Abstract
Protein 3D structures, determined by their amino acid sequences, are the support of major crucial biological functions. Post-translational modifications (PTMs) play an essential role in regulating these functions by altering the physicochemical properties of proteins. By virtue of their importance, several PTM databases have been developed and released in decades, but very few of these databases incorporate real 3D structural data. Since PTMs influence the function of the protein and their aberrant states are frequently implicated in human diseases, providing structural insights to understand the influence and dynamics of PTMs is crucial for unraveling the underlying processes. This review is dedicated to the current status of databases providing 3D structural data on PTM sites in proteins. Some of these databases are general, covering multiple types of PTMs in different organisms, while others are specific to one particular type of PTM, class of proteins or organism. The importance of these databases is illustrated with two major types of in silico applications: predicting PTM sites in proteins using machine learning approaches and investigating protein structure–function relationships involving PTMs. Finally, these databases suffer from multiple problems and care must be taken when analyzing the PTMs data.
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Abbreviations
- 3D:
-
Three-dimensional
- ADP:
-
Adenosine diphosphate
- CNN:
-
Convolutional neural network
- DNA:
-
Deoxyribonucleic acid
- GAG:
-
Glycosaminoglycan
- HPP:
-
Human Proteome Project
- IDP:
-
Intrinsically Disordered Protein
- IDR:
-
Intrinsically Disordered Region
- MBP:
-
Myelin basic protein
- MD:
-
Molecular dynamics
- MS:
-
Mass Spectrometry
- nsSNP:
-
Non-synonymous single nucleotide polymorphism
- PCA:
-
Pyrrolidone carboxylic acid
- PPI:
-
Protein–protein interaction
- P-site:
-
Phosphorylation site
- PTM:
-
Post-translational modification
- RF:
-
Random Forest
- RNA:
-
Ribonucleic acid
- SNO:
-
S-nitrosylation
- SVM:
-
Support Vector Machine
- TM:
-
Transmembrane
References
Adhikari S, Nice EC, Deutsch EW, Lane L, Omenn GS, Pennington SR, Paik YK, Overall CM, Corrales FJ, Cristea IM, Van Eyk JE, Uhlen M, Lindskog C, Chan DW, Bairoch A, Waddington JC, Justice JL, LaBaer J, Rodriguez H, He F, Kostrzewa M, Ping P, Gundry RL, Stewart P, Srivastava S, Srivastava S, Nogueira FCS, Domont GB, Vandenbrouck Y, Lam MPY, Wennersten S, Vizcaino JA, Wilkins M, Schwenk JM, Lundberg E, Bandeira N, Marko-Varga G, Weintraub ST, Pineau C, Kusebauch U, Moritz RL, Ahn SB, Palmblad M, Snyder MP, Aebersold R, Baker MS (2020) A high-stringency blueprint of the human proteome. Nat Commun 11(1):5301. https://doi.org/10.1038/s41467-020-19045-9
Aebersold R, Agar JN, Amster IJ, Baker MS, Bertozzi CR, Boja ES, Costello CE, Cravatt BF, Fenselau C, Garcia BA, Ge Y, Gunawardena J, Hendrickson RC, Hergenrother PJ, Huber CG, Ivanov AR, Jensen ON, Jewett MC, Kelleher NL, Kiessling LL, Krogan NJ, Larsen MR, Loo JA, Ogorzalek Loo RR, Lundberg E, MacCoss MJ, Mallick P, Mootha VK, Mrksich M, Muir TW, Patrie SM, Pesavento JJ, Pitteri SJ, Rodriguez H, Saghatelian A, Sandoval W, Schlüter H, Sechi S, Slavoff SA, Smith LM, Snyder MP, Thomas PM, Uhlén M, Van Eyk JE, Vidal M, Walt DR, White FM, Williams ER, Wohlschlager T, Wysocki VH, Yates NA, Young NL, Zhang B (2018) How many human proteoforms are there? Nat Chem Biol 14(3):206–214. https://doi.org/10.1038/nchembio.2576
Aggarwal S, Banerjee SK, Talukdar NC, Yadav AK (2020) Post-translational modification crosstalk and hotspots in sirtuin interactors implicated in cardiovascular diseases. Front Genet 11:356. https://doi.org/10.3389/fgene.2020.00356
Ajit D, Trzeciakiewicz H, Tseng JH, Wander CM, Chen Y, Ajit A, King DP, Cohen TJ (2019) A unique tau conformation generated by an acetylation-mimic substitution modulates P301S-dependent tau pathology and hyperphosphorylation. J Biol Chem 294(45):16698–16711. https://doi.org/10.1074/jbc.RA119.009674
