Skip to main content
Log in

Understanding the structural features of JAK2 inhibitors: a combined 3D-QSAR, DFT and molecular dynamics study

  • Original Article
  • Published:
Molecular Diversity Aims and scope Submit manuscript

Abstract

JAK2 plays a critical role in JAK/STAT signaling pathway and in patho-mechanism of myeloproliferative disorders and autoimmune diseases. Thus, effective JAK2 inhibitors provide a promising opportunity for the pharmaceutical intervention of many diseases. In this work, 3D-QSAR study was performed on a series of 1-amino-5H-pyrido-indole-4-carboxamide derivatives as JAK2 inhibitors to obtain reliable comparative molecular field analysis (CoMFA) and comparative molecular similarity analysis (CoMSIA) models with three different alignment methods. Among the different alignment methods, ligand-based (CoMFA: q2 = 0.676, r2 = 0.979; CoMSIA: q2 = 0.700, r2 = 0.953) and pharmacophore-based alignment (CoMFA: q2 = 0.710, r2 = 0.982; CoMSIA: q2 = 0.686, r2 = 0.960) has produced better statistical results when compared to receptor-based alignment (CoMFA: q2 = 0.507, r2 = 0.979; CoMSIA: q2 = 0.544, r2 = 0.917). Statistical parameters indicated that data are well fitted and have high predictive ability. The presence of electrostatic and hydrophobic field is highly desirable for potent inhibitory activity, and the steric field plays a minor role in modulating the activity. The contour analysis indicates ARG980, ASN981, ASP939 and LEU937 have more possibility of interacting with bulky, hydrophobic groups in pyrido and positive and negative groups in pyrazole ring. Based on our findings, we have designed sixteen molecules and predicted its activity and drug-like properties. Subsequently, molecular docking, molecular dynamics and DFT calculations were performed to evaluate its potency.

Graphical abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Leonard WJ, O’Shea JJ (1998) Jaks and STATs: biological implications. Annu Rev Immunol 16:293–322. https://doi.org/10.1146/annurev.immunol.16.1.293

    Article  CAS  PubMed  Google Scholar 

  2. Laurence A, Pesu M, Silvennoinen O, O’Shea J (2012) JAK kinases in health and disease: an update. Open Rheumatol J 6:232–244. https://doi.org/10.2174/1874312901206010232

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Vainchenker W, Constantinescu SN (2013) JAK/STAT signaling in hematological malignancies. Oncogene 32:2601–2613. https://doi.org/10.1038/onc.2012.347

    Article  CAS  PubMed  Google Scholar 

  4. Quintas-Cardama A, Vertovsek S (2011) New JAK2 inhibitors for myeloproliferative neoplasms. Expert Opin Invest Drugs 20:961–972. https://doi.org/10.1517/13543784.2011.579560

    Article  CAS  Google Scholar 

  5. Williams NK, Bamert RS, Patel O, Wang C, Walden PM, Wilks AF, Fantino E, Rossjohn J, Lucet IS (2009) Dissecting specificity in the Janus kinases: the structures of JAK-specific inhibitors complexed to the JAK1 and JAK2 protein tyrosine kinase domains. J Mol Biol 387:219–232. https://doi.org/10.1016/j.jmb.2009.01.041

    Article  CAS  PubMed  Google Scholar 

  6. Vrontaki E, Melagraki G, Afantitis A, Mavromoustakos T, Kollias G (2017) Searching for novel Janus kinase-2 inhibitors using a combination of pharmacophore modeling, 3D-QSAR studies and virtual screening. Mini Rev Med Chem 17:268–294. https://doi.org/10.2174/1389557516666160919163930

    Article  CAS  PubMed  Google Scholar 

  7. Kisseleva T, Bhattacharya S, Braunstein J, Schindler CW (2002) Signaling through the JAK/STAT pathway, recent advances and future challenges. Gene 285:1–24. https://doi.org/10.1016/S0378-1119(02)00398-0

