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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 final optimal features set.The impact on the domain prediction from (A) the five different feature types, and (B) each of the 13 subsites. See the text for further explanation.
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pone-0039308-g002: A 2-dimensional histogram to characterize the final optimal features set.The impact on the domain prediction from (A) the five different feature types, and (B) each of the 13 subsites. See the text for further explanation.

Mentions: The distribution of the number of each type of features in the final optimal feature set was investigated and shown in Fig. 2A. Of the 195 optimal features, 147 were from PSSM conservation scores, 21 from the amino acid factors, 4 from the disorder scores, 7 from the solvent accessibilities, and 16 from the secondary structural propensities. All these five kinds of features made contributions to the prediction of protein domain sites. It was revealed by the site-specific distribution of the optimal feature set (see Fig. 2B) that sites 1–2, site 10 and site 13 played most important roles in determining the domain sites. In addition, the features of site 4 and site 5 also had considerable impacts on the prediction of protein domain sites.


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 final optimal features set.The impact on the domain prediction from (A) the five different feature 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-g002: A 2-dimensional histogram to characterize the final optimal features set.The impact on the domain prediction from (A) the five different feature types, and (B) each of the 13 subsites. See the text for further explanation.
Mentions: The distribution of the number of each type of features in the final optimal feature set was investigated and shown in Fig. 2A. Of the 195 optimal features, 147 were from PSSM conservation scores, 21 from the amino acid factors, 4 from the disorder scores, 7 from the solvent accessibilities, and 16 from the secondary structural propensities. All these five kinds of features made contributions to the prediction of protein domain sites. It was revealed by the site-specific distribution of the optimal feature set (see Fig. 2B) that sites 1–2, site 10 and site 13 played most important roles in determining the domain sites. In addition, the features of site 4 and site 5 also had considerable impacts on the prediction of protein domain sites.

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