David Baker, University of Washington, Seattle, USA, Demis Hassabis, and John Jumper, both DeepMind Technologies Limited, London, UK, have received the Wiley Prize in Biomedical Sciences 2022 for procedures to predict highly accurate three-dimensional structures of protein molecules from their amino-acid sequences. The work of the three researchers has solved a long-standing question in biology: Can we predict how a protein folds simply based on its amino acid sequence? Their computational approaches provide highly accurate models of protein folds.
The prize is presented annually to recognize contributions that have opened new fields of research or have advanced concepts in a particular biomedical discipline. It includes USD 50,000 of prize money. The award was presented at a prize luncheon at Rockefeller University, New York, USA, on April 1, 2022.
David Baker received his Ph.D. in biochemistry under the supervision of Randy Schekman at the University of California, Berkeley, USA, and did postdoctoral work in biophysics working with David Agard at the University of California, San Francisco, USA. Today, Baker serves as Head of the Institute for Protein Design and The Henrietta and Aubrey Davis Endowed Professor of Biochemistry at the University of Washington.
Among many other honors, Baker has received the 2008 Sackler International Prize in Biophysics, the Biochemical Society Centenary Award in 2012, the Protein Society Hans Neurath Award in 2018, and the 2021 Breakthrough Prize in Life Sciences. He is a Fellow of the American Academy of Arts and Sciences.
Demis Hassabis studied computer science at the University of Cambridge, UK. He worked as a programmer and game designer before receiving his Ph.D. in cognitive neuroscience from University College London (UCL), UK, in 2009. Hassabis is the CEO and co-founder of DeepMind, a machine learning AI startup that was founded in London in 2010 together with Shane Legg and Mustafa Suleyman. In 2014, Google purchased DeepMind. DeepMind’s tool AlphaFold aims to accurately predict protein structures.
Among many other honors, Hassabis has received the Mullard Award of the Royal Society in 2014 and the Royal Academy of Engineering Silver Medal in 2016 and was appointed Commander of the Order of the British Empire (CBE) in 2018. He is a Fellow of the Royal Society, the Royal Society of Arts, and the Royal Academy of Engineering.
John Jumper studied mathematics and physics at Vanderbilt University, Nashville, TN, USA, physics at Cambridge University, UK, and chemistry at the University of Chicago, IL, USA, where he received his Ph.D. in 2017. Among other honors, John Jumper was featured in Nature‘s “Ten people who helped shape science in 2021”.
Selected Publications by David Baker
- Reconfigurable asymmetric protein assemblies through implicit negative design,
Danny D. Sahtoe, Florian Praetorius, Alexis Courbet, Yang Hsia, Basile I. M. Wicky, Natasha I. Edman, Lauren M. Miller, Bart J. R. Timmermans, Justin Decarreau, Hana M. Morris, Alex Kang, Asim K. Bera, David Baker,
Science 2022.
https://doi.org/10.1126/science.abj7662 - dCas9 fusion to computer-designed PRC2 inhibitor reveals functional TATA box in distal promoter region,
Shiri Levy, Logeshwaran Somasundaram, Infencia Xavier Raj, Diego Ic-Mex, Ashish Phal, Sven Schmidt, Weng I. Ng, Daniel Mar, Justin Decarreau, Nicholas Moss, Ammar Alghadeer, Henrik Honkanen, Jay Sarthy, Nicholas Vitanza, R. David Hawkins, Julie Mathieu, Yuliang Wang, David Baker, Karol Bomsztyk, Hannele Ruohola-Baker,
Cell Rep. 2022, 38, 110457.
https://doi.org/10.1016/j.celrep.2022.110457 - Interpreting neural networks for biological sequences by learning stochastic masks,
Johannes Linder, Alyssa La Fleur, Zibo Chen, Ajasja Ljubetič, David Baker, Sreeram Kannan, Georg Seelig,
Nat. Mach. Intell. 2022, 4, 41–54.
https://doi.org/10.1038/s42256-021-00428-6 - Computational Enzyme Design,
Gert Kiss, Çelebi-Ölçüm, Rocco Moretti, David Baker, K. N. Houk,
Angew. Chem. Int. Ed. 2013, 52, 5700–5725.
https://doi.org/10.1002/anie.201204077 - ROSETTA3: An Object-Oriented Software Suite for the Simulation and Design of Macromolecules,
Andrew Leaver-Fay, Michael Tyka, Steven M. Lewis, Oliver F. Lange, James Thompson, Ron Jacak, Kristian W. Kaufmann,
P. Douglas Renfrew, Colin A. Smith, Will Sheffler, Ian W. Davis, Seth Cooper, Adrien Treuille, Daniel J. Mandell, Florian Richter, Yih-En Andrew Ban, Sarel J. Fleishman, Jacob E. Corn, David E. Kim, Sergey Lyskov, Monica Berrondo, Stuart Mentzer, Zoran Popović, James J. Havranek, John Karanicolas, Rhiju Das, Jens Meiler, Tanja Kortemme, Jeffrey J. Gray, Brian Kuhlman, David Baker, Philip Bradley,
Methods Enzymol. 2011, 487, 545–574.
