OpenLB
Open Library of Bioscience
Advanced Search
Journal of applied genetics
Dec 14, 2024;

Computational analysis of MYC gene variants: structural and functional impact of non-synonymous SNPs.

Plabita Bhuyan, Varshabi Bharali, Sangju Basumatary, Aido Lego, Juman Sarma, Debasish Borbora
Author Information
  1. Plabita Bhuyan: Department of Biotechnology, Gauhati University, Guwahati, Assam, 781014, India.
  2. Varshabi Bharali: Department of Biotechnology, Gauhati University, Guwahati, Assam, 781014, India.
  3. Sangju Basumatary: Department of Biotechnology, Gauhati University, Guwahati, Assam, 781014, India.
  4. Aido Lego: Department of Biotechnology, Gauhati University, Guwahati, Assam, 781014, India.
  5. Juman Sarma: Department of Biotechnology, Gauhati University, Guwahati, Assam, 781014, India.
  6. Debasish Borbora: Department of Biotechnology, Gauhati University, Guwahati, Assam, 781014, India. debasish.borbora@gauhati.ac.in. ORCID
PMID: 39673052 DOI: 10.1007/s13353-024-00929-1

Abstract

The MYC proto-oncogene encodes a basic helix-loop-helix leucine zipper (HLH-LZ) transcription factor, acting as a master regulator of genes involved in cellular proliferation, differentiation, and immune surveillance. Dysregulation of MYC is implicated in over 70% of human cancers, driving oncogenic processes through altered gene expression and disrupted cellular functions. Non-synonymous single nucleotide polymorphisms (nsSNPs) within coding regions can significantly impact protein structure and function, leading to abnormal cellular behaviours. This study employed 29 in silico tools to systematically evaluate the deleteriousness of nsSNPs within the MYC gene. These tools assessed the variants' effects on protein structure, disease association, functional domains, and post-translational modification sites. This study investigated if these variants may disrupt protein-protein interactions, critical for MYC's oncogenic roles and normal cellular functions. Our analysis identified 21 nsSNPs that were predicted to be deleterious and pathogenic. These variants correspond to residues D63H, D63Y, P74L, P75L, N375D, N375I, E378K, E378Q, E378A, E378G, E378V, R379P, R381K, R381T, R382W, L392P, R393C, R393H, R393P, L411H, and L411P. Stability assessments indicated that these variants could destabilise the MYC protein. None of the variants affected post-translational modifications. Protein-protein interaction and docking analysis revealed that variants within bHLH and LZ domains may disrupt MYC/MAX binding, potentially impacting MYC's oncogenic activity and transcriptional regulation. This computational assessment enhances our understanding of genetic variations within the MYC gene and prioritises candidate nsSNPs for experimental validation and therapeutic exploration.

Keywords

In-silico analysis BHLH-LZ proteins MYC-MAX dimerization Molecular interactions Protein stability

