Skip to main content
SpringerLink
Log in

Molecular dynamics, MMGBSA, and docking studies of natural products conjugated to tumor-targeted peptide for targeting BRAF V600E and MERTK receptors

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

Abstract

Recent studies have revealed that MERTK and BRAF V600E receptors have been found to be over-expressed in several types of cancers including melanoma, making these receptors targets for drug design. In this study, we have designed novel peptide conjugates with the natural products vanillic acid, thiazole-2-carboxylic acid, cinnamic acid, theanine, and protocatechuic acid. Each of these compounds was conjugated with the tumor targeting peptide sequence TAASGVRSMH, known to bind to NG2 and target tumor neovasculature. We examined their binding affinities and stability with MERTK and BRAF V600E receptors using molecular docking and molecular dynamics studies. Compared to the neat compounds, the peptide conjugates displayed higher binding affinity toward both receptors. In the case of MERTK, the most stable complexes were formed with di-theaninate-peptide, vanillate-peptide, and thiazole-2-amido peptide conjugates and binding occurred in the hinge region. Additionally, it was discovered that the peptide alone also had high binding ability and stability with the MERTK receptor. In the case of BRAF V600E, the peptide conjugates of protocatechuate, vanillate and thiazole-2-amido peptide conjugates showed the formation of the most stable complexes and binding occurred in the ATP binding cleft. Further analysis revealed that the number of hydrogen bonds and hydrophobic interactions played a critical role in enhanced stability of the complexes. Docking studies also revealed that binding affinities for NG2 were similar to MERTK and higher for BRAF V600E. MMGBSA studies of the trajectories revealed that the protocatechuate–peptide conjugate showed the highest binding energy with BRAF V600E while the peptide-TAASGVRSMH showed the highest binding energy with MERTK. ADME studies revealed that each of the compounds showed medium to high permeability toward MDCK cells and were not hERG blockers. Furthermore, the conjugates were not CYP inhibitors or substrates, but they were found to be Pgp substrates. Our results indicated that the protocatechuate-TAASGVRSMH, thiazole-2-amido-TAASGVRSMH, and vanillate-TAASGVRSMH conjugates may be furthered developed for in vitro and in vivo studies as novel tumor targeting compounds for tumor cells over-expressing BRAF V600E, while di-theaninate-amido-TAASGVRSMH and thiazole-2-amido-TAASGVRSMH conjugates may be developed for targeting MERTK receptors. These studies provide insight into the molecular interactions of natural product-peptide conjugates and their potential for binding to and targeting MERTK and BRAF V600E receptors in developing new therapeutics for targeting cancer.

Graphical abstract

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Availability of data and material

Associated data are included in the supplementary information. The manuscript will be shared on faculty website.

Code availability

NA.

References

  1. Garbe Claus, Ulrike Leiter (2009) Melanoma epidemiology and trends. Clin Dermatol 27(1):3–9. https://doi.org/10.1016/j.clindermatol.2008.09.001.

  2. Chavda J, Bhatt H, 3D-QSAR (2019) (CoMFA, CoMSIA, HQSAR and topomer CoMFA), MD simulations and molecular docking studies on purinylpyridine derivatives as B-Raf inhibitors for the treatment of melanoma cancer. Struct Chem 30:2093–2107. https://doi.org/10.1007/s11224-019-01334-9

    Article  CAS  Google Scholar 

  3. Walter K (2000) Meaningful relationships: the regulation of the Ras/Raf/MEK/ERK pathway by protein interactions. Biochem J 351(2):289–305. https://doi.org/10.1042/bj3510289

  4. Peyssonnaux C, Alain E (2001) The RAF/MEK/Erk Pathway: new concepts of activation. Biol Cell 93(1–2):53–62. https://doi.org/10.1016/s0248-4900(01)01125-x

    Article  CAS  Google Scholar 

  5. Cohn A, Day B-M, Abhyankar S, McKenna E, Riehl T, Puzanov I (2017) Braf V600 mutations in solid tumors, other than metastatic melanoma and papillary thyroid cancer, or multiple myeloma: a screening study. Onco Targets Ther 10:965–971. https://doi.org/10.2147/ott.s120440

    Article  CAS  Google Scholar 

  6. Wan PT, Garnett MJ, Roe SM, Lee S, Niculescu-Duvaz D, Good VM, Jones CM, Marshall CJ, Springer CJ, Barford D, Marais R (2004) Mechanism of activation of the RAF-ERK signaling pathway by oncogenic mutations of B-RAF. Cell 116:855–867