Ayyappan V, Wat R, Barber C, Vivelo CA, Gauch K, Visanpattanasin P, Cook G, Sazeides C, Leung AKL (2021) ADPriboDB 2.0: an updated database of ADP-ribosylated proteins. Nucleic Acids Res 49(D1):D261–D265. https://doi.org/10.1093/nar/gkaa941
Bagdonas H, Ungar D, Agirre J (2020) Leveraging glycomics data in glycoprotein 3D structure validation with Privateer. Beilstein J Org Chem 16:2523–2533. https://doi.org/10.3762/bjoc.16.204
Bagdonas H, Fogarty CA, Fadda E, Agirre J (2021) The case for post-predictional modifications in the AlphaFold Protein Structure Database. Nat Struct Mol Biol 28(11):869–870. https://doi.org/10.1038/s41594-021-00680-9
Bah A, Forman-Kay JD (2016) Modulation of intrinsically disordered protein function by post-translational modifications. J Biol Chem 291(13):6696–705. https://doi.org/10.1074/jbc.R115.695056
Beattie JF, Breault GA, Ellston RP, Green S, Jewsbury PJ, Midgley CJ, Naven RT, Minshull CA, Pauptit RA, Tucker JA, Pease JE (2003) Cyclin-dependent kinase 4 inhibitors as a treatment for cancer. Part 1: identification and optimisation of substituted 4,6-bis anilino pyrimidines. Bioorg Med Chem Lett 13(18):2955–2960. https://doi.org/10.1016/s0960-894x(03)00202-6
Berezovsky IN, Guarnera E, Zheng Z, Eisenhaber B, Eisenhaber F (2017) Protein function machinery: from basic structural units to modulation of activity. Curr Opin Struct Biol 42:67–74. https://doi.org/10.1016/j.sbi.2016.10.021
Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The protein data bank. Nucleic Acids Res 28(1):235–242. https://doi.org/10.1093/nar/28.1.235
Bignon E, Allega MF, Lucchetta M, Tiberti M, Papaleo E (2018) Computational structural biology of S-nitrosylation of cancer targets. Front Oncol 8:272. https://doi.org/10.3389/fonc.2018.00272
Bode AM, Dong Z (2004) Post-translational modification of p53 in tumorigenesis. Nat Rev Cancer 4(10):793–805. https://doi.org/10.1038/nrc1455
Bohm M, Bohne-Lang A, Frank M, Loss A, Rojas-Macias MA, Lutteke T (2019) Glycosciences.DB: an annotated data collection linking glycomics and proteomics data (2018 update). Nucleic Acids Res 47(D1):D1195–D1201. https://doi.org/10.1093/nar/gky994
Chen YJ, Lu CT, Su MG, Huang KY, Ching WC, Yang HH, Liao YC, Chen YJ, Lee TY (2015) dbSNO 2.0: a resource for exploring structural environment, functional and disease association and regulatory network of protein S-nitrosylation. Nucleic Acids Res 43(Database issue):503–511. https://doi.org/10.1093/nar/gku1176
Choudhary C, Kumar C, Gnad F, Nielsen ML, Rehman M, Walther TC, Olsen JV, Mann M (2009) Lysine acetylation targets protein complexes and co-regulates major cellular functions. Science 325(5942):834–840. https://doi.org/10.1126/science.1175371
Craveur P, Rebehmed J, de Brevern AG (2014) PTM-SD: a database of structurally resolved and annotated posttranslational modifications in proteins. Database. https://doi.org/10.1093/database/bau041
Craveur P, Narwani TJ, Rebehmed J, de Brevern AG (2019) Investigation of the impact of PTMs on the protein backbone conformation. Amino Acids 51(7):1065–1079. https://doi.org/10.1007/s00726-019-02747-w
Dai C, Gu W (2010) p53 post-translational modification: deregulated in tumorigenesis. Trends Mol Med 16(11):528–536. https://doi.org/10.1016/j.molmed.2010.09.002
Deribe YL, Pawson T, Dikic I (2010) Post-translational modifications in signal integration. Nat Struct Mol Biol 17(6):666–672. https://doi.org/10.1038/nsmb.1842
Deshpande N, Addess KJ, Bluhm WF, Merino-Ott JC, Townsend-Merino W, Zhang Q, Knezevich C, Xie L, Chen L, Feng Z, Green RK, Flippen-Anderson JL, Westbrook J, Berman HM, Bourne PE (2005) The RCSB Protein Data Bank: a redesigned query system and relational database based on the mmCIF schema. Nucleic Acids Res 33(Databse issue):D233-237. https://doi.org/10.1093/nar/gki057