    Article  CAS  PubMed  Google Scholar 

  8. Lindauer K, Loerting T, Liedl KR, Kroemer RT (2001) Prediction of the structure of human Janus kinase 2 (JAK2) comprising the two carboxy-terminal domains reveals a mechanism for autoregulation. Protein Eng 14:27–37. https://doi.org/10.1093/protein/14.1.27

    Article  CAS  PubMed  Google Scholar 

  9. Remy I, Wilson IA, Michnick SW (1990) Erythropoietin receptor activation by a ligand-induced conformation change. Science 283:990–993. https://doi.org/10.2307/2897727

    Article  Google Scholar 

  10. Ishihara K, Hirano T (2002) Molecular basis of the cell specificity of cytokine action. Biochim Biophys Acta 1592:281–296. https://doi.org/10.1016/S0167-4889(02)00321-X

    Article  CAS  PubMed  Google Scholar 

  11. Burns CJ, Bourke DG, Andrau L, Bu X, Charman SA, Donohue AC, Fantino E, Farrugia M, Feutrill JT, Joffe M, Kling MR, Kurek M, Nero TL, Nguyen T, Palmer JT, Phillips I, Shackleford DM, Sikanyika H, Styles M, Su S, Treutlein H, Zeng J, Wilks AF (2009) Phenylaminopyrimidines as inhibitors of Janus kinases (JAKs). Bioorg Med Chem Lett 19:5887–5892. https://doi.org/10.1016/j.bmcl.2009.08.071

    Article  CAS  PubMed  Google Scholar 

  12. Chen E, Staudt LM, Green AR (2012) Janus kinase deregulation in leukemia and lymphoma. Immunity 36:529–541. https://doi.org/10.1016/j.immuni.2012.03.017

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Ghoreschi K, Laurence A, O’Shea JJ (2009) Janus kinases in immune cell signaling. Immunol Rev 228:2730–2787. https://doi.org/10.1111/j.1600-065X.2008.00754.x

    Article  Google Scholar 

  14. Rawlings JS, Rosler KM, Harrison DA (2004) The JAK/STAT signaling pathway. J Cell Sci 117:1281–1283. https://doi.org/10.1242/jcs.00963

    Article  CAS  PubMed  Google Scholar 

  15. Timofeeva OA, Tarasova NI (2012) Alternative ways of modulating JAK-STAT pathway: looking beyond phosphorylation. JAKSTAT 1:274–284. https://doi.org/10.4161/jkst.22313

    Article  PubMed  PubMed Central  Google Scholar 

  16. Aaronson DS, Horvath CM (2002) A road map for those who don’t know JAK-STAT. Science 296:1653–1655. https://doi.org/10.1126/science.1071545

    Article  CAS  PubMed  Google Scholar 

  17. Imada K, Leonard WJ (2000) The Jak-STAT pathway. Mol Immunol 37:1–11. https://doi.org/10.1016/S0161-5890(00)00018-3

    Article  CAS  PubMed  Google Scholar 

  18. Wu W, Sun XH (2011) Janus kinase 3: the controller and the controlled. Acta Biochim Biophys Sin 44:187–196. https://doi.org/10.1093/abbs/gmr105

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Yeh TC, Pellegrini S (1999) The Janus kinase family of protein tyrosine kinases and their role in signaling. Cell Mol Life Sci 55:1523–1534. https://doi.org/10.1007/s000180050392

    Article  CAS  PubMed  Google Scholar 

  20. Yamaoka K, Saharinen P, Pesu M, Holt VE, Silvennoinen O, O’Shea JJ (2004) The Janus kinases (Jaks). Genome Biol 5:253–258. https://doi.org/10.1186/gb-2004-5-12-253

    Article  PubMed  PubMed Central  Google Scholar 

  21. Lucet IS, Fantino E, Styles M, Bamert R, Patel O, Broughton SE, Walter M, Burns CJ, Treutlein H, Wilks AF, Rossjohn J (2003) The structural basis of Janus kinase 2 inhibition by a potent and specific pan-Janus kinase inhibitor. Blood 107:176–183. https://doi.org/10.1182/blood-2005-06-2413

