title: Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning.
abstract: Direct prediction of protein structure from sequence is a challenging problem. An effective approach is to break it up into independent sub-problems. These sub-problems such as prediction of protein secondary structure can then be solved independently. In a previous study, we found that an iterative use of predicted secondary structure and backbone torsion angles can further improve secondary structure and torsion angle prediction. In this study, we expand the iterative features to include solvent accessible surface area and backbone angles and dihedrals based on Cα atoms. By using a deep learning neural network in three iterations, we achieved 82% accuracy for secondary structure prediction, 0.76 for the correlation coefficient between predicted and actual solvent accessible surface area, 19° and 30° for mean absolute errors of backbone φ and ψ angles, respectively, and 8° and 32° for mean absolute errors of Cα-based θ and τ angles, respectively, for an independent test dataset of 1199 proteins. The accuracy of the method is slightly lower for 72 CASP 11 targets but much higher than those of model structures from current state-of-the-art techniques. This suggests the potentially beneficial use of these predicted properties for model assessment and ranking.
authors.affiliation: Signal Processing Laboratory, School of Engineering, Griffith University, Brisbane, Australia.
authors.forename: Rhys
authors.initials: R
authors.lastname: Heffernan
authors.affiliation: Signal Processing Laboratory, School of Engineering, Griffith University, Brisbane, Australia.
authors.forename: Kuldip
authors.initials: K
authors.lastname: Paliwal
authors.affiliation: Signal Processing Laboratory, School of Engineering, Griffith University, Brisbane, Australia.
authors.forename: James
authors.initials: J
authors.lastname: Lyons
authors.affiliation: 1] Signal Processing Laboratory, School of Engineering, Griffith University, Brisbane, Australia [2] Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia.
authors.forename: Abdollah
authors.initials: A
authors.lastname: Dehzangi
authors.affiliation: 1] Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia [2] School of Engineering and Physics, University of the South Pacific, Private Mail Bag, Laucala Campus, Suva, Fiji.
authors.forename: Alok
authors.initials: A
authors.lastname: Sharma
authors.affiliation: Shandong Provincial Key Laboratory of Functional Macromolecular Biophysics, Dezhou University, Dezhou, Shandong, China.
authors.forename: Jihua
authors.initials: J
authors.lastname: Wang
authors.affiliation: 1] Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia [2] National ICT Australia (NICTA), Brisbane, Australia.
authors.forename: Abdul
authors.initials: A
authors.lastname: Sattar
authors.affiliation: Institute for Glycomics and School of Information and Communication Technique, Griffith University, Parklands Dr. Southport, QLD 4222, Australia.
authors.forename: Yuedong
authors.initials: Y
authors.lastname: Yang
authors.affiliation: 1] Shandong Provincial Key Laboratory of Functional Macromolecular Biophysics, Dezhou University, Dezhou, Shandong, China [2] Institute for Glycomics and School of Information and Communication Technique, Griffith University, Parklands Dr. Southport, QLD 4222, Australia.
authors.forename: Yaoqi
authors.initials: Y
authors.lastname: Zhou
mesh_terms: D000465:Algorithms; D000596:Amino Acids / Q000737:chemistry; D030562:Databases, Protein; D000069550:Machine Learning; D008958:Models, Molecular; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*; D012997:Solvents / Q000737:chemistry*
chemical_list: D000596:Amino Acids; D011506:Proteins; D012997:Solvents