    Article  CAS  Google Scholar 

  7. Bhujbal SP, Keretsu S, Balasubramanian PK, Cho SJ (2019) Macrocyclic effect on inhibitory activity: a modeling study on MerTK inhibitors. Med Chem Res 28:1923–1938. https://doi.org/10.1007/s00044-019-02424-3

    Article  CAS  Google Scholar 

  8. Behrens EM, Gadue P, Gong S-Y, Garrett S, Stein PL, Cohen PL (2003) The Mer receptor tyrosine kinase: expression and function suggest a role in innate immunity. Eur J Immunol 33(8):2160–2167. https://doi.org/10.1002/eji.200324076

    Article  CAS  Google Scholar 

  9. Nguyen K-QN, Tsou W-I, Calarese DA, Kimani SG, Singh S, Hsieh S, Liu Y, Lu B, Wu Y, Garforth SJ, Almo SC, Kotenko SV, Birge RB (2014) Overexpression of MERTK receptor tyrosine kinase in epithelial cancer cells drives efferocytosis in a gain-of-function capacity. J Biol Chem 289(37):25737–25749. https://doi.org/10.1074/jbc.m114.570838

    Article  CAS  Google Scholar 

  10. Huang X, Finerty P, Walker JR, Butler-Cole C, Vedadi M, Schapira M, Parker SA, Turk BE, Thompson DA, Dhe-Paganon S (2009) Structural insights into the inhibited states of the Mer receptor tyrosine kinase. J Struct Biol 165(2):88–96. https://doi.org/10.1016/j.jsb.2008.10.003

    Article  CAS  Google Scholar 

  11. Hospital A, Goni JR, Orozco M, Gelpi J (2015) Molecular dynamics simulations: advances and applications. Adv Appl Bioinform Chem 8:37–47. https://doi.org/10.2147/aabc.s70333

    Article  Google Scholar 

  12. Johannessen CM, Boehm JS, Kim SY, Thomas SR, Wardwell L, Johnson LA, Emery CM, Stransky N, Cogdill AP, Barretina J, Caponigro G, Hieronymus H, Murray RR, Salehi-Ashtiani K, Hill DE, Vidal M, Zhao JJ, Yang X, Alkan O, Kim S, Harris JL, Wilson CJ, Myer VE, Finan PM, Root DE, Roberts TM, Golub T, Flaherty KT, Dummer R, Weber BL, Sellers WR, Schlegel R, Wargo JA, Hahn WC, Garraway LA (2010) COT drives resistance to RAF inhibition through MAP kinase pathway reactivation. Nature 468(7326):968–972. https://doi.org/10.1038/nature09627

    Article  CAS  Google Scholar 

  13. Poulikakos PI, Zhang C, Bollag G, Shokat KM, Rosen N (2010) RAF inhibitors transactivate RAF dimers and ERK signalling in cells with wild-type BRAF. Nature 464(7287):427–430. https://doi.org/10.1038/nature08902

    Article  CAS  Google Scholar 

  14. Tsai J, Lee J, Wang W, Zhang J, Cho H, Mamo S, Bremer R, Gillette S, Kong J, Haass N, Sproesser K, Li L, Smalley K, Fong D, Zhu Y, Marimuthu A, Nguyen H, Lam B, Liu J, Cheung I, Rice J, Suzuki Y, Luu C, Settachatgul C, Shellooe R, Cantwell J, Kim S, Schlesinger J, Zhang K, West B, Powell B, Habets G, Zhang C, Ibrahim P, Hirth P, Artis D, Herlyn M, Bollag G (2008) Discovery of a selective inhibitor of oncogenic B-Raf kinase with potent antimelanoma activity. PNAS 105(8):3041–3046. https://doi.org/10.1073/pnas.0711741105

    Article  Google Scholar 

  15. Wan PT, Garnett MJ, Roe SM, Lee S, Niculescu-Duvaz D, Good VM, Jones CM, Marshall CJ, Springer CJ, Barford D, Marais R (2004) Cancer Genome Project. Mechanism of activation of the RAF-ERK signaling pathway by oncogenic mutations of B-RAF. Cell 116(6):855–867. https://doi.org/10.1016/s0092-8674(04)00215-6.

  16. Garbe C, Abusaif S, Eigentler TK (2014) Vemurafenib. Recent Results Cancer Res 201:215–225. https://doi.org/10.1007/978-3-642-54490-3_13

    Article  CAS  Google Scholar 

  17. Ahn Y, Claire M, Ensinger CL, Hood MM, Lord JW, Lu W-P, Miller DF, Patt WC, Smith BD, Vogeti L, Kaufman MD, Petillo PA, Wise SC, Abendroth J, Chun L, Clark R, Feese M, Kim H, Stewart L, Flynn DL (2010) Switch control pocket iInhibitors of p38-MAP kinase. Durable type II inhibitors that do not require binding into the canonical ATP Hinge Region. Bioorg Med Chem Lett 20(19):5793–5798. https://doi.org/10.1016/j.bmcl.2010.07.134.

