Towards Computational Models of Identifying Protein Ubiquitination Sites

Author(s): Lidong Wang*, Ruijun Zhang.

Journal Name: Current Drug Targets

Volume 20 , Issue 5 , 2019

  Journal Home
Translate in Chinese
Submit Manuscript
Submit Proposal

Graphical Abstract:


Abstract:

Ubiquitination is an important post-translational modification (PTM) process for the regulation of protein functions, which is associated with cancer, cardiovascular and other diseases. Recent initiatives have focused on the detection of potential ubiquitination sites with the aid of physicochemical test approaches in conjunction with the application of computational methods. The identification of ubiquitination sites using laboratory tests is especially susceptible to the temporality and reversibility of the ubiquitination processes, and is also costly and time-consuming. It has been demonstrated that computational methods are effective in extracting potential rules or inferences from biological sequence collections. Up to the present, the computational strategy has been one of the critical research approaches that have been applied for the identification of ubiquitination sites, and currently, there are numerous state-of-the-art computational methods that have been developed from machine learning and statistical analysis to undertake such work. In the present study, the construction of benchmark datasets is summarized, together with feature representation methods, feature selection approaches and the classifiers involved in several previous publications. In an attempt to explore pertinent development trends for the identification of ubiquitination sites, an independent test dataset was constructed and the predicting results obtained from five prediction tools are reported here, together with some related discussions.

Keywords: Protein ubiquitination, computational method, data collection, feature extraction, feature selection, prediction model.

