Artificial intelligence has mapped the shape of virtually every protein known to science.
The advance will help tackle major global challenges such as developing malaria vaccines and tackling plastic pollution, experts say.
Proteins are the building blocks of life, and their shape is closely tied to their function.
Being able to predict the structure of a protein gives scientists a better understanding of what it does and how it works.
The research was conducted by DeepMind and the EMBL European Bioinformatics Institute (EMBL-EBI), which used the AlphaFold AI system to predict the 3D structure of a protein.
The AlphaFold Protein Structure Database – which is freely available to the scientific community – has expanded from nearly one million protein structures to over 200 million structures, covering nearly every organism on Earth that has had its genome sequenced.
The expansion includes predicted forms for the widest possible range of species, including plants, bacteria, animals and other organisms, opening new avenues of research in the life sciences.
Demis Hassabis, founder and CEO of DeepMind, said: “We have been amazed at the rate at which AlphaFold has already become an essential tool for hundreds of thousands of scientists in laboratories and universities around the world.
“From fighting disease to fighting plastic pollution, AlphaFold has already made an incredible impact on some of our biggest global challenges.
“Our hope is that this expanded database will help countless other scientists in their important work and open up entirely new avenues of scientific discovery.”
In December 2020, AlphaFold was recognized as a solution to the grand challenge of 50 years of protein structure prediction by the organizers of the Critical Assessment of Protein Structure Prediction (Casp).
At the time, he demonstrated that he could accurately predict the shape of a protein, at scale and in minutes, with atomic precision.
The database works like an internet search for protein structures, providing instant access to predicted models.
This reduces the time it takes for scientists to learn more about the likely shapes of the proteins they are researching, speeding up experimental work.
Previous predictions have already helped scientists in their quest to create an effective malaria vaccine.
Scientists at the University of Oxford and the National Institute of Allergy and Infectious Diseases are researching a protein called Pfs48/45, which is one of the most promising candidates for inclusion in a malaria vaccine that blocks transmission.
Existing technology alone did not allow them to fully understand the protein’s structure to see where the most effective transmission-blocking antibodies bind on its surface.
Matthew Higgins, Professor of Molecular Parasitology and co-author of this study, said: “By combining AlphaFold models with our experimental crystallography information, we can reveal the structure of Pfs48/45, understand its dynamics, and show where transmission-blocking antibodies bind.
“This insight will now be used to design improved vaccines that induce the most potent transmission-blocking antibodies.”
DeepMind and EMBL-EBI said they will continue to update the database periodically, with the aim of improving features and functionality.