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Modified AlphaFold combines AI with new experimental data

Nov. 05, 2024.
2 mins. read. 8 Interactions

Researchers have developed a modified version of AlphaFold able to take into account new experimental data to predict how proteins fold.

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Giulio Prisco

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Giulio Prisco is Senior Editor at Mindplex. He is a science and technology writer mainly interested in fundamental science and space, cybernetics and AI, IT, VR, bio/nano, crypto technologies.

Proteins are essential in all living things, controlling everything from muscle movement to food digestion. They’re made up of amino acids, kind of like tiny beads on a string, which can come in many different orders and lengths, creating a huge number of possible proteins. How these amino acids are folded into a 3D shape determines what a protein does.

For a long time, figuring out how proteins fold was really hard and took a lot of time and money. Then came AlphaFold, an Artificial Intelligence (AI) tool by DeepMind, released in 2020. This tool uses neural networks to predict how proteins will fold, making it much easier to understand their functions and design new proteins for medical drugs.

This was such a big deal that Demis Hassabis and John Jumper, respectively CEO and senior research scientists at DeepMind, won half of the Nobel Prize in Chemistry.

However, AlphaFold had some limits; it struggled with very large proteins or using data from experiments. Researchers at Linköping University have modified AlphaFold, creating a tool called AF_unmasked.

The researchers describe the development of AF_unmasked and some early results of the project in a paper published in Nature Communications.

Comparison of AF_unmasked and standard AlphaFold predictions of the structure of a protein (Credit: Nature).

Endless possibilities for protein design

AF_unmasked can take in partial or experimental data, refining protein designs. The idea is to combine what we learn from experiments with what AI predicts, helping researchers design proteins more effectively.

“We’re giving a new type of input to AlphaFold, says researcher Claudio Mirabello in a Linköping University press release. “The idea is to get the whole picture, both from experiments and neural networks, making it possible to build larger structures.”

“The possibilities for protein design are endless, only the imagination sets limits,” adds researcher Björn Wallner. “It’s possible to develop proteins for use both inside and outside the body.”

Mirabello explains that AlphaFold “encodes the evolutionary history of a protein inside the neural network.” He developed this idea with Wallner. “So, you could say that AlphaFold was based on our idea, and now we are building on AlphaFold.”

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