Contribution to a book

FZJ-2017-06604

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Backbone Dihedral Angle Prediction

Zimmermann, O. (Corresponding author)FZJ*

2017
Springer New York New York, NY
ISBN: 978-1-4939-6404-8 (print), 978-1-4939-6406-2 (electronic)

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Please use a persistent id in citations: doi:10.1007/978-1-4939-6406-2_7

Abstract: 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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Contributing Institute(s):

1. Jülich Supercomputing Center (JSC)

Research Program(s):

1. 511 - Computational Science and Mathematical Methods (POF3-511) (POF3-511)

Appears in the scientific report 2017

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Database coverage:
; NCBI Molecular Biology Database ; SCOPUS

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