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| eman2:e2tomo_atpsyn [2026/06/03 20:46] – muyuanchen | eman2:e2tomo_atpsyn [2026/06/04 20:47] (current) – muyuanchen |
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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". |
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| {{http://blake.bcm.edu/dl/EMAN2/atpsyn_euler_view.png| Euler angle comparison |width="600"}} | {{http://blake.bcm.edu/dl/EMAN2/atpsyn_euler_view.png| Euler angle comparison |width="600"}} |
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| </code> | </code> |
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| The resolution should improve to ~10Å at this point. Next, we can focus the refinement on the rotation of F1 head. Make a mask using FilterTool that covers the F1 head only, and name it mask_f1.hdf. Here we use the deep learning based alignment to recover the large scale rotation. | The resolution should get slightly better than 10Å at this point. Next, we can focus the refinement on the rotation of F1 head. Make a mask using FilterTool that covers the F1 head only, and name it mask_f1.hdf. Here we first run one iteration of the deep learning based alignment to recover the large scale rotation, followed by 3 iterations of the direct alignment from the deep learning result. |
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| <code> | <code> |
| e2gmm_spt_refine_iter.py gmm_00/threed_03.hdf --initpts spt_02/threed_07_seg.pdb --startres 10 --maskpp mask_01.hdf --mask mask_f1.hdf --align_mlp | e2gmm_spt_refine_iter.py gmm_00/threed_03.hdf --initpts spt_03/threed_07_seg.pdb --startres 15 --maskpp mask_01.hdf --mask mask_f1.hdf --align_mlp --niter 1 |
| | e2gmm_spt_refine_iter.py gmm_01/threed_01.hdf --initpts spt_03/threed_07_seg.pdb --startres 10 --maskpp mask_01.hdf --mask mask_f1.hdf |
| </code> | </code> |
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| This should improve the structure features at the F1 head domain, and slightly improve the FSC resolution. 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... |
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| | {{http://blake.bcm.edu/dl/EMAN2/atpsyn_cmp_focus_refine.png| Focus refinement comparison | width="600"}} |
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| To visualize the dynamics, run the following. | To visualize the dynamics, run the following. |
| 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. |
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| {{http://blake.bcm.edu/dl/EMAN2/atpsyn_bad_ptcls.png| Bad particle removal | width="600"}} | {{http://blake.bcm.edu/dl/EMAN2/atpsyn_f1_motion.gif | F1 head motion | width="600"}} |
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| Finally, we can refine the local motion of the F1 domain a bit more, without the neural network part. Depending on the particle count and the type of motion, this sometimes improve the resolution of the target domain. | |
| <code> | |
| e2gmm_spt_refine_iter.py gmm_01/threed_03.hdf --initpts spt_02/threed_07_seg.pdb --startres 10 --maskpp mask_01.hdf --mask mask_f1.hdf | |
| </code> | |
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| {{http://blake.bcm.edu/dl/EMAN2/atpsyn_cmp_focus_refine.png| Focus refinement comparison | width="600"}} | |
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