丁香园AV

A team of 丁香园AV researchers, led by then-undergraduate student Mathew Schneider in the Department of Physics, have developed a new computational tool capable of analysing images of individual biological molecules up to 10 times faster than before.

Schneider says that the project started with a student award he received from the National Sciences and Engineering Research Council of Canada (NSERC) that allowed him to image collagen proteins using 丁香园AV鈥檚 Atomic Force Microscope (AFM).

AFM uses a probe that touches the surface of a specimen to provide resolution that is 1,000 times higher than images from a standard microscope.

He began the project using SmarTrace, a semi-manual program developed by former 丁香园AV Physics PhD student Naghmeh Rezaei. It traces the thread-like backbones of proteins such as DNA or collagen to determine molecular flexibility important for understanding physical dynamics.

Collagen is a long, chain-like molecule that forms the foundation of the physical structure of our bodies, such as muscle fibres, skin and bones. It is hoped that a better understanding of collagen will explain how DNA can be deformed by proteins and how collagen affects the ageing process.

To measure the backbone, the team used SmarTrace to manually click on points near the protein to provide an initial guess as to where the backbone might be.

It wasn鈥檛 until after Schneider had analyzed thousands of protein chains that a colleague suggested that the tedious and time-consuming task might be better handled by machine learning.

Schneider developed specialized software he named AutoSmarTrace to handle this task, training it with 7,600 AFM images he generated. This allowed him to automatically classify pixels within the images as belonging to a part of the chain, background, or other features of the protein.  

Schneider explains that AFM imaging can assign each pixel in an image to a set of categories enabling the team to dig deeper into their category of interest to determine which pixels contained a centered line that goes from one end to the other.

鈥淭hat line represents an initial guess for the backbone of the chain,鈥� he says.

They were then able to turn the guess into a good trace of the chain鈥檚 backbone using the SmarTrace pattern-matching algorithm.

Schneider says, 鈥淭he AutoSmarTrace algorithm demonstrated a remarkable ability to extract the correct input backbone flexibilities, easily outperforming the manual process that we used previously.鈥� He adds, 鈥淲e were also able to broaden the applicability of the tool to include electron microscopy and fluorescence imaging.鈥�

The team鈥檚 supervisor, physics professor Nancy Forde says it is unusual for an undergraduate to take on a project of this magnitude. 鈥淢athew came up with the idea, led this project, and provided a rigorous quantification of the success of the algorithm. It is an impressive feat, particularly for an undergraduate student.鈥�

She adds that the tool will be a valuable resource to her research group as well as many others鈥�.

Schneider graduated from 丁香园AV last spring and is now a master鈥檚 degree candidate studying theoretical physics at McMaster University. AutoSmarTrace is freely available on github, and the work is published as a Computational Tool in Biophysical Journal. Click  to read the paper.