Protein tertiary structure prediction and refinement using deep learning and Rosetta in CASP14-Reference-Cited by-同舟云学术

Protein tertiary structure prediction and refinement using deep learning and Rosetta in CASP14

Published:2021-08-17 Issue:12 Volume:89 Page:1722-1733
ISSN:0887-3585
Container-title:Proteins: Structure, Function, and Bioinformatics
language:en
Short-container-title:Proteins

Author:

Anishchenko Ivan¹^ORCID,Baek Minkyung¹^ORCID,Park Hahnbeom¹,Hiranuma Naozumi¹²,Kim David E.¹³,Dauparas Justas¹,Mansoor Sanaa¹,Humphreys Ian R.¹,Baker David¹³

Affiliation:

1. Department of Biochemistry and Institute for Protein Design University of Washington Seattle Washington USA

2. Paul G. Allen School of Computer Science & Engineering University of Washington Seattle Washington USA

3. Howard Hughes Medical Institute University of Washington Seattle Washington USA

Funder

Howard Hughes Medical Institute

National Science Foundation

Publisher

Wiley

Subject

Molecular Biology,Biochemistry,Structural Biology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/prot.26194

Reference29 articles.

1. Improved protein structure prediction using potentials from deep learning

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4. Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints

5. HH-suite3 for fast remote homology detection and deep protein annotation

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