SUMOgo: Prediction of sumoylation sites on lysines by motif screening models and the effects of various post-translational modifications

doi:10.1038/s41598-018-33951-5

. 2018 Oct 19;8(1):15512.

doi: 10.1038/s41598-018-33951-5.

SUMOgo: Prediction of sumoylation sites on lysines by motif screening models and the effects of various post-translational modifications

Chi-Chang Chang^{1

2}, Chi-Hua Tung³, Chi-Wei Chen^{4

5}, Chin-Hau Tu⁵, Yen-Wei Chu^{6

7}

Affiliations

¹ School of Medical Informatics, Chung-Shan Medical University, Taichung, Taiwan.
² IT Office, Chung Shan Medical University Hospital, Taichung, Taiwan.
³ Department of Bioinformatics, Chung-Hua University, Rm. S116, 707, Sec. 2, WuFu Rd., Hsinchu, 30012, Taiwan.
⁴ Department of Computer Science and Engineering, National Chung-Hsing University, 250, Kuo Kuang Rd., Taichung, 402, Taiwan.
⁵ Institute of Genomics and Bioinformatics, National Chung Hsing University, 250, Kuo Kuang Rd., Taichung, 402, Taiwan.
⁶ Institute of Genomics and Bioinformatics, National Chung Hsing University, 250, Kuo Kuang Rd., Taichung, 402, Taiwan. ywchu@nchu.edu.tw.
⁷ Biotechnology Center, Agricultural Biotechnology Center, Institute of Molecular Biology, National Chung Hsing University, 250, Kuo Kuang Rd., Taichung, 402, Taiwan. ywchu@nchu.edu.tw.

PMID: 30341374
PMCID: PMC6195521
DOI: 10.1038/s41598-018-33951-5

SUMOgo: Prediction of sumoylation sites on lysines by motif screening models and the effects of various post-translational modifications

Chi-Chang Chang et al. Sci Rep. 2018.

. 2018 Oct 19;8(1):15512.

doi: 10.1038/s41598-018-33951-5.

Authors

Chi-Chang Chang^{1

2}, Chi-Hua Tung³, Chi-Wei Chen^{4

5}, Chin-Hau Tu⁵, Yen-Wei Chu^{6

7}

Affiliations

¹ School of Medical Informatics, Chung-Shan Medical University, Taichung, Taiwan.
² IT Office, Chung Shan Medical University Hospital, Taichung, Taiwan.
³ Department of Bioinformatics, Chung-Hua University, Rm. S116, 707, Sec. 2, WuFu Rd., Hsinchu, 30012, Taiwan.
⁴ Department of Computer Science and Engineering, National Chung-Hsing University, 250, Kuo Kuang Rd., Taichung, 402, Taiwan.
⁵ Institute of Genomics and Bioinformatics, National Chung Hsing University, 250, Kuo Kuang Rd., Taichung, 402, Taiwan.
⁶ Institute of Genomics and Bioinformatics, National Chung Hsing University, 250, Kuo Kuang Rd., Taichung, 402, Taiwan. ywchu@nchu.edu.tw.
⁷ Biotechnology Center, Agricultural Biotechnology Center, Institute of Molecular Biology, National Chung Hsing University, 250, Kuo Kuang Rd., Taichung, 402, Taiwan. ywchu@nchu.edu.tw.

PMID: 30341374
PMCID: PMC6195521
DOI: 10.1038/s41598-018-33951-5

Abstract

Most modern tools used to predict sites of small ubiquitin-like modifier (SUMO) binding (referred to as SUMOylation) use algorithms, chemical features of the protein, and consensus motifs. However, these tools rarely consider the influence of post-translational modification (PTM) information for other sites within the same protein on the accuracy of prediction results. This study applied the Random Forest machine learning method, as well as motif screening models and a feature selection combination mechanism, to develop a SUMOylation prediction system, referred to as SUMOgo. With regard to prediction method, PTM sites were coded as new functional features in addition to structural features, such as sequence-based binary coding, encoded chemical features of proteins, and encoded secondary structure information that is important for PTM. Twenty cycles of prediction were conducted with a 1:1 combination of positive test data and random negative data. Matthew's correlation coefficient of SUMOgo reached 0.511, which is higher than that of current commonly used tools. This study further verified the important role of PTM in SUMOgo and includes a case study on CREB binding protein (CREBBP). The website for the final tool is http://predictor.nchu.edu.tw/SUMOgo .

