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2000
Volume 33, Issue 7
  • ISSN: 0929-8673
  • E-ISSN: 1875-533X

Abstract

Background

Colorectal cancer (CRC) stands as the third most widespread cancer worldwide in both men and women, witnessing a concerning rise, especially in younger demographics. Abnormal activation of the Non-Receptor Tyrosine Kinase c-Src has been linked to the advancement of several human cancers, including colorectal, breast, lung, and pancreatic ones. The interaction between c-Src and Hexokinase 2 (HK2) triggers enzyme phosphorylation, significantly boosting glycolysis, and ultimately contributing to the development of CRC.

Objectives

The objectives of this study are to examine the influence of newly identified mutations on the interaction between c-Src and the HK2 enzyme and to discover potent phytocompounds capable of disrupting this interaction.

Methods

In this study, we utilized molecular docking to check the effect of the identified mutation on the binding of c-Src with HK2. Virtual drug screening, MD simulation, and binding free energy were employed to identify potent drugs against the binding interface of c-Src and HK2.

Results

Among these mutations, six (W151C, L272P, A296S, A330D, R391H, and P434A) were observed to significantly disrupt the stability of the c-Src structure. Additionally, through molecular docking analysis, we demonstrated that the mutant forms of c-Src exhibited high binding affinities with HK2. The wildtype showed a docking score of -271.80 kcal/mol, while the mutants displayed scores of -280.77 kcal/mol, -369.01 kcal/mol, -324.41 kcal/mol, -362.18 kcal/mol, 266.77 kcal/mol, and -243.28 kcal/mol for W151C, L272P, A296S, A330D, R391H, and P434A respectively. Furthermore, we identified five lead phytocompounds showing strong potential to impede the binding of c-Src with HK2 enzyme, essential for colon cancer progression. These compounds exhibit robust bonding with c-Src with docking scores of -7.37 kcal/mol, -7.26 kcal/mol, -6.88 kcal/mol, -6.81 kcal/mol, and -6.73 kcal/mol. Moreover, these compounds demonstrate dynamic stability, structural compactness, and the lowest residual fluctuation during MD simulation. The binding free energies for the top five hits (-42.44±0.28 kcal/mol, -28.31±0.25 kcal/mol, -34.95±0.44 kcal/mol, -38.92±0.25 kcal/mol, and -30.34±0.27 kcal/mol), further affirm the strong interaction of these drugs with c-Src which might impede the cascade of events that drive the progression of colon cancer.

Conclusion

Our findings serve as a promising foundation, paving the way for future discoveries in the fight against colorectal cancer.

© 2026 Bentham Science Publishers
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References

