title: Prediction of structural features and application to outer membrane protein identification.
abstract: Protein three-dimensional (3D) structures provide insightful information in many fields of biology. One-dimensional properties derived from 3D structures such as secondary structure, residue solvent accessibility, residue depth and backbone torsion angles are helpful to protein function prediction, fold recognition and ab initio folding. Here, we predict various structural features with the assistance of neural network learning. Based on an independent test dataset, protein secondary structure prediction generates an overall Q3 accuracy of ~80%. Meanwhile, the prediction of relative solvent accessibility obtains the highest mean absolute error of 0.164, and prediction of residue depth achieves the lowest mean absolute error of 0.062. We further improve the outer membrane protein identification by including the predicted structural features in a scoring function using a simple profile-to-profile alignment. The results demonstrate that the accuracy of outer membrane protein identification can be improved by ~3% at a 1% false positive level when structural features are incorporated. Finally, our methods are available as two convenient and easy-to-use programs. One is PSSM-2-Features for predicting secondary structure, relative solvent accessibility, residue depth and backbone torsion angles, the other is PPA-OMP for identifying outer membrane proteins from proteomes.
authors.affiliation: Institute of Applied Genomics, School of Biological Sciences and Engineering, Fuzhou University, Fuzhou 350108, China.
authors.forename: Renxiang
authors.initials: R
authors.lastname: Yan
authors.affiliation: College of Mathematics and Computer Sciences, Shanxi Normal University, Linfen, 041004, China.
authors.forename: Xiaofeng
authors.initials: X
authors.lastname: Wang
authors.affiliation: Institute of Applied Genomics, School of Biological Sciences and Engineering, Fuzhou University, Fuzhou 350108, China.
authors.forename: Lanqing
authors.initials: L
authors.lastname: Huang
authors.affiliation: Institute of Applied Genomics, School of Biological Sciences and Engineering, Fuzhou University, Fuzhou 350108, China.
authors.forename: Feidi
authors.initials: F
authors.lastname: Yan
authors.affiliation: Institute of Applied Genomics, School of Biological Sciences and Engineering, Fuzhou University, Fuzhou 350108, China.
authors.forename: Xiaoyu
authors.initials: X
authors.lastname: Xue
authors.affiliation: Institute of Applied Genomics, School of Biological Sciences and Engineering, Fuzhou University, Fuzhou 350108, China.
authors.forename: Weiwen
authors.initials: W
authors.lastname: Cai
mesh_terms: D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D004926:Escherichia coli / Q000378:metabolism; D029968:Escherichia coli Proteins / Q000737:chemistry* / Q000378:metabolism; D008565:Membrane Proteins / Q000737:chemistry* / Q000378:metabolism; D017433:Protein Structure, Secondary; D020543:Proteome / Q000378:metabolism; D012372:ROC Curve
chemical_list: D029968:Escherichia coli Proteins; D008565:Membrane Proteins; D020543:Proteome