  18. Chen C-Y, Tang H-C (2015) Insight into Molecular Dynamics Simulation of Braf(v600e) and Potent Novel Inhibitors for Malignant Melanoma. Int J Nanomed 3131. https://doi.org/10.2147/ijn.s80150

  19. Park TH, Bae SH, Bong SM, Ryu SE, Jang H, Lee B (2020) Crystal structure of the kinase domain of MerTK in complex with AZD7762 provides clues for structure-based drug development. Int J Mol Sci 21(21):7878. https://doi.org/10.3390/ijms21217878

    Article  CAS  Google Scholar 

  20. Huang X, Cheng CC, Fischmann TO, Duca JS, Yang X, Richards M, Shipps GW (2012) Discovery of a novel series of CHK1 kinase inhibitors with a distinctive hinge binding mode. ACS Med Chem Lett 3(2):123–128. https://doi.org/10.1021/ml200249h

    Article  CAS  Google Scholar 

  21. Sausville E, LoRusso P, Carducci M, Carter J, Quinn MF, Malburg L, Azad N, Cosgrove D, Knight R, Barker P, Zabludoff S, Agbo F, Oakes P, Senderowicz A (2014) Phase I dose-escalation study of AZD7762, a checkpoint kinase inhibitor, in combination with gemcitabine in US patients with advanced solid tumors. Cancer Chemother Phamacol 73(3):539–549. https://doi.org/10.1007/s00280-014-2380-5

    Article  CAS  Google Scholar 

  22. Pittet AO, Hruza DE (1974) Comparative study of flavor properties of thiazole derivatives. J Agric Food Chem 22(2):264–269. https://doi.org/10.1021/jf60192a009

    Article  CAS  Google Scholar 

  23. Khan AS, Rashid R, Fatima N, Mahmood S, Mir S, Khan S, Jabeen N, Murtaza G (2015) Pharmaceutical activities of protocatechuic acid. Acta Pol Pharm 72(4):643–650

    CAS  Google Scholar 

  24. Burg MA, Pasqualini R, Arap W, Ruoslahti E, Stallcup WB (1999) NG2 proteoglycan-binding peptides target neovasculature. Cancer Res 59(12):2869–2874

    CAS  Google Scholar 

  25. Stallcup WB (2002) The NG2 proteoglycan: past insights and future prospects. J Neurocytol 31(6–7):423–435. https://doi.org/10.1023/a:1025731428581

    Article  CAS  Google Scholar 

  26. Nishiyama A, Lin XH, Giese N, Heldin CH, Stallcup WB (1996) Interaction between NG2 proteoglycan and PDGF alpha-receptor on O2A progenitor cells is required for optimal response to PDGF. J Neurosci Res 43(3):315–330. https://doi.org/10.1002/(SICI)1097-4547(19960201)43:3%3c315::AID-JNR6%3e3.0.CO;2-M

    Article  CAS  Google Scholar 

  27. Goretzki L, Burg MA, Grako KA, Stallcup WB (1999) High-affinity binding of basic fibroblast growth factor and platelet-derived growth factor-AA to the core protein of the NG2 proteoglycan. J Biol Chem 274(24):16831–16837

    Article  CAS  Google Scholar 

  28. Yadavalli S, Hwang E, Packer RJ, Nazarian J (2016) The role of NG2 proteoglycan in glioma. Trans Oncol 9(1):57–63. https://doi.org/10.1016/j.tranon.2015.12.005

    Article  Google Scholar 

  29. Uranowska K, Kalic T, Valtsanidis V, Kitzwogerer M, Breiteneder H, Hafner C (2021) Expression of chondroitin sulfate proteoglycan 4 (CSPG4) in melanoma cells is downregulated upon inhibition of BRAF. Onc Rep 45 (14). https://doi.org/10.3892/or.2021.7965

  30. Beech J, Kelly K (2013) JNK1 and MERTK are markers of MEK inhibitor resistance and new targets for therapy. Cancer Res. 73 (8_Supplement): 5594. https://doi.org/10.1158/1538-7445.AM2013-5594

  31. Gong J, Zhou S, Yang S (2019) Vanillic acid suppresses HIF expression via inhibition of mTOR/p70S6k/4E-BP1 and Raf/MEK/ERK Pathways in human colon cancer HCT116 cells. Int J Mol Sci 20(3):465. https://doi.org/10.3390/ijms20030465

