Scopus; WoS

Incorporating Natural Language-Based and Sequence-Based Features to Predict Protein Sumoylation Sites

Năm XB 2023 Tạp chí / Hội thảo Lecture Notes in Networks and Systems Volume 734 LNNS DOI / Link https://doi.org/10.1007/978-3-031-36886-8_7 ↗

Tác giả

Tóm tắt

The incidence of thyroid cancer and breast cancer is increasing every year, and the specific pathogenesis is unclear. Post-translational modifications are an important regulatory mechanism that affects the function of almost all proteins. They are essential for a...

Tài liệu tham khảo

[1] Geiss-Friedlander, R., Melchior, F.: Concepts in sumoylation: a decade on. Nat. Rev. Mol. Cell Biol. 8(12), 947–956 (2007)

[2] Hay, R.T.: SUMO: a history of modification. Mol. Cell 18(1), 1–12 (2005)

[3] Müller, S., et al.: SUMO, ubiquitin’s mysterious cousin. Nat. Rev. Mol. Cell Biol. 2(3), 202–210 (2001)

[4] Marmor-Kollet, H.S., et al.: Spatiotemporal proteomic analysis of stress granule disassembly using APEX reveals regulation by SUMOylation and links to ALS pathogenesis. Mol. Cell. 80, 15 (2020)

[5] Princz, A.T.: N. SUMOylation in neurodegenerative diseases. Gerontology 66, 8 (2020)

[6] Seeler, J.S.B., Nacerddine, K.O., Dejean, A.: SUMO, the three Rs and cancer. Curr. Top. Microbiol. Immunol. 313, 22 (2007)

[7] Ren, J., et al.: Systematic study of protein sumoylation: development of a site‐specific predictor of SUMOsp 2.0. Proteomics 9(12), 3409–3412 (2009)

[8] Jia, J., et al.: pSumo-CD: predicting sumoylation sites in proteins with covariance discriminant algorithm by incorporating sequence-coupled effects into general PseAAC. Bioinformatics 32(20), 3133–3141 (2016)

[9] Chang, C.C., et al.: SUMOgo: prediction of sumoylation sites on lysines by motif screening models and the effects of various post-translational modifications. Sci. Rep. 8(1), 15512 (2018)

[10] Dehzangi, A., et al.: SumSec: accurate prediction of sumoylation sites using predicted secondary structure. Molecules 23(12) (2018)

[11] Sharma, A., et al.: HseSUMO: sumoylation site prediction using half-sphere exposures of amino acids residues. BMC Gen. 19(Suppl. 9), 982 (2019)

[12] Qian, Y., et al.: SUMO-Forest: a cascade forest based method for the prediction of SUMOylation sites on imbalanced data. Gene 741, 144536 (2020)

[13] Lopez, Y., Dehzangi, A., Reddy, H.M., Sharma, A.: C-iSUMO: a sumoylation site predictor that incorporates intrinsic characteristics of amino acid sequences. Comput. Biol. Chem. 87 (2020)

[14] Khan, Y.D., et al.: iSUMOK-PseAAC: prediction of lysine sumoylation sites using statistical moments and Chou’s PseAAC. PeerJ 9, e11581 (2021)

[15] Zhao, Q., et al.: GPS-SUMO: a tool for the prediction of sumoylation sites and SUMO-interaction motifs. Nucleic Acids Res.. 42(Web Server issue), W325–W330 (2014)

[16] Chen, Y.Z., Chen, Z., Gong, Y.A., Ying, G.: SUMOhydro: a novel method for the prediction of sumoylation sites based on hydrophobic properties. PloS One 7(6), e39195 (2012)

[17] Nguyen, V.-N., Nguyen, H.-M., Tran, T.-X.: An approach by exploiting support vector machine to characterize and identify protein SUMOylation sites. JASSA. 505, 877 (2012)

[18] Nguyen, V.-N., et al.: Characterization and identification of ubiquitin conjugation sites with E3 ligase recognition specificities. BMC Bioinform. BioMed Central (2015)

[19] Nguyen, V.-N., et al.: A new scheme to characterize and identify protein ubiquitination sites. IEEE/ACM Trans. Comput. Biol. Bioinform. 14(2), 393–403 (2016)

[20] Bui, V.-M., Nguyen, V.-N.: The prediction of Succinylation site in protein by analyzing amino acid composition. In: Akagi, M., Nguyen, T.T., Vu, D.T., Phung, T.N., Huynh, V.N. (eds.) Advances in Information and Communication Technology. ICTA 2016. Advances in Intelligent Systems and Computing, vol. 538, pp. 633–642. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-49073-1_67

[21] Le, N.Q.K., Ho, Q.T., Ou, Y.Y.: Incorporating deep learning with convolutional neural networks and position specific scoring matrices for identifying electron transport proteins. J. Comput. Chem. 38(23), 2000–2006 (2017)

[22] Nguyen, V.-N., et al. A new schema to identify S-farnesyl cysteine prenylation sites with substrate motifs. in Advances in Information and Communication Technology: Proceedings of the International Conference, ICTA 2016. 2017. Springer

[23] Le, N.Q.K., et al.: Identification of Clathrin proteins by incorporating hyperparameter optimization in deep learning and PSSM profiles. Comput. Methods Programs Biomed. 177, 81–88 (2019)

[24] Nguyen, V.-N., et al.: Exploiting two-layer support vector machine to predict protein sumoylation sites. In: Fujita, H., Nguyen, D., Vu, N., Banh, T., Puta, H. (eds.) Advances in Engineering Research and Application. ICERA 2018. Lecture Notes in Networks and Systems, vol. 63, pp. 324–332. Springer, Cham. https://doi.org/10.1007/978-3-030-04792-4_43

[25] Lu, C.T., et al.: DbPTM 3.0: an informative resource for investigating substrate site specificity and functional association of protein post-translational modifications. Nucleic Acids Res. 41(Database issue), D295–305 (2013)

[26] Beauclair, G., et al.: JASSA: a comprehensive tool for prediction of SUMOylation sites and SIMs. Bioinformatics 31(21), 3483–3491 (2015)

[27] Teng, S., Luo, H., Wang, L.: Predicting protein sumoylation sites from sequence features. Amino Acids 43, 447–455 (2012)

[28] Ho Thanh Lam, L., et al.: Machine learning model for identifying antioxidant proteins using features calculated from primary sequences. Biology 9(10), 325 (2020)

[29] Huang, Y., et al.: CD-HIT Suite: a web server for clustering and comparing biological sequences. Bioinformatics 26(5), 680–682 (2010)

[30] Chen, Z., et al.: iFeature: a Python package and web server for features extraction and selection from protein and peptide sequences. Bioinformatics 34(14), 2499–2502 (2018)

[31] Sahlgren, M.: The distributional hypothesis. Ital. J. Disabil. Stud. 20, 33–53 (2008)

[32] Chiu, B., et al.: How to train good word embeddings for biomedical NLP. In: Proceedings of the 15th Workshop on Biomedical Natural Language Processing (2016)

[33] Lai, S., et al.: How to generate a good word embedding. IEEE Intell. Syst. 31(6), 5–14 (2016)

[34] Crooks, G.E., et al.: WebLogo: a sequence logo generator. Genome Res. 14(6), 1188–1190 (2004)

[35] Vacic, V., Iakoucheva, L.M., Radivojac, P.: Two Sample Logo: a graphical representation of the differences between two sets of sequence alignments. Bioinformatics 22(12), 1536–1537 (2006)