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Carboxylator: incorporating solvent-accessible surface area for identifying protein carboxylation sites

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Abstract

In proteins, glutamate (Glu) residues are transformed into γ-carboxyglutamate (Gla) residues in a process called carboxylation. The process of protein carboxylation catalyzed by γ-glutamyl carboxylase is deemed to be important due to its involvement in biological processes such as blood clotting cascade and bone growth. There is an increasing interest within the scientific community to identify protein carboxylation sites. However, experimental identification of carboxylation sites via mass spectrometry-based methods is observed to be expensive, time-consuming, and labor-intensive. Thus, we were motivated to design a computational method for identifying protein carboxylation sites. This work aims to investigate the protein carboxylation by considering the composition of amino acids that surround modification sites. With the implication of a modified residue prefers to be accessible on the surface of a protein, the solvent-accessible surface area (ASA) around carboxylation sites is also investigated. Radial basis function network is then employed to build a predictive model using various features for identifying carboxylation sites. Based on a five-fold cross-validation evaluation, a predictive model trained using the combined features of amino acid sequence (AA20D), amino acid composition, and ASA, yields the highest accuracy at 0.874. Furthermore, an independent test done involving data not included in the cross-validation process indicates that in silico identification is a feasible means of preliminary analysis. Additionally, the predictive method presented in this work is implemented as Carboxylator (http://csb.cse.yzu.edu.tw/Carboxylator/), a web-based tool for identifying carboxylated proteins with modification sites in order to help users in investigating γ-glutamyl carboxylation.

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Acknowledgments

The authors sincerely appreciate the National Science Council of the Republic of China for financially supporting this research under Contract Numbers of NSC 100-2221-E-155-079.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Yuan Ze University, Room 3312, 135 Yuan-Tung Road, Chungli, Taoyuan, 32003, Taiwan, ROC

    Cheng-Tsung Lu, Shu-An Chen, Neil Arvin Bretaña, Tzu-Hsiu Cheng & Tzong-Yi Lee

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  1. Cheng-Tsung Lu
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  2. Shu-An Chen
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  3. Neil Arvin Bretaña
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  4. Tzu-Hsiu Cheng
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  5. Tzong-Yi Lee
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Corresponding author

Correspondence to Tzong-Yi Lee.

Additional information

Availability: Carboxylator can be accessed via a web interface, and is freely available to all interested users at http://csb.cse.yzu.edu.tw/Carboxylator/. All of the data set that is used in this work is also available.

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Lu, CT., Chen, SA., Bretaña, N.A. et al. Carboxylator: incorporating solvent-accessible surface area for identifying protein carboxylation sites. J Comput Aided Mol Des 25, 987–995 (2011). https://doi.org/10.1007/s10822-011-9477-2

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  • DOI: https://doi.org/10.1007/s10822-011-9477-2

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