DeepMind releases most complete database of predicted human protein structures

The most complete database of predictions for the shape of proteins in the human body has been shared with the scientific community to open new avenues of discovery.  

Researchers at Alphabet subsidiary, DeepMind made the breakthrough using artificial intelligence (AI) system AlphaFold, which was hailed in December 2020 as a solution to the 50-year-old challenge of protein structure prediction.

In partnership with the European Molecular Biology Laboratory (EMBL), they have released

more than 350,000 protein structure predictions, including the entire human proteome, to enable and accelerate research.

A paper in the journal Nature provides the fullest picture of proteins that make up the human proteome, and the release of 20 additional organisms that are important for biological research.


The database dramatically expands the accumulated knowledge of protein structures, more than doubling the number of high-accuracy human protein structures available to researchers.

Being able to predict a protein’s shape computationally from its amino acid sequence can help scientists achieve in months what previously took years to determine through laborious and costly experimental techniques.


AlphaFold is already being used by partners such as the Drugs for Neglected Diseases Initiative (DNDi) to advance their research into life-saving cures for diseases that disproportionately affect the poorer parts of the world, and the Centre for Enzyme Innovation (CEI) to help engineer faster enzymes for recycling single-use plastics.


DeepMind founder and CEO Demis Hassabis, PhD, said: “Our goal at DeepMind has always been to build AI and then use it as a tool to help accelerate the pace of scientific discovery itself, thereby advancing our understanding of the world around us.

“We used AlphaFold to generate the most complete and accurate picture of the human proteome. We believe this represents the most significant contribution AI has made to advancing scientific knowledge to date and is a great illustration of the sorts of benefits AI can bring to society.”

EMBL director general, Edith Heard said: “The AlphaFold database is a perfect example of the virtuous circle of open science. AlphaFold was trained using data from public resources built by the scientific community, so it makes sense for its predictions to be public.

“Sharing AlphaFold predictions openly and freely will empower researchers everywhere to gain new insights and drive discovery. I believe that AlphaFold is truly a revolution for the life sciences, just as genomics was several decades ago and I’m very proud that EMBL has been able to help DeepMind in enabling open access to this remarkable resource.”

EMBL deputy director general, and EMBL-EBI director Ewan Birney, said: “This will be one of the most important datasets since the mapping of the human genome. Making AlphaFold predictions accessible to the international scientific community opens up so many new research avenues, from neglected diseases to new enzymes for biotechnology and everything in between.”

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