title: Backbone Dihedral Angle Prediction.
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.
authors: Jülich Supercomputing Centre (JSC), Institute for Advanced Simulation (IAS), Forschungszentrum Jülich GmbH, 52425, Jülich, Germany. olav.zimmermann@fz-juelich.de.
authors: Olav
authors: O
authors: Zimmermann
mesh_terms: D000595:Amino Acid Sequence / Q000235:genetics; D030562:Databases, Protein; D008958:Models, Molecular; D016571:Neural Networks, Computer; D011487:Protein Conformation; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry* / Q000235:genetics
chemical_list: D011506:Proteins
keywords: Dihedral angles; Machine learning; Structure prediction; Torsional angles