Using a supporting geometry#

In cryo-ET, objects of interest are often bound to or part of a larger supporting object such as a membrane, a vesicle or a filament. When this is the case, they often exhibit specific spatial relationships to the support.

This information, the position and orientation of particles relative to a supporting geometry, is often useful in subtomogram averaging experiments.

The process#

The general process for making use of this spatial information is simple

  1. Generate a 3D model of the supporting geometry from tomogram annotations

  2. Seed particle positions and orientations relative to the supporting geometry

Advantages#

Providing accurate initial estimates for the positions and orientations of particles at the start of a subtomogram averaging experiment allows you to

  • limit angular searches appropriately

  • restrict shifts appropriately

Constraining these parameters ensures that particles do not ‘drift’ during iterative refinement procedures and get trapped in a local minimum, a common phenomenon when working with low SNR cryo-ET data.

A reduced search space also alleviates some of the computational burden of performing global searches.

Disadvantages#

Generation of these 3D models is usually semi-automated at best, as opposed to template matching which is fully automated. This can mean it takes significantly longer for large datasets.