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Sequence based residue depth prediction using evolutionary information and predicted secondary structure.

Zhang H, Zhang T, Chen K, Shen S, Ruan J, Kurgan L - BMC Bioinformatics (2008)

Bottom Line: The mean absolute and the mean relative errors are shown to be statistically significantly better when compared with a method recently proposed by Yuan and Wang [Proteins 2008; 70:509-516].The results show that three-fold cross validation underestimates the variability of the prediction quality when compared with the results based on the ten-fold cross validation.The proposed method, RDPred, provides statistically significantly better predictions of residue depth when compared with the competing method.

View Article: PubMed Central - HTML - PubMed

Affiliation: College of Mathematical Science and LPMC, Nankai University, Tianjin, PR China. zerohua@gmail.com

ABSTRACT

Background: Residue depth allows determining how deeply a given residue is buried, in contrast to the solvent accessibility that differentiates between buried and solvent-exposed residues. When compared with the solvent accessibility, the depth allows studying deep-level structures and functional sites, and formation of the protein folding nucleus. Accurate prediction of residue depth would provide valuable information for fold recognition, prediction of functional sites, and protein design.

Results: A new method, RDPred, for the real-value depth prediction from protein sequence is proposed. RDPred combines information extracted from the sequence, PSI-BLAST scoring matrices, and secondary structure predicted with PSIPRED. Three-fold/ten-fold cross validation based tests performed on three independent, low-identity datasets show that the distance based depth (computed using MSMS) predicted by RDPred is characterized by 0.67/0.67, 0.66/0.67, and 0.64/0.65 correlation with the actual depth, by the mean absolute errors equal 0.56/0.56, 0.61/0.60, and 0.58/0.57, and by the mean relative errors equal 17.0%/16.9%, 18.2%/18.1%, and 17.7%/17.6%, respectively. The mean absolute and the mean relative errors are shown to be statistically significantly better when compared with a method recently proposed by Yuan and Wang [Proteins 2008; 70:509-516]. The results show that three-fold cross validation underestimates the variability of the prediction quality when compared with the results based on the ten-fold cross validation. We also show that the hydrophilic and flexible residues are predicted more accurately than hydrophobic and rigid residues. Similarly, the charged residues that include Lys, Glu, Asp, and Arg are the most accurately predicted. Our analysis reveals that evolutionary information encoded using PSSM is characterized by stronger correlation with the depth for hydrophilic amino acids (AAs) and aliphatic AAs when compared with hydrophobic AAs and aromatic AAs. Finally, we show that the secondary structure of coils and strands is useful in depth prediction, in contrast to helices that have relatively uniform distribution over the protein depth. Application of the predicted residue depth to prediction of buried/exposed residues shows consistent improvements in detection rates of both buried and exposed residues when compared with the competing method. Finally, we contrasted the prediction performance among distance based (MSMS and DPX) and volume based (SADIC) depth definitions. We found that the distance based indices are harder to predict due to the more complex nature of the corresponding depth profiles.

Conclusion: The proposed method, RDPred, provides statistically significantly better predictions of residue depth when compared with the competing method. The predicted depth can be used to provide improved prediction of both buried and exposed residues. The prediction of exposed residues has implications in characterization/prediction of interactions with ligands and other proteins, while the prediction of buried residues could be used in the context of folding predictions and simulations.

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The comparison (A) PCC and (B) MAE values at the fold level using three-fold cross validations (3 CV) and ten-fold cross validations (10 CV) for the three depth indices, i.e., MSMS, DPX and SADIC, on the YW923 dataset. The x-axis shows the depth index and test types, e.g., MSMS 10 CV corresponds to the results for the MSMS based depth derived by ten-fold cross validation. The results are averaged over the folds and the corresponding standard deviations are shown using error bars. The scale of the y-axis, which shows the average quality index values, varies between the two panels.
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Figure 7: The comparison (A) PCC and (B) MAE values at the fold level using three-fold cross validations (3 CV) and ten-fold cross validations (10 CV) for the three depth indices, i.e., MSMS, DPX and SADIC, on the YW923 dataset. The x-axis shows the depth index and test types, e.g., MSMS 10 CV corresponds to the results for the MSMS based depth derived by ten-fold cross validation. The results are averaged over the folds and the corresponding standard deviations are shown using error bars. The scale of the y-axis, which shows the average quality index values, varies between the two panels.

Mentions: We applied the same features and parameterization as in the original RDPred method (developed for MSMS based depth) and performed three-fold and ten-fold cross validations on the YW923 dataset using the other two depth indices. We tested two sets of features, PSSM+PS (which simulates the YW method) and RDPred's features. The prediction quality measured with PCC and MAE at the residue level is summarized in Table 7 while the PCC and MAE values at the fold level are visualized in Figure 7. We did not compute the MRE values because the two other depth indices include values of zero. The MAE values and the corresponding centiles shown in the Table 7 should not be compared across different depth indices due to large differences in the range of values for the three depth indices.