    Article  CAS  Google Scholar 

  32. De P, Baltas M, Bedos-Belval F (2011) Cinnamic acid derivatives as anticancer agents—a review. Current Med Chem 18(11):1672–1703. https://doi.org/10.2174/092986711795471347

    Article  CAS  Google Scholar 

  33. Humaedi A, Arsiyanti A, Radji M (2017) In silico molecular docking study of gallic acid and its derivatives as inhibitor of BRAF colon cancer. Int J ChemTech Res 10(1):310–315

    CAS  Google Scholar 

  34. Deb PK, Kaur R, Bala CB, Gill D, Kaki VR, Akkinepalli RR, Mailavaram R (2014) Synthesis, anti-inflammatory evaluation, and docking studies of some new thiazole derivatives. Med Chem Res 23:2780–2792. https://doi.org/10.1007/s00044-013-0861-4

    Article  CAS  Google Scholar 

  35. Sharma PC, Bansal KK, Sharma A, Sharma D, Deep A (2020) Thiazole-containing compounds as therapeutic targets for cancer therapy. Eur Med Chem 188:112016. https://doi.org/10.1016/j.ejmech.2019.112016

    Article  CAS  Google Scholar 

  36. Waizenegger IC, Baum A, Steurer S, Stadmuller H, Bader G, Schaaf O, Garin-Chesa P, Schlatti A, Schweifer N, Haslinger C, Colbatzky F, Mousa S, Kalkuhl A, Kraut N, Adolf GR (2016) A novel RAF kinase inhibitor with DFG-out-binding mode: high efficacy in BRAF-mutant tumor xenograft models in the absence of normal tissue hyper proliferation. Mol Cancer Therap 15(3):354–365. https://doi.org/10.1158/1535-7163.MCT-15-0617

    Article  CAS  Google Scholar 

  37. Khrapunovich-Baine M, Menon V, Yang C-P, Northcote PT, Miller JH, Angeletti RH, Fiser A, Horwitz SB, Xiao H (2011) Hallmarks of molecular action of microtubule stabilizing agents. J Biol Chem 286(13):11765–11778. https://doi.org/10.1074/jbc.M110.162214

    Article  CAS  Google Scholar 

  38. Yan Z, Zhong Y, Duan Y, Chen Q, Li F (2020) Antioxidant mechanism of tea polyphenols and its impact on health benefits. Anim Nutr 6(2):115–123. https://doi.org/10.1016/j.aninu.2020.01.001

    Article  Google Scholar 

  39. Zhang R, Zheng S, Guo Z, Wang Y, Yang G, Yin Z, Luo (2021) LL-theanine inhibits melanoma cell growth and migration via regulating epxresion of the clock gene BMAL1. Eur J Nutrition. https://doi.org/10.1007/s00394-021-02677-y

  40. Sadzuka Y, Sugiyama T, Sonobe T (2002) Improvement of idarubicin induced antitumour activity and bone marrow suppression by theanine, a component of tea. Cancer Lett 158:119–124. https://doi.org/10.1016/s0304-3835(00)00491-2

    Article  Google Scholar 

  41. Yoneda Y (2017) An L-glutamine transporter isoform form neurogenesis facilitated by L-theanine. Neurochem 42(10):2686–2697. https://doi.org/10.1007/s11064-017-2317-6

    Article  CAS  Google Scholar 

  42. Zhang Y (2008) I-TASSER server for protein 3D structure prediction. BMC Bioinformatics 9(1):40

    Article  Google Scholar 

  43. Yang J, Yan R, Roy A, Xu D, Poisson J, Zhang Y (2015) The I-TASSER suite: protein structure and function prediction. Nat Methods 12(1):7–8. https://doi.org/10.1038/nmeth.3213

    Article  CAS  Google Scholar 

  44. Aissaoui T, AlNashef IM, Benguerba Y (2016) Dehydration of natural gas using choline chloride based deep eutectic solvents: COSMO-RS prediction. J Nat Gas Sci Eng 30:571–577. https://doi.org/10.1016/j.jngse.2016.02.007

    Article  CAS  Google Scholar 

  45. Gonzalez-Miquel M, Massel M, DeSilva A, Palomar J, Rodriguez F, Brennecke JF (2014) Excess enthalpy of Monoethanolamine + ionic liquid mixtures: How good are COSMO-RS predictions? J Phys Chem B 118(39):11512–11522. https://doi.org/10.1021/jp507547q

    Article  CAS  Google Scholar 

  46. Mulyono S, Hizaddin HF, Alnashef IM, Hashim MA, Fakeeha AH, Hadj-Kali MK (2014) Separation of BTEX aromatics from n-octane using a (tetrabutylammonium bromide + sulfolane) deep eutectic solvent—experiments and COSMO-RS prediction. RSC Adv 4(34):17597–17606. https://doi.org/10.1039/C4RA01081G

