UNC’s novel way to visualize the human proteome could lead to improved clinical laboratory tests along with the development of new therapies
Diagnostic testing based on proteomics is considered to be a field with immense potential in diagnostics and therapeutics. News of a research breakthrough into how scientists can visualize protein activity within cells will be of major interest to the pathologists, PhDs, and medical laboratory scientists who specialize in clinical laboratory testing involving proteins.
Proteins are essential to all life and to the growth, maintenance, and repair of the human body. So, a thorough understanding of how they function within living cells would be essential to informed medical decision-making as well. And yet, how proteins go about doing their work is not well understood.
That may soon change. Scientists at the University of North Carolina (UNC) School of Medicine have developed an imaging method that could provide new insights into how proteins alter their shapes within living cells. And those insights may lead to the development of new therapies and medical treatments.
Dubbed “binder-tag” by the UNC scientists, their new technique “allows researchers to pinpoint and track proteins that are in a desired shape or ‘conformation,’ and to do so in real time inside living cells,” according to a UNC Health news release.
The technique involves two parts: a fluorescent binder and a molecular tag that is attached to the proteins of interest.
When inactive, the tag is hidden inside the protein, but when the protein is ready for action it changes shape and exposes the tag.
The binder then joins with the exposed tag and fluoresces. This new fluorescence can easily be tracked within the cell.
Nothing else in the cell can bind to the binder or tag, so they only light up when in contact on the active protein.
This type of visualization will help researchers understand the dynamics of a protein in a cell.
“The method is compatible with a wide range of beacons, including much more efficient ones than the interacting beacon pairs required for ordinary FRET [fluorescence resonance energy transfer]. Binder-tag can even be used to build FRET sensors more easily. Moreover, the binder-tag molecules were chosen so that nothing in cells can react with them and interfere with their imaging role,” Hahn said in the news release.
“Only upon exposure can the peptide specifically interact with a reporter protein (the binder). Thus, simple fluorescence localization reflects protein conformation. Through direct excitation of bright dyes, the trajectory and conformation of individual proteins can be followed,” the UNC researchers wrote in Cell. “The simplicity of binder-tag can provide access to diverse proteins.”
The UNC researchers’ binder-tag technique is a way to overcome the dire challenge of seeing tiny and hard-working proteins, Cosmos noted. Typical light microscopy does not enable a view of molecules at work. This paves the way for the new binder-tag technique, UNC pointed out.
“With this method, we can see, for example, how microenvironmental differences across a cell affect—and often profoundly—what a protein is doing,” Hahn said. “For a lot of protein-related diseases, scientists haven’t been able to understand why proteins start to do the wrong thing. The tools for obtaining that understanding just haven’t been available.”
More Proteins to Study
More research is needed before the binder-tag method can be used in diagnostics. Meanwhile, the UNC scientists intend to show how binder-tag can be applied to other protein structures and functions.
“The human proteome has between 80,000 and 400,000 proteins, but not all at one time. They are expressed by 20,000 to 25,000 human genes. So, the human proteome has great promise for use in diagnostics, understanding disease, and developing therapies,” said Robert Michel, Editor-in-Chief of Dark Daily and its sister publication The Dark Report.
Medical scientists and diagnostics professionals will want to stay tuned to discover more about the tiny—though mighty—protein’s contributions to understanding diseases and patient treatment.
Pathologists and clinical laboratory scientists developing proteomic assays understand the significance of this gesture. They know how difficult and expensive it is to determine protein structures using sequencing of amino acids. That’s because the various types of amino acids in use cause the [DNA] string to “fold.” Thus, the availability of this data may accelerate the development of more diagnostic tests based on proteomics.
“For decades, scientists have been trying to find a method to reliably determine a protein’s structure just from its sequence of amino acids. Attraction and repulsion between the 20 different types of amino acids cause the string to fold in a feat of ‘spontaneous origami,’ forming the intricate curls, loops, and pleats of a protein’s 3D structure. This grand scientific challenge is known as the protein-folding problem,” a DeepMind statement noted.
Enter DeepMind’s AlphaFold AI platform to help iron things out. “Experimental techniques for determining structures are painstakingly laborious and time consuming (sometimes taking years and millions of dollars). Our latest version [of AlphaFold] can now predict the shape of a protein, at scale and in minutes, down to atomic accuracy. This is a significant breakthrough and highlights the impact AI can have on science,” DeepMind stated.
Release of Data Will Be ‘Transformative’
In July, DeepMind announced it would begin releasing data from its AlphaFold Protein Structure Database which contains “predictions for the structure of some 350,000 proteins across 20 different organisms,” The Verge reported, adding, “Most significantly, the release includes predictions for 98% of all human proteins, around 20,000 different structures, which are collectively known as the human proteome. By the end of the year, DeepMind hopes to release predictions for 100 million protein structures.”
Free Data about Proteins Will Accelerate Research on Diseases, Treatments
Research into how protein folds and, thereby, functions could have implications to fighting diseases and developing new medicines, according to DeepMind.
“This will be one of the most important datasets since the mapping of the human genome,” said Ewan Birney, PhD, Deputy Director General of the EMBL, in the DeepMind statement. EMBL worked with DeepMind on the dataset.
DeepMind protein prediction data are already being used by scientists in medical research. “Anyone can use it for anything. They just need to credit the people involved in the citation,” said Demis Hassabis, DeepMind CEO and Co-founder, in The Verge.
In a blog article, Hassabis listed several projects and organizations already using AlphaFold. They include:
“As researchers seek cures for diseases and pursue solutions to other big problems facing humankind—including antibiotic resistance, microplastic pollution, and climate change—they will benefit from fresh insights in the structure of proteins,” Hassabis wrote.
Because of the deep financial backing that Alphabet/Google can offer, it is reasonable to predict that DeepMind will make progress with its AI technology that regularly adds capabilities and accuracy, allowing AlphaFold to be effective for many uses.
This will be particularly true for the development of new diagnostic assays that will give clinical laboratories better tools for diagnosing disease earlier and more accurately.
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