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    Prodepth: Predict Residue Depth by Support Vector Regression Approach from Protein Sequences Only

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    Author
    Song, J; Tan, H; Mahmood, K; Law, RHP; Buckle, AM; Webb, GI; Akutsu, T; Whisstock, JC
    Date
    2009-09-17
    Source Title
    PLoS One
    Publisher
    PUBLIC LIBRARY SCIENCE
    University of Melbourne Author/s
    Mahmood, Khalid
    Affiliation
    Medicine Dentistry & Health Sciences
    Metadata
    Show full item record
    Document Type
    Journal Article
    Citations
    Song, J., Tan, H., Mahmood, K., Law, R. H. P., Buckle, A. M., Webb, G. I., Akutsu, T. & Whisstock, J. C. (2009). Prodepth: Predict Residue Depth by Support Vector Regression Approach from Protein Sequences Only. PLOS ONE, 4 (9), https://doi.org/10.1371/journal.pone.0007072.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/254799
    DOI
    10.1371/journal.pone.0007072
    Abstract
    Residue depth (RD) is a solvent exposure measure that complements the information provided by conventional accessible surface area (ASA) and describes to what extent a residue is buried in the protein structure space. Previous studies have established that RD is correlated with several protein properties, such as protein stability, residue conservation and amino acid types. Accurate prediction of RD has many potentially important applications in the field of structural bioinformatics, for example, facilitating the identification of functionally important residues, or residues in the folding nucleus, or enzyme active sites from sequence information. In this work, we introduce an efficient approach that uses support vector regression to quantify the relationship between RD and protein sequence. We systematically investigated eight different sequence encoding schemes including both local and global sequence characteristics and examined their respective prediction performances. For the objective evaluation of our approach, we used 5-fold cross-validation to assess the prediction accuracies and showed that the overall best performance could be achieved with a correlation coefficient (CC) of 0.71 between the observed and predicted RD values and a root mean square error (RMSE) of 1.74, after incorporating the relevant multiple sequence features. The results suggest that residue depth could be reliably predicted solely from protein primary sequences: local sequence environments are the major determinants, while global sequence features could influence the prediction performance marginally. We highlight two examples as a comparison in order to illustrate the applicability of this approach. We also discuss the potential implications of this new structural parameter in the field of protein structure prediction and homology modeling. This method might prove to be a powerful tool for sequence analysis.

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