    Article  CAS  Google Scholar 

  47. Wong SW, Vivash L, Mudududdla R, Nguyen N, Hermans SJ, Shackleford DM, Field J, Xue L, Aprico A, Hancock NC, Haskali M, Stashko MA, Frye SV, Wang X, Binder MD, Ackermann U, Parker MW, Kilpatrick TJ, Baell JB (2021) Eur J Med Chem 226:113822–113822. https://doi.org/10.1016/j.ejmech.2021.113822

    Article  CAS  Google Scholar 

  48. Hailing JR, Sudhamsu J, Yen I, Sideris S, Sandoval W, Phung W, Bravo BJ, Giannetti AM, Peck A, Masselot A, Morales T, Smith D, Brandhuber BJ, Hymowitz SG, Malek S (2014) Structure of the BRAF-MEK complex reveals a kinase activity independent role for BRAF in MAPK signaling. Cancer Cell 26:402–413. https://doi.org/10.1016/j.ccr2014.07.007

    Article  Google Scholar 

  49. “RCSB Protein Data Bank.” https://www.rcsb.org/

  50. Yu J, Zhou Y, Tanaka I, Yao M (2010) Roll: a new algorithm for the detection of protein pockets and cavities with a rolling probe sphere. Bioinformatics 26(1):46–52. https://doi.org/10.1093/bioinformatics/btp599

    Article  CAS  Google Scholar 

  51. Trott O, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31(2):455–461. https://doi.org/10.1002/jcc.21334

    Article  CAS  Google Scholar 

  52. Guedes IA, Pereira FS, Dardenne LE (2018) Empirical scoring functions for structure-based virtual screening: applications, critical aspects, and challenges. Front Pharmacol 9:1–18. https://doi.org/10.3389/fphar.2018.01089

    Article  CAS  Google Scholar 

  53. Yan Y, Tao H, He J, Huang SY (2020) The HDOCK server for integrated protein–protein docking. Nat Protocols 15(5):1829–1852. https://doi.org/10.1038/s41596-020-0312-x

    Article  CAS  Google Scholar 

  54. Sebastian S, Sven S, Adasme JHV, Schroeder M (2015) PLIP: fully automated protein–ligand interaction profiler. Nucleic Acids Res 43(W1):W443–W447. https://doi.org/10.1093/nar/gkv315

    Article  CAS  Google Scholar 

  55. Desmond Molecular Dynamics System, D. E. Shaw Research, New York, NY, 2020. Maestro-Desmond Interoperability Tools, Schrödinger, New York, NY, 2020.

  56. Maestro, Schrödinger, LLC, New York, NY, 2020.

  57. Bergdorf M, Baxter S, Rendleman CA, Shae DE (2016) Desmond/GPU Performance as of November 2016. D.E. Shaw Research Technical Report DESRES/T-2019-01

  58. Hou T, Wang J, Li Y, Wang W (2011) Assessing the performance of the MM/PBSA and MM/GBSA methods. 1. The accuracy of binding free energy calculations based on molecular dynamics simulations. J Chem Inf Model 51(1):69–82. https://doi.org/10.1021/ci100275a

  59. Rastelli G, Del Rio A, Degliesposti G, Sgobba M (2009) Fast and accurate predictions of binding free energies using MM-PBSA and MM-GBSA. J Comput Chem 31(4):797–810. https://doi.org/10.1002/jcc.21372

    Article  CAS  Google Scholar 

  60. Du J, Sun H, Xi L, Li J, Yang Y, Liu H, Yao X (2011) Molecular modeling study of checkpoint kinase 1 inhibitors by multiple docking strategies and prime/MM GBSA calculation. J Comput Chem 32(13):2800–2809. https://doi.org/10.1002/jcc.21859

    Article  CAS  Google Scholar 

  61. Xiong G, Wu Z, Yi J, Li F, Yang Z, Hsieh C, Yin M, Zeng X, Wu C, Lu A, Chen X, Hou T, Cao D (2021) ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acids Res 49:W5–W14. https://doi.org/10.1093/nar/gkab255

    Article  CAS  Google Scholar 

  62. Zhang Y (2008) I-TASSER server for protein 3D structure prediction. BMC Bioinformatics 9:40. https://doi.org/10.1186/1471-2105-9-40

    Article  CAS  Google Scholar 

  63. Yang J, Zhang Y (2015) I-TASSER server: new development for protein structure and function predictions. Nucleic Acids Res 43(W1):W174–W181. https://doi.org/10.1093/nar/gkv342