[1]
Hershko A, Ciechanover A. The ubiquitin system. Nat Med 1998; 67: 1-17.
[2]
Gao T, Liu Z, Wang Y, et al. UUCD: a family-based database of ubiquitin and ubiquitin-like conjugation. Nucleic Acids Res 2013; 41: 445-51.
[3]
Pickart CM, Eddins MJ. Ubiquitin: structures, functions, mechanisms. BBA & Cell Res 2004; 1695: 55-72.
[4]
Tait SW, De VE, Maas C, et al. Apoptosis induction by Bid requires unconventional ubiquitination and degradation of its N-terminal fragment. J Cell Biol 2007; 179: 1453-66.
[5]
Mcdowell GS, Philpott A. Non-canonical ubiquitylation: mechanisms and consequences. Int J Biochem Cell Biol 2013; 45: 1833-42.
[6]
Kravtsova-Ivantsiv Y, Ciechanover A. Non-canonical ubiquitin-based signals for proteasomal degradation. J Cell Sci 2012; 125: 539-48.
[7]
Nguyen VN, Huang KY, Huang CH, et al. A new scheme to characterize and identify protein ubiquitination sites. IEEE/ACM Trans. Comput Biol Bioinform 2017; 14: 393-403.
[8]
Vogelstein B, Papadopoulos N, Velculescu VE, et al. Cancer genome landscapes. Sci 2013; 339: 1546-58.
[9]
Liu J, Shaik S, Dai X, et al. Targeting the ubiquitin pathway for cancer treatment. Biochim Biophys Acta 2015; 1855: 50-60.
[10]
Hoeller D, Dikic I. Targeting the ubiquitin system in cancer therapy. Nature 2009; 458: 438-44.
[11]
Liu J, Shaik S, Dai XP, et al. Targeting the ubiquitin pathway for cancer treatment. Biochim Biophys Acta 2015; 1855: 50-60.
[12]
Mansour MA. Ubiquitination: Friend and foe in cancer. Int J Biochem Cell Biol 2018; 101: 80-93.
[13]
Wang D, Ma LN, Wang B. Liu j, Wei W Y. E3 ubiquitin ligases in cancer and implications for therapies. Cancer Metastasis Rev 2017; 36: 683-702.
[14]
Xu GQ, Jaffrey SR. Proteomic identification of protein ubiquitination events. Biotechnol Genet Eng Rev 2013; 29: 73-109.
[15]
Lamsou I, Uttenweiler-Joseph S, Moog-Lutz C, Lutz PG. Cullin 5-RING E3 ubiquitin ligases, new therapeutic targets? Biochimie 2016; 122: 339-47.
[16]
Nalepa G, Rolfe M, Harper JW. Drug discovery in the ubiquitin-proteasome system. Nat Rev Drug Discov 2006; 5: 596-613.
[17]
Huang KY, Weng JZ, Lee TY, Weng SY. A new scheme to discover functional associations and regulatory networks of E3 ubiquitin ligases. BMC Syst Biol 2016; 10: 3.
[http://dx.doi.org/10.1186/s12918-015-0244-1]
[18]
Kar G, Keskin O, Fraternali F, Gursoy A. Emerging role of the Ubiquitin-proteasome system as drug targets. Curr Pharm Des 2013; 19: 3175-89.
[19]
Hou YC, Deng JY. Role of E3 ubiquitin ligases in gastric cancer. World J Gastroenterol 2015; 21: 786-93.
[20]
Bielskienė K, Bagdonienė L, Mozraitienė J, Kazbarienė B, Janulionis E. E3 ubiquitin ligases as drug targets and prognostic biomarkers in melanoma. Med 2015; 51: 1-9.
[21]
Goru SK, Kadakol A, Gaikwad AB. Hidden targets of ubiquitin proteasome system: To prevent diabetic nephropathy. Pharmacol Res 2017; 120: 170-9.
[22]
Powell SR, Herrmann J, Lerman A, Patterson C, Wang XJ. The ubiquitin-proteasome system and cardiovascular disease. Prog Mol Biol Transl Sci 2012; 109: 295-346.
[23]
Yin J, Zhu JM, Shen XZ. The role and therapeutic implications of RING-finger E3 ubiquitin ligases in hepatocellular carcinoma. Int J Cancer 2015; 136: 249-57.
[24]
Weathington NM, Mallampalli RK. New insights on the function of SCF ubiquitin E3 ligases in the lung. Cell Signal 2013; 25: 1792-8.
[25]
Yang LT, Guo WN, Zhang SL, Wang G. Ubiquitination-proteasome system: A new player in the pathogenesis of psoriasis and clinical implications. J Dermatol Sci 2018; 89: 219-25.
[26]
Harrigan JA, Jacq X, Martin NM, Jackson SP. Deubiquitylating enzymes and drug discovery: emerging opportunities. Nat Rev Drug Discov 2018; 17: 57-78.
[27]
Patel K, Ahmed ZS, Huang X, et al. Discovering proteasomal deubiquitinating enzyme inhibitors for cancer therapy: lessons from rational design, nature and old drug reposition. Future Med Chem 2018.
[http://dx.doi.org/10.4155/fmc-2018-0091]
[28]