    Article  CAS  Google Scholar 

  22. Sayyah J, Sayeski PP (2009) Jak2 inhibitors: rationale and role as therapeutic agents in hematologic malignancies. Curr Oncol Rep 11:117–124. https://doi.org/10.1007/s11912-009-0018-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Mascarenhas J, Mughal TI, Verstovsek S (2012) Biology and clinical management of myeloproliferative neoplasms and development of the JAK inhibitor Ruxolitinib. Curr Med Chem 19:4399–4413. https://doi.org/10.2174/092986712803251511

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Delhommeau F, Pisani DF, James C, Casadevall N, Constantinescu S, Vainchenker W (2006) Oncogenic mechanisms in myeloproliferative disorders. Cell Mol Life Sci 63:2939–2953. https://doi.org/10.1007/s00018-006-6272-7

    Article  CAS  PubMed  Google Scholar 

  25. Karoline G, Iris B, Claude H (2013) JAK2 mutants (e.g., JAK2V617F) and their importance as drug targets in myeloproliferative neoplasm. JAK-STAT 2. https://doi.org/10.4161/jkst.25025

    Article  PubMed  PubMed Central  Google Scholar 

  26. James C (2008) The JAK2V617F mutation in polycythemia vera and other myeloproliferative disorders: one mutation for three diseases? Hematol Am Soc Hematol Educ Progr 2008:69–75. https://doi.org/10.1182/asheducation-2008.1.69

    Article  Google Scholar 

  27. Falanga A, Marchetti M, Vignoli A, Balducci D, Russo L, Guerini V, Barbui T (2007) V617F JAK-2 mutation in patients with essential thrombocythemia: relation to platelet, granulocyte, and plasma hemostatic and inflammatory molecules. Exp Hematol 35:702–711. https://doi.org/10.1016/j.exphem.2007.01.053

    Article  CAS  PubMed  Google Scholar 

  28. Matthews DJ, Gerritsen ME (2010) Targeting protein kinases for cancer therapy. Wiley, New York. https://doi.org/10.1002/9780470555293

    Book  Google Scholar 

  29. Lippert E, Boissinot M, Kralovics R, Girodon F, Dobo I, Praloran V, Boiret-Dupre N, Skoda RC, Hermouet S (2006) The JAK2- V617F mutation is frequently present at diagnosis in patients with essential thrombocythemia and polycythemia vera. Blood 108:1865–1867. https://doi.org/10.1182/blood-2006-01-013540

    Article  CAS  PubMed  Google Scholar 

  30. Levine RL, Wadleigh M, Cools J, Ebert BL, Wernig G, Huntly BJP, Boggon TJ, Wlodarska I, Lark JJ, Moore S, Adelsperger J, Koo S, Lee JC, Gabriel S, Mercher T, D’Andrea A, Froehling S, Doehner K, Marynen P, Vandenberghe P, Mesa RA, Tefferi A, Griffin JD, Eck MJ, Sellers WR, Meyerson M, Golub TR, Lee SJ, Gilliland DG (2005) Activating mutation in the tyrosine kinase JAK2 in polycythemia vera, essential thrombocythemia, and myeloid metaplasia with myelofibrosis. Cancer Cell 7:387–397. https://doi.org/10.1016/j.ccr.2005.03.023

    Article  CAS  PubMed  Google Scholar 

  31. Baxter EJ, Scott LM, Campbell PJ, East C, Fourouclas N, Swanton S, Vassiliou GS, Bench AJ, Boyd EM, Curtin N, Scott MA, Erber WN, Green AR (2005) Acquired mutation of the tyrosine kinase JAK2 in human myeloproliferative disorders. Lancet 365:1054–1061. https://doi.org/10.1016/S0140-6736(05)71142-9