  1. AraghiM. SoerjomataramI. BardotA. FerlayJ. CabasagC.J. MorrisonD.S. DeP. TervonenH. WalshP.M. BucherO. EngholmG. JacksonC. McClureC. WoodsR.R. Saint-JacquesN. MorganE. RansomD. ThursfieldV. MøllerB. LeonfellnerS. GurenM.G. BrayF. ArnoldM. Changes in colorectal cancer incidence in seven high-income countries: a population-based study.Lancet Gastroenterol. Hepatol.20194751151810.1016/S2468‑1253(19)30147‑5 31105047
     [Google Scholar]
  2. LopesS.R. MartinsC. SantosI.C. TeixeiraM. GamitoÉ. AlvesA.L. Colorectal cancer screening: A review of current knowledge and progress in research.World J. Gastrointest. Oncol.20241641119113310.4251/wjgo.v16.i4.1119 38660635
     [Google Scholar]
  3. BillerL.H. SchragD. Diagnosis and treatment of metastatic colorectal cancer: a review.JAMA2021325766968510.1001/jama.2021.0106 33591350
     [Google Scholar]
  4. SawickiT. RuszkowskaM. DanielewiczA. NiedźwiedzkaE. ArłukowiczT. PrzybyłowiczK.E. A review of colorectal cancer in terms of epidemiology, risk factors, development, symptoms and diagnosis.Cancers2021139202510.3390/cancers13092025 33922197
     [Google Scholar]
  5. ArnoldM. SierraM.S. LaversanneM. SoerjomataramI. JemalA. BrayF. Global patterns and trends in colorectal cancer incidence and mortality.Gut201766468369110.1136/gutjnl‑2015‑310912 26818619
     [Google Scholar]
  6. World health statistics 2016: monitoring health for the SDGs, sustainable development goals.2016 Available from: https://www.who.int/publications/i/item/9789241565264(accessed on 12-7-2024)
  7. AstinM. GriffinT. NealR.D. RoseP. HamiltonW. The diagnostic value of symptoms for colorectal cancer in primary care: a systematic review.Br. J. Gen. Pract.201161586e231e24310.3399/bjgp11X572427 21619747
     [Google Scholar]
  8. RoshandelG. Ghasemi-KebriaF. MalekzadehR. Colorectal cancer: Epidemiology, risk factors, and prevention.Cancers2024168153010.3390/cancers16081530 38672612
     [Google Scholar]
  9. CollettM.S. EriksonE. PurchioA.F. BruggeJ.S. EriksonR.L. A normal cell protein similar in structure and function to the avian sarcoma virus transforming gene product.Proc. Natl. Acad. Sci.19797673159316310.1073/pnas.76.7.3159 226956
     [Google Scholar]
  10. IshizawarR. ParsonsS.J. c-Src and cooperating partners in human cancer.Cancer Cell20046320921410.1016/j.ccr.2004.09.001 15380511
     [Google Scholar]
  11. YeatmanT.J. A renaissance for SRC.Nat. Rev. Cancer20044647048010.1038/nrc1366 15170449
     [Google Scholar]
  12. SummyJ.M. GallickG.E. Src family kinases in tumor progression and metastasis.Cancer Metastasis Rev.200322433735810.1023/A:1023772912750 12884910
     [Google Scholar]
  13. TalamontiM.S. RohM.S. CurleyS.A. GallickG.E. Increase in activity and level of pp60c-src in progressive stages of human colorectal cancer.J. Clin. Invest.1993911536010.1172/JCI116200 7678609
     [Google Scholar]
  14. CartwrightC.A. KampsM.P. MeislerA.I. PipasJ.M. EckhartW. pp60c-src activation in human colon carcinoma.J. Clin. Invest.19898362025203310.1172/JCI114113 2498394
     [Google Scholar]
  15. JonesR.J. AvizienyteE. WykeA.W. OwensD.W. BruntonV.G. FrameM.C. Elevated c-Src is linked to altered cell–matrix adhesion rather than proliferation in KM12C human colorectal cancer cells.Br. J. Cancer200287101128113510.1038/sj.bjc.6600594 12402152
     [Google Scholar]
  16. DangX.W. DuanJ-L. YeE. MaoN-D. BaiR.R. ZhouX. YeX-Y. Recent advances of small-molecule c-Src inhibitors for potential therapeutic utilities.Bioorg. Chem.202414210693410.1016/j.bioorg.2023.106934
     [Google Scholar]
