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Sequence Conservation in the Prediction of Catalytic Sites

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Abstract

Predicting catalytic sites of a given enzyme is an important open problem of Bioinformatics. Recently, many machine learning-based methods have been developed which have the advantage that they can account for many sequential or structural features. We found that although many kinds of features are incorporated, protein sequence conservation is the main part of information they used and should play an important role in the future. So we tested several conservation features in their ability to predict catalytic sites by using the Support Vector Machine classifier. Our results suggest that position specific scoring matrix performs better than other features and incorporating conservation information of sequentially adjacent sites is more effective than that of structurally adjacent ones. Moreover, although conservation information is effective in predicting catalytic sites, it is a difficult problem to optimize the combination of conservation features and other ones.

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Abbreviations

SVM:

Support Vector Machine

JSD:

Jensen-Shannon divergence

PSSM:

Position specific scoring matrix

PCA:

Principal Component Analysis

PC:

Principal Component

WOP:

Weighted observed percentage

CR:

Contribution rate

ACR:

Accumulated contribution rate

ROC:

Receiver operating characteristic

P:

Precision

R:

Recall

FPR:

False positive rate

TPR:

True position rate

MCC:

Matthews correlation coefficient

RP:

Recall/Precision

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Acknowledgments

This work was partially supported by the National Natural Science Foundation of China (No. 10731040), Shanghai Leading Academic Discipline Project (No. S30405) and Innovation Program of Shanghai Municipal Education Commission (No. 09zz134). All the calculational tasks are applied on a LENOVO Shenteng 1800 COW, which is located in School of Mathematical Science, Dalian University of Technology. The authors thank DR. Elisa Cilia for providing their data on the HA-superfamily data set.

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Authors and Affiliations

  1. School of Mathematical Science, Dalian University of Technology, Dalian, 116024, People’s Republic of China

    Yongchao Dou, Xingbo Geng & Hongyun Gao

  2. College of Advanced Science and Technology, Dalian University of Technology, Dalian, 116024, People’s Republic of China

    Yongchao Dou, Xingbo Geng & Hongyun Gao

  3. MPI-CAS Institute of Computational Biology, Chinese Academy of Sciences, Shanghai, 200031, People’s Republic of China

    Jialiang Yang

  4. Department of Mathematics, Shanghai Normal University, Shanghai, 200234, People’s Republic of China

    Xiaoqi Zheng & Jun Wang

  5. Scientific Computing Key Laboratory of Shanghai Universities, Shanghai, 200234, People’s Republic of China

    Jun Wang

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  1. Yongchao Dou
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  2. Xingbo Geng
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  3. Hongyun Gao
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  4. Jialiang Yang
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  5. Xiaoqi Zheng
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  6. Jun Wang
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Correspondence to Jun Wang.

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Dou, Y., Geng, X., Gao, H. et al. Sequence Conservation in the Prediction of Catalytic Sites. Protein J 30, 229–239 (2011). https://doi.org/10.1007/s10930-011-9324-2

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