    Article  CAS  PubMed  Google Scholar 

  32. Jones AV, Kreil S, Zoi K, Waghorn K, Curtis C, Zhang L, Score J, Seear R, Chase AJ, Grand FH, White H, Zoi C, Loukopoulos D, Terpos E, Vervessou EC, Schultheis B, Emig M, Ernst T, Lengfelder E, Hehlmann R, Hochhaus A, Oscier D, Silver RT, Reiter A, Cross NC (2005) Widespread occurrence of the JAK2 V617F mutation in chronic myeloproliferative disorders. Blood 106:2162–2168. https://doi.org/10.1182/blood-2005-03-1320

    Article  CAS  PubMed  Google Scholar 

  33. Clark JD, Flanagan ME, Telliez JB (2014) Discovery and development of Janus kinase (JAK) inhibitors for inflammatory diseases. J Med Chem 57:5023–5038. https://doi.org/10.1021/jm401490p

    Article  CAS  PubMed  Google Scholar 

  34. O’Shea JJ, Kontzias A, Yamaoka K, Tanaka Y, Laurence A (2013) Janus kinase inhibitors in autoimmune diseases. Ann Rheum Dis 72:ii111–ii115. https://doi.org/10.1136/annrheumdis-2012-202576

    Article  CAS  PubMed  Google Scholar 

  35. Quintas-Cardama A, Kantarjian H, Cortes J, Verstovsek S (2011) Janus kinase inhibitors for the treatment of myeloproliferative neoplasias and beyond. Nat Rev Drug Discov 10:127–140. https://doi.org/10.1038/nrd3264

    Article  CAS  PubMed  Google Scholar 

  36. Tan SH, Nevalainen MT (2008) Signal transducer and activator of transcription 5A/B in prostate and breast cancers. Endocr Relat Cancer 15:367–390. https://doi.org/10.1677/ERC-08-0013

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Dearden JC (2016) The history and development of quantitative structure-activity relationships (QSARs). IJQSPR 1:1–44. https://doi.org/10.4018/IJQSPR.2016010101

    Article  CAS  Google Scholar 

  38. Itteboina R, Ballu S, Sivan SK, Manga V (2016) Molecular docking, 3D QSAR and dynamics simulation studies of imidazo-pyrrolopyridines as janus kinase 1 (JAK 1) inhibitors. Comput Biol Chem 64:33–46. https://doi.org/10.1016/j.compbiolchem.2016.04.009

    Article  CAS  PubMed  Google Scholar 

  39. Jasuja H, Chadha N, Kaur M, Silakari O (2014) Dual inhibitors of Janus kinase 2 and 3 (JAK2/3): designing by pharmacophore- and docking-based virtual screening approach. Mol Divers 18:253–267. https://doi.org/10.1007/s11030-013-9497-z

    Article  CAS  PubMed  Google Scholar 

  40. Dhanachandra Singh Kh, Karthikeyan M, Kirubakaran P, Nagamani S (2011) Pharmacophore filtering and 3D-QSAR in the discovery of new JAK2 inhibitors. J Mol Graph Model 30:186–197. https://doi.org/10.1016/j.jmgm.2011.07.004

    Article  CAS  PubMed  Google Scholar 

  41. Singh KhD, Naveena Q, Karthikeyan M (2014) Jak2 inhibitor–a jackpot for pharmaceutical industries: a comprehensive computational method in the discovery of new potent Jak2 inhibitors. Mol BioSyst 10:2146–2159. https://doi.org/10.1039/c4mb00071d

    Article  CAS  PubMed  Google Scholar 

  42. Gade DR, Kunala P, Raavi D, Reddy PK, Prasad RV (2015) Structural insights of JAK2 inhibitors: pharmacophore modeling and ligand-based 3D-QSAR studies of pyrido-indole derivatives. J Recept Signal Transduct Res 35:189–201. https://doi.org/10.3109/10799893.2014.948556

    Article  CAS  PubMed  Google Scholar 

  43. Chekkara R, Susithra E, Kandakatla N, Gorla VR, Tenkayala SR (2014) Pharmacophore generation and atom-based 3D-QSAR analysis of substituted aromatic bicyclic compounds containing pyrimidine and pyridine rings as Janus kinase 2 (JAK2) inhibitors. J Chem Pharm Res 6:1146–1152

