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000837815 0247_ $$2ISSN$$a1940-6029
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000837815 1001_ $$0P:(DE-Juel1)132307$$aZimmermann, Olav$$b0$$eCorresponding author$$ufzj
000837815 245__ $$aBackbone Dihedral Angle Prediction
000837815 260__ $$aNew York, NY$$bSpringer New York$$c2017
000837815 29510 $$aPrediction 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
000837815 300__ $$a65 - 82
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000837815 4900_ $$aMethods in Molecular Biology$$v1484
000837815 520__ $$aMore 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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