  17. IrbyR.B. YeatmanT.J. Role of Src expression and activation in human cancer.Oncogene200019495636564210.1038/sj.onc.1203912 11114744
     [Google Scholar]
  18. BrauerP.M. TynerA.L. Building a better understanding of the intracellular tyrosine kinase PTK6 - BRK by BRK.Biochim. Biophys. Acta2010180616673 20193745
     [Google Scholar]
  19. BruntonV.G. OzanneB.W. ParaskevaC. FrameM.C. A role for epidermal growth factor receptor, c-Src and focal adhesion kinase in an in vitro model for the progression of colon cancer.Oncogene199714328329310.1038/sj.onc.1200827 9018114
     [Google Scholar]
  20. HanahanD. WeinbergR.A. Hallmarks of cancer: the next generation.Cell2011144564667410.1016/j.cell.2011.02.013
     [Google Scholar]
  21. WarburgO. On the origin of cancer cells.Science1956123319130931410.1126/science.123.3191.309 13298683
     [Google Scholar]
  22. ZhangH. XuD. HuangH. JiangH. HuL. LiuL. SunG. GaoJ. LiY. XiaC. ChenS. ZhouH. KongX. WangM. LuoC. Discovery of a covalent inhibitor selectively targeting the autophosphorylation site of c-Src Kinase.ACS Chem. Biol.2024194999101010.1021/acschembio.4c00048 38513196
     [Google Scholar]
  23. ChakrabortyP. LubnaS. BhuinS. K, D.; Chakravarty, M.; Jamma, T.; Yogeeswari, P. Targeting hexokinase 2 for oral cancer therapy: structure-based design and validation of lead compounds.Front. Pharmacol.202415134627010.3389/fphar.2024.1346270 38529190
     [Google Scholar]
  24. ŠimčíkováD. GardášD. HložkováK. HrudaM. ŽáčekP. RobL. HenebergP. Loss of hexokinase 1 sensitizes ovarian cancer to high-dose metformin.Cancer Metab.2021914110.1186/s40170‑021‑00277‑2 34895333
     [Google Scholar]
  25. YangL. YanX. ChenJ. ZhanQ. HuaY. XuS. LiZ. WangZ. DongY. ZuoD. XueM. TangY. HerschmanH.R. LuS. ShiQ. WeiW. Hexokinase 2 discerns a novel circulating tumor cell population associated with poor prognosis in lung cancer patients.Proc. Natl. Acad. Sci.202111811e201222811810.1073/pnas.2012228118 33836566
     [Google Scholar]
  26. TeliG. PalR. MajiL. Purawarga MatadaG.S. SenguptaS. Explanatory review on pyrimidine/fused pyrimidine derivatives as anticancer agents targeting Src kinase.J. Biomol. Struct. Dyn.20244231582161410.1080/07391102.2023.2205943 37144746
     [Google Scholar]
  27. CheungE.C. LudwigR.L. VousdenK.H. Mitochondrial localization of TIGAR under hypoxia stimulates HK2 and lowers ROS and cell death.Proc. Natl. Acad. Sci.201210950204912049610.1073/pnas.1206530109 23185017
     [Google Scholar]
  28. RohJ. KimY. OhJ. KimY. LeeJ. LeeJ. ChunK.H. LeeH.W. Hexokinase 2 is a molecular bridge linking telomerase and autophagy.PLoS One2018132e019318210.1371/journal.pone.0193182 29462198
     [Google Scholar]
  29. ZhangJ. WangS. JiangB. HuangL. JiZ. LiX. ZhouH. HanA. ChenA. WuY. MaH. ZhaoW. ZhaoQ. XieC. SunX. ZhouY. HuangH. SulemanM. LinF. ZhouL. TianF. JinM. CaiY. ZhangN. LiQ. c-Src phosphorylation and activation of hexokinase promotes tumorigenesis and metastasis.Nat. Commun.2017811373210.1038/ncomms13732 28054552
     [Google Scholar]
  30. SulemanM. Tahir ul Qamar, M.; Saleem, S.; Ahmad, S.; Ali, S.S.; Khan, H.; Akbar, F.; Khan, W.; Alblihy, A.; Alrumaihi, F.; Waseem, M.; Allemailem, K.S. Mutational landscape of pirin and elucidation of the impact of most detrimental missense variants that accelerate the breast cancer pathways: A computational modelling study.Front. Mol. Biosci.2021869283510.3389/fmolb.2021.692835 34262943
     [Google Scholar]
  31. MagraneM. UniProt Consortium. UniProt Knowledgebase: a hub of integrated protein data.Database20112011bar00910.1093/database/bar009
