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How a Google AI program for protein folding could speed drug discovery

By Brian Buntz | December 1, 2020

AlphaFold

[The AlphaFold software uncovered this protein structure. Image from DeepMind.]

A Google-owned lab has accurately predicted protein structures, potentially paving the way toward advances in drug discovery.

London-based DeepMind lab has developed a deep-learning program known as AlphaFold that bested roughly 100 efforts to predict protein structures in a contest known as Critical Assessment of Structure Prediction (CASP).

Such proteins are found within the cells of organisms ranging from bacteria to humans. Abnormalities in such proteins play a role in numerous diseases.

Predicting the 3D shape of such proteins has been a vexing challenge for decades, although researchers have made breakthroughs in doing so in recent years.

The structure of such proteins dictates how it functions.

While CASP participants were more accurate in the competition than in years’ past, AlphaFold was an outlier. For protein-folding puzzles deemed moderately difficult, the AlphaFold team scored approximately 90 on a 100-point scale for prediction accuracy. The nearest competitors scored roughly 75.

This was the first year that any team came close to accurately predicting protein shapes. The CASP contest was launched in 1994. Scientists have attempted to solve the “protein-folding problem” for some 50 years.

The improved ability to predict protein structures based on their amino-acid sequence will likely be a boon for drug discovery as it will improve scientists’ ability to comprehend the inner workings of cells.

The traditional manner of determining protein structures has relied on lab experiments. Often, figuring out a precise 3D protein shape can take months or years. And in some respects, however, AlphaFold’s predictions replicated those from laboratory techniques such as X-ray crystallography and cryo-transmission electron microscopy.

But the ability to use artificial intelligence to understand protein folding opens up new avenues for studying organisms and disease.

AlphaFold currently does have limitations. It could not accurately predict structures found using nuclear magnetic resonance imaging. It also struggled to predict individual structures in complexes of proteins.

The peer-reviewed research from DeepMind was published in Nature and Proteins.


Filed Under: Drug Discovery, Drug Discovery and Development
Tagged With: AlphaFold, CASP, Critical Assessment of Structure Prediction, DeepMind, Google, protein folding, protein folding problem, protein shapes
 

About The Author

Brian Buntz

As the pharma and biotech editor at WTWH Media, Brian has almost two decades of experience in B2B media, with a focus on healthcare and technology. While he has long maintained a keen interest in AI, more recently Brian has made making data analysis a central focus, and is exploring tools ranging from NLP and clustering to predictive analytics.

Throughout his 18-year tenure, Brian has covered an array of life science topics, including clinical trials, medical devices, and drug discovery and development. Prior to WTWH, he held the title of content director at Informa, where he focused on topics such as connected devices, cybersecurity, AI and Industry 4.0. A dedicated decade at UBM saw Brian providing in-depth coverage of the medical device sector. Engage with Brian on LinkedIn or drop him an email at bbuntz@wtwhmedia.com.

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