Deutsch EW, Csordas A, Sun Z, Jarnuczak A, Perez-Riverol Y, Ternent T, Campbell DS, Bernal-Llinares M, Okuda S, Kawano S, Moritz RL, Carver JJ, Wang M, Ishihama Y, Bandeira N, Hermjakob H, Vizcaino JA (2017) The ProteomeXchange consortium in 2017: supporting the cultural change in proteomics public data deposition. Nucleic Acids Res 45(D1):D1100–D1106. https://doi.org/10.1093/nar/gkw936
Diella F, Cameron S, Gemund C, Linding R, Via A, Kuster B, Sicheritz-Ponten T, Blom N, Gibson TJ (2004) Phospho.ELM: a database of experimentally verified phosphorylation sites in eukaryotic proteins. BMC Bioinform 5:79. https://doi.org/10.1186/1471-2105-5-79
Dinkel H, Chica C, Via A, Gould CM, Jensen LJ, Gibson TJ, Diella F (2011) Phospho.ELM: a database of phosphorylation sites—update 2011. Nucleic Acids Res 39(Database issue):D261-267. https://doi.org/10.1093/nar/gkq1104
Donnelly C, Williams A (2020) Investigating the potential impact of post translational modification of auto-antigens by tissue transglutaminase on humoral islet autoimmunity in type 1 diabetes. Metabol Open 8:100062. https://doi.org/10.1016/j.metop.2020.100062
Egorova KS, Kondakova AN, Toukach PV (2015) Carbohydrate Structure Database: tools for statistical analysis of bacterial, plant and fungal glycomes. Database. https://doi.org/10.1093/database/bav073
Eisenhaber B, Eisenhaber F (2010) Prediction of posttranslational modification of proteins from their amino acid sequence. Methods Mol Biol (clifton, NJ) 609:365–384. https://doi.org/10.1007/978-1-60327-241-4_21
Etchebest C, Benros C, Hazout S, de Brevern AG (2005) A structural alphabet for local protein structures: improved prediction methods. Proteins 59(4):810–827. https://doi.org/10.1002/prot.20458
Farriol-Mathis N, Garavelli JS, Boeckmann B, Duvaud S, Gasteiger E, Gateau A, Veuthey AL, Bairoch A (2004) Annotation of post-translational modifications in the Swiss-Prot knowledge base. Proteomics 4(6):1537–1550. https://doi.org/10.1002/pmic.200300764
Gao J, Xu D (2012) Correlation between posttranslational modification and intrinsic disorder in protein. In: Pacific Symposium on Biocomputing, pp 94–103
Gao J, Shao K, Chen X, Li Z, Liu Z, Yu Z, Aung LHH, Wang Y, Li P (2020) The involvement of post-translational modifications in cardiovascular pathologies: focus on SUMOylation, neddylation, succinylation, and prenylation. J Mol Cell Cardiol 138:49–58. https://doi.org/10.1016/j.yjmcc.2019.11.146
Gibson AE, Arris CE, Bentley J, Boyle FT, Curtin NJ, Davies TG, Endicott JA, Golding BT, Grant S, Griffin RJ, Jewsbury P, Johnson LN, Mesguiche V, Newell DR, Noble ME, Tucker JA, Whitfield HJ (2002) Probing the ATP ribose-binding domain of cyclin-dependent kinases 1 and 2 with O(6)-substituted guanine derivatives. J Med Chem 45(16):3381–3393. https://doi.org/10.1021/jm020056z
Glaser F, Rosenberg Y, Kessel A, Pupko T, Ben-Tal N (2005) The ConSurf-HSSP database: the mapping of evolutionary conservation among homologs onto PDB structures. Proteins 58(3):610–617. https://doi.org/10.1002/prot.20305
Gong CX, Liu F, Grundke-Iqbal I, Iqbal K (2005) Post-translational modifications of tau protein in Alzheimer’s disease. J Neural Transm (vienna) 112(6):813–838. https://doi.org/10.1007/s00702-004-0221-0
Gu Y, Rosenblatt J, Morgan DO (1992) Cell cycle regulation of CDK2 activity by phosphorylation of Thr160 and Tyr15. EMBO J 11(11):3995–4005
Gupta R, Birch H, Rapacki K, Brunak S, Hansen JE (1999) O-GLYCBASE version 4.0: a revised database of O-glycosylated proteins. Nucleic Acids Res 27(1):370–372. https://doi.org/10.1093/nar/27.1.370
Gutmanas A, Alhroub Y, Battle GM, Berrisford JM, Bochet E, Conroy MJ, Dana JM, Fernandez Montecelo MA, van Ginkel G, Gore SP, Haslam P, Hatherley R, Hendrickx PM, Hirshberg M, Lagerstedt I, Mir S, Mukhopadhyay A, Oldfield TJ, Patwardhan A, Rinaldi L, Sahni G, Sanz-Garcia E, Sen S, Slowley RA, Velankar S, Wainwright ME, Kleywegt GJ (2014) PDBe: protein data bank in Europe. Nucleic Acids Res 42(Database issue):D285-291. https://doi.org/10.1093/nar/gkt1180
