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Sequence-based identification of interface residues by an integrative profile combining hydrophobic and evolutionary information.

Chen P, Li J - BMC Bioinformatics (2010)

Bottom Line: Numerous methods have been proposed to recognize their interaction sites, however, only a small proportion of protein complexes have been successfully resolved due to the high cost.Results show that evolutionary context of residue with respect to hydrophobicity makes better the identification of protein interface residues.In addition, the ensemble of SVM classifiers improves the prediction performance.

View Article: PubMed Central - HTML - PubMed

Affiliation: Bioinformatics Research Center, School of Computer Engineering, Nanyang Technological University, 639798 Singapore.

ABSTRACT

Background: Protein-protein interactions play essential roles in protein function determination and drug design. Numerous methods have been proposed to recognize their interaction sites, however, only a small proportion of protein complexes have been successfully resolved due to the high cost. Therefore, it is important to improve the performance for predicting protein interaction sites based on primary sequence alone.

Results: We propose a new idea to construct an integrative profile for each residue in a protein by combining its hydrophobic and evolutionary information. A support vector machine (SVM) ensemble is then developed, where SVMs train on different pairs of positive (interface sites) and negative (non-interface sites) subsets. The subsets having roughly the same sizes are grouped in the order of accessible surface area change before and after complexation. A self-organizing map (SOM) technique is applied to group similar input vectors to make more accurate the identification of interface residues. An ensemble of ten-SVMs achieves an MCC improvement by around 8% and F1 improvement by around 9% over that of three-SVMs. As expected, SVM ensembles constantly perform better than individual SVMs. In addition, the model by the integrative profiles outperforms that based on the sequence profile or the hydropathy scale alone. As our method uses a small number of features to encode the input vectors, our model is simpler, faster and more accurate than the existing methods.

Conclusions: The integrative profile by combining hydrophobic and evolutionary information contributes most to the protein-protein interaction prediction. Results show that evolutionary context of residue with respect to hydrophobicity makes better the identification of protein interface residues. In addition, the ensemble of SVM classifiers improves the prediction performance.

Availability: Datasets and software are available at http://mail.ustc.edu.cn/~bigeagle/BMCBioinfo2010/index.htm.

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Comparison between the three profiles on the complex of Bacillus pasteurii urease with acetohydroxamate anion(PDB id: 4UBP, chain A). (a) Prediction results for hydropathy scale; (b) Results for sequence profile; (c) Results for the integrative profile. True prediction interface residues are in red, false predicted non-interface residues are shown in green, false predicted interface residues are in blue, while other ones are in white.
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Figure 3: Comparison between the three profiles on the complex of Bacillus pasteurii urease with acetohydroxamate anion(PDB id: 4UBP, chain A). (a) Prediction results for hydropathy scale; (b) Results for sequence profile; (c) Results for the integrative profile. True prediction interface residues are in red, false predicted non-interface residues are shown in green, false predicted interface residues are in blue, while other ones are in white.

Mentions: The difference between the integrative profile and each individual profile is that in Equation 4, one profile term would be removed for the model keeping only one profile left. The three pictures in Figure 3 illustrate the interaction identification results by the use of the three profiles: hydrophobic scale, sequence profile, and the integrative profile. From the Figure 3, results show that the model with the integrative profile outperformed the other two, and predicted interface sites more accurately. In addition, the model with sequence profile alone performed better than that with hydropathy scale alone.


Sequence-based identification of interface residues by an integrative profile combining hydrophobic and evolutionary information.

Chen P, Li J - BMC Bioinformatics (2010)

Comparison between the three profiles on the complex of Bacillus pasteurii urease with acetohydroxamate anion(PDB id: 4UBP, chain A). (a) Prediction results for hydropathy scale; (b) Results for sequence profile; (c) Results for the integrative profile. True prediction interface residues are in red, false predicted non-interface residues are shown in green, false predicted interface residues are in blue, while other ones are in white.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2921408&req=5

Figure 3: Comparison between the three profiles on the complex of Bacillus pasteurii urease with acetohydroxamate anion(PDB id: 4UBP, chain A). (a) Prediction results for hydropathy scale; (b) Results for sequence profile; (c) Results for the integrative profile. True prediction interface residues are in red, false predicted non-interface residues are shown in green, false predicted interface residues are in blue, while other ones are in white.
Mentions: The difference between the integrative profile and each individual profile is that in Equation 4, one profile term would be removed for the model keeping only one profile left. The three pictures in Figure 3 illustrate the interaction identification results by the use of the three profiles: hydrophobic scale, sequence profile, and the integrative profile. From the Figure 3, results show that the model with the integrative profile outperformed the other two, and predicted interface sites more accurately. In addition, the model with sequence profile alone performed better than that with hydropathy scale alone.

Bottom Line: Numerous methods have been proposed to recognize their interaction sites, however, only a small proportion of protein complexes have been successfully resolved due to the high cost.Results show that evolutionary context of residue with respect to hydrophobicity makes better the identification of protein interface residues.In addition, the ensemble of SVM classifiers improves the prediction performance.

View Article: PubMed Central - HTML - PubMed

Affiliation: Bioinformatics Research Center, School of Computer Engineering, Nanyang Technological University, 639798 Singapore.

ABSTRACT

Background: Protein-protein interactions play essential roles in protein function determination and drug design. Numerous methods have been proposed to recognize their interaction sites, however, only a small proportion of protein complexes have been successfully resolved due to the high cost. Therefore, it is important to improve the performance for predicting protein interaction sites based on primary sequence alone.

Results: We propose a new idea to construct an integrative profile for each residue in a protein by combining its hydrophobic and evolutionary information. A support vector machine (SVM) ensemble is then developed, where SVMs train on different pairs of positive (interface sites) and negative (non-interface sites) subsets. The subsets having roughly the same sizes are grouped in the order of accessible surface area change before and after complexation. A self-organizing map (SOM) technique is applied to group similar input vectors to make more accurate the identification of interface residues. An ensemble of ten-SVMs achieves an MCC improvement by around 8% and F1 improvement by around 9% over that of three-SVMs. As expected, SVM ensembles constantly perform better than individual SVMs. In addition, the model by the integrative profiles outperforms that based on the sequence profile or the hydropathy scale alone. As our method uses a small number of features to encode the input vectors, our model is simpler, faster and more accurate than the existing methods.

Conclusions: The integrative profile by combining hydrophobic and evolutionary information contributes most to the protein-protein interaction prediction. Results show that evolutionary context of residue with respect to hydrophobicity makes better the identification of protein interface residues. In addition, the ensemble of SVM classifiers improves the prediction performance.

Availability: Datasets and software are available at http://mail.ustc.edu.cn/~bigeagle/BMCBioinfo2010/index.htm.

Show MeSH