    Google Scholar 

  44. Wang JL, Cheng LP, Wang TC, Deng W, Wu FH (2017) Molecular modeling study of CP-690550 derivatives as JAK3 kinase inhibitors through combined 3D-QSAR, molecular docking, and dynamics simulation techniques. J Mol Graph Model 72:178–186. https://doi.org/10.1016/j.jmgm.2016.12.020

    Article  CAS  PubMed  Google Scholar 

  45. Anand B, Pavithra KB, Seung JC (2017) 3D-QSAR, docking, molecular dynamics simulation and free energy calculation studies of some pyrimidine derivatives as novel JAK3 inhibitors. Arab J Chem. https://doi.org/10.1016/j.arabjc.2017.09.009

    Article  Google Scholar 

  46. Rajeswari M, Santhi N, Bhuvaneswari V (2014) Pharmacophore and virtual screening of JAK3 inhibitors. Bioinformation 10:157–163. https://doi.org/10.6026/97320630010157

    Article  PubMed  PubMed Central  Google Scholar 

  47. Lim J, Taoka B, Otte RD, Spencer K, Dinsmore CJ, Altman MD, Chan G, Rosenstein C, Sharma S, Su HP, Szewczak AA, Xu L, Yin H, Zugay-Murphy J, Marshall CG, Young JR (2011) Discovery of 1-Amino-5H-pyrido[4,3-b]indol-4-carboxamide inhibitors of Janus kinase 2 (JAK2) for the treatment of myeloproliferative disorders. J Med Chem 54:7334–7349. https://doi.org/10.1021/jm200909u

    Article  CAS  PubMed  Google Scholar 

  48. Powell MJD (1977) Restart procedures for conjugate gradient method. Math Progr 12:241–254. https://doi.org/10.1007/BF01593790

    Article  Google Scholar 

  49. Gasteiger J, Marsili M (1980) Iterative partial equalization of orbital electronegativity—a rapid access to atomic charges. Tetrahedron 36:3219–3228. https://doi.org/10.1016/0040-4020(80)80168-2

    Article  CAS  Google Scholar 

  50. SYBYLX -2.1Software, Tripos Associates Inc, St. Louis

  51. Cho SJ, Tropsha A (1995) Cross-validated R2-guided region selection for comparative molecular field analysis: a simple method to achieve consistent results. J Med Chem 38:1060–1066. https://doi.org/10.1021/jm00007a003

    Article  CAS  PubMed  Google Scholar 

  52. Jones G, Willett P, Glen RC (1995) A genetic algorithm for flexible molecular overlay and pharmacophore elucidation. J Comput Aided Mol Des 9:532–549. https://doi.org/10.1007/BF00124324

    Article  CAS  PubMed  Google Scholar 

  53. Jain AN (2003) Surflex: fully automatic flexible molecular docking using a molecular similarity-based search engine. J Med Chem 46:499–511. https://doi.org/10.1021/jm020406h

    Article  CAS  PubMed  Google Scholar 

  54. Jain AN (2006) Scoring functions for protein-ligand docking. Curr Protein Pept Sci 7:407–420. https://doi.org/10.2174/138920306778559395

    Article  CAS  PubMed  Google Scholar 

  55. Klebe G, Abraham U, Mietzner T (1994) Molecular similarity indices in a comparative analysis CoMSIA of drug molecules to correlate and predict their biological activity. J Med Chem 37:4130–4146. https://doi.org/10.1021/jm00050a010

    Article  CAS  PubMed  Google Scholar 

  56. Wold S, Sjostrom M, Eriksson L (2011) PLS-regression: a basic tool of chemometrics. Chemom Intell Lab Syst 58:109–130. https://doi.org/10.1016/S0169-7439(01)00155-1

    Article  Google Scholar 

  57. Cramer RD (1993) Partial least squares (PLS): its strength and limitations. Perspect Drug Discov Des 1:269–278. https://doi.org/10.1007/BF02174528