     [Google Scholar]
  32. KarczewskiK.J. FrancioliL.C. TiaoG. CummingsB.B. AlföldiJ. WangQ. CollinsR.L. LaricchiaK.M. GannaA. BirnbaumD.P. GauthierL.D. The mutational constraint spectrum quantified from variation in 141,456 humans.Nature20205817809434443
     [Google Scholar]
  33. BendlJ. StouracJ. SalandaO. PavelkaA. WiebenE.D. ZendulkaJ. BrezovskyJ. DamborskyJ. PredictSNP: robust and accurate consensus classifier for prediction of disease-related mutations.PLOS Comput. Biol.2014101e100344010.1371/journal.pcbi.1003440 24453961
     [Google Scholar]
  34. CapriottiE. CalabreseR. CasadioR. Predicting the insurgence of human genetic diseases associated to single point protein mutations with support vector machines and evolutionary information.Bioinformatics200622222729273410.1093/bioinformatics/btl423 16895930
     [Google Scholar]
  35. AdzhubeiI.A. SchmidtS. PeshkinL. RamenskyV.E. GerasimovaA. BorkP. KondrashovA.S. SunyaevS.R. A method and server for predicting damaging missense mutations.Nat. Methods20107424824910.1038/nmeth0410‑248 20354512
     [Google Scholar]
  36. StoneE.A. SidowA. Physicochemical constraint violation by missense substitutions mediates impairment of protein function and disease severity.Genome Res.200515797898610.1101/gr.3804205 15965030
     [Google Scholar]
  37. SimN.L. KumarP. HuJ. HenikoffS. SchneiderG. NgP.C. SIFT web server: predicting effects of amino acid substitutions on proteins.Nucleic Acids Res.201240W1W452W45710.1093/nar/gks539 22689647
     [Google Scholar]
  38. CalabreseR. CapriottiE. FariselliP. MartelliP.L. CasadioR. Functional annotations improve the predictive score of human disease-related mutations in proteins.Hum. Mutat.20093081237124410.1002/humu.21047 19514061
     [Google Scholar]
  39. RodriguesC.H.M. PiresD.E.V. AscherD.B. DYNAMUT2: Assessing changes in stability and flexibility upon single and multiple point missense mutations.Protein Sci.2021301606910.1002/pro.3942 32881105
     [Google Scholar]
  40. PiresD.E.V. AscherD.B. BlundellT.L. mCSM: predicting the effects of mutations in proteins using graph-based signatures.Bioinformatics201430333534210.1093/bioinformatics/btt691 24281696
     [Google Scholar]
  41. WebbB. SaliA. Comparative protein structure modeling using MODELLER.Current prot. bioinform.2016541137
     [Google Scholar]
  42. YanY. TaoH. HeJ. HuangS.Y. The HDOCK server for integrated protein–protein docking.Nat. Protoc.20201551829185210.1038/s41596‑020‑0312‑x 32269383
     [Google Scholar]
  43. LaskowskiR.A. JabłońskaJ. PravdaL. VařekováR.S. ThorntonJ.M. PDBsum: Structural summaries of PDB entries.Protein Sci.201827112913410.1002/pro.3289 28875543
     [Google Scholar]
  44. SulemanM. Elucidating the binding mechanism of SARS-CoV-2 NSP6-TBK1 and structure-based designing of phytocompounds inhibitors for instigating the host immune response.Front Chem.2023202311 38293247
     [Google Scholar]
  45. Ntie-KangF. ZofouD. BabiakaS.B. MeudomR. ScharfeM. LifongoL.L. MbahJ.A. MbazeL.M. SipplW. EfangeS.M.N. AfroDb: a select highly potent and diverse natural product library from African medicinal plants.PLoS One2013810e7808510.1371/journal.pone.0078085 24205103
     [Google Scholar]
  46. LagorceD. BouslamaL. BecotJ. MitevaM.A. VilloutreixB.O. FAF-Drugs4: free ADME-tox filtering computations for chemical biology and early stages drug discovery.Bioinformatics201733223658366010.1093/bioinformatics/btx491 28961788
     [Google Scholar]