    Article  CAS  Google Scholar 

  64. Lemaoui T, Darwish AS, Hammoudi NEH, Hatab FA, Attoui A, Alnashef IM, Benguerba Y (2020) Prediction of electrical conductivity of deep eutectic solvents using COSMO-RS sigma profiles as molecular descriptors: a quantitative structure-property relationship study. Ind Eng Chem Res 59(29):13343–13354. https://doi.org/10.1021/acs.iecr.0c02542

    Article  CAS  Google Scholar 

  65. Volkamer A, Kuhn D, Grombacher G, Rippmann F, Rarey M (2012) Combining global and local measures for structure-based druggability predictions. J Chem Inf Model 52(2):360–372. https://doi.org/10.1021/ci200454v

    Article  CAS  Google Scholar 

  66. Peng X, Streu C, Qin J, Bregman H, Pagano N, Meggers E, Marmorsein R (2009) The crystal structure of BRAF in complex with an organoruthenium inhibitor reveals a mechanism for inhibition of an active form of BRAF kinase. Biochemistry 48(23):5187–5198. https://doi.org/10.1021/bi802067u

    Article  CAS  Google Scholar 

  67. Chen P, Zeng J, Liu Z, Thaker H, Wang S, Tian S, Zhang J, Tao L, Gutierrez CB, Xing L, Gerhard R, Huang L, Dong M, Jin R (2021) Structural basis for CSPG4 as a receptor for TcdB and a therapeutic target in the Clostridioides difficile infection. Nature Commun 12:3748. https://doi.org/10.1038/s41467-021-23878-3

    Article  CAS  Google Scholar 

  68. Yan Y, Zhang D, Zhou P, Li B, Huang SY (2017) HDOCK: a web server for protein-protein and protein-DNA/RNA docking based on hybrid strategy. Nucleic Acids Res 45(W1):W364–W373. https://doi.org/10.1093/nar/gkx407

    Article  CAS  Google Scholar 

  69. Poli G, Tuccinardi T (2020) Consensus docking in drug discovery. Curr. Bioact. Compd, 2020, 16, 182–190 https://doi.org/10.2174/1573407214666181023114820

  70. Guedes IA, de Magalhaes CS, Dardene LE (2014) Receptor-ligand molecular docking. Biophys Rev 6(1):75–87

    Article  CAS  Google Scholar 

  71. Madhavilatha NK and Mohan Babu GR (2018) Systematic approach for enrichment of docking outcome using consensus scoring functions. J Phys Confer Ser 1228(1):012019. https://doi.org/10.1088/1742-6596/1228/1/012019.

  72. Eberhardt J, Santos-Martins D, Tillack AF, Forli S (2021) AutoDock Vina 1.2.0: New docking methods, expanded force field and python bindings. J Chem Inform Model 61 (8):3891–3898. https://doi.org/10.1021/acs.jcim.1c00203

  73. Zhao J, Zhang D, Zhang W, Stashko M, DeRyckere D, Vasileiadi E, Parker RE, Hunter D, Liu Q, Zhang Y, Norris-Drouin J, Li B, Drewry DH, Kireev D, Graham DK, Earp HS, Frye SV, Wang X (2018) Highly selective Mertk inhibitors achieved by a single methyl group. J Med Chem 61(22):10242–10254. https://doi.org/10.1021/acs.jmedchem.8b01229

    Article  CAS  Google Scholar 

  74. Zhou S, Cui R, Tian Y, Li X, You R, Zhong L (2016) Pharmacophore-based 3D-QSAR modeling, virtual screening and molecular docking analysis for the detection of MERTK inhibitors with Novel Scaffold. Comb Chem High Throughput Screen 19(1):73–96. https://doi.org/10.2174/1386207319666151203002228

    Article  CAS  Google Scholar 

  75. Wan PT, Garnett MJ, Roe SM, Lee S, Niculescu-Duvaz D, Good VM, Jones CM, Marshall CJ, Springer CJ, Barford D, Marais R (2004) Cancer genome project. Cell 116(6):855–867

    Article  CAS  Google Scholar 

  76. Dong J-J, Li Q-S, Wang S-F, Li C-Y, Zhao X, Qiu H-Y, Zhao H-Y, Zhu H-L (2013) Synthesis, biological evaluation and molecular docking of Novel 5-Phenyl-1H-Pyrazol derivatives as potential BRAFV600E inhibitors. Org Biomol Chem 11:6328–6337. https://doi.org/10.1039/C3OB40776D

  77. Thundimadathil J (2012) Cancer treatment using peptides: current therapies and future prospects. J Amino Acids. Article ID 967347 https://doi.org/10.1155/2012/967347