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Conflict of interest statement

The authors declare no competing interests.

Figures

**Figure 1**
The experimental architecture of SUMOgo.

**Figure 2**
Performance of using different consensus motif types. (a)The average MCC for each of the CN models. (b) The average MCC for each of the CY models.

**Figure 3**
A comparison of MCC values resulting in the CNCY (with consensus motif classification) and noCNCY systems (without consensus motif classification).

**Figure 4**
A ROC curve comparison of prediction results from SUMOgo with other SUMOylation prediction tools. (a) The best case and (b) worst case of area under curve of SUMOgo in the testing data set by conducting 20 cycles of random extraction of negative data at a 1:1 ratio.

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References

1. Zhao Q, et al. GPS-SUMO: a tool for the prediction of sumoylation sites and SUMO-interaction motifs. Nucleic Acids Res. 2014;42:W325–330. doi: 10.1093/nar/gku383. - DOI - PMC - PubMed
1. Beauclair G, Bridier-Nahmias A, Zagury JF, Saib A, Zamborlini A. JASSA: a comprehensive tool for prediction of SUMOylation sites and SIMs. Bioinformatics. 2015;31:3483–3491. doi: 10.1093/bioinformatics/btv403. - DOI - PubMed
1. Lu L, et al. Protein sumoylation sites prediction based on two-stage feature selection. Molecular diversity. 2010;14:81–86. doi: 10.1007/s11030-009-9149-5. - DOI - PubMed
1. Geiss-Friedlander R, Melchior F. Concepts in sumoylation: a decade on. Nature reviews. Molecular cell biology. 2007;8:947–956. doi: 10.1038/nrm2293. - DOI - PubMed
1. Hay RT. SUMO: a history of modification. Molecular cell. 2005;18:1–12. doi: 10.1016/j.molcel.2005.03.012. - DOI - PubMed

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[1] Zhao Q, et al. GPS-SUMO: a tool for the prediction of sumoylation sites and SUMO-interaction motifs. Nucleic Acids Res. 2014;42:W325–330. doi: 10.1093/nar/gku383. - DOI - PMC - PubMed

[2] Zhao Q, et al. GPS-SUMO: a tool for the prediction of sumoylation sites and SUMO-interaction motifs. Nucleic Acids Res. 2014;42:W325–330. doi: 10.1093/nar/gku383. - DOI - PMC - PubMed

[3] Beauclair G, Bridier-Nahmias A, Zagury JF, Saib A, Zamborlini A. JASSA: a comprehensive tool for prediction of SUMOylation sites and SIMs. Bioinformatics. 2015;31:3483–3491. doi: 10.1093/bioinformatics/btv403. - DOI - PubMed

[4] Beauclair G, Bridier-Nahmias A, Zagury JF, Saib A, Zamborlini A. JASSA: a comprehensive tool for prediction of SUMOylation sites and SIMs. Bioinformatics. 2015;31:3483–3491. doi: 10.1093/bioinformatics/btv403. - DOI - PubMed

[5] Lu L, et al. Protein sumoylation sites prediction based on two-stage feature selection. Molecular diversity. 2010;14:81–86. doi: 10.1007/s11030-009-9149-5. - DOI - PubMed

[6] Lu L, et al. Protein sumoylation sites prediction based on two-stage feature selection. Molecular diversity. 2010;14:81–86. doi: 10.1007/s11030-009-9149-5. - DOI - PubMed

[7] Geiss-Friedlander R, Melchior F. Concepts in sumoylation: a decade on. Nature reviews. Molecular cell biology. 2007;8:947–956. doi: 10.1038/nrm2293. - DOI - PubMed

[8] Geiss-Friedlander R, Melchior F. Concepts in sumoylation: a decade on. Nature reviews. Molecular cell biology. 2007;8:947–956. doi: 10.1038/nrm2293. - DOI - PubMed

[9] Hay RT. SUMO: a history of modification. Molecular cell. 2005;18:1–12. doi: 10.1016/j.molcel.2005.03.012. - DOI - PubMed

[10] Hay RT. SUMO: a history of modification. Molecular cell. 2005;18:1–12. doi: 10.1016/j.molcel.2005.03.012. - DOI - PubMed

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SUMOgo: Prediction of sumoylation sites on lysines by motif screening models and the effects of various post-translational modifications

Affiliations

SUMOgo: Prediction of sumoylation sites on lysines by motif screening models and the effects of various post-translational modifications

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Conflict of interest statement

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