References

  1. Abramson J, Adler J, Dunger J, Evans R, Green T, Pritzel A, Ronneberger O, Willmore L, Ballard AJ, Bambrick J, Bodenstein SW, Evans DA, Hung C-C, O’Neill M, Reiman D, Tunyasuvunakool K, Wu Z, Žemgulytė A, Arvaniti E, … Jumper JM (2024) Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature, 630(8016):493–500. https://doi.org/10.1038/s41586-024-07487-w
  2. Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P, Kondrashov AS, Sunyaev SR (2010) A method and server for predicting damaging missense mutations. Nat Methods 7(4):248–249. https://doi.org/10.1038/nmeth0410-248 [DOI: 10.1038/nmeth0410-248]
  3. Albihn A, Johnsen JI, Arsenian Henriksson M (2010) MYC in Oncogenesis and as a target for cancer therapies. In Advances in Cancer Research (Vol. 107, pp. 163–224). Elsevier. https://doi.org/10.1016/S0065-230X(10)07006-5
  4. Arede L, Pina C (2021) Buffering noise: KAT2A modular contributions to stabilization of transcription and cell identity in cancer and development. Exp Hematol 93:25–37. https://doi.org/10.1016/j.exphem.2020.10.003 [DOI: 10.1016/j.exphem.2020.10.003]
  5. Azmi MB, Naeem U, Saleem A, Jawed A, Usman H, Qureshi SA, Azim MK (2022) In silico identification of the rare-coding pathogenic mutations and structural modeling of human NNAT gene associated with anorexia nervosa. Eating and Weight Disorders: EWD 27(7):2725–2744. https://doi.org/10.1007/s40519-022-01422-6 [DOI: 10.1007/s40519-022-01422-6]
  6. Bédard M, Maltais L, Montagne M, Lavigne P (2017) Miz-1 and Max compete to engage c-Myc: Implication for the mechanism of inhibition of c-Myc transcriptional activity by Miz-1. Proteins 85(2):199–206. https://doi.org/10.1002/prot.25214 [DOI: 10.1002/prot.25214]
  7. Bendl J, Stourac J, Salanda O, Pavelka A, Wieben ED, Zendulka J, Brezovsky J, Damborsky J (2014) PredictSNP: Robust and Accurate Consensus Classifier for Prediction of Disease-Related Mutations. PLoS Comput Biol 10(1):e1003440. https://doi.org/10.1371/journal.pcbi.1003440 [DOI: 10.1371/journal.pcbi.1003440]
  8. Bromberg Y, Yachdav G, Rost B (2008) SNAP predicts effect of mutations on protein function. Bioinformatics 24(20):2397–2398. https://doi.org/10.1093/bioinformatics/btn435 [DOI: 10.1093/bioinformatics/btn435]
  9. Capriotti E, Fariselli P, Casadio R (2005) I-Mutant2.0: Predicting stability changes upon mutation from the protein sequence or structure. Nucleic Acids Res 33(Web Server):W306–W310. https://doi.org/10.1093/nar/gki375
  10. Capriotti E, Altman RB, Bromberg Y (2013) Collective judgment predicts disease-associated single nucleotide variants. BMC Genomics 14 Suppl 3(Suppl 3):S2. https://doi.org/10.1186/1471-2164-14-S3-S2
  11. Capriotti E, Fariselli P (2017) PhD-SNPg: A webserver and lightweight tool for scoring single nucleotide variants. Nucleic Acids Res 45(W1):W247–W252. https://doi.org/10.1093/nar/gkx369 [DOI: 10.1093/nar/gkx369]
  12. Cheng J, Randall A, Baldi P (2006). Prediction of protein stability changes for single‐site mutations using support vector machines. Proteins Struct Funct Bioinforma 62(4):1125–1132. https://doi.org/10.1002/prot.20810
  13. Choi SH, Mahankali M, Lee SJ, Hull M, Petrassi HM, Chatterjee AK, Schultz PG, Jones KA, Shen W (2017) Targeted Disruption of Myc-Max Oncoprotein Complex by a Small Molecule. ACS Chem Biol 12(11):2715–2719. https://doi.org/10.1021/acschembio.7b00799 [DOI: 10.1021/acschembio.7b00799]
  14. Colovos C, Yeates TO (1993) Verification of protein structures: Patterns of nonbonded atomic interactions. Protein Sci 2(9):1511–1519. https://doi.org/10.1002/pro.5560020916 [DOI: 10.1002/pro.5560020916]
