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

Date:
July 23, 2021
Source:
European Molecular Biology Laboratory - European Bioinformatics
Institute
Summary:
DeepMind is partnering with EMBL to make the most complete and
accurate database yet of the predicted human protein structures
freely and openly available to the scientific community. The
AlphaFold Protein Structure Database will enable research
that advances understanding of these building blocks of life,
accelerating research across a variety of fields. AlphaFold's
impact is already being realized by early partners researching
neglected diseases, studying antibiotic resistance, and recycling
single-use plastics.



FULL STORY ========================================================================== 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. The database
and artificial intelligence system provide structural biologists with
powerful new tools for examining a protein's three- dimensional structure,
and offer a treasure trove of data that could unlock future advances
and herald a new era for AI-enabled biology.


========================================================================== AlphaFold's recognition in December 2020 by the organisers 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. Advancing the understanding
of these building blocks of life, which underpin every biological process
in every living thing, will help enable researchers across a huge variety
of fields to accelerate their work.

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.

"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," said DeepMind Founder
and CEO Demis Hassabis, PhD. "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." AlphaFold is already helping
scientists to accelerate discovery The ability to predict a protein's
shape computationally from its amino acid sequence -- rather than
determining it experimentally through years of painstaking, laborious
and often costly techniques -- is already helping scientists to achieve
in months what previously took years.



==========================================================================
"The AlphaFold database is a perfect example of the virtuous circle of
open science," said EMBL Director General Edith Heard. "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 am very proud that EMBL has been able to help DeepMind in enabling
open access to this remarkable resource." AlphaFold is already being used
by partners such as the Drugs for Neglected Diseases Initiative (DNDi),
which has advanced their research into life-saving cures for diseases that disproportionately affect the poorer parts of the world, and the Centre
for Enzyme Innovation (CEI) is using AlphaFold to help engineer faster
enzymes for recycling some of our most polluting single-use plastics. For
those scientists who rely on experimental protein structure determination, AlphaFold's predictions have helped accelerate their research.

For example, a team at the University of Colorado Boulder is finding
promise in using AlphaFold predictions to study antibiotic resistance,
while a group at the University of California San Francisco has used
them to increase their understanding of SARS-CoV-2 biology.

The AlphaFold Protein Structure Database 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.

"This will be one of the most important datasets since the mapping of the
Human Genome," said EMBL Deputy Director General, and EMBL-EBI Director
Ewan Birney.

"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. This is a
great new scientific tool, which complements existing technologies, and
will allow us to push the boundaries of our understanding of the world."
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.

Research into these organisms has been the subject of countless research
papers and numerous major breakthroughs. These structures will enable researchers across a huge variety of fields -- from neuroscience to
medicine -- to accelerate their work.

The future of AlphaFold 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 [cited below] describing the
full method and the human_proteome, and read the Authors'_Notes. See
the open-source code_to_AlphaFold if you want to view the workings of
the system, and Colab notebook to run individual sequences. To explore
the structures, visit EMBL- EBI's searchable_database that is open and
free to all.

========================================================================== Story Source: Materials provided by European_Molecular_Biology_Laboratory_-_European
Bioinformatics_Institute. Note: Content may be edited for style and
length.


========================================================================== Journal References:
1. John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael
Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates,
Augustin Ži'dek, Anna Potapenko, Alex Bridgland, Clemens Meyer,
Simon A. A.

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 Ži'dek, 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 A. A. 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 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/07/210723095647.htm

--- up 11 weeks, 22 hours, 45 minutes
* Origin: -=> Castle Rock BBS <=- Now Husky HPT Powered! (1:317/3)