title: Prediction of protein solvent accessibility using PSO-SVR with multiple sequence-derived features and weighted sliding window scheme.
abstract: BACKGROUND
  The prediction of solvent accessibility could provide valuable clues for analyzing protein structure and functions, such as protein 3-Dimensional structure and B-cell epitope prediction. To fully decipher the protein-protein interaction process, an initial but crucial step is to calculate the protein solvent accessibility, especially when the tertiary structure of the protein is unknown. Although some efforts have been put into the protein solvent accessibility prediction, the performance of existing methods is far from satisfaction.
  
  
  METHODS
  In order to develop the high-accuracy model, we focus on some possible aspects concerning the prediction performance, including several sequence-derived features, a weighted sliding window scheme and the parameters optimization of machine learning approach. To address above issues, we take following strategies. Firstly, we explore various features which have been observed to be associated with the residue solvent accessibility. These discriminative features include protein evolutionary information, predicted protein secondary structure, native disorder, physicochemical propensities and several sequence-based structural descriptors of residues. Secondly, the different contributions of adjacent residues in sliding window are observed, thus a weighted sliding window scheme is proposed to differentiate the contributions of adjacent residues on the central residue. Thirdly, particle swarm optimization (PSO) is employed to search the global best parameters for the proposed predictor.
  
  
  RESULTS
  Evaluated by 3-fold cross-validation, our method achieves the mean absolute error (MAE) of 14.1% and the person correlation coefficient (PCC) of 0.75 for our new-compiled dataset. When compared with the state-of-the-art prediction models in the two benchmark datasets, our method demonstrates better performance. Experimental results demonstrate that our PSAP achieves high performances and outperforms many existing predictors. A web server called PSAP is built and freely available at http://59.73.198.144:8088/SolventAccessibility/.
authors.affiliation: School of Computer Science and Information Technology, Northeast Normal University, Changchun, 1300117 P.R. China.
authors.forename: Jian
authors.initials: J
authors.lastname: Zhang
authors.affiliation: School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Queensland Australia.
authors.forename: Wenhan
authors.initials: W
authors.lastname: Chen
authors.affiliation: School of Computer Science and Information Technology, Northeast Normal University, Changchun, 1300117 P.R. China ; The Engineering Laboratory for Drug-Gene and Protein Screening, Northeast Normal University, Changchun, 130117 P.R. China.
authors.forename: Pingping
authors.initials: P
authors.lastname: Sun
authors.affiliation: School of Computer Science and Information Technology, Northeast Normal University, Changchun, 1300117 P.R. China ; The Engineering Laboratory for Drug-Gene and Protein Screening, Northeast Normal University, Changchun, 130117 P.R. China.
authors.forename: Xiaowei
authors.initials: X
authors.lastname: Zhao
authors.affiliation: School of Computer Science and Information Technology, Northeast Normal University, Changchun, 1300117 P.R. China.
authors.forename: Zhiqiang
authors.initials: Z
authors.lastname: Ma
keywords: Particle swarm optimization; Protein sequence; Solvent accessibility; Support vector regression