  15. Comino-Méndez I, Gracia-Aznárez FJ, Schiavi F, Landa I, Leandro-García LJ, Letón R, Honrado E, Ramos-Medina R, Caronia D, Pita G, Gómez-Graña Á, De Cubas AA, Inglada-Pérez L, Maliszewska A, Taschin E, Bobisse S, Pica G, Loli P, Hernández-Lavado R, … Cascón A (2011). Exome sequencing identifies MAX mutations as a cause of hereditary pheochromocytoma. Nature Genetics 43(7):663–667. https://doi.org/10.1038/ng.861
  16. Dai Y, Zhang J, Wang Y, Liu L, Gao J (2022) Computational insights into the differentiated binding affinities of Myc, Max, and Omomyc dimers to the E-boxes of DNA. J Mol Model 28(10):329. https://doi.org/10.1007/s00894-022-05261-1 [DOI: 10.1007/s00894-022-05261-1]
  17. Dakal TC, Kala D, Dhiman G, Yadav V, Krokhotin A, Dokholyan NV (2017) Predicting the functional consequences of non-synonymous single nucleotide polymorphisms in IL8 gene. Sci Rep 7(1):6525. https://doi.org/10.1038/s41598-017-06575-4 [DOI: 10.1038/s41598-017-06575-4]
  18. Davis IW, Leaver-Fay A, Chen VB, Block JN, Kapral GJ, Wang X, Murray LW, Arendall WB, Snoeyink J, Richardson JS, Richardson DC (2007) MolProbity: All-atom contacts and structure validation for proteins and nucleic acids. Nucleic Acids Res 35(Web Server):W375–W383. https://doi.org/10.1093/nar/gkm216
  19. del Toro N, Shrivastava A, Ragueneau E, Meldal B, Combe C, Barrera E, Perfetto L, How K, Ratan P, Shirodkar G, Lu O, Mészáros B, Watkins X, Pundir S, Licata L, Iannuccelli M, Pellegrini M, Martin MJ, Panni S, … Hermjakob H (2022) The IntAct database: Efficient access to fine-grained molecular interaction data. Nucleic Acids Research 50(D1):D648–D653. 10.1093/nar/gkab1006
  20. Demma MJ, Mapelli C, Sun A, Bodea S, Ruprecht B, Javaid S, Wiswell D, Muise E, Chen S, Zelina J, Orvieto F, Santoprete A, Altezza S, Tucci F, Escandon E, Hall B, Ray K, Walji A, O’Neil J (2019) Omomyc Reveals New Mechanisms To Inhibit the MYC Oncogene. Mol Cell Biol 39(22):e00248-e319. https://doi.org/10.1128/MCB.00248-19 [DOI: 10.1128/MCB.00248-19]
  21. Deng W, Wang Y, Ma L, Zhang Y, Ullah S, Xue Y (2016) Computational prediction of methylation types of covalently modified lysine and arginine residues in proteins. Brief Bioinforma bbw041. https://doi.org/10.1093/bib/bbw041
  22. Destefanis F, Manara V, Bellosta P (2020) Myc as a Regulator of Ribosome Biogenesis and Cell Competition: A Link to Cancer. Int J Mol Sci 21(11):4037. https://doi.org/10.3390/ijms21114037 [DOI: 10.3390/ijms21114037]
  23. Dhanasekaran R, Deutzmann A, Mahauad-Fernandez WD, Hansen AS, Gouw AM, Felsher DW (2022) The MYC oncogene—The grand orchestrator of cancer growth and immune evasion. Nat Rev Clin Oncol 19(1):23–36. https://doi.org/10.1038/s41571-021-00549-2 [DOI: 10.1038/s41571-021-00549-2]
  24. Dong Y, Tu R, Liu H, Qing G (2020) Regulation of cancer cell metabolism: Oncogenic MYC in the driver’s seat. Signal Transduct Target Ther 5(1):124. https://doi.org/10.1038/s41392-020-00235-2 [DOI: 10.1038/s41392-020-00235-2]
  25. Glaser F, Pupko T, Paz I, Bell RE, Bechor-Shental D, Martz E, Ben-Tal N (2003) ConSurf: Identification of Functional Regions in Proteinsby Surface-Mapping of Phylogenetic Information. Bioinformatics 19(1):163–164. https://doi.org/10.1093/bioinformatics/19.1.163 [DOI: 10.1093/bioinformatics/19.1.163]
  26. Guo Y, Ning W, Jiang P, Lin S, Wang C, Tan X, Yao L, Peng D, Xue Y (2020) GPS-PBS: A Deep Learning Framework to Predict Phosphorylation Sites that Specifically Interact with Phosphoprotein-Binding Domains. Cells 9(5):1266. https://doi.org/10.3390/cells9051266 [DOI: 10.3390/cells9051266]
