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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.

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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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SVM ensemble for identifying protein-protein interface residues.
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Figure 8: SVM ensemble for identifying protein-protein interface residues.

Mentions: A simple method was used to combine the outputs of SVMs in this paper. A residue was predicted as interface residue if at least TH outputs of the SVMs corresponding to the same residue were labeled as positive class 1, otherwise the corresponding residue was identified as non-interface residue. Here TH, a threshold value, is ranged from 1 to the total number of SVM classifiers. For example, threshold 2 denotes that one residue was identified as interface residue if at least two outputs of those SVMs were labeled as 1, otherwise as non-interface residue. The flowchart of the whole method is demonstrated in Figure 8. In Figure 8 there are M × N SVM classifiers, each of which contains balanced training positive and negative input vector sets i and j.


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

Chen P, Li J - BMC Bioinformatics (2010)

SVM ensemble for identifying protein-protein interface residues.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 8: SVM ensemble for identifying protein-protein interface residues.
Mentions: A simple method was used to combine the outputs of SVMs in this paper. A residue was predicted as interface residue if at least TH outputs of the SVMs corresponding to the same residue were labeled as positive class 1, otherwise the corresponding residue was identified as non-interface residue. Here TH, a threshold value, is ranged from 1 to the total number of SVM classifiers. For example, threshold 2 denotes that one residue was identified as interface residue if at least two outputs of those SVMs were labeled as 1, otherwise as non-interface residue. The flowchart of the whole method is demonstrated in Figure 8. In Figure 8 there are M × N SVM classifiers, each of which contains balanced training positive and negative input vector sets i and j.

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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