Skip to main content
SpringerLink
Log in

In-silico identification of Coumarin-based natural compounds as potential VEGFR-2 inhibitors

  • Original Paper
  • Published:
Chemical Papers Aims and scope Submit manuscript

Abstract

The coumarin nucleus is a simple privileged scaffold distributed in many plants. It has recently gained attention for its diverse biological activities and interactions with enzymes and receptors. The vascular endothelial growth factor receptor-2 (VEGFR-2), a receptor tyrosine kinase, is a crucial cancer target as it is involved in angiogenesis. This study employs virtual screening, molecular docking, and molecular simulation studies to identify potential coumarin candidates against VEGFR-2 from the COCONUT database. After thorough docking studies, CNP0056360, CNP0340213, and CNP0366287 were identified as final hits. Molecular dynamics simulation studies revealed strong stability and better binding energies for CNP0056360 and CNP0340213, outperforming lenvatinib; CNP0366287 showed comparable behaviour. The identified coumarins exhibited good in-silico pharmacokinetics and demonstrated low toxicity.

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

Data availability

The files associated with the research could be freely assessed on “https://zenodo.org/records/10646050”.

References

  • Abolibda TZ, Fathalla M, Farag B, Zaki MEA, Gomha SM (2023) Synthesis and molecular docking of some novel 3-Thiazolyl-coumarins as inhibitors of VEGFR-2 Kinase. Molecules [Online], 28

  • Alshabanah LA, Al-Mutabagani LA, Gomha SM, Ahmed HA (2022) Three-component synthesis of some new coumarin derivatives as anticancer agents. Front Chem 9:762248

    Article  PubMed  PubMed Central  Google Scholar 

  • Al-Warhi T, Sabt A, Elkaeed EB, Eldehna WM (2020) Recent advancements of coumarin-based anticancer agents: an up-to-date review. Bioorg Chem 103:104163

    Article  CAS  PubMed  Google Scholar 

  • Annunziata F, Pinna C, Dallavalle S, Tamborini L, Pinto A (2020) An overview of coumarin as a versatile and readily accessible scaffold with broad-ranging biological activities. Int J Mol Sci [Online] 21

  • Aziz MA, Serya RAT, Lasheen DS, Abdel-Aziz AK, Esmat A, Mansour AM, Singab ANB, Abouzid KAM (2016) Discovery of potent VEGFR-2 inhibitors based on furopyrimidine and thienopyrimidne scaffolds as cancer targeting agents. Sci Rep 6:24460

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  • Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The protein data bank. Nucl Acids Res 28:235–242

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Berthold MR, Cebron N, Dill F, Gabriel TR, Kötter T, Meinl T, Ohl P, Sieb C, Thiel K, Wiswedel B (2008) KNIME: the Konstanz information miner. In: Data analysis, machine learning and applications. Springer, Berlin pp 319–326

  • Colovos C, Yeates TO (1993) Verification of protein structures: patterns of nonbonded atomic interactions. Protein Sci 2:1511–1519

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Daina A, Michielin O, Zoete V (2017) SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep 7:42717

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  • Davis IW, Leaver-Fay A, Chen VB, Block JN, Kapral GJ, Wang X, Murray LW, Arendall WB III, Snoeyink J, Richardson JS, Richardson DC (2007) MolProbity: all-atom contacts and structure validation for proteins and nucleic acids. Nucl Acids Res 35:W375–W383

    Article  PubMed  PubMed Central  Google Scholar 

  • Dolinsky TJ, Nielsen JE, McCammon JA, Baker NA (2004) PDB2PQR: an automated pipeline for the setup of Poisson–Boltzmann electrostatics calculations. Nucl Acids Res 32:W665–W667

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Durrant JD, McCammon JA (2011) Molecular dynamics simulations and drug discovery. BMC Biol 9:71

