600"}}+{{:eman2:atpsyn_bad_ptcls.png| Bad particle removal | width="600"}}
  
 <code> <code>
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 This should improve the structure features at the F1 head domain, but the FSC resolution does not necessarily improve here. Because the even/odd half set only are only aligned to the "neutral" struture of their half-set and never see each other, there is a possiblity that they converge to slightly different states, and the FSC resolution decrease even though the feature in each half-set improves. This is less of a problem in datasets with more particles since the "neutral" state would be better defined, but here there are some uncertainties with only 5 tomograms... This should improve the structure features at the F1 head domain, but the FSC resolution does not necessarily improve here. Because the even/odd half set only are only aligned to the "neutral" struture of their half-set and never see each other, there is a possiblity that they converge to slightly different states, and the FSC resolution decrease even though the feature in each half-set improves. This is less of a problem in datasets with more particles since the "neutral" state would be better defined, but here there are some uncertainties with only 5 tomograms...
  
-{{http://blake.bcm.edu/dl/EMAN2/atpsyn_cmp_focus_refine.png| Focus refinement comparison | width="600"}}+{{:eman2:atpsyn_cmp_focus_refine.png| Focus refinement comparison | width="600"}}
  
 To visualize the dynamics, run the following.  To visualize the dynamics, run the following. 
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 This only shows the motion of the even set, and the same can be done to the odd half. Since the deep learning models for the two half-sets are trained independently, visualizing the motion in the combined dataset without breaking the "gold-standard" validation is impossible. Still, the rotation movement should be visible already even with the small dataset. This only shows the motion of the even set, and the same can be done to the odd half. Since the deep learning models for the two half-sets are trained independently, visualizing the motion in the combined dataset without breaking the "gold-standard" validation is impossible. Still, the rotation movement should be visible already even with the small dataset.
  
-{{http://blake.bcm.edu/dl/EMAN2/atpsyn_f1_motion.gif | F1 head motion | width="600"}}+{{:eman2:atpsyn_f1_motion.gif | F1 head motion | width="600"}}
  
  
  
eman2/e2tomo_atpsyn.1780683042.txt.gz · Last modified: by muyuanchen