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Assessment of contact predictions in CASP12: Co-evolution and deep learning coming of age.

作者信息

Schaarschmidt Joerg, Monastyrskyy Bohdan, Kryshtafovych Andriy, Bonvin Alexandre M J J

机构信息

Faculty of Science - Chemistry, Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Utrecht University, Utrecht, The Netherlands.

Genome Center, University of California, Davis, California.

出版信息

Proteins. 2018 Mar;86 Suppl 1(Suppl Suppl 1):51-66. doi: 10.1002/prot.25407. Epub 2017 Nov 7.


DOI:10.1002/prot.25407
PMID:29071738
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5820169/
Abstract

Following up on the encouraging results of residue-residue contact prediction in the CASP11 experiment, we present the analysis of predictions submitted for CASP12. The submissions include predictions of 34 groups for 38 domains classified as free modeling targets which are not accessible to homology-based modeling due to a lack of structural templates. CASP11 saw a rise of coevolution-based methods outperforming other approaches. The improvement of these methods coupled to machine learning and sequence database growth are most likely the main driver for a significant improvement in average precision from 27% in CASP11 to 47% in CASP12. In more than half of the targets, especially those with many homologous sequences accessible, precisions above 90% were achieved with the best predictors reaching a precision of 100% in some cases. We furthermore tested the impact of using these contacts as restraints in ab initio modeling of 14 single-domain free modeling targets using Rosetta. Adding contacts to the Rosetta calculations resulted in improvements of up to 26% in GDT_TS within the top five structures.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3430/5836953/d69acb6e8759/PROT-86-51-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3430/5836953/0695ba35fad6/PROT-86-51-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3430/5836953/c183b696f601/PROT-86-51-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3430/5836953/36ddb31328ab/PROT-86-51-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3430/5836953/9232f3ff567d/PROT-86-51-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3430/5836953/53a19cf73bae/PROT-86-51-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3430/5836953/00ae5aefb940/PROT-86-51-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3430/5836953/859cebcf7f07/PROT-86-51-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3430/5836953/809602a79e58/PROT-86-51-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3430/5836953/39d58b7d1135/PROT-86-51-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3430/5836953/155c1bebba6b/PROT-86-51-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3430/5836953/cb5f4a2659c6/PROT-86-51-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3430/5836953/c6bd8d2d44ef/PROT-86-51-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3430/5836953/eeda5176f186/PROT-86-51-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3430/5836953/de6dd0165615/PROT-86-51-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3430/5836953/22570a247ec3/PROT-86-51-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3430/5836953/d69acb6e8759/PROT-86-51-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3430/5836953/0695ba35fad6/PROT-86-51-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3430/5836953/c183b696f601/PROT-86-51-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3430/5836953/36ddb31328ab/PROT-86-51-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3430/5836953/9232f3ff567d/PROT-86-51-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3430/5836953/53a19cf73bae/PROT-86-51-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3430/5836953/00ae5aefb940/PROT-86-51-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3430/5836953/859cebcf7f07/PROT-86-51-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3430/5836953/809602a79e58/PROT-86-51-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3430/5836953/39d58b7d1135/PROT-86-51-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3430/5836953/155c1bebba6b/PROT-86-51-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3430/5836953/cb5f4a2659c6/PROT-86-51-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3430/5836953/c6bd8d2d44ef/PROT-86-51-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3430/5836953/eeda5176f186/PROT-86-51-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3430/5836953/de6dd0165615/PROT-86-51-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3430/5836953/22570a247ec3/PROT-86-51-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3430/5836953/d69acb6e8759/PROT-86-51-g016.jpg

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本文引用的文献

[1]
Improved protein contact predictions with the MetaPSICOV2 server in CASP12.

Proteins. 2018-3

[2]
Analysis of deep learning methods for blind protein contact prediction in CASP12.

Proteins. 2018-3

[3]
Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model.

PLoS Comput Biol. 2017-1-5

[4]
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Bioinformatics. 2016-4-10

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J Chem Inf Model. 2016-3-28

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Proteins. 2016-9

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Nucleic Acids Res. 2015-7-1

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Nat Methods. 2015-1

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Proteins. 2015-1-24

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Bioinformatics. 2015-4-1

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