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• W1964655247 endingPage "2046" @default.
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• W1964655247 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." @default.
• W1964655247 created "2016-06-24" @default.
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• W1964655247 date "2014-09-12" @default.
• W1964655247 modified "2023-10-17" @default.
• W1964655247 title "Predicting backbone Cα angles and dihedrals from protein sequences by stacked sparse auto-encoder deep neural network" @default.
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• W1964655247 doi "https://doi.org/10.1002/jcc.23718" @default.
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• W1964655247 hasPublicationYear "2014" @default.
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