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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: When compared with the solvent accessibility, the depth allows studying deep-level structures and functional sites, and formation of the protein folding nucleus.We found that the distance based indices are harder to predict due to the more complex nature of the corresponding depth profiles.The predicted depth can be used to provide improved prediction of both buried and exposed residues.

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 of the observed depth values (the top plot) and the predicted depth values (the bottom plot) for 1QFTA protein chain. Blue, green, and red plots correspond to actual and predicted MSMS, DPX, and SADIC depth values.
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Figure 9: The comparison of the observed depth values (the top plot) and the predicted depth values (the bottom plot) for 1QFTA protein chain. Blue, green, and red plots correspond to actual and predicted MSMS, DPX, and SADIC depth values.

Mentions: Following we analyze why the depth defined with SADIC algorithm yields higher quality predictions. The reason for the differences in the prediction performance between SADIC and the other two depth indices is due to the fact that SADIC is a volume based depth, while the other two are distance-based. The volume based depth is negatively correlated with the other two distance based depth indices. As shown in Figure 8, the maximal and mean distance based depth values (defined with MSMS or DPX) show larger variability with the increasing size of the protein chain, while the maximal and mean volume based depth values are independent of the chain size. The definitions of the three depth indices imply that the volume based depth has an upper bound of 2 while both distance based indices have no upper bound. The prediction of the indices that are characterized by larger variability and wider range of values is more challenging. The wider range of values implies that the corresponding MAE values (which are not normalized against the range) are higher. The reason for the improved PCC values in the case of the SADIC depth index is that these depth values along the protein sequence (referred to as the depth profile) are smoother. On the other hand, the other two indices are characterized by larger number of high spikes, see Figure 9, in which case it is harder to generate highly correlated values. To show that, we counted the number of spike points for each depth index for sequences from the YW923 dataset. Residue i is called an ε-spike point if its depth satisfies


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 of the observed depth values (the top plot) and the predicted depth values (the bottom plot) for 1QFTA protein chain. Blue, green, and red plots correspond to actual and predicted MSMS, DPX, and SADIC depth values.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 9: The comparison of the observed depth values (the top plot) and the predicted depth values (the bottom plot) for 1QFTA protein chain. Blue, green, and red plots correspond to actual and predicted MSMS, DPX, and SADIC depth values.
Mentions: Following we analyze why the depth defined with SADIC algorithm yields higher quality predictions. The reason for the differences in the prediction performance between SADIC and the other two depth indices is due to the fact that SADIC is a volume based depth, while the other two are distance-based. The volume based depth is negatively correlated with the other two distance based depth indices. As shown in Figure 8, the maximal and mean distance based depth values (defined with MSMS or DPX) show larger variability with the increasing size of the protein chain, while the maximal and mean volume based depth values are independent of the chain size. The definitions of the three depth indices imply that the volume based depth has an upper bound of 2 while both distance based indices have no upper bound. The prediction of the indices that are characterized by larger variability and wider range of values is more challenging. The wider range of values implies that the corresponding MAE values (which are not normalized against the range) are higher. The reason for the improved PCC values in the case of the SADIC depth index is that these depth values along the protein sequence (referred to as the depth profile) are smoother. On the other hand, the other two indices are characterized by larger number of high spikes, see Figure 9, in which case it is harder to generate highly correlated values. To show that, we counted the number of spike points for each depth index for sequences from the YW923 dataset. Residue i is called an ε-spike point if its depth satisfies

Bottom Line: When compared with the solvent accessibility, the depth allows studying deep-level structures and functional sites, and formation of the protein folding nucleus.We found that the distance based indices are harder to predict due to the more complex nature of the corresponding depth profiles.The predicted depth can be used to provide improved prediction of both buried and exposed residues.

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