Hanan EJ, Eigenbrot C, Bryan MC, Burdick DJ, Chan BK, Chen Y, Dotson J, Heald RA, Jackson PS, La H, Lainchbury MD, Malek S, Purkey HE, Schaefer G, Schmidt S, Seward EM, Sideris S, Tam C, Wang S, Yeap SK, Yen I, Yin J, Yu C, Zilberleyb I, Heffron TP (2014) Discovery of selective and noncovalent diaminopyrimidine-based inhibitors of epidermal growth factor receptor containing the T790M resistance mutation. J Med Chem 57(23):10176–10191. https://doi.org/10.1021/jm501578n
Hatos A, Hajdu-Soltesz B, Monzon AM, Palopoli N, Alvarez L, Aykac-Fas B, Bassot C, Benitez GI, Bevilacqua M, Chasapi A, Chemes L, Davey NE, Davidovic R, Dunker AK, Elofsson A, Gobeill J, Foutel NSG, Sudha G, Guharoy M, Horvath T, Iglesias V, Kajava AV, Kovacs OP, Lamb J, Lambrughi M, Lazar T, Leclercq JY, Leonardi E, Macedo-Ribeiro S, Macossay-Castillo M, Maiani E, Manso JA, Marino-Buslje C, Martinez-Perez E, Meszaros B, Micetic I, Minervini G, Murvai N, Necci M, Ouzounis CA, Pajkos M, Paladin L, Pancsa R, Papaleo E, Parisi G, Pasche E, Barbosa Pereira PJ, Promponas VJ, Pujols J, Quaglia F, Ruch P, Salvatore M, Schad E, Szabo B, Szaniszlo T, Tamana S, Tantos A, Veljkovic N, Ventura S, Vranken W, Dosztanyi Z, Tompa P, Tosatto SCE, Piovesan D (2020) DisProt: intrinsic protein disorder annotation in 2020. Nucleic Acids Res 48(D1):D269–D276. https://doi.org/10.1093/nar/gkz975
Hornbeck PV, Chabra I, Kornhauser JM, Skrzypek E, Zhang B (2004) PhosphoSite: a bioinformatics resource dedicated to physiological protein phosphorylation. Proteomics 4(6):1551–1561. https://doi.org/10.1002/pmic.200300772
Hornbeck PV, Zhang B, Murray B, Kornhauser JM, Latham V, Skrzypek E (2015) PhosphoSitePlus, 2014: mutations, PTMs and recalibrations. Nucleic Acids Res 43(Database issue):D512-520. https://doi.org/10.1093/nar/gku1267
Huang Q, Chang J, Cheung MK, Nong W, Li L, Lee MT, Kwan HS (2014) Human proteins with target sites of multiple post-translational modification types are more prone to be involved in disease. J Proteome Res 13(6):2735–2748. https://doi.org/10.1021/pr401019d
Huang KY, Su MG, Kao HJ, Hsieh YC, Jhong JH, Cheng KH, Huang HD, Lee TY (2016) dbPTM 2016: 10-year anniversary of a resource for post-translational modification of proteins. Nucleic Acids Res 44(D1):D435-446. https://doi.org/10.1093/nar/gkv1240
Huang KY, Lee TY, Kao HJ, Ma CT, Lee CC, Lin TH, Chang WC, Huang HD (2019) dbPTM in 2019: exploring disease association and cross-talk of post-translational modifications. Nucleic Acids Res 47(D1):D298-d308. https://doi.org/10.1093/nar/gky1074
Hubbard T, Andrews D, Caccamo M, Cameron G, Chen Y, Clamp M, Clarke L, Coates G, Cox T, Cunningham F, Curwen V, Cutts T, Down T, Durbin R, Fernandez-Suarez XM, Gilbert J, Hammond M, Herrero J, Hotz H, Howe K, Iyer V, Jekosch K, Kahari A, Kasprzyk A, Keefe D, Keenan S, Kokocinsci F, London D, Longden I, McVicker G, Melsopp C, Meidl P, Potter S, Proctor G, Rae M, Rios D, Schuster M, Searle S, Severin J, Slater G, Smedley D, Smith J, Spooner W, Stabenau A, Stalker J, Storey R, Trevanion S, Ureta-Vidal A, Vogel J, White S, Woodwark C, Birney E (2005) Ensembl 2005. Nucleic Acids Res 33(Database issue):D447-453. https://doi.org/10.1093/nar/gki138
Jimenez JL, Hegemann B, Hutchins JR, Peters JM, Durbin R (2007) A systematic comparative and structural analysis of protein phosphorylation sites based on the mtcPTM database. Genome Biol 8(5):R90. https://doi.org/10.1186/gb-2007-8-5-r90
Jo S, Kim T, Iyer VG, Im W (2008) CHARMM-GUI: a web-based graphical user interface for CHARMM. J Comput Chem 29(11):1859–1865. https://doi.org/10.1002/jcc.20945
Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, Tunyasuvunakool K, Bates R, Zidek A, Potapenko A, Bridgland A, Meyer C, Kohl SAA, Ballard AJ, Cowie A, Romera-Paredes B, Nikolov S, Jain R, Adler J, Back T, Petersen S, Reiman D, Clancy E, Zielinski M, Steinegger M, Pacholska M, Berghammer T, Bodenstein S, Silver D, Vinyals O, Senior AW, Kavukcuoglu K, Kohli P, Hassabis D (2021) Highly accurate protein structure prediction with AlphaFold. Nature. https://doi.org/10.1038/s41586-021-03819-2
