title: SPIDER2: A Package to Predict Secondary Structure, Accessible Surface Area, and Main-Chain Torsional Angles by Deep Neural Networks.
abstract: Predicting one-dimensional structure properties has played an important role to improve prediction of protein three-dimensional structures and functions. The most commonly predicted properties are secondary structure and accessible surface area (ASA) representing local and nonlocal structural characteristics, respectively. Secondary structure prediction is further complemented by prediction of continuous main-chain torsional angles. Here we describe a newly developed method SPIDER2 that utilizes three iterations of deep learning neural networks to improve the prediction accuracy of several structural properties simultaneously. For an independent test set of 1199 proteins SPIDER2 achieves 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. The method provides state-of-the-art, all-in-one accurate prediction of local structure and solvent accessible surface area. The method is implemented, as a webserver along with a standalone package that are available in our website: http://sparks-lab.org .
authors: Institute for Glycomics and School of Information and Communication Technology, Griffith University, Gold Coast Campus, Science 1 (G24) 2.10, Parklands Drive, Southport, QLD, 4222, Australia.
authors: Yuedong
authors: Y
authors: Yang
authors: Signal Processing Laboratory, School of Engineering, Griffith University, Brisbane, QLD, Australia.
authors: Rhys
authors: R
authors: Heffernan
authors: Signal Processing Laboratory, School of Engineering, Griffith University, Brisbane, QLD, Australia.
authors: Kuldip
authors: K
authors: Paliwal
authors: Signal Processing Laboratory, School of Engineering, Griffith University, Brisbane, QLD, Australia.
authors: James
authors: J
authors: Lyons
authors: Department of Psychiatry, Medical Research Center, University of Iowa, Iowa City, IA, USA.
authors: Abdollah
authors: A
authors: Dehzangi
authors: Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, QLD, Australia.|School of Engineering and Physics, University of the South Pacific, Private Mail Bag, Laucala Campus, Suva, Fiji.
authors: Alok
authors: A
authors: Sharma
authors: Shandong Provincial Key Laboratory of Functional Macromolecular Biophysics, Dezhou University, Dezhou, Shandong, China.
authors: Jihua
authors: J
authors: Wang
authors: Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, QLD, Australia.|National ICT Australia (NICTA), Brisbane, QLD, Australia.
authors: Abdul
authors: A
authors: Sattar
authors: Institute for Glycomics and School of Information and Communication Technology, Griffith University, Gold Coast Campus, Science 1 (G24) 2.10, Parklands Drive, Southport, QLD, 4222, Australia. yaoqi.zhou@griffith.edu.au.
authors: Yaoqi
authors: Y
authors: Zhou
mesh_terms: D000596:Amino Acids / Q000737:chemistry* / Q000235:genetics; D030562:Databases, Protein; D016571:Neural Networks, Computer; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry* / Q000235:genetics
chemical_list: D000596:Amino Acids; D011506:Proteins
keywords: Backbone torsion angles; C alpha-based angles; Deep neural networks; Secondary structure prediction; Solvent accessible surface area