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231224s2012 xx |||||o 00| ||eng c |
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|a 10.1002/jcc.21968
|2 doi
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|a pubmed24n1405.xml
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|a (DE-627)NLM212675702
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|a (NLM)22045506
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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|a Faraggi, Eshel
|e verfasserin
|4 aut
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| 245 |
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|a SPINE X
|b improving protein secondary structure prediction by multistep learning coupled with prediction of solvent accessible surface area and backbone torsion angles
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|c 2012
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
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|2 rdacarrier
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|a Date Completed 03.04.2012
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|a Date Revised 12.05.2024
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a Copyright © 2011 Wiley Periodicals, Inc.
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|a Accurate prediction of protein secondary structure is essential for accurate sequence alignment, three-dimensional structure modeling, and function prediction. The accuracy of ab initio secondary structure prediction from sequence, however, has only increased from around 77 to 80% over the past decade. Here, we developed a multistep neural-network algorithm by coupling secondary structure prediction with prediction of solvent accessibility and backbone torsion angles in an iterative manner. Our method called SPINE X was applied to a dataset of 2640 proteins (25% sequence identity cutoff) previously built for the first version of SPINE and achieved a 82.0% accuracy based on 10-fold cross validation (Q(3)). Surpassing 81% accuracy by SPINE X is further confirmed by employing an independently built test dataset of 1833 protein chains, a recently built dataset of 1975 proteins and 117 CASP 9 targets (critical assessment of structure prediction techniques) with an accuracy of 81.3%, 82.3% and 81.8%, respectively. The prediction accuracy is further improved to 83.8% for the dataset of 2640 proteins if the DSSP assignment used above is replaced by a more consistent consensus secondary structure assignment method. Comparison to the popular PSIPRED and CASP-winning structure-prediction techniques is made. SPINE X predicts number of helices and sheets correctly for 21.0% of 1833 proteins, compared to 17.6% by PSIPRED. It further shows that SPINE X consistently makes more accurate prediction in helical residues (6%) without over prediction while PSIPRED makes more accurate prediction in coil residues (3-5%) and over predicts them by 7%. SPINE X Server and its training/test datasets are available at http://sparks.informatics.iupui.edu/
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|a Journal Article
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|a Research Support, N.I.H., Extramural
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|a Validation Study
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| 650 |
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|a Proteins
|2 NLM
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| 650 |
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|a Solvents
|2 NLM
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| 700 |
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|a Zhang, Tuo
|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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| 700 |
1 |
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|a Kurgan, Lukasz
|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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| 773 |
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|i Enthalten in
|t Journal of computational chemistry
|d 1984
|g 33(2012), 3 vom: 30. Jan., Seite 259-67
|w (DE-627)NLM098138448
|x 1096-987X
|7 nnns
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| 773 |
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|g volume:33
|g year:2012
|g number:3
|g day:30
|g month:01
|g pages:259-67
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|u http://dx.doi.org/10.1002/jcc.21968
|3 Volltext
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