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Ganeshpurkar A, Singh R, Gore PG, Kumar D, Gutti G, Kumar A, Singh SK (2020) Structure-based screening and molecular dynamics simulation studies for the identification of potential acetylcholinesterase inhibitors. Mol Simul 46:169–185

    Article  CAS  Google Scholar 

  • Ghosh P, Singh R, Ganeshpurkar A, Swetha R, Kumar D, Singh SK, Kumar A (2022) Identification of potential death-associated protein kinase-1 (DAPK1) inhibitors by an integrated ligand-based and structure-based computational drug design approach. J Biomol Struct Dyn 1–13

  • Gomha SM, Abdel-Aziz HM, El-Reedy AAM (2018) Facile synthesis of Pyrazolo[3,4-c]pyrazoles bearing coumarine ring as anticancer agents. J Heterocycl Chem 55:1960–1965

    Article  CAS  Google Scholar 

  • Hao Z, Wang P (2020) Lenvatinib in management of solid tumors. Oncologist 25:e302–e310

    Article  PubMed  Google Scholar 

  • Hata H, Phuoc Tran D, MarzoukSobeh M, Kitao A (2021) Binding free energy of protein/ligand complexes calculated using dissociation parallel cascade selection molecular dynamics and Markov state model. Biophys Physicobiol 18:305–316

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Hicklin DJ, Ellis LM (2005) Role of the vascular endothelial growth factor pathway in tumor growth and angiogenesis. J Clin Oncol 23:1011–1027

    Article  CAS  PubMed  Google Scholar 

  • Jabeen A, Mohamedali A, Ranganathan S (2019) Protocol for Protein structure modelling. Encyclopedia of bioinformatics and computational biology. Academic Press, Oxford

    Google Scholar 

  • Jiang Z, You L, Dou W, Sun T, Xu P (2019) Effects of an electric field on the conformational transition of the protein: a molecular dynamics simulation study. Polymers [Online] 11

  • Jitendra S, Vinay R (2011) Structure based drug designing of a novel antiflaviviral inhibitor for nonstructural 3 protein. Bioinformation 6:57–60

    Article  PubMed  PubMed Central  Google Scholar 

  • Kumar R, Sharma A (2023) Chapter 15—Computational strategies and tools for protein tertiary structure prediction. Basic biotechniques for bioprocess and bioentrepreneurship. Academic Press, Cambridge

    Google Scholar 

  • Küpeli Akkol E, Genç Y, Karpuz B, Sobarzo-Sánchez E, Capasso R (2020) Coumarins and coumarin-related compounds in pharmacotherapy of cancer. Cancers [Online] 12

  • Lugano R, Ramachandran M, Dimberg A (2020) Tumor angiogenesis: causes, consequences, challenges and opportunities. Cell Mol Life Sci 77:1745–1770

    Article  CAS  PubMed  Google Scholar 

  • Ma X-L, Chen C, Yang J (2005) Predictive model of blood-brain barrier penetration of organic compounds. Acta Pharmacol Sin 26:500–512

    Article  CAS  PubMed  Google Scholar 

  • Maria João M, Lourdes S, Eugenio U, Orlando AA, Enrique M, Estela Guardado Y (2015) Coumarins—an important class of phytochemicals. Phytochemicals. IntechOpen

  • Meng XY, Zhang HX, Mezei M, Cui M (2011) Molecular docking: a powerful approach for structure-based drug discovery. Curr Comput Aided Drug Des 7:146–157

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ (2009) AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem 30:2785–2791

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • NaderiAlizadeh M, Rashidi M, Muhammadnejad A, MoeiniZanjani T, Ziai SA (2018) Antitumor effects of umbelliprenin in a mouse model of colorectal cancer. Iran J Pharm Res 17:976–985

    Google Scholar 

  • Nilsson M, Heymach JV (2006) Vascular endothelial growth factor (VEGF) pathway. J Thorac Oncol 1:768–770

    Article  PubMed  Google Scholar 

  • O’Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR (2011) Open babel: an open chemical toolbox. J Cheminformatics 3:33