  27. Han H, Jain AD, Truica MI, Izquierdo-Ferrer J, Anker JF, Lysy B, Sagar V, Luan Y, Chalmers ZR, Unno K, Mok H, Vatapalli R, Yoo YA, Rodriguez Y, Kandela I, Parker JB, Chakravarti D, Mishra RK, Schiltz GE, Abdulkadir SA (2019) Small-Molecule MYC Inhibitors Suppress Tumor Growth and Enhance Immunotherapy. Cancer Cell 36(5):483-497.e15. https://doi.org/10.1016/j.ccell.2019.10.001 [DOI: 10.1016/j.ccell.2019.10.001]
  28. Heo L, Park H, Seok C (2013) GalaxyRefine: Protein structure refinement driven by side-chain repacking. Nucleic Acids Res 41(W1):W384–W388. https://doi.org/10.1093/nar/gkt458 [DOI: 10.1093/nar/gkt458]
  29. Hoda A, Lika Çekani M, Kolaneci V (2023) Identification of deleterious nsSNPs in human HGF gene: In silico approach. J Biomol Struct Dyn 41(21):11889–11903. https://doi.org/10.1080/07391102.2022.2164060 [DOI: 10.1080/07391102.2022.2164060]
  30. Ioannidis NM, Rothstein JH, Pejaver V, Middha S, McDonnell SK, Baheti S, Musolf A, Li Q, Holzinger E, Karyadi D, Cannon-Albright LA, Teerlink CC, Stanford JL, Isaacs WB, Xu J, Cooney KA, Lange EM, Schleutker J, Carpten JD, … Sieh W (2016) REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants. Am J Human Genet 99(4):877–885. https://doi.org/10.1016/j.ajhg.2016.08.016
  31. Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, Tunyasuvunakool K, Bates R, Žídek A, Potapenko A, Bridgland A, Meyer C, Kohl SAA, Ballard AJ, Cowie A, Romera-Paredes B, Nikolov S, Jain R, Adler J, … Hassabis D (2021) Highly accurate protein structure prediction with AlphaFold. Nature 596(7873):583–589. https://doi.org/10.1038/s41586-021-03819-2
  32. Karadkhelkar NM, Lin M, Eubanks LM, Janda KD (2023) Demystifying the Druggability of the MYC Family of Oncogenes. J Am Chem Soc 145(6):3259–3269. https://doi.org/10.1021/jacs.2c12732 [DOI: 10.1021/jacs.2c12732]
  33. Khoruddin NA, Noorizhab MN, Teh LK, Mohd Yusof FZ, Salleh MZ (2021) Pathogenic nsSNPs that increase the risks of cancers among the Orang Asli and Malays. Sci Rep 11(1):16158. https://doi.org/10.1038/s41598-021-95618-y [DOI: 10.1038/s41598-021-95618-y]
  34. Kozakov D, Hall DR, Xia B, Porter KA, Padhorny D, Yueh C, Beglov D, Vajda S (2017) The ClusPro web server for protein–protein docking. Nat Protoc 12(2):255–278. https://doi.org/10.1038/nprot.2016.169 [DOI: 10.1038/nprot.2016.169]
  35. Krieger KL, Hu W-F, Ripperger T, Woods NT (2019) Functional Impacts of the BRCA1-mTORC2 Interaction in Breast Cancer. Int J Mol Sci 20(23):5876. https://doi.org/10.3390/ijms20235876 [DOI: 10.3390/ijms20235876]
  36. Laskowski RA, MacArthur MW, Moss DS, Thornton JM (1993) PROCHECK: A program to check the stereochemical quality of protein structures. J Appl Crystallogr 26(2):283–291. https://doi.org/10.1107/S0021889892009944 [DOI: 10.1107/S0021889892009944]
  37. Liang M-Q, Yu F-Q, Chen C (2020b) C-Myc regulates PD-L1 expression in esophageal squamous cell carcinoma. American Journal of Translational Research 12(2):379–388 [PMID: 32194890]
  38. Liang M, Yu F, Wu W, Chen H, Zheng B, Zheng W, Zhu Y, Chen C (2020) High programmed death-ligand 1 expression is a poor prognostic indicator for esophageal squamous cell carcinoma and is correlated with two-field lymph node metastasis. Transl Cancer Res 9(4):2472–2481. https://doi.org/10.21037/tcr.2020.03.22
  39. Madden SK, De Araujo AD, Gerhardt M, Fairlie DP, Mason JM (2021) Taking the Myc out of cancer: Toward therapeutic strategies to directly inhibit c-Myc. Mol Cancer 20(1):3. https://doi.org/10.1186/s12943-020-01291-6 [DOI: 10.1186/s12943-020-01291-6]
  40. Manfredi M, Savojardo C, Martelli PL, Casadio R (2022) E-SNPs&GO: Embedding of protein sequence and function improves the annotation of human pathogenic variants. Bioinformatics 38(23):5168–5174. https://doi.org/10.1093/bioinformatics/btac678 [DOI: 10.1093/bioinformatics/btac678]