Jungblut PR, Holzhutter HG, Apweiler R, Schluter H (2008) The speciation of the proteome. Chem Cent J 2:16. https://doi.org/10.1186/1752-153X-2-16
Kabsch W, Sander C (1983) Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22(12):2577–2637. https://doi.org/10.1002/bip.360221211
Kawahara R, Recuero S, Srougi M, Leite KRM, Thaysen-Andersen M, Palmisano G (2021) The complexity and dynamics of the tissue glycoproteome associated with prostate cancer progression. Mol Cell ProteomMCP 20:100026. https://doi.org/10.1074/mcp.RA120.002320
Kern F, Fehlmann T, Keller A (2020) On the lifetime of bioinformatics web services. Nucleic Acids Res 48(22):12523–12533. https://doi.org/10.1093/nar/gkaa1125
Khater S, Mohanty D (2015) novPTMenzy: a database for enzymes involved in novel post-translational modifications. Database J Biol Databases Curation. https://doi.org/10.1093/database/bav039
Kozma D, Simon I, Tusnady GE (2013) PDBTM: Protein Data Bank of transmembrane proteins after 8 years. Nucleic Acids Res 41(Database issue):D524-529. https://doi.org/10.1093/nar/gks1169
Lee TY, Huang HD, Hung JH, Huang HY, Yang YS, Wang TH (2006) dbPTM: an information repository of protein post-translational modification. Nucleic Acids Res 34(Database issue):D622-627. https://doi.org/10.1093/nar/gkj083
Lee TY, Hsu JB, Chang WC, Wang TY, Hsu PC, Huang HD (2009) A comprehensive resource for integrating and displaying protein post-translational modifications. BMC Res Notes 2:111. https://doi.org/10.1186/1756-0500-2-111
Lee TY, Chen YJ, Lu CT, Ching WC, Teng YC, Huang HD, Chen YJ (2012) dbSNO: a database of cysteine S-nitrosylation. Bioinformatics (oxford, England) 28(17):2293–2295. https://doi.org/10.1093/bioinformatics/bts436
Legrain P, Aebersold R, Archakov A, Bairoch A, Bala K, Beretta L, Bergeron J, Borchers CH, Corthals GL, Costello CE, Deutsch EW, Domon B, Hancock W, He F, Hochstrasser D, Marko-Varga G, Salekdeh GH, Sechi S, Snyder M, Srivastava S, Uhlen M, Wu CH, Yamamoto T, Paik YK, Omenn GS (2011) The human proteome project: current state and future direction. Mol Cell Proteom MCP 10:M111 009993. https://doi.org/10.1074/mcp.M111.009993
Lernmark A (2013) Is there evidence for post-translational modification of beta cell autoantigens in the aetiology and pathogenesis of type 1 diabetes? Diabetologia. https://doi.org/10.1007/s00125-013-3041-7
Li F, Fan C, Marquez-Lago TT, Leier A, Revote J, Jia C, Zhu Y, Smith AI, Webb GI, Liu Q, Wei L, Li J, Song J (2020) PRISMOID: a comprehensive 3D structure database for post-translational modifications and mutations with functional impact. Brief Bioinform 21(3):1069–1079. https://doi.org/10.1093/bib/bbz050
Lo A, Cheng CW, Chiu YY, Sung TY, Hsu WL (2011) TMPad: an integrated structural database for helix-packing folds in transmembrane proteins. Nucleic Acids Res 39(Database issue):D347-355. https://doi.org/10.1093/nar/gkq1255
Lodish HF (2013) Molecular cell biology, 7th edn. W.H. Freeman and Co., New York
Lomize MA, Pogozheva ID, Joo H, Mosberg HI, Lomize AL (2012) OPM database and PPM web server: resources for positioning of proteins in membranes. Nucleic Acids Res 40(Database issue):D370-376. https://doi.org/10.1093/nar/gkr703
Lu CT, Huang KY, Su MG, Lee TY, Bretaña NA, Chang WC, Chen YJ, Chen YJ, Huang HD (2013) 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):D395–D305. https://doi.org/10.1093/nar/gks1229
Mann M, Jensen ON (2003) Proteomic analysis of post-translational modifications. Nat Biotechnol 21(3):255–261. https://doi.org/10.1038/nbt0303-255
Margreitter C, Petrov D, Zagrovic B (2013) Vienna-PTM web server: a toolkit for MD simulations of protein post-translational modifications. Nucleic Acids Res 41(Web Server issue):W422-426. https://doi.org/10.1093/nar/gkt416
