title: Predicting backbone Cα angles and dihedrals from protein sequences by stacked sparse auto-encoder deep neural network.
abstract: Because a nearly constant distance between two neighbouring Cα atoms, local backbone structure of proteins can be represented accurately by the angle between C(αi-1)-C(αi)-C(αi+1) (θ) and a dihedral angle rotated about the C(αi)-C(αi+1) bond (τ). θ and τ angles, as the representative of structural properties of three to four amino-acid residues, offer a description of backbone conformations that is complementary to φ and ψ angles (single residue) and secondary structures (>3 residues). Here, we report the first machine-learning technique for sequence-based prediction of θ and τ angles. Predicted angles based on an independent test have a mean absolute error of 9° for θ and 34° for τ with a distribution on the θ-τ plane close to that of native values. The average root-mean-square distance of 10-residue fragment structures constructed from predicted θ and τ angles is only 1.9Å from their corresponding native structures. Predicted θ and τ angles are expected to be complementary to predicted ϕ and ψ angles and secondary structures for using in model validation and template-based as well as template-free structure prediction. The deep neural network learning technique is available as an on-line server called Structural Property prediction with Integrated DEep neuRal network (SPIDER) at http://sparks-lab.org.
authors.affiliation: Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia.
authors.forename: James
authors.initials: J
authors.lastname: Lyons
authors.forename: Abdollah
authors.initials: A
authors.lastname: Dehzangi
authors.forename: Rhys
authors.initials: R
authors.lastname: Heffernan
authors.forename: Alok
authors.initials: A
authors.lastname: Sharma
authors.forename: Kuldip
authors.initials: K
authors.lastname: Paliwal
authors.forename: Abdul
authors.initials: A
authors.lastname: Sattar
authors.forename: Yaoqi
authors.initials: Y
authors.lastname: Zhou
authors.forename: Yuedong
authors.initials: Y
authors.lastname: Yang
mesh_terms: D016571:Neural Networks, Computer; D011506:Proteins / Q000737:chemistry*
chemical_list: D011506:Proteins
keywords: deep learning; fold recognition; fragment structure prediction; local structure prediction; neural network; protein structure prediction; secondary structure prediction