Machine learning generates pictures of proteins in 5D

Machine learning generates pictures of proteins in 5D
Estimating 3D orientations and 2D positions of single molecules (SMs) using Deep-SMOLM. Credit: Optics Express (2022). DOI: 10.1364/OE.470146

By combining machine learning with the laws of physics, researchers in the lab of Matthew Lew, associate professor of electrical and systems engineering at Washington University in St. Louis, have been able to sort out the orientation and position of overlapping single molecules in 5D from a single image.

Their research was published Sept. 26 in the journal Optics Express.

The five in question aren't new or hidden . Instead, a team headed by Tingting Wu, a Ph.D. student in the McKelvey School of Engineering's imaging sciences program, was able to design a system that could tell the orientation of a molecule in 3D space as well as its position in 2D: five parameters from a single, noisy, pixelated image.

Using a machine learning algorithm coupled with post-processing allows the Lew lab to uncover the structureon the right from the noisy, pixelated image on the left. The image on the right is color coded with estimated 3D orientation. Credit: Lew lab

To wrest this additional complex information from a seemingly simple spot of light, the team did design a machine learning algorithm, but added an extra step.

"A lot of people use AI end-to-end," Wu said. "Just put in the thing you have and ask the neural network to give you the thing you want." She decided to break the problem into two steps to lighten the load on the algorithm, making it more robust.

The kind of imaging carried out in the Lew lab—of —tends to be very "noisy," containing "specks" or fluctuations that can obscure an image. For most machine learning , Lew said, "robustly dealing with that kind of noise can be very complicated to learn."

Machine learning generates pictures of proteins in 5D
Amyloid proteins. Their lengths and directions indicate the magnitude of their in-plane orientations and their orientations, respectively. Credit: Lew Lab

Humans, however, have already learned how signals from the molecules of interest and this noise are combined together within microscope images. Instead of asking the algorithm to re-learn the laws of physics, the team added a second "post-processing" algorithm–a straightforward computation that applied these physical laws to the results from the first algorithm.

"It's like I've separated two problems into two algorithms," Wu said.

After processing thousands of snapshots, the result, Wu said, is a "beautiful image" that uses color, curvature and direction to indicate how thousands of molecules are connected to each other.

Ultimately, this system will be able to help researchers better understand at tiny scales—like the way in which assemble to form the tangled structures associated with Alzheimer's disease.

More information: Tingting Wu et al, Deep-SMOLM: deep learning resolves the 3D orientations and 2D positions of overlapping single molecules with optimal nanoscale resolution, Optics Express (2022). DOI: 10.1364/OE.470146

Journal information: Optics Express
Citation: Machine learning generates pictures of proteins in 5D (2022, November 2) retrieved 20 July 2024 from
This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission. The content is provided for information purposes only.

Explore further

Extraction of bouton-like structures from neuropil calcium imaging data


Feedback to editors