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| Hauptseite > Publikationsdatenbank > Backbone Dihedral Angle Prediction > print |
| 001 | 837815 | ||
| 005 | 20210129231432.0 | ||
| 020 | _ | _ | |a 978-1-4939-6404-8 (print) |
| 020 | _ | _ | |a 978-1-4939-6406-2 (electronic) |
| 024 | 7 | _ | |a 10.1007/978-1-4939-6406-2_7 |2 doi |
| 024 | 7 | _ | |a 1064-3745 |2 ISSN |
| 024 | 7 | _ | |a 1940-6029 |2 ISSN |
| 024 | 7 | _ | |a WOS:000400734600008 |2 WOS |
| 037 | _ | _ | |a FZJ-2017-06604 |
| 082 | _ | _ | |a 570 |
| 100 | 1 | _ | |a Zimmermann, Olav |0 P:(DE-Juel1)132307 |b 0 |e Corresponding author |u fzj |
| 245 | _ | _ | |a Backbone Dihedral Angle Prediction |
| 260 | _ | _ | |a New York, NY |c 2017 |b Springer New York |
| 295 | 1 | 0 | |a Prediction of Protein Secondary Structure / Zhou, Yaoqi (Editor) ; New York, NY : Springer New York, 2017, Chapter 7 ; ISSN: 1064-3745=1940-6029 ; ISBN: 978-1-4939-6404-8=978-1-4939-6406-2 ; doi:10.1007/978-1-4939-6406-2 |
| 300 | _ | _ | |a 65 - 82 |
| 336 | 7 | _ | |a BOOK_CHAPTER |2 ORCID |
| 336 | 7 | _ | |a Book Section |0 7 |2 EndNote |
| 336 | 7 | _ | |a bookPart |2 DRIVER |
| 336 | 7 | _ | |a INBOOK |2 BibTeX |
| 336 | 7 | _ | |a Output Types/Book chapter |2 DataCite |
| 336 | 7 | _ | |a Contribution to a book |b contb |m contb |0 PUB:(DE-HGF)7 |s 1505477831_21918 |2 PUB:(DE-HGF) |
| 490 | 0 | _ | |a Methods in Molecular Biology |v 1484 |
| 520 | _ | _ | |a More than two decades of research have enabled dihedral angle predictions at an accuracy that makes them an interesting alternative or supplement to secondary structure prediction that provides detailed local structure information for every residue of a protein. The evolution of dihedral angle prediction methods is closely linked to advancements in machine learning and other relevant technologies. Consequently recent improvements in large-scale training of deep neural networks have led to the best method currently available, which achieves a mean absolute error of 19° for phi, and 30° for psi. This performance opens interesting perspectives for the application of dihedral angle prediction in the comparison, prediction, and design of protein structures. |
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| 773 | _ | _ | |a 10.1007/978-1-4939-6406-2_7 |
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| 914 | 1 | _ | |y 2017 |
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