Limits...
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

Related in: MedlinePlus

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 tertile, Mean ± SD, mean and median as RSA thresholds.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2553345&req=5

Figure 3: 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 tertile, Mean ± SD, mean and median as RSA thresholds.

Mentions: For fixed thresholds, the prediction accuracy dropped by 0.17% to 8.26% (data not shown). When we used means or medians as the residue-specific thresholds, the prediction accuracy was more than original method in all cases. However, when we used tertiles or mean ± standard deviation as the thresholds, the resulting accuracies were more than original method in GOR and HMM methods, but surprisingly, not in Chou-Fasman method (Figure 3).


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 tertile, Mean ± SD, mean and median as RSA thresholds.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: 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 tertile, Mean ± SD, mean and median as RSA thresholds.
Mentions: For fixed thresholds, the prediction accuracy dropped by 0.17% to 8.26% (data not shown). When we used means or medians as the residue-specific thresholds, the prediction accuracy was more than original method in all cases. However, when we used tertiles or mean ± standard deviation as the thresholds, the resulting accuracies were more than original method in GOR and HMM methods, but surprisingly, not in Chou-Fasman method (Figure 3).

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
Related in: MedlinePlus