https://doi.org/10.1016/S0076-6879(11)87019-9 - Quantitative reactivity profiling predicts functional cysteines in proteomes,
Eranthie Weerapana, Chu Wang, Gabriel M. Simon, Florian Richter, Sagar Khare, Myles B. D. Dillon, Daniel A. Bachovchin, Kerri Mowen, David Baker, Benjamin F. Cravatt,
Nature 2010, 468, 790–795.
https://doi.org/10.1038/nature09472 - Kemp elimination catalysts by computational enzyme design,
Daniela Röthlisberger, Olga Khersonsky, Andrew M. Wollacott, Lin Jiang, Jason DeChancie, Jamie Betker, Jasmine L. Gallaher, Eric A. Althoff, Alexandre Zanghellini, Orly Dym, Shira Albeck, Kendall N. Houk, Dan S. Tawfik, David Baker,
Nature 2008, 453, 190–195.
https://doi.org/10.1038/nature06879
Selected Publications by Demis Hassabis
- Magnetic control of tokamak plasmas through deep reinforcement learning,
Jonas Degrave, Federico Felici, Jonas Buchli, Michael Neunert, Brendan Tracey, Francesco Carpanese, Timo Ewalds, Roland Hafner, Abbas Abdolmaleki, Diego de las Casas, Craig Donner, Leslie Fritz, Cristian Galperti, Andrea Huber, James Keeling, Maria Tsimpoukelli, Jackie Kay, Antoine Merle, Jean-Marc Moret, Seb Noury, Federico Pesamosca, David Pfau, Olivier Sauter, Cristian Sommariva, Stefano Coda, Basil Duval, Ambrogio Fasoli, Pushmeet Kohli, Koray Kavukcuoglu, Demis Hassabis, Martin Riedmiller,
Nature 2022, 602, 414–419.
https://doi.org/10.1038/s41586-021-04301-9 - Reimagining chess with AlphaZero,
Nenad Tomašev, Ulrich Paquet, Demis Hassabis, Vladimir Kramnik,
Commun. ACM 2022, 65, 60–66.
https://doi.org/10.1145/3460349 - Mastering the game of Go with deep neural networks and tree search,
David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, Demis Hassabis,
Nature 2016, 529, 484–489.
https://doi.org/10.1038/nature16961 - Human-level control through deep reinforcement learning,
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, Demis Hassabis,
Nature 2015, 518, 529–533.
https://doi.org/10.1038/nature14236
Selected Publications by John Jumper
- Structure of the decoy module of human glycoprotein 2 and uromodulin and its interaction with bacterial adhesin FimH,
Alena Stsiapanava, Chenrui Xu, Shunsuke Nishio, Ling Han, Nao Yamakawa, Marta Carroni, Kathryn Tunyasuvunakool, John Jumper, Daniele de Sanctis, Bin Wu, Luca Jovine,
Nat. Struct. Mol. Biol. 2022, 29, 190–193.
https://doi.org/10.1038/s41594-022-00729-3
Selected Publications by Demis Hassabis and John Jumper
- AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models,
Mihaly Varadi, Stephen Anyango, Mandar Deshpande, Sreenath Nair, Cindy Natassia, Galabina Yordanova, David Yuan, Oana Stroe, Gemma Wood, Agata Laydon, Augustin Žídek, Tim Green, Kathryn Tunyasuvunakool, Stig Petersen, John Jumper, Ellen Clancy, Richard Green, Ankur Vora, Mira Lutfi, Michael Figurnov, Andrew Cowie, Nicole Hobbs, Pushmeet Kohli, Gerard Kleywegt, Ewan Birney, Demis Hassabis, Sameer Velankar,
Nucleic Acids Res. 2022, 50, D439–D444.
https://doi.org/10.1093/nar/gkab1061 - Protein structure predictions to atomic accuracy with AlphaFold,
John Jumper, Demis Hassabis,
Nat. Methods 2022, 19, 11–12.
https://doi.org/10.1038/s41592-021-01362-6 - Highly accurate protein structure prediction for the human proteome,
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 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,
Nature 2021, 596, 590–596.
https://doi.org/10.1038/s41586-021-03828-1 - Highly accurate protein structure prediction with AlphaFold,
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. 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,
Nature 2021, 596, 583–589.
https://doi.org/10.1038/s41586-021-03819-2 - Improved protein structure prediction using potentials from deep learning,
Andrew W. Senior, Richard Evans, John Jumper, James Kirkpatrick, Laurent Sifre, Tim Green, Chongli Qin, Augustin Žídek, Alexander W. R. Nelson, Alex Bridgland, Hugo Penedones, Stig Petersen, Karen Simonyan, Steve Crossan, Pushmeet Kohli, David T. Jones, David Silver, Koray Kavukcuoglu, Demis Hassabis,
Nature 2020, 577, 706–710.
https://doi.org/10.1038/s41586-019-1923-7
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