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231224s2014 xx |||||o 00| ||eng c |
| 024 |
7 |
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|a 10.1002/jcc.23718
|2 doi
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| 028 |
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|a pubmed24n0806.xml
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|a (DE-627)NLM241821843
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|a (NLM)25212657
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|a DE-627
|b ger
|c DE-627
|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Lyons, James
|e verfasserin
|4 aut
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| 245 |
1 |
0 |
|a Predicting backbone Cα angles and dihedrals from protein sequences by stacked sparse auto-encoder deep neural network
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| 264 |
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|c 2014
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| 336 |
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|a Text
|b txt
|2 rdacontent
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| 337 |
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|a ƒaComputermedien
|b c
|2 rdamedia
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| 338 |
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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| 500 |
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|a Date Completed 20.05.2015
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| 500 |
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|a Date Revised 10.12.2019
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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| 520 |
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|a Copyright © 2014 Wiley Periodicals, Inc.
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|a Because a nearly constant distance between two neighbouring Cα atoms, local backbone structure of proteins can be represented accurately by the angle between C(αi-1)-C(αi)-C(αi+1) (θ) and a dihedral angle rotated about the C(αi)-C(αi+1) bond (τ). θ and τ angles, as the representative of structural properties of three to four amino-acid residues, offer a description of backbone conformations that is complementary to φ and ψ angles (single residue) and secondary structures (>3 residues). Here, we report the first machine-learning technique for sequence-based prediction of θ and τ angles. Predicted angles based on an independent test have a mean absolute error of 9° for θ and 34° for τ with a distribution on the θ-τ plane close to that of native values. The average root-mean-square distance of 10-residue fragment structures constructed from predicted θ and τ angles is only 1.9Å from their corresponding native structures. Predicted θ and τ angles are expected to be complementary to predicted ϕ and ψ angles and secondary structures for using in model validation and template-based as well as template-free structure prediction. The deep neural network learning technique is available as an on-line server called Structural Property prediction with Integrated DEep neuRal network (SPIDER) at http://sparks-lab.org
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| 650 |
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4 |
|a Journal Article
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| 650 |
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4 |
|a Research Support, Non-U.S. Gov't
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| 650 |
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4 |
|a deep learning
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| 650 |
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4 |
|a fold recognition
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| 650 |
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4 |
|a fragment structure prediction
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| 650 |
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4 |
|a local structure prediction
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| 650 |
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4 |
|a neural network
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| 650 |
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4 |
|a protein structure prediction
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| 650 |
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4 |
|a secondary structure prediction
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| 650 |
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7 |
|a Proteins
|2 NLM
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| 700 |
1 |
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|a Dehzangi, Abdollah
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Heffernan, Rhys
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Sharma, Alok
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Paliwal, Kuldip
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Sattar, Abdul
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhou, Yaoqi
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yang, Yuedong
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t Journal of computational chemistry
|d 1984
|g 35(2014), 28 vom: 30. Okt., Seite 2040-6
|w (DE-627)NLM098138448
|x 1096-987X
|7 nnns
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| 773 |
1 |
8 |
|g volume:35
|g year:2014
|g number:28
|g day:30
|g month:10
|g pages:2040-6
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| 856 |
4 |
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|u http://dx.doi.org/10.1002/jcc.23718
|3 Volltext
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|a GBV_USEFLAG_A
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|a GBV_ILN_350
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|a AR
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| 952 |
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|d 35
|j 2014
|e 28
|b 30
|c 10
|h 2040-6
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