TY  - CHAP
AU  - Zimmermann, Olav
TI  - Backbone Dihedral Angle Prediction
VL  - 1484
CY  - New York, NY
PB  - Springer New York
M1  - FZJ-2017-06604
SN  - 978-1-4939-6404-8 (print)
T2  - Methods in Molecular Biology
SP  - 65 - 82
PY  - 2017
AB  - 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.
LB  - PUB:(DE-HGF)7
UR  - <Go to ISI:>//WOS:000400734600008
DO  - DOI:10.1007/978-1-4939-6406-2_7
UR  - https://juser.fz-juelich.de/record/837815
ER  -