    Article  CAS  Google Scholar 

  58. Geladi P, Xie YL, Polissar A, Hopke P (1998) Regression on parameters from three way decomposition. J Chemom 12:337–354. https://doi.org/10.1002/(SICI)1099-128X(199809/10)12:5<337::AID-CEM517>3.0.CO;2-1

    Article  CAS  Google Scholar 

  59. Wold S, Ruhe A, Wold H, Dunn WJ (1984) The collinearity problem in linear regression. the partial least squares (PLS) approach to generalized inverses. SIAM J Sci Stat Comput 5:735–743. https://doi.org/10.1137/0905052

    Article  Google Scholar 

  60. Roy K, Supratik K, Ambure P (2015) On a simple approach for determining applicability domain of QSAR models. Chemom Intell Lab Syst 145:22–29. https://doi.org/10.1016/j.chemolab.2015.04.013

    Article  CAS  Google Scholar 

  61. Roy K, Das RN, Ambure P, Aher RB (2016) Be aware of error measures. Further studies on validation of predictive QSAR models. Chemom Intell Lab Sys 152:18–33. https://doi.org/10.1016/j.chemolab.2016.01.008

    Article  CAS  Google Scholar 

  62. Rücker C, Rücker G, Meringer M (2007) y-Randomization and its variants in QSPR/QSAR. J Chem Inf Model 47:2345–2357. https://doi.org/10.1021/ci700157b

    Article  CAS  PubMed  Google Scholar 

  63. http://dtclab.webs.com/software-tools

  64. Desmond Molecular Dynamics System, version 3.6, D. E. Shaw Research, New York, NY, 2013. Maestro- Desmond Interoperability Tools, version 3.6, Schrodinger, New York, NY

  65. Kaminski G, Friesner RA, Tirado-Rives J, Jorgensen WL (2001) Evaluation and reparameterization of the OPLS-AA force field for proteins via comparison with accurate quantum chemical calculations on peptides. J Phys Chem B 105:6474–6487. https://doi.org/10.1021/jp003919d

    Article  CAS  Google Scholar 

  66. Becke AD (1993) Density-functional thermochemistry. III. The role of exact exchange. J Chem Phys 98:5648–5652. https://doi.org/10.1063/1.464913

    Article  CAS  Google Scholar 

  67. Frisch MJ, Trucks GW, Schlegel HB, Scuseria GE, Robb MA, Cheeseman JR, Scalmani G, Barone V, Petersson GA, Nakatsuji H, Li X, Caricato M, Marenich AV, Bloino J, Janesko BG, Gomperts R, Mennucci B, Hratchian HP, Ortiz JV, Izmaylov AF, Sonnenberg JL, Williams-Young D, Ding F, Lipparini F, Egidi F, Goings J, Peng B, Petrone A, Henderson T, Ranasinghe D, Zakrzewski VG, Gao J, Rega N, Zheng G, Liang W, Hada M, Ehara M, Toyota K, Fukuda R, Hasegawa J, Ishida M, Nakajima T, Honda Y, Kitao O, Nakai H, Vreven T, Throssell K, Montgomery JA, Peralta JE, Ogliaro F, Bearpark MJ, Heyd JJ, Brothers EN, Kudin KN, Staroverov VN, Keith TA, Kobayashi R, Normand J, Raghavachari K, Rendell A P, Burant JC, Iyengar SS, Tomasi J, Cossi M, Millam JM, Klene M, Adamo C, Cammi R, Ochterski JW, Martin RL, Morokuma K, Farkas O, Foresman JB, Fox DJ (2016) Gaussian 16, Revision B.01, Gaussian Inc., Wallingford CT

  68. Lee C, Yang W, Parr RG (1988) Development of the Colle–Salvetti correlation-energy formula into a functional of the electron density. Phys Rev B 37:785–789. https://doi.org/10.1103/PhysRevB.37.785

    Article  CAS  Google Scholar 

  69. Domingo LR, Ríos-Gutiérrez M, Pérez P (2016) Applications of the conceptual density functional theory indices to organic chemistry reactivity. Molecules 21:748–770. https://doi.org/10.3390/molecules21060748