Sequence based residue depth prediction using evolutionary information and predicted secondary structure.

Zhang H, Zhang T, Chen K, Shen S, Ruan J, Kurgan L - BMC Bioinformatics (2008)

The comparison (A) PCC and (B) MAE values at the fold level using three-fold cross validations (3 CV) and ten-fold cross validations (10 CV) for the three depth indices, i.e., MSMS, DPX and SADIC, on the YW923 dataset. The x-axis shows the depth index and test types, e.g., MSMS 10 CV corresponds to the results for the MSMS based depth derived by ten-fold cross validation. The results are averaged over the folds and the corresponding standard deviations are shown using error bars. The scale of the y-axis, which shows the average quality index values, varies between the two panels.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2567998&req=5

Figure 7: The comparison (A) PCC and (B) MAE values at the fold level using three-fold cross validations (3 CV) and ten-fold cross validations (10 CV) for the three depth indices, i.e., MSMS, DPX and SADIC, on the YW923 dataset. The x-axis shows the depth index and test types, e.g., MSMS 10 CV corresponds to the results for the MSMS based depth derived by ten-fold cross validation. The results are averaged over the folds and the corresponding standard deviations are shown using error bars. The scale of the y-axis, which shows the average quality index values, varies between the two panels.
Mentions: We applied the same features and parameterization as in the original RDPred method (developed for MSMS based depth) and performed three-fold and ten-fold cross validations on the YW923 dataset using the other two depth indices. We tested two sets of features, PSSM+PS (which simulates the YW method) and RDPred's features. The prediction quality measured with PCC and MAE at the residue level is summarized in Table 7 while the PCC and MAE values at the fold level are visualized in Figure 7. We did not compute the MRE values because the two other depth indices include values of zero. The MAE values and the corresponding centiles shown in the Table 7 should not be compared across different depth indices due to large differences in the range of values for the three depth indices.

Bottom Line: The mean absolute and the mean relative errors are shown to be statistically significantly better when compared with a method recently proposed by Yuan and Wang [Proteins 2008; 70:509-516].The results show that three-fold cross validation underestimates the variability of the prediction quality when compared with the results based on the ten-fold cross validation.The proposed method, RDPred, provides statistically significantly better predictions of residue depth when compared with the competing method.

View Article: PubMed Central - HTML - PubMed

Affiliation: College of Mathematical Science and LPMC, Nankai University, Tianjin, PR China. zerohua@gmail.com

ABSTRACT

Background: Residue depth allows determining how deeply a given residue is buried, in contrast to the solvent accessibility that differentiates between buried and solvent-exposed residues. When compared with the solvent accessibility, the depth allows studying deep-level structures and functional sites, and formation of the protein folding nucleus. Accurate prediction of residue depth would provide valuable information for fold recognition, prediction of functional sites, and protein design.

Results: A new method, RDPred, for the real-value depth prediction from protein sequence is proposed. RDPred combines information extracted from the sequence, PSI-BLAST scoring matrices, and secondary structure predicted with PSIPRED. Three-fold/ten-fold cross validation based tests performed on three independent, low-identity datasets show that the distance based depth (computed using MSMS) predicted by RDPred is characterized by 0.67/0.67, 0.66/0.67, and 0.64/0.65 correlation with the actual depth, by the mean absolute errors equal 0.56/0.56, 0.61/0.60, and 0.58/0.57, and by the mean relative errors equal 17.0%/16.9%, 18.2%/18.1%, and 17.7%/17.6%, respectively. The mean absolute and the mean relative errors are shown to be statistically significantly better when compared with a method recently proposed by Yuan and Wang [Proteins 2008; 70:509-516]. The results show that three-fold cross validation underestimates the variability of the prediction quality when compared with the results based on the ten-fold cross validation. We also show that the hydrophilic and flexible residues are predicted more accurately than hydrophobic and rigid residues. Similarly, the charged residues that include Lys, Glu, Asp, and Arg are the most accurately predicted. Our analysis reveals that evolutionary information encoded using PSSM is characterized by stronger correlation with the depth for hydrophilic amino acids (AAs) and aliphatic AAs when compared with hydrophobic AAs and aromatic AAs. Finally, we show that the secondary structure of coils and strands is useful in depth prediction, in contrast to helices that have relatively uniform distribution over the protein depth. Application of the predicted residue depth to prediction of buried/exposed residues shows consistent improvements in detection rates of both buried and exposed residues when compared with the competing method. Finally, we contrasted the prediction performance among distance based (MSMS and DPX) and volume based (SADIC) depth definitions. We found that the distance based indices are harder to predict due to the more complex nature of the corresponding depth profiles.

Conclusion: The proposed method, RDPred, provides statistically significantly better predictions of residue depth when compared with the competing method. The predicted depth can be used to provide improved prediction of both buried and exposed residues. The prediction of exposed residues has implications in characterization/prediction of interactions with ligands and other proteins, while the prediction of buried residues could be used in the context of folding predictions and simulations.

Show MeSH
Related in: MedlinePlus