DeepMind and EMBL jointly released the most complete human protein three-dimensional structure prediction database to date

DeepMind announced on July 22, 2021 that it will cooperate with the European Molecular Biology Laboratory (EMBL), the flagship laboratory of European life sciences, to establish the most complete and accurate protein structure model database for the human proteome to date

AlphaFold was recognized by the organizers of the Critical Assessment of Protein Structure Prediction (CASP) benchmark in December 2020 as a solution to the 50-year-old major challenge of protein structure prediction.

The AlphaFold protein structure database is built on this innovation and the discoveries of several generations of scientists.

Last week, Nature magazine published the methodology behind the latest highly innovative version of AlphaFold and its open source code

Dr.

AlphaFold is already helping scientists accelerate discovery

The ability to calculate the shape of a protein from the amino acid sequence of a protein--rather than experimentally determining it through years of painstaking, laborious, and often expensive techniques--has helped scientists achieve within a few months what previously took several years to achieve.

"The AlphaFold database is a perfect example of the virtuous circle of open science," said Edith Heard, director of EMBL

AlphaFold is already being used by partners such as the Drugs for Neglected Diseases Initiative (DNDi), which advances research on life-saving treatments for diseases that severely affect poor regions of the world, and the Center for Enzyme Innovation (CEI) is Use AlphaFold to help accelerate the development of enzymes to recycle some of the most polluting disposable plastics

AlphaFold protein structure database

The AlphaFold protein structure database* is based on many contributions from the international scientific community, as well as AlphaFold’s complex algorithm innovation and EMBL-EBI’s decades of experience in sharing world biological data

"This will be one of the most important data sets since mapping the human genome," said Ewan Birney, deputy director of EMBL and director of EMBL-EBI

In addition to the human proteome, the database also contains about 350,000 structures, including 20 biologically significant organisms, such as E.

The future of AlphaFold

As we continue to invest in AlphaFold's future improvements, the database and system will be updated regularly.

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Comments from independent chief scientists:

Paul Nurse, winner of the 2001 Nobel Prize in Physiology or Medicine, Director of the Francis Crick Institute, Chairman of the EMBL Scientific Advisory Board

"Computational methods are changing scientific research, opening up new possibilities for the discovery and application of public interest

Venki Ramakrishnan, 2009 Nobel Laureate in Chemistry, former President of the Royal Society

"This computational work represents an amazing advancement in the problem of protein folding.

Elizabeth Blackburn, 2009 Nobel Laureate in Physiology or Medicine, Professor Emeritus of the University of California, San Francisco

"As these revolutionary methods of protein structure pioneered by DeepMind become feasible, this will open a new window for the scientific community to the biological significance of genome sequences

Patrick Cramer, Director of the Max Planck Institute for Biophysical Chemistry

"The fantastic resources provided by DeepMind and EMBL will change the way we study structural biology
.
These predictions prove the power of machine learning and serve the global community, which provided open data to make this breakthrough achievement possible
.
This It is an important example of scientific development in the 21st century
.
"

Research partners' comments on using AlphaFold:

Ben Perry, Neglected Disease Drugs Initiative (DNDi) Discovers Open Innovation Leader

“We need to strengthen the discovery of new drugs for millions of people around the world who are at risk of neglected diseases
.
Artificial intelligence can change the rules of the game: by predicting protein structure quickly and accurately, AlphaFold opens up new research horizons and increases the scope and scope of research and development.
Efficiency, and promoted our research in endemic countries
.
It is encouraging to see that powerful cutting-edge artificial intelligence makes it possible to treat diseases that are almost exclusively concentrated in the poor
.
"

John McGeehan, Professor of Structural Biology, Director of the Enzyme Innovation Center, University of Portsmouth

"Our mission is to develop an enzyme-catalyzed solution for plastic recycling
.
This technology is accelerating our research in an unprecedented way
.
The open access provided by DeepMind will transform the entire community and allow everyone to conduct this type of experiment.

What we took months or even years to complete, AlphaFold only took one weekend to complete
.
I think we are at least one year ahead of yesterday
.

Marcelo Sousa, Department of Biochemistry, University of Colorado Boulder

"AlphaFold's predictions finally resolved the experimental data we have been obsessed with for more than 10 years and helped speed up our research on antibiotic resistance
.
These predictions are very accurate.
At first I thought that my settings were wrong!"

