[3dem] Overcoming orientation preference issue

[Jillian Chase]

Hi Yang,

You could also try discarding a portion of large dataset to even out the population percentages. Alternatively, if there is a similar pdb available, you could try 2D backprojections of that structure and use projections as templates for automated picking (I prefer gAutomatch). This may help pick up poorly electron dense orientations that are not easily visible or not readily picked with auto threshold parameters.

Hope this helps,
Jillian



> On May 17, 2017, at 9:06 AM, Andreas Boland <aboland at mrc-lmb.cam.ac.uk> wrote:
> 
> Dear Yang,
> 
> we have recently used graphene oxide as a support layer to increase our orientation distribution (compared to no support) for a separase-securin complex (http://www.nature.com/nsmb/journal/v24/n4/full/nsmb.3386.html)
> 
> A protocol how to prepare graphene oxide coated girds can be found online here (https://figshare.com/articles/Graphene_Oxide_Grid_Preparation/3178669).
> 
> Also this topic has been discussed in many reviews. One example here:
> https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4409659/
> 
> All the best,
> 
> Andreas 
> 
>> On 17 May 2017, at 13:46, Yang Li <yanglixtal at gmail.com> wrote:
>> 
>> Dear colleagues,
>> 
>> We have a protein sample that suffers from severe orientation preference, that most of the particles cluster into two distinct orientations. This way we have to collect large amounts of data in order to obtain enough effective particles, which hiders us from reaching high resolution. We have tried to make thicker ice or adding tiny amount of detergent such as Tween20, but not working very well so far. I wonder if there are any tricks we can try to overcome this orientation preference issue? Thank you in advance for suggestions!
>> 
>> Best,
>> Yang 
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