    Article  CAS  PubMed Central  Google Scholar 

  70. Pissot-Soldermann C, Gerspacher M, Furet P, Gaul C, Holzer P, McCarthy C, Radimerski T, Regnier CH, Baffert F, Drueckes P, Tavares GA, Vangrevelinghe E, Blasco F, Ottaviani G, Ossola F, Scesa J, Reetz J (2010) Discovery and SAR of potent, orally available 2,8-diaryl-quinoxalines as a new class of JAK2 inhibitors. Bioorg Med Chem Lett 20:2609–2613. https://doi.org/10.1016/j.bmcl.2010.02.056

    Article  CAS  PubMed  Google Scholar 

  71. Golbraikh A, Tropsha A (2002) Beware of q2. J Mol Graph Model 20:269–276. https://doi.org/10.1016/S1093-3263(01)00123-1

    Article  CAS  PubMed  Google Scholar 

  72. Hart AC, Schroeder GM, Wan H, Grebinski J, Inghrim J, Kempson J, Guo J, Pitts WJ, Tokarski JS, Sack JS, Khan JA, Lippy J, Lorenzi MV, You D, McDevitt T, Vuppugalla R, Zhang Y, Lombardo LJ, Trainor GL, Purandare AV (2015) Structure-based design of selective Janus kinase 2 Imidazo[4,5d]pyrrolo[2,3b] pyridine Inhibitors. ACS Med Chem Lett 6:845–849. https://doi.org/10.1021/acsmedchemlett.5b00225

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Schenkel LB, Huang X, Cheng A, Deak HL, Doherty E, Emkey R, Gu Y, Gunaydin H, Kim JL, Lee J, Loberg R, Olivieri P, Pistillo J, Tang J, Wan Q, Wang HL, Wang SW, Wells MC, Wu B, Yu V, Liu L, Geuns-Meyer S (2011) Discovery of potent and highly selective thienopyridine Janus kinase 2 inhibitors. J Med Chem 54:8440–8450. https://doi.org/10.1021/jm200911r

    Article  CAS  PubMed  Google Scholar 

  74. Hanan EJ, Abbema AV, Barrett K, Blair WS, Blaney J, Chang C, Eigenbrot C, Flynn S, Gibbons P, Hurley CA, Kenny JR, Kulagowski J, Lee L, Magnuson SR, Morris C, Murray J, Pastor RM, Rawson T, Siu M, Ultsch M, Zhou A, Sampath D, Lyssikatos JP (2012) Discovery of potent and selective pyrazolopyrimidine Janus kinase 2 inhibitors. J Med Chem 55:10090–10107. https://doi.org/10.1021/jm3012239

    Article  CAS  PubMed  Google Scholar 

  75. Parr RG, Donnelly RA, Levy M, Palke WE (1978) Electronegativity—density functional viewpoint. J Chem Phys 68:3801–3807. https://doi.org/10.1063/1.436185

    Article  CAS  Google Scholar 

  76. Mert BD, Mert ME, Kardas G, Yazici B (2011) Experimental and theoretical investigation of 3-amino-1, 2, 4-triazole-5-thiol as a corrosion inhibitor for carbon steel in HCl medium. Corros Sci 53:4265–4272. https://doi.org/10.1016/j.corsci.2011.08.038

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This research was supported by Start-Up Research Grant for Young Scientist (SB/YS/LS-128/2013), funded by the Science and Engineering Research Board (SERB), Department of Science and Technology (DST), Government of India. Author SB thanks CSIR, New Delhi, India for providing Senior Research Fellowship (SRF).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Thirumurthy Madhavan.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 1156 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Babu, S., Nagarajan, S.K. & Madhavan, T. Understanding the structural features of JAK2 inhibitors: a combined 3D-QSAR, DFT and molecular dynamics study. Mol Divers 23, 845–874 (2019). https://doi.org/10.1007/s11030-018-09913-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11030-018-09913-4

Keywords

Navigation