Accurate prediction of protein structures and interactions using a three-track neural network-Reference-Cited by-同舟云学术

Accurate prediction of protein structures and interactions using a three-track neural network

Published:2021-08-20 Issue:6557 Volume:373 Page:871-876
ISSN:0036-8075
Container-title:Science
language:en
Short-container-title:Science

Author:

Baek Minkyung¹²^ORCID,DiMaio Frank¹²^ORCID,Anishchenko Ivan¹²^ORCID,Dauparas Justas¹²^ORCID,Ovchinnikov Sergey³⁴^ORCID,Lee Gyu Rie¹²^ORCID,Wang Jue¹²^ORCID,Cong Qian⁵⁶^ORCID,Kinch Lisa N.⁷^ORCID,Schaeffer R. Dustin⁶^ORCID,Millán Claudia⁸^ORCID,Park Hahnbeom¹²^ORCID,Adams Carson¹²,Glassman Caleb R.⁹¹⁰¹¹^ORCID,DeGiovanni Andy¹²,Pereira Jose H.¹²^ORCID,Rodrigues Andria V.¹²,van Dijk Alberdina A.¹³,Ebrecht Ana C.¹³^ORCID,Opperman Diederik J.¹⁴^ORCID,Sagmeister Theo¹⁵^ORCID,Buhlheller Christoph¹⁵¹⁶^ORCID,Pavkov-Keller Tea¹⁵¹⁷^ORCID,Rathinaswamy Manoj K.¹⁸^ORCID,Dalwadi Udit¹⁹,Yip Calvin K.¹⁹^ORCID,Burke John E.¹⁸^ORCID,Garcia K. Christopher⁹¹⁰¹¹²⁰^ORCID,Grishin Nick V.⁶⁷²¹^ORCID,Adams Paul D.¹²²²^ORCID,Read Randy J.⁸^ORCID,Baker David¹²²³^ORCID

Affiliation:

1. Department of Biochemistry, University of Washington, Seattle, WA 98195, USA.

2. Institute for Protein Design, University of Washington, Seattle, WA 98195, USA.

3. Faculty of Arts and Sciences, Division of Science, Harvard University, Cambridge, MA 02138, USA.

4. John Harvard Distinguished Science Fellowship Program, Harvard University, Cambridge, MA 02138, USA.

5. Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX, USA.

6. Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, USA.

7. Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, Dallas, TX, USA.

8. Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK.

9. Program in Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA.

10. Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94305, USA.

11. Department of Structural Biology, Stanford University School of Medicine, Stanford, CA 94305, USA.

12. Molecular Biophysics & Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.

13. Department of Biochemistry, Focus Area Human Metabolomics, North-West University, 2531 Potchefstroom, South Africa.

14. Department of Biotechnology, University of the Free State, 205 Nelson Mandela Drive, Bloemfontein 9300, South Africa.

15. Institute of Molecular Biosciences, University of Graz, Humboldtstrasse 50, 8010 Graz, Austria.

16. Medical University of Graz, Graz, Austria.

17. BioTechMed-Graz, Graz, Austria.

18. Department of Biochemistry and Microbiology, University of Victoria, Victoria, BC, Canada.

19. Life Sciences Institute, Department of Biochemistry and Molecular Biology, The University of British Columbia, Vancouver, BC, Canada.

20. Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA 94305, USA.

21. Department of Biochemistry, University of Texas Southwestern Medical Center, Dallas, TX, USA.

22. Department of Bioengineering, University of California, Berkeley, Berkeley, CA 94720, USA.

23. Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA.

Abstract

Deep learning takes on protein folding In 1972, Anfinsen won a Nobel prize for demonstrating a connection between a protein’s amino acid sequence and its three-dimensional structure. Since 1994, scientists have competed in the biannual Critical Assessment of Structure Prediction (CASP) protein-folding challenge. Deep learning methods took center stage at CASP14, with DeepMind’s Alphafold2 achieving remarkable accuracy. Baek et al . explored network architectures based on the DeepMind framework. They used a three-track network to process sequence, distance, and coordinate information simultaneously and achieved accuracies approaching those of DeepMind. The method, RoseTTA fold, can solve challenging x-ray crystallography and cryo–electron microscopy modeling problems and generate accurate models of protein-protein complexes. —VV

Funder

National Institutes of Health

Welch Foundation

Washington Research Foundation

Microsoft

Schmidt Family Foundation

Canadian Institutes of Health Research

Global Challenges Research Fund

Austrian Science Fund

Wellcome

Open Philanthropy Project

G. Harold and Leila Y. Mathers Foundation

National Science Foundation

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

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