DeepMind, a company dedicated to artificial intelligence (AI) research, recently tested an advancement that might one day save biologists time and anxiety when it comes to “protein-folding,” The Next Web reported. That advancement, known as AlphaFold AI, is able to rapidly find the 3D structure of proteins from the sequence of amino-acids they’re comprised of, a critical piece of protein-folding.
According to The Next Web, biologists have struggled with the notion of protein-folding for years: “the ‘protein folding problem‘ has haunted biologists when conceiving the 3D structure of proteins from the sequence of amino-acids that make them up. When researchers find a new protein, they compare its amino-acid sequence to a database of other proteins whose structures are already known. From the best matches, they predict how the new protein would also look like in 3D.”
In the past, researchers have been able to determine 3D structures using techniques like cryo-electron microscopy and X-ray crystallography; however, those methods take years worth of trial and error to land on a solution, and “cost tends of thousands of dollars per structure.”
However, AlphaFold AI might be able to fix these issues, according to The Next Web:
“AlphaFold was built by training a neural network with thousands of proteins whose structures were known, until the software could predict the 3D structures of proteins from their amino acid sequence alone… Once AlphaFold is provided a new protein, it uses its neural network to predict the distances between pairs of its constituent amino acids, and the angles between their connecting chemical bonds, forming a draft structure. Then, AlphaFold tweaks this structure to find the most energy-efficient structure.”
The technology was put to the test in a recent international protein-folding competition run by the Protein Structure Prediction Center. AlphaFold won against 98 competitors; after the end results were analyzed, AlphaFold correctly predicted the structures of 25 out of 43 proteins; the second place winner only predicted 3 out of 25 proteins.
A promising outlook for decision makers in STEM:
Based on the results of protein-folding competition, The Next Web suggests that AlphaFold might bring in a new wave of protein-folding technology that will make researchers’ jobs easier, especially biologists and other STEM fields. For example, protein-folding technology used to take weeks to predict proteins structures (including the first iteration of AlphaFold), and now AlphaFold is able to predict proteins in a couple of hours. Also demonstrated by AlphaFold’s victory, protein-folding technology is getting more accurate, minimizing the margin of error.
While this has been a huge step for AlphaFold and DeepMind alike, Demis Hassabis, co-founder and CEO of DeepMind, said that the work to improve protein-folding technology isn’t over. Things will continue to improve for researchers, biologists, and other decision makers who are interested in these types of solutions.
“We’ve not solved the protein folding problem, this is just a first step,” he told The Next Web. “It’s a hugely challenging problem, but we have a good system and we have a ton of ideas we haven’t implemented yet.”
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