  47. RavindranathP.A. ForliS. GoodsellD.S. OlsonA.J. SannerM.F. AutoDockFR: advances in protein-ligand docking with explicitly specified binding site flexibility.PLOS Comput. Biol.20151112e100458610.1371/journal.pcbi.1004586 26629955
     [Google Scholar]
  48. SulemanM. YousafiQ. AliJ. AliS.S. HussainZ. AliS. WaseemM. IqbalA. AhmadS. KhanA. WangY. WeiD.Q. Bioinformatics analysis of the differences in the binding profile of the wild-type and mutants of the SARS-CoV-2 spike protein variants with the ACE2 receptor.Comput. Biol. Med.202113810493610.1016/j.compbiomed.2021.104936 34655895
     [Google Scholar]
  49. KhanA. UmbreenS. HameedA. FatimaR. ZahoorU. BabarZ. WaseemM. HussainZ. RizwanM. ZamanN. AliS. SulemanM. ShahA. AliL. AliS.S. WeiD.Q. In silico mutagenesis-based remodelling of SARS-CoV-1 peptide (ATLQAIAS) to inhibit SARS-CoV-2: structural-dynamics and free energy calculations.Interdiscip. Sci.202113352153410.1007/s12539‑021‑00447‑2 34324157
     [Google Scholar]
  50. Salomon-FerrerR. CaseD.A. WalkerR.C. An overview of the Amber biomolecular simulation package.Wiley Interdiscip. Rev. Comput. Mol. Sci.20133219821010.1002/wcms.1121
     [Google Scholar]
  51. CaseD.A. CheathamT.E.III DardenT. GohlkeH. LuoR. MerzK.M.Jr OnufrievA. SimmerlingC. WangB. WoodsR.J. The Amber biomolecular simulation programs.J. Comput. Chem.200526161668168810.1002/jcc.20290 16200636
     [Google Scholar]
  52. WatowichS.J. MeyerE.S. HagstromR. JosephsR. A stable, rapidly converging conjugate gradient method for energy minimization.J. Comput. Chem.19889665066110.1002/jcc.540090611
     [Google Scholar]
  53. MezaJ.C. Steepest descent.Wiley Interdiscip. Rev. Comput. Stat.20102671972210.1002/wics.117
     [Google Scholar]
  54. BabarZ. KhanM. ZahraM. AnwarM. NoorK. HashmiH.F. SulemanM. WaseemM. ShahA. AliS. AliS.S. Drug similarity and structure-based screening of medicinal compounds to target macrodomain-I from SARS-CoV-2 to rescue the host immune system: a molecular dynamics study.J. Biomol. Struct. Dyn.202240152353710.1080/07391102.2020.1815583 32897173
     [Google Scholar]
  55. Salomon-FerrerR. GötzA.W. PooleD. Le GrandS. WalkerR.C. Routine microsecond molecular dynamics simulations with AMBER on GPUs. 2. Explicit solvent particle mesh Ewald.J. Chem. Theory Comput.2013993878388810.1021/ct400314y 26592383
     [Google Scholar]
  56. AhmadI. AliS.S. ZafarB. HashmiH.F. ShahI. KhanS. SulemanM. KhanM. UllahS. AliS. KhanJ. AliM. KhanA. WeiD.Q. Development of multi-epitope subunit vaccine for protection against the norovirus’ infections based on computational vaccinology.J. Biomol. Struct. Dyn.20224073098310910.1080/07391102.2020.1845799 33170093
     [Google Scholar]
  57. LaguninA.A. DubovskajaV.I. RudikA.V. PogodinP.V. DruzhilovskiyD.S. GloriozovaT.A. FilimonovD.A. SastryN.G. PoroikovV.V. CLC-Pred: A freely available web-service for in silico prediction of human cell line cytotoxicity for drug-like compounds.PLoS One2018131e019183810.1371/journal.pone.0191838 29370280
     [Google Scholar]
  58. AshktorabH. KupferS.S. BrimH. CarethersJ.M. Racial disparity in gastrointestinal cancer risk.Gastroenterology2017153491092310.1053/j.gastro.2017.08.018 28807841
     [Google Scholar]
  59. AL-Eitan, L.N.; Al-Ahmad, B.H.; Almomani, F.A. The association of il-1 and HRAS gene polymorphisms with breast cancer susceptibility in a Jordanian population of] Arab descent: A genotype-phenotype study.Cancers (Basel)202012228310.3390/cancers12020283 31979384
     [Google Scholar]
