DeepMind Releases Accurate Picture of the Human Proteome – “The Most Significant Contribution AI Has Made to Advancing Scientific Knowledge to Date” - SciTechDaily

DeepMind and EMBL release the most complete database of predicted 3D structures of human proteins.

Partners use AlphaFold, the AI system recognized last year as a solution to the protein structure prediction problem, to release more than 350,000 protein structure predictions including the entire human proteome to the scientific community.

DeepMind today announced its partnership with the European Molecular Biology Laboratory (EMBL), Europe’s flagship laboratory for the life sciences, to make the most complete and accurate database yet of predicted protein structure models for the human proteome.

This will cover all ~20,000 proteins expressed by the human genome, and the data will be freely and openly available to the scientific community.

AlphaFold’s recognition in December 2020 by the organizers of the Critical Assessment of protein Structure Prediction (CASP) benchmark as a solution to the 50-year-old grand challenge of protein structure prediction was a stunning breakthrough for the field.

The AlphaFold Protein Structure Database builds on this innovation and the discoveries of generations of scientists, from the early pioneers of protein imaging and crystallography, to the thousands of prediction specialists and structural biologists who’ve spent years experimenting with proteins since.

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

Last week, the methodology behind the latest highly innovative version of AlphaFold, the sophisticated AI system announced last December that powers these structure predictions, and its open source code were published in Nature.

Today’s announcement coincides with a second Nature paper that 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.

“We used AlphaFold to generate the most complete and accurate picture of the human proteome.

“AlphaFold was trained using data from public resources built by the scientific community so it makes sense for its predictions to be public.

For those scientists who rely on experimental protein structure determination, AlphaFold’s predictions have helped accelerate their research.

The AlphaFold Protein Structure Database* builds on many contributions from the international scientific community, as well as AlphaFold’s sophisticated algorithmic innovations and EMBL-EBI’s decades of experience in sharing the world’s biological data.

DeepMind and EMBL’s European Bioinformatics Institute (EMBL-EBI) are providing access to AlphaFold’s predictions so that others can use the system as a tool to enable and accelerate research and open up completely new avenues of scientific discovery.

“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.

In addition to the human proteome, the database launches with ~350,000 structures including 20 biologically-significant organisms such as E.coli, fruit fly, mouse, zebrafish, malaria parasite and tuberculosis bacteria.

The database and system will be periodically updated as we continue to invest in future improvements to AlphaFold, and over the coming months we plan to vastly expand the coverage to almost every sequenced protein known to science — over 100 million structures covering most of the UniProt reference database.

To learn more, please see the Nature papers describing our full method and the human proteome*, and read the Authors’ Notes*.

DeepMind’s release of the AlphaFold Protein Structure Database with EMBL, Europe’s flagship organization for molecular biology, is a great leap for biological innovation that demonstrates the impact of interdisciplinary collaboration for scientific progress.

“As these revolutionary approaches to protein structures pioneered by DeepMind become accessible, this will open new windows for the scientific community onto the biological meaning of the genome sequence.”.

AI can be a game changer: by quickly and accurately predicting protein structures, AlphaFold opens new research horizons, improving both the scope and efficiency of R&D and facilitating our research in endemic countries.

“The AlphaFold database shows the potential for AI to profoundly accelerate scientific progress.

Not only has DeepMind’s machine learning system greatly expanded our accumulated knowledge of protein structures and the human proteome overnight, its deep insights into the building blocks of life hold extraordinary promise for the future of scientific discovery.”.

We are making AlphaFold’s predictions available to everyone via a database to maximize the scientific progress that can be made from these insights.

This database and AlphaFold have the potential to open up new avenues of scientific inquiry that will ultimately advance our understanding of many areas of biology and life itself.

We are optimistic that the promise and machine learning advances of AlphaFold will spur the development of an exciting new phase of protein research, where deep learning tools enable quantitative understanding of biology hand-in-hand with experimental methods.”.

Making these models available will undoubtedly galvanize both the experimental and theoretical protein structure researchers to apply this new knowledge to their own areas of research and to open up new areas of interest

“Twenty years on from the human genome revolution, AlphaFold is a significant breakthrough in biological research

Protein function is dictated by its structure, and the AlphaFold Protein Structure Database will deliver millions of predicted protein structures, accelerating the discovery process

AlphaFold structure predictions will greatly speed up structural biology research and will put three-dimensional protein structures even more into the limelight in life sciences research.”

“Highly accurate protein structure prediction for the human proteome” by Kathryn Tunyasuvunakool, Jonas Adler, Zachary Wu, Tim Green, Michal Zielinski, Augustin Žídek, Alex Bridgland, Andrew Cowie, Clemens Meyer, Agata Laydon, Sameer Velankar, Gerard J

“Highly accurate protein structure prediction with AlphaFold” by John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, Alex Bridgland, Clemens Meyer, Simon A