Minguez P, Letunic I, Parca L, Bork P (2013) PTMcode: a database of known and predicted functional associations between post-translational modifications in proteins. Nucleic Acids Res 41(Database issue):D306-311. https://doi.org/10.1093/nar/gks1230
Minguez P, Letunic I, Parca L, Garcia-Alonso L, Dopazo J, Huerta-Cepas J, Bork P (2015) PTMcode v2: a resource for functional associations of post-translational modifications within and between proteins. Nucleic Acids Res 43(Database issue):D494-502. https://doi.org/10.1093/nar/gku1081
Mulder NJ, Apweiler R, Attwood TK, Bairoch A, Bateman A, Binns D, Biswas M, Bradley P, Bork P, Bucher P, Copley R, Courcelle E, Durbin R, Falquet L, Fleischmann W, Gouzy J, Griffith-Jones S, Haft D, Hermjakob H, Hulo N, Kahn D, Kanapin A, Krestyaninova M, Lopez R, Letunic I, Orchard S, Pagni M, Peyruc D, Ponting CP, Servant F, Sigrist CJ, InterPro C (2002) InterPro: an integrated documentation resource for protein families, domains and functional sites. Brief Bioinform 3(3):225–235. https://doi.org/10.1093/bib/3.3.225
Muller MM (2018) Post-translational modifications of protein backbones: unique functions, mechanisms, and challenges. Biochemistry 57(2):177–185. https://doi.org/10.1021/acs.biochem.7b00861
Nekooki-Machida Y, Hagiwara H (2020) Role of tubulin acetylation in cellular functions and diseases. Med Mol Morphol 53(4):191–197. https://doi.org/10.1007/s00795-020-00260-8
Pérez S, Sarkar A, Rivet A, Breton C, Imberty A (2015) Glyco3D: a portal for structural glycosciences. Methods Mol Biol (clifton, NJ) 1273:241–258. https://doi.org/10.1007/978-1-4939-2343-4_18
Perez S, Bonnardel F, Lisacek F, Imberty A, Ricard Blum S, Makshakova O (2020) GAG-DB, the new interface of the three-dimensional landscape of glycosaminoglycans. Biomolecules. https://doi.org/10.3390/biom10121660
Perez-Riverol Y, Csordas A, Bai J, Bernal-Llinares M, Hewapathirana S, Kundu DJ, Inuganti A, Griss J, Mayer G, Eisenacher M, Perez E, Uszkoreit J, Pfeuffer J, Sachsenberg T, Yilmaz S, Tiwary S, Cox J, Audain E, Walzer M, Jarnuczak AF, Ternent T, Brazma A, Vizcaino JA (2019) The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res 47(D1):D442–D450. https://doi.org/10.1093/nar/gky1106
Piovesan D, Hatos A, Minervini G, Quaglia F, Monzon AM, Tosatto SCE (2020) Assessing predictors for new post translational modification sites: a case study on hydroxylation. PLoS Comput Biol 16(6):e1007967. https://doi.org/10.1371/journal.pcbi.1007967
Piovesan D, Necci M, Escobedo N, Monzon AM, Hatos A, Micetic I, Quaglia F, Paladin L, Ramasamy P, Dosztanyi Z, Vranken WF, Davey NE, Parisi G, Fuxreiter M, Tosatto SCE (2021) MobiDB: intrinsically disordered proteins in 2021. Nucleic Acids Res 49(D1):D361–D367. https://doi.org/10.1093/nar/gkaa1058
Radivojac P, Baenziger PH, Kann MG, Mort ME, Hahn MW, Mooney SD (2008) Gain and loss of phosphorylation sites in human cancer. Bioinformatics (oxford, England) 24(16):i241-247. https://doi.org/10.1093/bioinformatics/btn267
Ramasamy P, Turan D, Tichshenko N, Hulstaert N, Vandermarliere E, Vranken W, Martens L (2020) Scop3P: a comprehensive resource of human phosphosites within their full context. J Proteome Res 19(8):3478–3486. https://doi.org/10.1021/acs.jproteome.0c00306
Rao RM, Wong H, Dauchez M, Baud S (2021) Effects of changes in glycan composition on glycoprotein dynamics: example of N-glycans on insulin receptor. Glycobiology. https://doi.org/10.1093/glycob/cwab049
Rigden DJ, Fernandez XM (2021) The 2021 Nucleic Acids Research database issue and the online molecular biology database collection. Nucleic Acids Res 49(D1):D1–D9. https://doi.org/10.1093/nar/gkaa1216
Rose AS, Hildebrand PW (2015) NGL Viewer: a web application for molecular visualization. Nucleic Acids Res 43(W1):W576-579. https://doi.org/10.1093/nar/gkv402
Scherbinina SI, Toukach PV (2020) Three-dimensional structures of carbohydrates and where to find them. Int J Mol Sci. https://doi.org/10.3390/ijms21207702
