Impact of residue accessible surface area on the prediction of protein secondary structures

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• 作者：Amir Momen-Roknabadi (1) (3)
  Mehdi Sadeghi (2) (3)
  Hamid Pezeshk (4)
  Sayed-Amir Marashi (1) (5) (6)
  
• 刊名：BMC Bioinformatics
• 出版年：2008
• 出版时间：December 2008
• 年：2008
• 卷：9
• 期：1
• 全文大小：372KB
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• 作者单位：Amir Momen-Roknabadi (1) (3)
  Mehdi Sadeghi (2) (3)
  Hamid Pezeshk (4)
  Sayed-Amir Marashi (1) (5) (6)
  
  1. Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran
  3. Bioinformatics Group, School of Computer Science, Institute for Studies in Theoretical Physics and Mathematics (IPM), Niavaran Square, Tehran, Iran
  2. National Institute of Genetic Engineering and Biotechnology, Tehran-Karaj Highway, Tehran, Iran
  4. School of Mathematics, Statistics and Computer Sciences and Center of Excellence in Biomathematics, College of Science, University of Tehran, Tehran, Iran
  5. IMPRS-CBSC, Max Planck Institute for Molecular Genetics, Ihnestr. 63-73, D-14195, Berlin, Berlin, Germany
  6. DFG-Research Center Matheon, FB Mathematik und Informatik, Freie Universit盲t Berlin, Arnimallee 6, D-14195, Berlin, Germany
  
• ISSN：1471-2105

文摘

Background The problem of accurate prediction of protein secondary structure continues to be one of the challenging problems in Bioinformatics. It has been previously suggested that amino acid relative solvent accessibility (RSA) might be an effective factor for increasing the accuracy of protein secondary structure prediction. Previous studies have either used a single constant threshold to classify residues into discrete classes (buries vs. exposed), or used the real-value predicted RSAs in their prediction method. Results We studied the effect of applying different RSA threshold types (namely, fixed thresholds vs. residue-dependent thresholds) on a variety of secondary structure prediction methods. With the consideration of DSSP-assigned RSA values we realized that improvement in the accuracy of prediction strictly depends on the selected threshold(s). Furthermore, we showed that choosing a single threshold for all amino acids is not the best possible parameter. We therefore used residue-dependent thresholds and most of residues showed improvement in prediction. Next, we tried to consider predicted RSA values, since in the real-world problem, protein sequence is the only available information. We first predicted the RSA classes by RVP-net program and then used these data in our method. Using this approach, improvement in prediction was also obtained. Conclusion The success of applying the RSA information on different secondary structure prediction methods suggest that prediction accuracy can be improved independent of prediction approaches. Thus, solvent accessibility can be considered as a rich source of information to help the improvement of these methods.