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

Momen-Roknabadi A, Sadeghi M, Pezeshk H, Marashi SA - BMC Bioinformatics (2008)

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran. roknabadi@khayam.ut.ac.ir

ABSTRACT

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.

Show MeSH
Percentage of improvement in secondary structure prediction accuracy by addition of RSA information for the GOR (A), Chou-Fasman (B) and HMM(C) methods using leave-one-out cross-validation and different thresholds in two-state classification of RSA.
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Figure 1: Percentage of improvement in secondary structure prediction accuracy by addition of RSA information for the GOR (A), Chou-Fasman (B) and HMM(C) methods using leave-one-out cross-validation and different thresholds in two-state classification of RSA.

Mentions: In our analysis, we first investigated the effect of adding the actual RSA values (obtained from DSSP files), for different RSA thresholds using GOR, Chou-Fasman and HMM (Hidden Markov Method). Accuracies of SS prediction for GOR, Chou-Fasman and HMM methods, without consideration of RSA information are summarized in Additional file 1. Figure 1 depicts the level of improvement of SS prediction, compared to the prediction accuracy of classical method [see also Additional file 2, 3, 4]. For all selected thresholds, some improvements are obtained which is consistent with the results obtained by other investigators [32,33]. Our results suggest that the best threshold for the improvement of SS prediction in GOR and Chou-Fasman methods is about 16%, while HMM performs best with a 4% RSA threshold. Therefore, the 7% cutoff used by Zhu and Blundell [33], and also the 50% cutoff used by Macdonald and Johnson [32] might not be optimal.


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

Momen-Roknabadi A, Sadeghi M, Pezeshk H, Marashi SA - BMC Bioinformatics (2008)

Percentage of improvement in secondary structure prediction accuracy by addition of RSA information for the GOR (A), Chou-Fasman (B) and HMM(C) methods using leave-one-out cross-validation and different thresholds in two-state classification of RSA.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2553345&req=5

Figure 1: Percentage of improvement in secondary structure prediction accuracy by addition of RSA information for the GOR (A), Chou-Fasman (B) and HMM(C) methods using leave-one-out cross-validation and different thresholds in two-state classification of RSA.
Mentions: In our analysis, we first investigated the effect of adding the actual RSA values (obtained from DSSP files), for different RSA thresholds using GOR, Chou-Fasman and HMM (Hidden Markov Method). Accuracies of SS prediction for GOR, Chou-Fasman and HMM methods, without consideration of RSA information are summarized in Additional file 1. Figure 1 depicts the level of improvement of SS prediction, compared to the prediction accuracy of classical method [see also Additional file 2, 3, 4]. For all selected thresholds, some improvements are obtained which is consistent with the results obtained by other investigators [32,33]. Our results suggest that the best threshold for the improvement of SS prediction in GOR and Chou-Fasman methods is about 16%, while HMM performs best with a 4% RSA threshold. Therefore, the 7% cutoff used by Zhu and Blundell [33], and also the 50% cutoff used by Macdonald and Johnson [32] might not be optimal.

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran. roknabadi@khayam.ut.ac.ir

ABSTRACT

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.

Show MeSH