Gu S, Cui D, Chen X, Xiong X, Zhao Y. PROTACs: An emerging targeting technique for protein degradation in drug discovery. Bioessays 2018; 40: e1700247.
[http://dx.doi.org/10.1002/bies.201700247]
[29]
Bednash JS, Mallampalli RK. Targeting deubiquitinases in cancer. Methods Mol Biol 2018; 1731: 295-305.
[30]
Soave CL, Guerin T, Liu J, Dou QP. Targeting the ubiquitin-proteasome system for cancer treatment: discovering novel inhibitors from nature and drug repurposing. Cancer Metastasis Rev 2017; 36: 717-36.
[31]
Chen X, Wu J, Yang Q, et al. Cadmium pyrithione suppresses tumor growth in vitro and in vivo through inhibition of proteasomal deubiquitinase.. Biometals 2018; 31: 29-43.
[32]
Kaushal K, Antao AM, Kim KS, Ramakrishna S. Deubiquitinating enzymes in cancer stem cells: functions and targeted inhibition for cancer therapy. Drug Discov Today 2018.
[http://dx.doi.org/10.1016/j.drudis.2018.05.035]
[33]
Yeasmin Khusbu F, Chen FZ, Chen HC. Targeting ubiquitin specific protease 7 in cancer: A deubiquitinase with great prospects. Cell Biochem Funct 2018; 36: 244-54.
[34]
McClurg UL, Azizyan M, Dransfield DT, et al. Thenovelanti-androgen candidate galeterone targets deubiquitinating enzymes, USP12 and USP46, to control prostatecancer growth and survival. Oncotarget 2018; 9: 24992-5007.
[35]
Ahmed ZSO, Li X, Li F, et al. Elbargeesy GAEH, Dou QP. Computational and biochemical studies of isothiocyanates as inhibitors of proteasomal cysteine deubiquitinases in human cancer cells. J Cell Biochem 2018.
[http://dx.doi.org/10.1002/jcb.27157]
[36]
Li S, Zhao J, Shang D, Kass DJ, Zhao Y. Ubiquitination and deubiquitination emerge as players in idiopathic pulmonary fibrosis pathogenesis and treatment. JCI Insight 2018; 3
[http://dx.doi.org/10.1172/jci.insight.120362]
[37]
Anderson C, Crimmins S, Wilson JA, et al. Loss of Usp14 results in reduced levels of ubiquitin in ataxia mice. J Neurochem 2005; 95: 724-31.
[38]
Gao TS, Liu ZX, Wang YB, Xue Y. Ubiquitin and Ubiquitin-Like conjugations in complex diseases: a computational perspective; Shen B. Bioinformatics for Diagnosis, Prognosis and Treatment of ComplexDiseases: Springer Netherlands 2013; 171-87.
[39]
Maor R, Jones A, Nhse TS, et al. Multidimensional protein identification technology (MudPIT) analysis of ubiquitinated proteins in plants. Mol Cell Proteomics 2007; 6: 601-10.
[40]
Tung CW, Ho SY. Computational identification of ubiquitylation sites from protein sequences. BMC Bioinformatics 2008; 9: 310.
[http://dx.doi.org/10.1186/1471-2105-9-310]
[41]
Hitchcock AL, Auld K, Gygi SP, et al. A subset of membrane-associated proteins is ubiquitinated in response to mutations in the endoplasmic reticulum degradation machinery. Proc Natl Acad Sci USA 2003; 100: 12735-40.
[42]
Peng J, Schwartz D, Elias JE, et al. A proteomics approach to understanding protein ubiquitination. Nat Biotechnol 2003; 21: 921-6.
[43]
Radivojac P, Vacic V, Haynes C, et al. Identification, analysis and prediction of protein ubiquitination sites. Proteins 2010; 78: 365-80.
[44]
Chen Z, Chen YZ, Wang XF, et al. Prediction of ubiquitination sites by using the composition of k-spaced amino acid pairs. PLoS One 2011; 6: e22930.
[http://dx.doi.org/10.1371/journal.pone.0022930]
[45]
Cai Y, Huang T, Hu L, et al. Prediction of lysine ubiquitination with mRMR feature selection. Amino Acids 2012; 42: 1387-95.
[46]
Chen Z, Zhou Y, Song J, et al. hCKSAAP_UbSite: Improved prediction of human ubiquitination sites by exploiting amino acid pattern and properties. Biochim Biophys Acta 2013; 1834: 1461-7.
[47]
Wagner SA, Beli P, Weinert BT, et al. A proteome-wide, quantitative survey of in vivo ubiquitylation sites reveals widespread regulatory roles.. Mol Cell Proteomics 2011.
[http://dx.doi.org/10.1074/mcp.M111.013284]
[48]
Walsh I, Di DT, Tosatto SC. RUBI: rapid proteomic-scale prediction of lysine ubiquitination and factors influencing predictor performance. Amino Acids 2014; 46: 853-62.
[49]
