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

Author

Listed:
  • Jiangning Song
  • Hao Tan
  • Khalid Mahmood
  • Ruby H P Law
  • Ashley M Buckle
  • Geoffrey I Webb
  • Tatsuya Akutsu
  • James C Whisstock

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.

Suggested Citation

  • Jiangning Song & Hao Tan & Khalid Mahmood & Ruby H P Law & Ashley M Buckle & Geoffrey I Webb & Tatsuya Akutsu & James C Whisstock, 2009. "Prodepth: Predict Residue Depth by Support Vector Regression Approach from Protein Sequences Only," PLOS ONE, Public Library of Science, vol. 4(9), pages 1-14, September.
  • Handle: RePEc:plo:pone00:0007072
    DOI: 10.1371/journal.pone.0007072
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    Cited by:

    1. Jiangning Song & Hao Tan & Andrew J Perry & Tatsuya Akutsu & Geoffrey I Webb & James C Whisstock & Robert N Pike, 2012. "PROSPER: An Integrated Feature-Based Tool for Predicting Protease Substrate Cleavage Sites," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-23, November.
    2. Shohreh Ariaeenejad & Maryam Mousivand & Parinaz Moradi Dezfouli & Maryam Hashemi & Kaveh Kavousi & Ghasem Hosseini Salekdeh, 2018. "A computational method for prediction of xylanase enzymes activity in strains of Bacillus subtilis based on pseudo amino acid composition features," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-16, October.
    3. Jiangning Song & Hao Tan & Mingjun Wang & Geoffrey I Webb & Tatsuya Akutsu, 2012. "TANGLE: Two-Level Support Vector Regression Approach for Protein Backbone Torsion Angle Prediction from Primary Sequences," PLOS ONE, Public Library of Science, vol. 7(2), pages 1-16, February.

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