Language:

Highly accurate protein structure prediction with AlphaFold

Nature (London), 2021-08, Vol.596 (7873), p.583-589 [Peer Reviewed Journal]

2021. The Author(s). ;COPYRIGHT 2021 Nature Publishing Group ;Copyright Nature Publishing Group Aug 26, 2021 ;The Author(s) 2021 ;ISSN: 0028-0836 ;EISSN: 1476-4687 ;DOI: 10.1038/s41586-021-03819-2 ;PMID: 34265844

Full text available

Citations Cited by

Actions
1. Add to My Research
2. Remove from My Research
3. E-mail
4. Print
5. Permalink
6. Citation
7. EasyBib
8. EndNote
9. RefWorks
10. Delicious
11. Export RIS
12. Export BibTeX

Title:
Highly accurate protein structure prediction with AlphaFold
Author: Jumper, John ; Evans, Richard ; Pritzel, Alexander ; Green, Tim ; Figurnov, Michael ; Ronneberger, Olaf ; Tunyasuvunakool, Kathryn ; Bates, Russ ; Žídek, Augustin ; Potapenko, Anna ; Bridgland, Alex ; Meyer, Clemens ; Kohl, Simon A A ; Ballard, Andrew J ; Cowie, Andrew ; Romera-Paredes, Bernardino ; Nikolov, Stanislav ; Jain, Rishub ; Adler, Jonas ; Back, Trevor ; Petersen, Stig ; Reiman, David ; Clancy, Ellen ; Zielinski, Michal ; Steinegger, Martin ; Pacholska, Michalina ; Berghammer, Tamas ; Bodenstein, Sebastian ; Silver, David ; Vinyals, Oriol ; Senior, Andrew W ; Kavukcuoglu, Koray ; Kohli, Pushmeet ; Hassabis, Demis
Subjects: Accuracy ; Algorithms ; Amino Acid Sequence ; Amino acids ; Bioinformatics ; Computational Biology - methods ; Computational Biology - standards ; Computer applications ; Confidence intervals ; Databases, Protein ; Deep learning ; Deep Learning - standards ; Homology ; Learning algorithms ; Machine learning ; Methods ; Models, Molecular ; Neural networks ; Neural Networks, Computer ; Predictions ; Protein Conformation ; Protein Folding ; Protein structure ; Protein structure prediction ; Proteins ; Proteins - chemistry ; Reproducibility of Results ; Sequence Alignment
Is Part Of: Nature (London), 2021-08, Vol.596 (7873), p.583-589
Description: Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort , the structures of around 100,000 unique proteins have been determined , but this represents a small fraction of the billions of known protein sequences . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence-the structure prediction component of the 'protein folding problem' -has been an important open research problem for more than 50 years . Despite recent progress , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm.
Publisher: England: Nature Publishing Group
Language: English
Identifier: ISSN: 0028-0836
EISSN: 1476-4687
DOI: 10.1038/s41586-021-03819-2
PMID: 34265844
Source: ProQuest One Psychology
MEDLINE
ProQuest Central

Back to results list


INSPIRE LIBRARY - TON DUC THANG UNIVERSITY	(84-028) 37 755 057	Feedback
19 Nguyen Huu Tho St. Dist.7, HCM	thuvien@tdtu.edu.vn	Feedback

Highly accurate protein structure prediction with AlphaFold

2021. The Author(s). ;COPYRIGHT 2021 Nature Publishing Group ;Copyright Nature Publishing Group Aug 26, 2021 ;The Author(s) 2021 ;ISSN: 0028-0836 ;EISSN: 1476-4687 ;DOI: 10.1038/s41586-021-03819-2 ;PMID: 34265844

Searching Remote Databases, Please Wait