Schwartz D (2012) Prediction of lysine post-translational modifications using bioinformatic tools. Essays Biochem 52:165–177. https://doi.org/10.1042/bse0520165
Sidney J, Vela JL, Friedrich D, Kolla R, von Herrath M, Wesley JD, Sette A (2018) Low HLA binding of diabetes-associated CD8+ T-cell epitopes is increased by post translational modifications. BMC Immunol 19(1):12. https://doi.org/10.1186/s12865-018-0250-3
Su MG, Huang KY, Lu CT, Kao HJ, Chang YH, Lee TY (2014) topPTM: a new module of dbPTM for identifying functional post-translational modifications in transmembrane proteins. Nucleic Acids Res 42(Database issue):D537-545. https://doi.org/10.1093/nar/gkt1221
Timofeev O, Cizmecioglu O, Settele F, Kempf T, Hoffmann I (2010) Cdc25 phosphatases are required for timely assembly of CDK1-cyclin B at the G2/M transition. J Biol Chem 285(22):16978–16990. https://doi.org/10.1074/jbc.M109.096552
Tompa P, Davey NE, Gibson TJ, Babu MM (2014) A million peptide motifs for the molecular biologist. Mol Cell. 17;55(2):161–169: https://doi.org/10.1016/j.molcel.2014.05.032
Tunyasuvunakool K, Adler J, Wu Z, Green T, Zielinski M, Zidek A, Bridgland A, Cowie A, Meyer C, Laydon A, Velankar S, Kleywegt GJ, Bateman A, Evans R, Pritzel A, Figurnov M, Ronneberger O, Bates R, Kohl SAA, Potapenko A, Ballard AJ, Romera-Paredes B, Nikolov S, Jain R, Clancy E, Reiman D, Petersen S, Senior AW, Kavukcuoglu K, Birney E, Kohli P, Jumper J, Hassabis D (2021) Highly accurate protein structure prediction for the human proteome. Nature 596(7873):590–596. https://doi.org/10.1038/s41586-021-03828-1
Tusnady GE, Kalmar L, Simon I (2008) TOPDB: topology data bank of transmembrane proteins. NucleicAcids Res 36(Database issue):D234-239. https://doi.org/10.1093/nar/gkm751
UniProt C (2019) UniProt: a worldwide hub of protein knowledge. Nucleic Acids Res 47(D1):D506–D515. https://doi.org/10.1093/nar/gky1049
Van Eyk JE (2011) Overview: the maturing of proteomics in cardiovascular research. Circ Res 108(4):490–498. https://doi.org/10.1161/CIRCRESAHA.110.226894
Velankar S, Best C, Beuth B, Boutselakis CH, Cobley N, Sousa Da Silva AW, Dimitropoulos D, Golovin A, Hirshberg M, John M, Krissinel EB, Newman R, Oldfield T, Pajon A, Penkett CJ, Pineda-Castillo J, Sahni G, Sen S, Slowley R, Suarez-Uruena A, Swaminathan J, van Ginkel G, Vranken WF, Henrick K, Kleywegt GJ (2010) PDBe: Protein Data Bank in Europe. Nucleic Acids Res 38(Database issue):D308-317. https://doi.org/10.1093/nar/gkp916
Vidal CJ (2011) Post-translational modifications in health and disease. Springer, New York
Vivelo CA, Wat R, Agrawal C, Tee HY, Leung AK (2017) ADPriboDB: the database of ADP-ribosylated proteins. Nucleic Acids Res 45(D1):D204–D209. https://doi.org/10.1093/nar/gkw706
Walsh CT, Garneau-Tsodikova S, Gatto GJ Jr (2005) Protein posttranslational modifications: the chemistry of proteome diversifications. Angew Chem Int Ed Engl 44(45):7342–7372. https://doi.org/10.1002/anie.200501023
Walsh I, Fishman D, Garcia-Gasulla D, Titma T, Pollastri G, Harrow J, Psomopoulos FE, Tosatto SCE, Group EMLF (2021) DOME: recommendations for supervised machine learning validation in biology. Nat Methods 18(10):1122–1127. https://doi.org/10.1038/s41592-021-01205-4
Wang D, Liu D, Yuchi J, He F, Jiang Y, Cai S, Li J, Xu D (2020a) MusiteDeep: a deep-learning based webserver for protein post-translational modification site prediction and visualization. Nucleic Acids Res 48(W1):W140-w146. https://doi.org/10.1093/nar/gkaa275
Wang H, Wang Z, Li Z, Lee TY (2020b) Incorporating deep learning with word embedding to identify plant ubiquitylation sites. Front Cell Dev Biol 8:572195. https://doi.org/10.3389/fcell.2020.572195
Wang R, Wang Z, Wang H, Pang Y, Lee TY (2020c) Characterization and identification of lysine crotonylation sites based on machine learning method on both plant and mammalian. Sci Rep 10(1):20447. https://doi.org/10.1038/s41598-020-77173-0