Wang JR, Huang WL, Tsai MJ, et al. ESA-UbiSite: accurate prediction of human ubiquitination sites by identifying a set of effective negatives. Bioinformatics 2017; 33: 661-8.
[50]
Cai B, Jiang X. Computational methods for ubiquitination site prediction using physicochemical properties of protein sequences. BMC Bioinformatics 2016; 17: 116.
[http://dx.doi.org/10.1186/s12859-016-0959-z]
[51]
Yadav S, Gupta M, Bist AS. Prediction of ubiquitination sites using ubiNets. Adv Fuzzy Syst 2018.
[http://dx.doi.org/10.1155/2018/5125103]
[52]
Kim W, Bennett EJ, Huttlin EL, et al. Systematic and quantitative assessment of the ubiquitin-modified proteome. Mol Cell 2011; 44: 325-40.
[53]
Chen X, Qiu JD, Shi SP, et al. Incorporating key position and amino acid residue features to identify general and species-specific Ubiquitin conjugation sites. Bioinformatics 2013; 29: 1614-22.
[54]
Huang CH, Su MG, Kao HJ, et al. UbiSite: incorporating two-layered machine learning method with substrate motifs to predict ubiquitin-conjugation site on lysines. BMC Syst Biol 2016; 10: S6.
[http://dx.doi.org/10.1186/s12918-015-0246-z]
[55]
Starita LM, Lo RS, Eng JK, et al. Sites of ubiquitin attachment in saccharomyces cerevisiae. Proteomics 2012; 12: 236-40.
[56]
Kim DY, Scalf M, Smith LM, et al. Advanced proteomic analyses yield a deep catalog of ubiquitylation targets in Arabidopsis. Plant Cell 2013; 25: 1523-40.
[57]
Wagner SA, Beli P, Weinert BT, et al. Proteomic analyses reveal divergent ubiquitylation site Patterns in murine tissues. Mol Cell Proteomics 2012; 11(12): 1578-85.
[58]
Mertins P, Qiao JW, Patel J, et al. Integrated proteomic analysis of post-translational modifications by serial enrichment. Nat Methods 2013; 10: 634-7.
[59]
Udeshi ND, Svinkina T, Mertins P, et al. Refined preparation and use of anti-diglycine remnant (K-epsilon-GG) antibody enables routine quantification of 10,000s of ubiquitination sites in single proteomics experiments. Mol Cell Proteomics 2013; 12: 825-31.
[60]
Chen Z, Zhou Y, Zhang Z, et al. Towards more accurate prediction of ubiquitination sites: a comprehensive review of current methods, tools and features. Briefing Bioinform 2015; 16: 640-57.
[61]
Zhao X, Li X, Ma Z, et al. Prediction of lysine ubiquitylation with ensemble classifier and feature selection. Int J Mol Sci 2011; 12: 8347-61.
[62]
Lee TY, Chen SA, Hung HY, et al. Incorporating distant sequence features and radial basis function networks to identify ubiquitin conjugation sites. PLoS One 2010; 6-e17331.
[http://dx.doi.org/10.1371/journal.pone.0017331]
[63]
Consortium UP. UniProt: a hub for protein information. Nucleic Acids Res 2015; 43: 204-12.
[64]
Boeckmann B, Bairoch A, Apweiler R, et al. The Swiss-Prot knowledgebase and its supplement mTREMBL in 2003. Nucleic Acids Res 2003; 31: 365-70.
[65]
Cherry JM, Adler C, Ball C, et al. SGD: saccharomyces genome database. Nucleic Acids Res 1998; 26: 73-9.
[66]
Li H, Xing X, Ding G, et al. SysPTM: a systematic resource for proteomic research on post-translational modifications. Mol Cell Proteomics 2009; 8: 1839-49.
[67]
Lee TY, Huang HD, Hung JH, et al. dbPTM: an information repository of protein post-translational modification. Nucleic Acids Res 2006; 34: D622-7.
[68]
Chen T, Zhou T, He B, et al. mUbiSiDa: a comprehensive database for protein ubiquitination sites in mammals. PLoS One 2014; 9: e85744.
[http://dx.doi.org/10.1371/journal.pone.0085744]
[69]
Hornbeck PV, Kornhauser JM, Sasha T, et al. PhosphoSitePlus: A comprehensive resource for investigating the structure and function of experimentally determined post-translational modifications in man and mouse. Nucleic Acids Res 2011; 40: 261-70.
[70]
Woo JJ, Minho L, Won-Chul L, et al. SCUD: Saccharomyces cerevisiae ubiquitination database. BMC Genomics 2008; 9: 440.
[http://dx.doi.org/10.1186/1471-2164-9-440]
[71]
Liu Z, Wang Y, Gao T, et al. CPLM: a database of protein lysine modifications. Nucleic Acids Res 2014; 42: 531-6.