  41. Marfil V, Blazquez M, Serrano F, Castell JV, Bort R (2015) Growth-promoting and tumourigenic activity of c-Myc is suppressed by Hhex. Oncogene 34(23):3011–3022. https://doi.org/10.1038/onc.2014.240 [DOI: 10.1038/onc.2014.240]
  42. McLaren W, Gil L, Hunt SE, Riat HS, Ritchie GRS, Thormann A, Flicek P, Cunningham F (2016) The Ensembl Variant Effect Predictor. Genome Biol 17(1):122. https://doi.org/10.1186/s13059-016-0974-4 [DOI: 10.1186/s13059-016-0974-4]
  43. Meng EC, Goddard TD, Pettersen EF, Couch GS, Pearson ZJ, Morris JH, Ferrin TE (2023) UCSF CHIMERAX: Tools for structure building and analysis. Protein Sci 32(11):e4792. https://doi.org/10.1002/pro.4792 [DOI: 10.1002/pro.4792]
  44. Mulder NJ, Apweiler R (2008) The Interpro database and tools for protein domain analysis. Curr Protoc Bioinforma 21(1). https://doi.org/10.1002/0471250953.bi0207s21
  45. Oughtred R, Rust J, Chang C, Breitkreutz B-J, Stark C, Willems A, Boucher L, Leung G, Kolas N, Zhang F, Dolma S, Coulombe-Huntington J, Chatr-Aryamontri A, Dolinski K, Tyers M (2021) The BioGRID database: A comprehensive biomedical resource of curated protein, genetic, and chemical interactions. Protein Sci A Publ Protein Soc 30(1):187–200. https://doi.org/10.1002/pro.3978 [DOI: 10.1002/pro.3978]
  46. Papadimitropoulou A, Makri M, Zoidis G (2024) MYC the oncogene from hell: Novel opportunities for cancer therapy. Eur J Med Chem 267:116194. https://doi.org/10.1016/j.ejmech.2024.116194 [DOI: 10.1016/j.ejmech.2024.116194]
  47. Parthiban V, Gromiha MM, Schomburg D (2006) CUPSAT: Prediction of protein stability upon point mutations. Nucleic Acids Res 34(Web Server):W239–W242. https://doi.org/10.1093/nar/gkl190
  48. Pejaver V, Urresti J, Lugo-Martinez J, Pagel KA, Lin GN, Nam H-J, Mort M, Cooper DN, Sebat J, Iakoucheva LM, Mooney SD, Radivojac P (2020) Inferring the molecular and phenotypic impact of amino acid variants with MutPred2. Nat Commun 11(1):5918. https://doi.org/10.1038/s41467-020-19669-x [DOI: 10.1038/s41467-020-19669-x]
  49. Pires DEV, Ascher DB, Blundell TL (2014) mCSM: Predicting the effects of mutations in proteins using graph-based signatures. Bioinformatics (Oxford, England) 30(3):335–342. https://doi.org/10.1093/bioinformatics/btt691 [DOI: 10.1093/bioinformatics/btt691]
  50. Rentzsch P, Witten D, Cooper GM, Shendure J, Kircher M (2019) CADD: Predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Res 47(D1):D886–D894. https://doi.org/10.1093/nar/gky1016 [DOI: 10.1093/nar/gky1016]
  51. Reva B, Antipin Y, Sander C (2011) Predicting the functional impact of protein mutations: Application to cancer genomics. Nucleic Acids Res 39(17):e118–e118. https://doi.org/10.1093/nar/gkr407 [DOI: 10.1093/nar/gkr407]
  52. Rozario LT, Sharker T, Nila TA (2021) In silico analysis of deleterious SNPs of human MTUS1 gene and their impacts on subsequent protein structure and function. PLoS ONE 16(6):e0252932. https://doi.org/10.1371/journal.pone.0252932 [DOI: 10.1371/journal.pone.0252932]
  53. Shen W, Zhou Q, Peng C, Li J, Yuan Q, Zhu H, Zhao M, Jiang X, Liu W, Ren C (2022) FBXW7 and the Hallmarks of Cancer: Underlying Mechanisms and Prospective Strategies. Front Oncol 12:880077. https://doi.org/10.3389/fonc.2022.880077 [DOI: 10.3389/fonc.2022.880077]
  54. Shostak A, Ruppert B, Ha N, Bruns P, Toprak UH, ICGC MMML-Seq Project, Eils R, Schlesner M, Diernfellner A, Brunner M (2016) MYC/MIZ1-dependent gene repression inversely coordinates the circadian clock with cell cycle and proliferation. Nature Communications 7:11807. https://doi.org/10.1038/ncomms1180
