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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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Flowchart of generating residue profiles. Each row of the sequence profile corresponds to a residue in the protein, while each column in the sequence profile or the KD hydropathy scale corresponds to each amino acid type.
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Figure 7: Flowchart of generating residue profiles. Each row of the sequence profile corresponds to a residue in the protein, while each column in the sequence profile or the KD hydropathy scale corresponds to each amino acid type.

Mentions: where vector vi for residue i is the multiplication of the standard deviation value SDi by its influencing coefficient pi. More details of generating the profile vectors can be referred to an example in Figure 7. For each residue in protein chains, in summary, the input of our model is an array Vi, while the corresponding target Ti is another state value 1 or 0 that denotes whether the residue is located at interface or non-interface region. Similar to most other machine learning methods, our method aims to learn the mapping from the input array V onto the corresponding target array T. Suppose that O is the output from our method, it is trained to make the output O as close as possible to the target T.


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

Chen P, Li J - BMC Bioinformatics (2010)

Flowchart of generating residue profiles. Each row of the sequence profile corresponds to a residue in the protein, while each column in the sequence profile or the KD hydropathy scale corresponds to each amino acid type.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 7: Flowchart of generating residue profiles. Each row of the sequence profile corresponds to a residue in the protein, while each column in the sequence profile or the KD hydropathy scale corresponds to each amino acid type.
Mentions: where vector vi for residue i is the multiplication of the standard deviation value SDi by its influencing coefficient pi. More details of generating the profile vectors can be referred to an example in Figure 7. For each residue in protein chains, in summary, the input of our model is an array Vi, while the corresponding target Ti is another state value 1 or 0 that denotes whether the residue is located at interface or non-interface region. Similar to most other machine learning methods, our method aims to learn the mapping from the input array V onto the corresponding target array T. Suppose that O is the output from our method, it is trained to make the output O as close as possible to the target T.

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