Artificial intelligence in structural biology is here to stay - Nature.com

The AlphaFold machine-learning tool can predict 3D structures of full protein chains for 98% of human proteinsCredit: Karen Arnott/EMBL-EBI.

“I didn’t think we would get to this point in my lifetime.” That’s how one research leader in structural biology responded to last week’s publication of research in which artificial intelligence (AI) was used to predict the structure of more than 20,000 human proteins, as well as that of nearly all the known proteins produced by 20 model organisms such as Escherichia coli, fruit flies and yeast, but also soya bean and Asian rice.

The data, publicly accessible for the first time (see https://alphafold.ebi.ac.uk), were released online on 22 July by researchers at DeepMind, a London-based AI company owned by Google’s parent company, Alphabet, and the European Bioinformatics Institute, based at the European Molecular Biology Laboratory (EBI-EMBL) near Cambridge, UK.

DeepMind’s AI predicts structures for a vast trove of proteins.

The unveiling of this catalogue of predicted structures would not be nearly such good news were the data and the methodology not open and freely available.

Before AI, structure prediction from sequence was an intensely time-consuming, not to say labour-intensive, process with little guarantee of getting an accurate result.

But the AI tools can accurately predict protein structures in minutes to hours — compared with the months, or years, that it used to take to determine the structure of just one or two proteins.

Read the paper: Highly accurate protein structure prediction for the human proteome.

Last week’s breakthrough depended not just on the sharing of open data, but on advances in fundamental science and technology.

One involves piecing together the structures of proteins by understanding the underlying physical forces.

Another attempts to predict the shapes by making comparisons with closely related proteins, using an organism’s evolutionary history.

Although AI in science and technology is good at producing accurate results, it doesn’t (at least for now) explain how, or why, those results happened.

But there is still work to be done to unlock the science — the essential biology, chemistry and physics — of how and why proteins fold?

The human genome’s first draft was the result of a race.

Solving protein folding has also benefited from a kind of competition — an annual event called the Critical Assessment of Protein Structure Prediction (or CASP), which has been essential to getting a result.

Read the paper: Highly accurate protein structure prediction with AlphaFold.

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