  55. Sim N-L, Kumar P, Hu J, Henikoff S, Schneider G, Ng PC (2012) SIFT web server: Predicting effects of amino acid substitutions on proteins. Nucleic Acids Res 40(W1):W452–W457. https://doi.org/10.1093/nar/gks539 [DOI: 10.1093/nar/gks539]
  56. Snel B (2000) STRING: A web-server to retrieve and display the repeatedly occurring neighbourhood of a gene. Nucleic Acids Res 28(18):3442–3444. https://doi.org/10.1093/nar/28.18.3442 [DOI: 10.1093/nar/28.18.3442]
  57. Soucek L, Whitfield J, Martins CP, Finch AJ, Murphy DJ, Sodir NM, Karnezis AN, Swigart LB, Nasi S, Evan GI (2008) Modelling Myc inhibition as a cancer therapy. Nature 455(7213):679–683. https://doi.org/10.1038/nature07260 [DOI: 10.1038/nature07260]
  58. Sun H, Yu G (2019) New insights into the pathogenicity of non-synonymous variants through multi-level analysis. Sci Rep 9(1):1667. https://doi.org/10.1038/s41598-018-38189-9 [DOI: 10.1038/s41598-018-38189-9]
  59. Tang H, Thomas PD (2016) PANTHER-PSEP: Predicting disease-causing genetic variants using position-specific evolutionary preservation. Bioinformatics 32(14):2230–2232. https://doi.org/10.1093/bioinformatics/btw222 [DOI: 10.1093/bioinformatics/btw222]
  60. Varadi M, Bertoni D, Magana P, Paramval U, Pidruchna I, Radhakrishnan M, Tsenkov M, Nair S, Mirdita M, Yeo J, Kovalevskiy O, Tunyasuvunakool K, Laydon A, Žídek A, Tomlinson H, Hariharan D, Abrahamson J, Green T, Jumper J, … Velankar S (2024) AlphaFold Protein structure database in 2024: Providing structure coverage for over 214 million protein sequences. Nucleic Acids Res 52(D1):D368–D375. https://doi.org/10.1093/nar/gkad1011
  61. Wang C, Zhang J, Yin J, Gan Y, Xu S, Gu Y, Huang W (2021) Alternative approaches to target Myc for cancer treatment. Signal Transduct Target Ther 6(1):117. https://doi.org/10.1038/s41392-021-00500-y [DOI: 10.1038/s41392-021-00500-y]
  62. Wang C, Tan X, Tang D, Gou Y, Han C, Ning W, Lin S, Zhang W, Chen M, Peng D, Xue Y (2022) GPS-Uber: A hybrid-learning framework for prediction of general and E3-specific lysine ubiquitination sites. Brief Bioinforma 23(2):bbab574. https://doi.org/10.1093/bib/bbab574
  63. Wiese KE, Walz S, von Eyss B, Wolf E, Athineos D, Sansom O, Eilers M (2013) The role of MIZ-1 in MYC-dependent tumorigenesis. Cold Spring Harb Perspect Med 3(12):a014290. https://doi.org/10.1101/cshperspect.a014290 [DOI: 10.1101/cshperspect.a014290]
  64. Yates CM, Sternberg MJE (2013) The effects of non-synonymous single nucleotide polymorphisms (nsSNPs) on protein-protein interactions. J Mol Biol 425(21):3949–3963. https://doi.org/10.1016/j.jmb.2013.07.012 [DOI: 10.1016/j.jmb.2013.07.012]
  65. Yazar M, Özbek P (2021) In silico tools and approaches for the prediction of functional and structural effects of single-nucleotide polymorphisms on proteins: An expert review. Omics: A J Integr Biol 25(1):23–37. https://doi.org/10.1089/omi.2020.0141
  66. Zhang Y (2005) TM-align: A protein structure alignment algorithm based on the TM-score. Nucleic Acids Res 33(7):2302–2309. https://doi.org/10.1093/nar/gki524 [DOI: 10.1093/nar/gki524]
  67. Zhou Y, Gao X, Yuan M, Yang B, He Q, Cao J (2021) Targeting Myc Interacting Proteins as a Winding Path in Cancer Therapy. Front Pharmacol 12:748852. https://doi.org/10.3389/fphar.2021.748852 [DOI: 10.3389/fphar.2021.748852]
  68. Zhu Y, Wang Z, Li Y, Peng H, Liu J, Zhang J, Xiao X (2023) The Role of CREBBP/EP300 and Its Therapeutic Implications in Hematological Malignancies. Cancers 15(4):1219. https://doi.org/10.3390/cancers15041219 [DOI: 10.3390/cancers15041219]