  60. ZhouY. LiN. ZhuangW. LiuG.J. WuT.X. YaoX. DuL. WeiM.L. WuX.T. P53 codon 72 polymorphism and gastric cancer: A meta-analysis of the literature.Int. J. Cancer200712171481148610.1002/ijc.22833 17546594
     [Google Scholar]
  61. TanfordC. Protein Denaturation.Adv. Protein Chem.19682312128210.1016/S0065‑3233(08)60401‑5 4882248
     [Google Scholar]
  62. ShoichetB.K. BaaseW.A. KurokiR. MatthewsB.W. A relationship between protein stability and protein function.Proc. Natl. Acad. Sci. USA199592245245610.1073/pnas.92.2.452 7831309
     [Google Scholar]
  63. ZhangN. LuH. ChenY. ZhuZ. YangQ. WangS. LiM. PremPRI: Predicting the effects of missense mutations on protein–RNA interactions.Int. J. Mol. Sci.20202115556010.3390/ijms21155560 32756481
     [Google Scholar]
  64. CapriottiE. FariselliP. CasadioR. I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure.Nucleic Acids Res.200533Web Server(Suppl. 2), W306W310.10.1093/nar/gki375 15980478
     [Google Scholar]
  65. PiresD.E.V. AscherD.B. BlundellT.L. DUET: a server for predicting effects of mutations on protein stability using an integrated computational approach.Nucleic Acids Res.201442W1W314W31910.1093/nar/gku411 24829462
     [Google Scholar]
  66. SulemanM. Umme-I-HaniS. SalmanM. AljuaidM. KhanA. IqbalA. HussainZ. AliS.S. AliL. SherH. WaheedY. WeiD.Q. Sequence-structure functional implications and molecular simulation of high deleterious nonsynonymous substitutions in IDH1 revealed the mechanism of drug resistance in glioma.Front. Pharmacol.20221392757010.3389/fphar.2022.927570 36188571
     [Google Scholar]
  67. SulemanM. MurtazaA. KhanH. RashidF. AlshammariA. AliL. KhanA. WeiD.Q. The XBB.1.5 slightly increase the binding affinity for host receptor ACE2 and exhibit strongest immune escaping features: molecular modeling and free energy calculation.Front. Mol. Biosci.202310115304610.3389/fmolb.2023.1153046 37325478
     [Google Scholar]
  68. KhanA. WarisH. RafiqueM. SulemanM. MohammadA. AliS.S. KhanT. WaheedY. LiaoC. WeiD.Q. The Omicron (B.1.1.529) variant of SARS-CoV-2 binds to the hACE2 receptor more strongly and escapes the antibody response: Insights from structural and simulation data.Int. J. Biol. Macromol.202220043844810.1016/j.ijbiomac.2022.01.059 35063482
     [Google Scholar]
  69. KhanA. ZiaT. SulemanM. KhanT. AliS.S. AbbasiA.A. MohammadA. WeiD.Q. Higher infectivity of the SARS-CoV-2 new variants is associated with K417N/T, E484K, and N501Y mutants: An insight from structural data.J. Cell. Physiol.2021236107045705710.1002/jcp.30367 33755190
     [Google Scholar]
  70. SulemanM. LuqmanM. WeiD.Q. AliS. AliS.S. KhanA. KhanH. AliZ. KhanW. RizwanM. UllahN. Structural insights into the effect of mutations in the spike protein of SARS-CoV-2 on the binding with human furin protein.Heliyon202394e1508310.1016/j.heliyon.2023.e15083 37064465
     [Google Scholar]
  71. MaiaE.H.B. AssisL.C. de OliveiraT.A. da SilvaA.M. TarantoA.G. Structure-based virtual screening: from classical to artificial intelligence.Front Chem.2020834310.3389/fchem.2020.00343 32411671
     [Google Scholar]
  72. OliveiraT. SilvaM. MaiaE. SilvaA. TarantoA. Virtual screening algorithms in drug discovery: A review focused on machine and deep learning methods.Drugs and Drug Candidates20232231133410.3390/ddc2020017
     [Google Scholar]
  73. MengX.Y. ZhangH.X. MezeiM. CuiM. Molecular docking: a powerful approach for structure-based drug discovery.Curr. Computeraided Drug Des.20117214615710.2174/157340911795677602 21534921
     [Google Scholar]
  74. PhillipsM.A. Has molecular docking ever brought us a medicine.Molecular Docking.Intechopen201810.5772/intechopen.72898
