Closing the door on colds and flu

First-of-its-kind structural data about protein family is key for drug discovery

New research by scientists at the University of Toronto and the Structural Genomics Consortium has deepened our understanding of how viruses like the flu, common cold, and COVID-19 get into cells in human airways.

Using the Canadian Light Source at the University of Saskatchewan, the researchers identified for the first time the crystal structures of a human protein (TMPRSS11D) that viruses use as a doorway into our body.

Understanding how viruses use our proteins to gain entry into our cells will help researchers develop better ways to stop infections in their tracks.

“This paper is really the stepping stone for building out more effective antiviral agents,” says lead author Bryan Fraser, a University of Toronto postdoctoral researcher at the Structural Genomics Consortium.

“We’re using the structure-based information that we’ve gained here to guide us in improving molecules that we hope will become drug candidates.”

Knowing the crystal structure of this “doorway” protein, says Fraser, is key to finding helpful drugs to stop coronavirus and influenza viruses, because it is very similar to other important proteins in the human body.

“Many of the important proteins for coagulation that are present in your blood look a lot like the TMPRSS proteins,” Fraser explains.

Successfully drugging subtle features on the TMPRSS proteins that are not present in coagulation proteins can be the difference between stopping infections and interfering with how wounds heal.

“The major challenge in our field is finding really effective compounds or drug candidates that show they’re selective for the target you’re interested in, and don’t block those other essential functions,” says Fraser.

While precise targeting is a challenge, the promise of these proteins as drug targets is immense.

Read more on CLS website

Quantitative analysis of cell organelles with artificial intelligence

The analysis of cryo-X-Ray-microscopy data still requires a lot of time. Scientists developed a convolutional neural network, which identifies structures at high accuracy within a few minutes.

BESSY II’s high-brilliance X-rays can be used to produce microscopic images with spatial resolution down to a few tens of nanometres. Whole cell volumes can be examined without the need for complex sample preparation as in electron microscopy. Under the X-ray microscope, the tiny cell organelles with their fine structures and boundary membranes appear clear and detailed, even in three dimensions. This makes cryo x-ray tomography ideal for studying changes in cell structures caused, for example, by external triggers. Until now, however, the evaluation of 3D tomograms has required largely manual and labour-intensive data analysis. To overcome this problem, teams led by computer scientist Prof. Dr. Frank Noé and cell biologist Prof. Dr. Helge Ewers (both from Freie Universität Berlin) have now collaborated with the X-ray microscopy department at HZB. The computer science team has developed a novel, self-learning algorithm. This AI-based analysis method is based on the automated detection of subcellular structures and accelerates the quantitative analysis of 3D X-ray data sets. The 3D images of the interior of biological samples were acquired at the U41 beamline at BESSY II.

“In this study, we have now shown how well the AI-based analysis of cell volumes works, using mammalian cells from cell cultures that have so-called filopodia,” says Dr Stephan Werner, an expert in X-ray microscopy at HZB. Mammalian cells have a complex structure with many different cell organelles, each of which has to fulfil different cellular functions. Filopodia are protrusions of the cell membrane and serve in particular for cell migration. “For cryo X-ray microscopy, the cell samples are first shock-frozen, so quickly that no ice crystals form inside the cell. This leaves the cells in an almost natural state and allows us to study the structural influence of external factors inside the cell,” Werner explains.

“Our work has already aroused considerable interest among experts,” says first author Michael Dyhr from Freie Universität Berlin. The neural network correctly recognises about 70% of the existing cell features within a very short time, thus enabling a very fast evaluation of the data set. “In the future, we could use this new analysis method to investigate how cells react to environmental influences such as nanoparticles, viruses or carcinogens much faster and more reliably than before,” says Dyhr.

Read more in the Proceedings of the National Academy of Sciences journal article

Image: The images show part of a frozen mammalian cell. On the left is a section from the 3D X-ray tomogram (scale: 2 μm). The right figure shows the reconstructed cell volume after applying the new AI-supported algorithm

Credit: HZB