Grants

  1. DST/INSPIRE Fellowship/IF210714/DEPARTMENT OF SCIENCE AND TECHNOLOGY,INDIA
  2. 202223-NFST-ASS-00016/National Fellowship for Scheduled Tribe, Minstry of Tribal affairs, Govt.of India
Journal Article

Word Cloud

Created with Highcharts 10.0.0MYCvariantscellulargenensSNPswithinanalysisoncogenicproteinfunctionsimpactstructurestudytoolsfunctionaldomainspost-translationalmaydisruptinteractionsMYC'sproto-oncogeneencodesbasichelix-loop-helixleucinezipperHLH-LZtranscriptionfactoractingmasterregulatorgenesinvolvedproliferationdifferentiationimmunesurveillanceDysregulationimplicated70%humancancersdrivingprocessesalteredexpressiondisruptedNon-synonymoussinglenucleotidepolymorphismscodingregionscansignificantlyfunctionleadingabnormalbehavioursemployed29silicosystematicallyevaluatedeleteriousnessassessedvariants'effectsdiseaseassociationmodificationsitesinvestigatedprotein-proteincriticalrolesnormalidentified21predicteddeleteriouspathogeniccorrespondresiduesD63HD63YP74LP75LN375DN375IE378KE378QE378AE378GE378VR379PR381KR381TR382WL392PR393CR393HR393PL411HL411PStabilityassessmentsindicateddestabiliseNoneaffectedmodificationsProtein-proteininteractiondockingrevealedbHLHLZMYC/MAXbindingpotentiallyimpactingactivitytranscriptionalregulationcomputationalassessmentenhancesunderstandinggeneticvariationsprioritisescandidateexperimentalvalidationtherapeuticexplorationComputationalvariants:structuralnon-synonymousSNPsIn-silicoBHLH-LZproteinsMYC-MAXdimerizationMolecularProteinstability

Similar Articles

Cited By