     [Google Scholar]
  75. KarplusM. KuriyanJ. Molecular dynamics and protein function.Proc. Natl. Acad. Sci. USA2005102196679668510.1073/pnas.0408930102 15870208
     [Google Scholar]
  76. SulemanM. RashidF. AliS. SherH. LuoS. XieL. XieZ. Immunoinformatic-based design of immune-boosting multiepitope subunit vaccines against monkeypox virus and validation through molecular dynamics and immune simulation.Front. Immunol.202213104299710.3389/fimmu.2022.1042997 36311718
     [Google Scholar]
  77. RashidF. SulemanM. ShahA. DzakahE.E. ChenS. WangH. TangS. Structural analysis on the severe acute respiratory syndrome coronavirus 2 non-structural protein 13 mutants revealed altered bonding network with TANK binding kinase 1 to evade host immune system.Front. Microbiol.20211278906210.3389/fmicb.2021.789062 34925297
     [Google Scholar]
  78. ShahA. RehmatS. AslamI. SulemanM. BatoolF. AzizA. RashidF. Midrarullah NawazM.A. AliS.S. JunaidM. KhanA. WeiD.Q. Comparative mutational analysis of SARS-CoV-2 isolates from Pakistan and structural-functional implications using computational modelling and simulation approaches.Comput. Biol. Med.202214110517010.1016/j.compbiomed.2021.105170 34968862
     [Google Scholar]
  79. KhanA. HussainS. AhmadS. SulemanM. BukhariI. KhanT. RashidF. AzadA.K. WaseemM. KhanW. HussainZ. KhanA. AliS.S. QinQ. WeiD.Q. Computational modelling of potentially emerging SARS-CoV-2 spike protein RBDs mutations with higher binding affinity towards ACE2: A structural modelling study.Comput. Biol. Med.202214110516310.1016/j.compbiomed.2021.105163 34979405
     [Google Scholar]
  80. RashidF. SulemanM. ShahA. DzakahE.E. WangH. ChenS. TangS. Mutations in SARS-CoV-2 ORF8 altered the bonding network with interferon regulatory factor 3 to evade host immune system.Front. Microbiol.20211270314510.3389/fmicb.2021.703145 34335535
     [Google Scholar]
  81. ChenD. OezguenN. UrvilP. FergusonC. DannS.M. SavidgeT.C. Regulation of protein-ligand binding affinity by hydrogen bond pairing.Sci. Adv.201623e150124010.1126/sciadv.1501240 27051863
     [Google Scholar]
  82. ChoderaJ.D. MobleyD.L. Entropy-enthalpy compensation: role and ramifications in biomolecular ligand recognition and design.Annu. Rev. Biophys.201342112114210.1146/annurev‑biophys‑083012‑130318 23654303
     [Google Scholar]
  83. JewkesR. SikweyiyaY. MorrellR. DunkleK. Gender inequitable masculinity and sexual entitlement in rape perpetration South Africa: findings of a cross-sectional study.PLoS One2011612e2959010.1371/journal.pone.0029590 22216324
     [Google Scholar]
  84. WangE. SunH. WangJ. WangZ. LiuH. ZhangJ.Z.H. HouT. End-point binding free energy calculation with MM/PBSA and MM/GBSA: strategies and applications in drug design.Chem. Rev.2019119169478950810.1021/acs.chemrev.9b00055 31244000
     [Google Scholar]
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Deciphering Mutational Impacts on c-Src-HK2 Interaction in Colorectal Cancer Progression, and Identification of Potential Phytocompounds Inhibitors: A Molecular Simulation and Free Energy Calculation Approach
Curr. Med. Chem. 33, 1338 (2026); https://doi.org/10.2174/0109298673311962240815055821
/content/journals/cmc/10.2174/0109298673311962240815055821
/content/journals/cmc/10.2174/0109298673311962240815055821
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  • Article Type:     Research Article
Keyword(s): binding energy; c-Src; colon cancer; HK2; MD simulation; molecular docking

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