[72]
Boutet E, Lieberherr D, Tognolli M, et al. UniProtKB/Swiss-Prot. Methods Mol Biol 2007; 406: 89-112.
[73]
Shi SP, Qiu JD, Sun XY, et al. PMeS: Prediction of methylation sites based on enhanced feature encoding scheme. PLoS One 2012; 7: e38772.
[http://dx.doi.org/10.1371/journal.pone.0038772]
[74]
Shi SP, Xu HD, Wen PP, et al. Progress and challenges in predicting protein methylation sites. Mol Biosyst 2015; 11: 2610-9.
[75]
Huang Y, Niu B, Gao Y, et al. CD-HIT Suite: a web server for clustering and comparing biological sequences. Bioinform 2010; 26: 680-2.
[76]
Jia C, Zuo Y, Zou Q, et al. O-GlcNAcPRED-II: an integrated classification algorithm for identifying O-GlcNAcylation sites based on fuzzy undersampling and a K-means PCA oversampling technique. Bioinform 2018; 34: 2029-36.
[77]
Kawashima S, Pokarowski P, Pokarowska M, et al. AAindex: amino acid index database, progress report 2008. Nucleic Acids Res 2008; 36: 202-5.
[78]
Bryson K, Mcguffin LJ, Marsden RL, et al. Protein structure prediction servers at University College London. Nucleic Acids Res 2005; 33: 36-8.
[79]
Sickmeier M, Hamilton JA, Legall T, et al. DisProt: the database of disordered proteins. Nucleic Acids Res 2007; 35: D786-93.
[80]
Peng K, Radivojac P, Vucetic S, et al. Length-dependent prediction of protein intrinsic disorder. BMC Bioinformatics 2006; 7: 208.
[http://dx.doi.org/10.1186/1471-2105-7-208]
[81]
Walsh I, Martin AJM, Domenico TD, et al. ESpritz: accurate and fast prediction of protein disorder. Bioinform 2012; 28: 503-9.
[82]
Pang CN, Hayen A, Wilkins MR. Surface accessibility of protein post-translational modifications. J Proteome Res 2007; 6: 1833-45.
[83]
Ahmad S, Gromiha MM, Sarai A. RVP-net: online prediction of real valued accessible surface area of proteins from single sequences. Bioinform 2003; 19: 1849-51.
[84]
Lin S, Song Q, Tao H, et al. Rice_Phospho 1.0: a new rice-specific SVM predictor for protein phosphorylation sites. Sci Rep 2015; 5: 11940.
[85]
Lee TY, Hsu JBK, Lin FM, et al. N-Ace: using solvent accessibility and physicochemical properties to identify protein N-acetylation sites. J Comput Chem 2010; 31: 2759-71.
[86]
Niu S, Huang T, Feng K, et al. Prediction of tyrosine sulfation with mRMR feature selection and analysis. Proteome Res 2010; 9: 6490-7.
[87]
Saeys Y, Inza I, Larrañaga P. A review of feature selection techniques in bioinformatics. Bioinform 2007; 23: 2507-17.
[88]
Peng H, Long F, Ding C. Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 2005; 27: 1226-38.
[89]
Ho SY, Chen JH, Huang MH. Inheritable genetic algorithm for biobjective 0/1 combinatorial optimization problems and its applications. IEEE Trans Syst Man Cybern 2004; 34: 609-20.
[90]
Hans C. Bayesian lasso regression. Biometrika 2009; 96: 835-45.
[91]
Casella TPG. The Bayesian Lasso. J Am Stat Assoc 2008; 103: 681-6.
[92]
Meszlényi R, Peska L, Gál V, et al. Classification of fMRI data using dynamic time warping based functional connectivity analysis. Signal Processing Conf 2016; 245-49.
[93]
Chen W, Feng PM, Lin H, et al. iSS-PseDNC: Identifying splicing sites using pseudo dinucleotide composition. Biomed Res Int 2014; 2014: 623149.
[http://dx.doi.org/10.1155/2014/623149]
[94]
Yang H, Qiu WR, Liu G, et al. iRSpot-Pse6NC: Identifying recombination spots in Saccharomyces cerevisiae by incorporating hexamer composition into general PseKNC. Int J Biol Sci 2018; 14: 883-91.
[95]
Feng P, Yang H, Ding H, et al. iDNA6mA-PseKNC: Identifying DNA N6-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC. Genomics 2018.
[http://dx.doi.org/10.1016/j.ygeno.2018.01.005]
[96]
Fawcett T. An introduction to ROC analysis. Pattern Recognit Lett 2005; 27: 861-74.


Rights & PermissionsPrintExport Cite as


Article Details

VOLUME: 20
ISSUE: 5
Year: 2019
Page: [565 - 578]
Pages: 14
DOI: 10.2174/1389450119666180924150202
Price: $58

Article Metrics

PDF: 17
HTML: 1