Walkthrough for template matching

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Dynamo includes a set of tools for location of particles inside tomograms. The most simple one is template matching.

Template matching

In this technique, a template representing a molecule of interest is systematically cross-correlated against a tomogram, producing a cross-correlation map of the tomogram. Each pixel in this map represents a score assigned the corresponding pixel in the tomogram map. This score measures the similarity of the neighbourhood of the tomogram pixel to the used template. This similarity is measured exclusively inside a mask.

Data set

Tomogram description

The tomogram contains a buffer with T20S proteasome on a holey carbon grid collected on a Krios + K2. Original pixelsize was 1.76 angstroms. The tomogram provided here has bin binned twice (yielding thus 7.04 ang), defocus is 4.4 microns, no CTF correction, no energy filter.


The tomogram has been kindly provided by Alex Noble, from the New York Structural Biology Center. Data collection was performed using Leginon and Appion-Protomo at the Simons Electron Microscopy Center and National Resource for Automated Molecular Microscopy located at the New York Structural Biology Center, supported by grants from the Simons Foundation (349247), NYSTAR, and the NIH National Institute of General Medical Sciences (GM103310) with additional support from Agouron Institute [Grant Number: F00316].

Getting the tomogram


Visualizing the tomogram

We can get a first glance on how the tomogram looks like:

dtmshow -otf t20s.mrc  

As you can see, the proteasomes are densely packed in an oblique layer.

Creating a template

Through manual alignment

Through geometrical shapes

Alternatively, you can use dynamo_mask or dynamo_tube to create a synthetic model.

Creating a cross correlation process

The Process object

Considerations when creating a process

Locating cross correlation peaks

Looking at the cross correlation map

Looking at the cross correlation profile

Extracting a table

A table can be extracted through:

myTable = pff.peaks.computeTable('mcc',0.378);

Visual evaluation of results

Looking at cropped particles

Looking at averages