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Prediction of protein domain with mRMR feature selection and analysis.

Li BQ, Hu LL, Chen L, Feng KY, Cai YD, Chou KC - PLoS ONE (2012)

Bottom Line: With the avalanche of protein sequences generated in the postgenomic age, it is highly desired to develop effective methods for predicting the protein domains according to the sequences information alone, so as to facilitate the structure prediction of proteins and speed up their functional annotation.The overall success rate achieved by the new method on an independent dataset was around 73%, which was about 28-40% higher than those by the existing method on the same benchmark dataset.Finally, it has not escaped our notice that the current approach can also be utilized to study protein signal peptides, B-cell epitopes, HIV protease cleavage sites, among many other important topics in protein science and biomedicine.

View Article: PubMed Central - PubMed

Affiliation: Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.

ABSTRACT
The domains are the structural and functional units of proteins. With the avalanche of protein sequences generated in the postgenomic age, it is highly desired to develop effective methods for predicting the protein domains according to the sequences information alone, so as to facilitate the structure prediction of proteins and speed up their functional annotation. However, although many efforts have been made in this regard, prediction of protein domains from the sequence information still remains a challenging and elusive problem. Here, a new method was developed by combing the techniques of RF (random forest), mRMR (maximum relevance minimum redundancy), and IFS (incremental feature selection), as well as by incorporating the features of physicochemical and biochemical properties, sequence conservation, residual disorder, secondary structure, and solvent accessibility. The overall success rate achieved by the new method on an independent dataset was around 73%, which was about 28-40% higher than those by the existing method on the same benchmark dataset. Furthermore, it was revealed by an in-depth analysis that the features of evolution, codon diversity, electrostatic charge, and disorder played more important roles than the others in predicting protein domains, quite consistent with experimental observations. It is anticipated that the new method may become a high-throughput tool in annotating protein domains, or may, at the very least, play a complementary role to the existing domain prediction methods, and that the findings about the key features with high impacts to the domain prediction might provide useful insights or clues for further experimental investigations in this area. Finally, it has not escaped our notice that the current approach can also be utilized to study protein signal peptides, B-cell epitopes, HIV protease cleavage sites, among many other important topics in protein science and biomedicine.

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A 2-dimensional histogram to characterize the amino acid factor types in the final optimal features set.The impact on the domain prediction from (A) the five different amino acid types, and (B) each of the 13 subsites. See the text for further explanation.
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pone-0039308-g004: A 2-dimensional histogram to characterize the amino acid factor types in the final optimal features set.The impact on the domain prediction from (A) the five different amino acid types, and (B) each of the 13 subsites. See the text for further explanation.

Mentions: Illustrated in Fig. 4 are the impacts of different amino acid factors and their subsite locations to the protein domain prediction. It can be seen from Fig. 4A that the codon diversity was the most important feature to the protein domain site prediction, as supported by [98], [99]. Besides, it was reported that “codon harmonization” would put some non-preferred codons into the positions corresponding to the predicted protein domain boundaries [100]. Furthermore, the electrostatic charge has proved to be essential for the localization and activation of many proteins containing polycationic domains in their amino acid sequence [101]. Meanwhile, it has also been revealed that binding of oppositely charged proteins via electrostatic interactions can induce domain formation [102]. As shown in Fig. 4B, the amino acid residues at the subsite 2 and site 13 would have the highest impact to the protein domain sites prediction. Interestingly, the electrostatic feature at the subsite 13 had an index of 2 in our final optimal feature set, indicating that it was one of the most important features for the protein domain site prediction.


Prediction of protein domain with mRMR feature selection and analysis.

Li BQ, Hu LL, Chen L, Feng KY, Cai YD, Chou KC - PLoS ONE (2012)

A 2-dimensional histogram to characterize the amino acid factor types in the final optimal features set.The impact on the domain prediction from (A) the five different amino acid types, and (B) each of the 13 subsites. See the text for further explanation.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3376124&req=5

pone-0039308-g004: A 2-dimensional histogram to characterize the amino acid factor types in the final optimal features set.The impact on the domain prediction from (A) the five different amino acid types, and (B) each of the 13 subsites. See the text for further explanation.
Mentions: Illustrated in Fig. 4 are the impacts of different amino acid factors and their subsite locations to the protein domain prediction. It can be seen from Fig. 4A that the codon diversity was the most important feature to the protein domain site prediction, as supported by [98], [99]. Besides, it was reported that “codon harmonization” would put some non-preferred codons into the positions corresponding to the predicted protein domain boundaries [100]. Furthermore, the electrostatic charge has proved to be essential for the localization and activation of many proteins containing polycationic domains in their amino acid sequence [101]. Meanwhile, it has also been revealed that binding of oppositely charged proteins via electrostatic interactions can induce domain formation [102]. As shown in Fig. 4B, the amino acid residues at the subsite 2 and site 13 would have the highest impact to the protein domain sites prediction. Interestingly, the electrostatic feature at the subsite 13 had an index of 2 in our final optimal feature set, indicating that it was one of the most important features for the protein domain site prediction.

Bottom Line: With the avalanche of protein sequences generated in the postgenomic age, it is highly desired to develop effective methods for predicting the protein domains according to the sequences information alone, so as to facilitate the structure prediction of proteins and speed up their functional annotation.The overall success rate achieved by the new method on an independent dataset was around 73%, which was about 28-40% higher than those by the existing method on the same benchmark dataset.Finally, it has not escaped our notice that the current approach can also be utilized to study protein signal peptides, B-cell epitopes, HIV protease cleavage sites, among many other important topics in protein science and biomedicine.

View Article: PubMed Central - PubMed

Affiliation: Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.

ABSTRACT
The domains are the structural and functional units of proteins. With the avalanche of protein sequences generated in the postgenomic age, it is highly desired to develop effective methods for predicting the protein domains according to the sequences information alone, so as to facilitate the structure prediction of proteins and speed up their functional annotation. However, although many efforts have been made in this regard, prediction of protein domains from the sequence information still remains a challenging and elusive problem. Here, a new method was developed by combing the techniques of RF (random forest), mRMR (maximum relevance minimum redundancy), and IFS (incremental feature selection), as well as by incorporating the features of physicochemical and biochemical properties, sequence conservation, residual disorder, secondary structure, and solvent accessibility. The overall success rate achieved by the new method on an independent dataset was around 73%, which was about 28-40% higher than those by the existing method on the same benchmark dataset. Furthermore, it was revealed by an in-depth analysis that the features of evolution, codon diversity, electrostatic charge, and disorder played more important roles than the others in predicting protein domains, quite consistent with experimental observations. It is anticipated that the new method may become a high-throughput tool in annotating protein domains, or may, at the very least, play a complementary role to the existing domain prediction methods, and that the findings about the key features with high impacts to the domain prediction might provide useful insights or clues for further experimental investigations in this area. Finally, it has not escaped our notice that the current approach can also be utilized to study protein signal peptides, B-cell epitopes, HIV protease cleavage sites, among many other important topics in protein science and biomedicine.

Show MeSH