  78. Hamzah N, Tjahjono DH (2016) A quantitative structure-activity relationship study, compound development, pharmacophore feature, and molecular docking of pyrazolo-[3,4-d]-pyrimidine derivatives as Mer Tyrosine kinase inhibitor. Int J ChemTech Res 9:323–337

    CAS  Google Scholar 

  79. Guan Y, Luan X, Xu J-R, Liu Y-R, Lu Q, Wang C, Liu H-J, Gao YG, Chen HZ, Fang C (2014) Selective eradication of tumor vascular pericytes by peptide-conjugated nanoparticles for antiangiogenic therapy of melanoma lung metastasis. Biomaterials 35(9):3060–3070. https://doi.org/10.1016/j.biomaterials.2013.12.027

    Article  CAS  Google Scholar 

  80. DeRyckere D, Lee-Sherick AB, Huey MG, Hill A, Tyner JW, Jacobsen KM, Page LS, Kirkpatrick GG, Eryildiz F, Montgomery SA, Zhang W, Wang X, Frye SV, Earp S, Graham DK (2017) UNC2025, a MERTK small molecule-inhibitor, is therapeutically effective alone and in combination with methotrexate in leukemia models. Clin Cancer Res 23(6):1481–1492. https://doi.org/10.1158/1078-0432.CCR-16-1330

    Article  CAS  Google Scholar 

  81. Pflug A, Schiml M, Nissink JWM, Overman R, Rawlins P, Truman C, Underwood E, Warwicker J, Winter-Holt JJ, McCoull W (2020) A-loop interactions in Mer tyrosine kinase give rise to inhibitors with two-step mechanism and long residence time of binding. Biochem J 477(22):4443–4452. https://doi.org/10.1042/BCJ20200735

    Article  CAS  Google Scholar 

  82. Modi V, Dunbrack RL (2019) Defining a new nomenclature for the structures of active and inactive kinases. Proc Natl Acad Sci USA 116:6818–6827. https://doi.org/10.1073/pnas.1814279116

    Article  CAS  Google Scholar 

  83. Wang S-B, Cui M-T, Wang X-F, Ohkoshi E, Goto M, Yang D-X, Li L, Yuan S, Morris-Natschke SL, Lee K-H, Xie L (2016) Synthesis, biological evaluation, and physicochemical property assessment of 4-substituted 2-phenylaminoquinazolines as Mer Tyrosine Kinase Inhibitors. Bioorg Med Chem 24(13):3083–3092. https://doi.org/10.1016/j.bmc.2016.05.025

    Article  CAS  Google Scholar 

  84. Verma A, Warner SL, Vankayalapati H, Bearss DJ, Sharma S (2011) Targeting Axl and Mer Kinases in cancer. Mol Cancer Ther 10(10):1763–1773. https://doi.org/10.1158/1535-7163.MCT-11-0116

    Article  CAS  Google Scholar 

  85. Gajiwala K, Grodsky N, Bolanos B, Feng J, Ferre RA, Timofeevski S, Xu M, Murray BW, Johnson TW, Stewart A (2017) The Axl kinase domain in complex with a macrocyclic inhibitor offers first structural insights into an active tam receptor kinase. J Biol Chem 292(38):15705–15716. https://doi.org/10.1074/jbc.m116.771485

    Article  CAS  Google Scholar 

  86. Tessoulin B, Moreau-Aubry A, Descamps G, Gomez-Bougie P, Maiga S, Gaignard A, Chiron D, Menoret E, Le Gouill S, Moreau P, Amiot M, Pellat-Deceunynck C (2018) Whole-exon sequencing of human myeloma cell lines shows mutations related to myeloma patients with major hits in the DNA regulation and repair pathways. J Hematol Oncol 11:137. https://doi.org/10.1186/s13045-018-0679-0

    Article  CAS  Google Scholar 

  87. Loo E, Khalili P, Beuhler K, Siddiqi I, Vasef M (2018) BRAFV600E mutation across multiple tumor types: Correlation between DNA-based sequencing and mutation specific immunohistochemistry. Appl Immunohistochem Mol Morphol 26(10):79–713. https://doi.org/10.1097/PAI.0000000000000516

    Article  CAS  Google Scholar 

  88. Lemech C, Infante J, Arkenau H-T (2012) The potential for BRAF V600 inhibitors in advanced cutaneous melanoma: rationale and latest evidence. Ther Adv Med Oncol 4(2):61–73. https://doi.org/10.1177/1758834011432949

