title: PredRSA: a gradient boosted regression trees approach for predicting protein solvent accessibility.
abstract: BACKGROUND
  Protein solvent accessibility prediction is a pivotal intermediate step towards modeling protein tertiary structures directly from one-dimensional sequences. It also plays an important part in identifying protein folds and domains. Although some methods have been presented to the protein solvent accessibility prediction in recent years, the performance is far from satisfactory. In this work, we propose PredRSA, a computational method that can accurately predict relative solvent accessible surface area (RSA) of residues by exploring various local and global sequence features which have been observed to be associated with solvent accessibility. Based on these features, a novel and efficient approach, Gradient Boosted Regression Trees (GBRT), is first adopted to predict RSA.
  
  
  RESULTS
  Experimental results obtained from 5-fold cross-validation based on the Manesh-215 dataset show that the mean absolute error (MAE) and the Pearson correlation coefficient (PCC) of PredRSA are 9.0 % and 0.75, respectively, which are better than that of the existing methods. Moreover, we evaluate the performance of PredRSA using an independent test set of 68 proteins. Compared with the state-of-the-art approaches (SPINE-X and ASAquick), PredRSA achieves a significant improvement on the prediction quality.
  
  
  CONCLUSIONS
  Our experimental results show that the Gradient Boosted Regression Trees algorithm and the novel feature combination are quite effective in relative solvent accessibility prediction. The proposed PredRSA method could be useful in assisting the prediction of protein structures by applying the predicted RSA as useful restraints.
authors.affiliation: School of Software, Central South University, No.22 Shaoshan South Road, Changsha, 410075, China. chaofan0427@csu.edu.cn.
authors.forename: Chao
authors.initials: C
authors.lastname: Fan
authors.affiliation: School of Software, Central South University, No.22 Shaoshan South Road, Changsha, 410075, China. csu.ldw@csu.edu.cn.
authors.forename: Diwei
authors.initials: D
authors.lastname: Liu
authors.affiliation: School of Software, Central South University, No.22 Shaoshan South Road, Changsha, 410075, China. ryanhwang1124@csu.edu.cn.
authors.forename: Rui
authors.initials: R
authors.lastname: Huang
authors.affiliation: School of Software, Central South University, No.22 Shaoshan South Road, Changsha, 410075, China. czg@csu.edu.cn.
authors.forename: Zhigang
authors.initials: Z
authors.lastname: Chen
authors.affiliation: School of Software, Central South University, No.22 Shaoshan South Road, Changsha, 410075, China. leideng@csu.edu.cn.|Shanghai Key Laboratory of Intelligent Information Processing, No.220 Handan Road, Shanghai, 200433, China. leideng@csu.edu.cn.
authors.forename: Lei
authors.initials: L
authors.lastname: Deng
mesh_terms: D000465:Algorithms; D000596:Amino Acids / Q000737:chemistry*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D016014:Linear Models; D011506:Proteins / Q000737:chemistry*; D012984:Software; D012997:Solvents / Q000737:chemistry*
chemical_list: D000596:Amino Acids; D011506:Proteins; D012997:Solvents
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