Deepmind AI Predicts 214 Million Protein Structures

Find out more about the machine learning algorithms that have helped the AlphaFold project predict more than 214 million protein structures.
AUSTIN, Texas - Dec. 21, 2022 - PRLog -- In this Formaspace laboratory report, we take a look behind the headlines at the machine learning algorithms that have helped the AlphaFold project predict more than 214 million protein structures.

Why Understanding The 3D Structure Of Proteins Is Difficult Yet Vital For Biological Research

Understanding the 3D structures of a protein can be enormously useful in uncovering the function and evolution of specific metabolic processes, such as identifying the cause (and hopefully cure) of a disease or evaluating whether an animal protein can be used as a human disease research model.

Yet visualizing the complex 3D structure of proteins accurately has remained a challenge, requiring time-consuming, painstaking direct observations using X-ray crystallography or (increasingly) cryo-EM techniques to create density maps of the protein structure in 3D space.

Researchers have long been looking for a shortcut, asking whether it would be possible to use our knowledge of the sequence of amino acids found in each protein to predict what its 3D structure would look like.

Why Are Protein Structures So Complicated?

To borrow the words of Winston Churchill (speaking about Russia in 1939):

"It's a riddle, wrapped in a mystery, inside an enigma."

Calculating 3D protein structures is hard. To illustrate this, here is a breakdown of the first three ways they can fold in 3D space:

1) Primary Protein Structure

2) Secondary Protein Structure

3) Tertiary Protein Structure

CASP, The Bi-Annual Scientific Challenge For Predicting 3D Protein Structures

As you can imagine, it's challenging to derive the 3D version of a protein if all you have to work with is the original sequence of amino acids in the chain.

But where there is a daunting scientific challenge, there is often a contest designed to spur innovation.

In 1994, Professor John Moult, Institute for Bioscience and Biotechnology Research at the University of Maryland, co-founded CASP, the Critical Assessment of Techniques for Protein Structure Prediction.

Every two years, CASP holds an international protein folding prediction contest that has drawn participation from over 100 top research groups from around the world.

What's Behind AlphaFold's Prowess In Predicting 3D Protein Structures?

The AlphaFold project is one of several emerging AI-based tools that can predict the complex 3D structure of proteins.


Julia Solodovnikova
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Location:Austin - Texas - United States
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Page Updated Last on: Dec 21, 2022
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