Welburn JP, Tucker JA, Johnson T, Lindert L, Morgan M, Willis A, Noble ME, Endicott JA (2007) How tyrosine 15 phosphorylation inhibits the activity of cyclin-dependent kinase 2-cyclin A. J Biol Chem 282(5):3173–3181. https://doi.org/10.1074/jbc.M609151200
Wilkins MR, Gasteiger E, Bairoch A, Sanchez JC, Williams KL, Appel RD, Hochstrasser DF (1999a) Protein identification and analysis tools in the ExPASy server. Methods Mol Biol (clifton, NJ) 112:531–552. https://doi.org/10.1385/1-59259-584-7:531
Wilkins MR, Gasteiger E, Gooley AA, Herbert BR, Molloy MP, Binz PA, Ou K, Sanchez JC, Bairoch A, Williams KL, Hochstrasser DF (1999b) High-throughput mass spectrometric discovery of protein post-translational modifications. J Mol Biol 289(3):645–657. https://doi.org/10.1006/jmbi.1999.2794
Wu CH, Yeh LS, Huang H, Arminski L, Castro-Alvear J, Chen Y, Hu Z, Kourtesis P, Ledley RS, Suzek BE, Vinayaka CR, Zhang J, Barker WC (2003) The protein information resource. Nucleic Acids Res 31(1):345–347. https://doi.org/10.1093/nar/gkg040
Xin F, Radivojac P (2012) Post-translational modifications induce significant yet not extreme changes to protein structure. Bioinformatics (oxford, England) 28(22):2905–2913. https://doi.org/10.1093/bioinformatics/bts541
Yalinca H, Gehin CJC, Oleinikovas V, Lashuel HA, Gervasio FL, Pastore A (2019) The role of post-translational modifications on the energy landscape of Huntingtin N-Terminus. Front Mol Biosci 6:95. https://doi.org/10.3389/fmolb.2019.00095
Zahn-Zabal M, Michel PA, Gateau A, Nikitin F, Schaeffer M, Audot E, Gaudet P, Duek PD, Teixeira D, Rech de Laval V, Samarasinghe K, Bairoch A, Lane L (2020) The neXtProt knowledgebase in 2020: data, tools and usability improvements. Nucleic Acids Res 48(D1):D328–D334. https://doi.org/10.1093/nar/gkz995
Zanzoni A, Ausiello G, Via A, Gherardini PF, Helmer-Citterich M (2007) Phospho3D: a database of three-dimensional structures of protein phosphorylation sites. Nucleic Acids Res 35(Database issue):D229-231. https://doi.org/10.1093/nar/gkl922
Zanzoni A, Carbajo D, Diella F, Gherardini PF, Tramontano A, Helmer-Citterich M, Via A (2011) Phospho3D 2.0: an enhanced database of three-dimensional structures of phosphorylation sites. Nucleic Acids Res 39(Database issue):D268-271. https://doi.org/10.1093/nar/gkq936
Zhang C, Walker AK, Zand R, Moscarello MA, Yan JM, Andrews PC (2012) Myelin basic protein undergoes a broader range of modifications in mammals than in lower vertebrates. J Proteome Res 11(10):4791–4802. https://doi.org/10.1021/pr201196e
Zhang L, Liu M, Qin X, Liu G (2020) Succinylation site prediction based on protein sequences using the IFS-LightGBM (BO) model. Comput Math Methods Med 2020:8858489. https://doi.org/10.1155/2020/8858489
Zhou F, Xue Y, Yao X, Xu Y (2006) A general user interface for prediction servers of proteins’ post-translational modification sites. Nat Protoc 1(3):1318–1321. https://doi.org/10.1038/nprot.2006.209
Acknowledgements
The authors would like to thank Dr Tsikas Dimitros, the editor of this special issue for allowing us to present this review and for his kindness in granting us multiple extensions to finalize the manuscript. The authors also wish to thank all the authors of the various databases and tools whose contributions made it possible to tackle this difficult and arduous research area. AdB acknowledges the French National Research Agency with grant ANR-19-CE17-0021 (BASIN) and the Indo-French Center for the Promotion of Advanced Research/CEFIPRA for collaborative grants (numbers 5302-2).
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de Brevern, A.G., Rebehmed, J. Current status of PTMs structural databases: applications, limitations and prospects. Amino Acids 54, 575–590 (2022). https://doi.org/10.1007/s00726-021-03119-z
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DOI: https://doi.org/10.1007/s00726-021-03119-z