[3dem] 3D classification

[Eric Hanssen]

Thank you all for the suggestions. I ll give it a try see how that goes ...
Cheers
Eric




----------------------------------------------------------------------------
Assoc Prof Eric Hanssen
Principal Research Fellow
Head Bio21 Advanced Microscopy Facility
Bio21 Molecular Science and Biotechnology Institute
30 Flemington Road - The University of Melbourne - Victoria 3010 - Australia
email: ehanssen at unimelb.edu.au<mailto:ehanssen at unimelb.edu.au> | Office: +61 3 83442449 | Microscope: +61 3 83442509 | Fax: +61 3 9347 826


On 13 Jul 2019, at 03:13, Penczek, Pawel A <Pawel.A.Penczek at uth.tmc.edu<mailto:Pawel.A.Penczek at uth.tmc.edu>> wrote:

Hi

there has to be some kind of misunderstanding here as in general it is impossible to perform eigeanalysis in the way described in the manual.

Regards,
Pawel Pawel

On Jul 12, 2019, at 11:53 AM, Ali Punjani <alipunjani at cs.toronto.edu<mailto:alipunjani at cs.toronto.edu>> wrote:


**** EXTERNAL EMAIL ****

Hi Eric,

As a direct method to resolve the heterogeneity, you may wish to try the new 3D Variability Analysis method available in cryoSPARC v2.9 (released earlier this month). This algorithm can detect very small conformational changes at high resolution, by computing the eigenvectors of the 3D density covariance of the density. These eigenvectors correspond to the modes of maximal variability in the particle data, and therefore can be used to resolve both discrete and continuous heterogeneity. More details, including examples and a detailed tutorial can be found here:
https://cryosparc.com/docs/tutorials/3d-variability-analysis/<https://urldefense.proofpoint.com/v2/url?u=https-3A__cryosparc.com_docs_tutorials_3d-2Dvariability-2Danalysis_&d=DwMFaQ&c=bKRySV-ouEg_AT-w2QWsTdd9X__KYh9Eq2fdmQDVZgw&r=yEYHb4SF2vvMq3W-iluu41LlHcFadz4Ekzr3_bT4-qI&m=uikbWAmVC_qT6eK3I3JsUspENOZfAhHTGFHkCL4iUGQ&s=ij-rYNgTenO1SXXpZdrGsUv_6--ECLDYbb-Z2PRaX70&e=>
3D Variability Analysis can also be used with a mask, to focus on the relevant region of the structure, ignoring conformational variability that may be occurring in other irrelevant regions (e.g. it can help to mask out the micelle or nanodisc for a membrane protein).

Note that, as Sjors already mentioned, the major determinant of success will be detectability of the change caused by binding. If there is no conformational change associated with the binding, it is unlikely that the ligand alone would cause enough change in each particle image to be statistically distinguishable from noise, regardless of the computational technique used.

Good luck with your processing!
Ali


It can also be used in a masked fashion,
On Thu, Jul 11, 2019 at 8:24 PM Eric Hanssen <ehanssen at unimelb.edu.au<mailto:ehanssen at unimelb.edu.au>> wrote:
Hi all,
Before I commit some computer resource to a job I’d rather know if it is doable.

I have 200,000 particle of a 600kDa protein, a current map at 3.4A that looks very nice, I do not need a better resolution for now. I have a drug (dipeptide) attached to the protein but  only a subset of the protein has the drug bound. Is it possible with a 3D classification to separate such a small difference? I don’t mind which software, relion, cryosparc, eman2 ….

Or is there a better way, eg. 3D classification of a very small box size centered around the binding site?

Cheers
Eric


-----------------
Assoc. Prof Eric Hanssen

Head - Advanced Microscopy Facility
Honorary Principal Fellow – Department of Biochemistry and Molecular Biology

President Australian Microscopy and Microanalysis Society

Bio21 Molecular Science and Biotechnology Institute
30 Flemington Road - The University of Melbourne - Victoria 3010 - Australia
email: ehanssen at unimelb.edu.au<mailto:ehanssen at unimelb.edu.au> | Office: +61 3 83442449 | Microscope: +61 3 83442509
Web: www.microscopy.unimelb.edu.au<https://urldefense.proofpoint.com/v2/url?u=http-3A__www.microscopy.unimelb.edu.au_&d=DwMFaQ&c=bKRySV-ouEg_AT-w2QWsTdd9X__KYh9Eq2fdmQDVZgw&r=yEYHb4SF2vvMq3W-iluu41LlHcFadz4Ekzr3_bT4-qI&m=uikbWAmVC_qT6eK3I3JsUspENOZfAhHTGFHkCL4iUGQ&s=RJXjZmqvLh-xira2OF7B8KXDn-433k3cR2vBc2V4f8U&e=>

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