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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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Proposed prediction system.
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Figure 1: Proposed prediction system.

Mentions: The sequence based prediction methods work in two steps: (1) a fixed-length feature vector is computed based on the sequence and sequence-derived information; (2) the vector is inputted into the prediction model to generate the outcome. The design of the prediction method usually requires a training dataset that is used to construct the model and parameterize the algorithm used to generate the model. In our case we choose SVR to construct the model due to its extensive prior use in bioinformatics application that perform real-valued prediction [12,32-35], including the existing sequence based depth prediction [30]. More specifically, given a sequence, the features were extracted from the sequence itself, and from the sequence-derived PSI-BLAST scoring matrix [36] and secondary structure predicted with PSIPRED [37,38], see Figure 1.


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)

Proposed prediction system.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: Proposed prediction system.
Mentions: The sequence based prediction methods work in two steps: (1) a fixed-length feature vector is computed based on the sequence and sequence-derived information; (2) the vector is inputted into the prediction model to generate the outcome. The design of the prediction method usually requires a training dataset that is used to construct the model and parameterize the algorithm used to generate the model. In our case we choose SVR to construct the model due to its extensive prior use in bioinformatics application that perform real-valued prediction [12,32-35], including the existing sequence based depth prediction [30]. More specifically, given a sequence, the features were extracted from the sequence itself, and from the sequence-derived PSI-BLAST scoring matrix [36] and secondary structure predicted with PSIPRED [37,38], see Figure 1.

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