Evaluation from DeepMind/Alphabet:

Sundar Pichai, CEO of Google and Alphabet

"The AlphaFold database shows the potential of artificial intelligence in profoundly accelerating scientific progress.

DeepMind's machine learning system has not only greatly expanded our accumulated knowledge of protein structure and human proteome overnight, but it also has a profound impact on the building blocks of life.
Insights bring extraordinary hope for future scientific discoveries
.
"

Dr.
Pushmeet Kohli, Head of Artificial Intelligence Science, DeepMind

"Our team has been studying AlphaFold to decipher and unlock the protein world by predicting the structure of proteins
.
We are providing AlphaFold predictions to everyone through the database to maximize the use of these insights to make scientific progress
.
This database and AlphaFold have potential Opening up new avenues of scientific exploration will ultimately advance our understanding of many areas of biology and life itself
.
We believe that this will produce variability in research on issues related to health and disease, drug design processes, and environmental sustainability And I’m very excited to see what applications will be developed in the coming months and years
.
"

Dr.
John Jumper, Principal Investigator of DeepMind AlphaFold

"As the database expands, almost every model for cataloging proteins will be available.

AlphaFold DB may change the way we study bioinformatics, that is, large-scale studies of DNA and proteins, because it will allow us to approach Atomic precision studies the proteins of all known organisms
.
We are optimistic that the prospects of AlphaFold and advances in machine learning will stimulate the development of an exciting new phase of protein research, in which deep learning tools can interact with experiments Methods work hand in hand to quantitatively understand biology
.
"

Dr.
Kathryn Tunyasuvunakool, Research Scientist at DeepMind

"The AlphaFold model can help determine the structure through experimental methods
.
A sufficiently accurate preliminary prediction of the structure will allow researchers to revisit and resolve old x-ray data sets and cryo-EM maps
for which models could not be built before .
This is a good one.
The example illustrates how the calculation method complements the experimental method
.
"

Voice from EMBL:

Dame Janet Thornton, Honorary Director of EMBL-EBI

"AlphaFold's predictions are based on data collected by scientists from all over the world over the past 50 years.
It is based on the power of artificial intelligence
.
Making these models available will undoubtedly inspire experimental and theoretical protein structure researchers to apply these new knowledge to their own research fields, and Open up new areas of interest
.
This will help our knowledge and understanding of life systems, which will open up all opportunities for mankind
.
"

Sameer Velankar, Head of EMBL-EBI Department

"Twenty years after the human genome revolution, AlphaFold is a major breakthrough in biological research
.
The function of a protein is determined by its structure.
The AlphaFold protein structure database will provide millions of predicted protein structures and accelerate the discovery process
.
This unprecedented scale will Trigger a new wave of innovation and help us deal with challenges ranging from health to climate change
.
"

Christoph Müller, Head of EMBL Structural and Computational Biology Department

"This is a huge advancement.

AlphaFold structure prediction will greatly accelerate structural biology research and will make three-dimensional protein structures more compelling in life science research
.
"

Related papers:

1. 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 AA Kohl, Andrew J.
   Ballard, Andrew Cowie, Bernardino Romera -Paredes, Stanislav Nikolov, Rishub Jain, Jonas Adler, Trevor Back, Stig Petersen, David Reiman, Ellen Clancy, Michal Zielinski, Martin Steinegger, Michalina Pacholska, Tamas Berghammer, Sebastian Bodenstein, David Silver, Oriol Vinyals, Andrew W.
   Senior, Koray Kavukcuoglu, Pushmeet Kohli, Demis Hassabis.
   Highly accurate protein structure prediction with AlphaFold .
   Nature , 2021; DOI: 10.
   1038/s41586-021-03819-2

2. Kathryn Tunyasuvunakool, Jonas Adler, Zachary Wu, Tim Green, Michal Zielinski, Augustin Žídek, Alex Bridgland, Andrew Cowie, Clemens Meyer, Agata Laydon, Sameer Velankar, Gerard J.
   Kleywegt, Alex Bateman, Richard Evans, Alexander Pritzel, Michael Figurnov, Olaf Ronneberger, Russ Bates, Simon AA Kohl, Anna Potapenko, Andrew J.
   Ballard, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Ellen Clancy, David Reiman, Stig Petersen, Andrew W.
   Senior, Koray Kavukcuoglu, Ewan Birney, Pushmeet Kohli, John Jumper, Demis Hassabis.
   Highly accurate protein structure prediction for the human proteome .
   Nature , 2021; DOI: 10.
   1038/s41586-021-03828-1