    Article  CAS  Google Scholar 

  89. Zaharie F, Cojocneanu-Petric R, Muresan M, Frinc I, Dima D, Petrushev B, Tanase A, Berce C, Chitic M, Berindan-Neagoe I, Pileczki V, Irimie A, Tomuleasa C (2015) Small molecules against B-RAF (BRAF) Val600 Glu (V600E) single mutation. Int J Nanomed 10:4897–4899. https://doi.org/10.2147/IJN.S87405

    Article  CAS  Google Scholar 

  90. Davies H, Bignell GR, Cox C, Stephens P, Edkins S, Clegg S, Teague J, Woffendin H, Garnett MJ, Bottomley W, Davis N, Dicks E, Ewing R, Floyd Y, Gray K, Hall S, Hawes R, Hughes J, Kosmidou V, Menzies A, Mould C, Parker A, Stevens C, Watt S, Hooper S, Wilson R, Jayatilake H, Gusterson BA, Cooper C, Shipley J, Hargrave D, Pritchard-Jones K, Maitland N, Chenevix-Trench G, Riggins GJ, Bigner DD, Palmieri G, Cossu A, Flanagan A, Nicholson A, Ho JW, Leung SY, Yuen ST, Weber BL, Seigler HF, Darrow TL, Paterson H, Marais R, Marshall CJ, Wooster R, Stratton MR, Futreal PA (2002) Mutations of the BRAF gene in human cancer. Nature 417:949–954. https://doi.org/10.1038/nature00766

    Article  CAS  Google Scholar 

  91. Genheden S, Ryde U (2015) The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin Drug Discov 10(5):449–461. https://doi.org/10.1517/17460441.2015.1032936

    Article  CAS  Google Scholar 

  92. Ahinko M, Niinivehmas S, Jokinen E, Pentikanen OT (2019) Suitability of MMGBSA for the celection of correct ligand binding modes from docking results. Chem Biol Drug Design 93(4):522–538. https://doi.org/10.1111/cbdd.13446

    Article  CAS  Google Scholar 

  93. Kalyaanamoorthy S, Barakat KH (2018) Development of safe drugs: the hERG challenge. Med Res Rev 38(2):525–555. https://doi.org/10.1002/med.21445

    Article  Google Scholar 

  94. McCarren P, Springer C, Whitehead L (2011) An investigation into pharmaceutically relevant mutagenicity data and the influence on Ames predictive potential. J Cheminform 3:15. https://doi.org/10.1186/1758-2946-3-51

    Article  CAS  Google Scholar 

  95. Lynch T, Price A (2007) The effect of cytochrome P450 metabolism on drug response, interactions and adverse effects. Am Fam Physician 76(3):391–396

    Google Scholar 

  96. Kumar Gondi N, Sekhar S (2001) Role of drug metabolism in drug discovery and development. Med Res Rev 21(5):397–411. https://doi.org/10.1002/med.1016

    Article  Google Scholar 

  97. Raub TJ (2005) P-Glycoprotein recognition of substrates and circumvention through rational drug design. Mol Pharm 3(1):3–25. https://doi.org/10.1021/mp0500871

    Article  CAS  Google Scholar 

  98. Broccatelli F, Plise SE, Cheong J, Gobbi A, Lee ML, Aliagas I (2016) Predicting passive permeability of drug-like molecules from chemical structure: where are we? Mol Pharm 13(12):4199–4208. https://doi.org/10.1021/acs.molpharmaceut.6b00836

    Article  CAS  Google Scholar 

Download references

Acknowledgements

The authors thank Fordham University Research Grants for financial support of this work.

Author information

Authors and Affiliations

  1. Department of Chemistry, Fordham University, 441 E. Fordham Rd, Bronx, NY, 10458, USA

    Dominic J. Lambo, Charlotta G. Lebedenko, Paige A. McCallum & Ipsita A. Banerjee

Authors
  1. Dominic J. Lambo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Charlotta G. Lebedenko
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Paige A. McCallum
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ipsita A. Banerjee
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

IAB was involved in conception, design of study; DJL, CGL, and PAM helped in acquisition of data. IAB and DJL contributed to analysis, writing, and interpretation of data.

Corresponding author

Correspondence to Ipsita A. Banerjee.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Ethical approval

NA.

Consent for publication

All authors give full consent for publication.

Consent to participate

NA.

Additional information

Publisher's Note

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

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (PDF 1780 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lambo, D.J., Lebedenko, C.G., McCallum, P.A. et al. Molecular dynamics, MMGBSA, and docking studies of natural products conjugated to tumor-targeted peptide for targeting BRAF V600E and MERTK receptors. Mol Divers 27, 389–423 (2023). https://doi.org/10.1007/s11030-022-10430-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11030-022-10430-8

Keywords

Search

Navigation