    Article  CAS  Google Scholar 

  • Önder A (2020) Chapter 3—Anticancer activity of natural coumarins for biological targets. In: Atta Ur R (ed) Studies in natural products chemistry. Elsevier, Amsterdam

    Google Scholar 

  • Peng F-W, Liu D-K, Zhang Q-W, Xu Y-G, Shi L (2017) VEGFR-2 inhibitors and the therapeutic applications thereof: a patent review (2012–2016). Expert Opin Ther Pat 27:987–1004

    Article  CAS  PubMed  Google Scholar 

  • Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, Ferrin TE (2004) UCSF Chimera—A visualization system for exploratory research and analysis. J Comput Chem 25:1605–1612

    Article  CAS  PubMed  Google Scholar 

  • Pinedo HM, Slamon DJ (2000) INTRODUCTION: Translational Research: The Role of VEGF in Tumor Angiogenesis. Oncologist 5:1–2

    Article  Google Scholar 

  • S V, Kajal K, Mondal S, Wahan SK, Das Kurmi B, Das Gupta G, Patel P (2023) Novel VEGFR-2 kinase inhibitors as anticancer agents: a review focusing on SAR and molecular docking studies (2016–2021). Chem Biodivers 20:e202200847

  • Sharifi-Rad J, Cruz-Martins N, López-Jornet P, Lopez EP-F, Harun N, Yeskaliyeva B, Beyatli A, Sytar O, Shaheen S, Sharopov F, Taheri Y, Docea AO, Calina D, Cho WC (2021) Natural coumarins: exploring the pharmacological complexity and underlying molecular mechanisms. Oxid Med Cell Longev 2021:6492346

    Article  PubMed  PubMed Central  Google Scholar 

  • Singh R, Pokle AV, Ghosh P, Ganeshpurkar A, Swetha R, Singh SK, Kumar A (2022) Pharmacophore-based virtual screening, molecular docking and molecular dynamics simulations study for the identification of LIM kinase-1 inhibitors. J Biomol Struct Dyn 41:1–15

    Google Scholar 

  • Skariyachan S, Garka S (2018) Chapter 1—Exploring the binding potential of carbon nanotubes and fullerene towards major drug targets of multidrug resistant bacterial pathogens and their utility as novel therapeutic agents. In: Grumezescu AM (ed) Fullerens graphenes and nanotubes. William Andrew Publishing

    Google Scholar 

  • Sneha P, George Priya Doss C (2016) Chapter seven—Molecular dynamics: new frontier in personalized medicine. In: Donev R (ed) Advances in protein chemistry and structural biology. Academic Press

  • Sobolev OV, Afonine PV, Moriarty NW, Hekkelman ML, Joosten RP, Perrakis A, Adams PD (2020) A global Ramachandran score identifies protein structures with unlikely stereochemistry. Structure 28:1249-1258.e2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Sorokina M, Merseburger P, Rajan K, Yirik MA, Steinbeck C (2021) COCONUT online: collection of open natural products database. J Cheminformatics 13:1–13

    Article  Google Scholar 

  • Subramaniam S, Mehrotra M, Gupta D (2008) Virtual high throughput screening (vHTS)—a perspective. Bioinformation 3:14–17

    Article  PubMed  PubMed Central  Google Scholar 

  • Sunseri J, Koes DR (2016) Pharmit: interactive exploration of chemical space. Nucl Acids Res 44:W442–W448

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Swetha R, Sharma A, Singh R, Ganeshpurkar A, Kumar D, Kumar A, Singh SK (2022) Combined ligand-based and structure-based design of PDE 9A inhibitors against Alzheimer’s disease. Mol Divers 26:2877–2892

    Article  CAS  PubMed  Google Scholar 

  • Tripathi N, Goel B, Bhardwaj N, Sahu B, Kumar H, Jain SK (2022) Virtual screening and molecular simulation study of natural products database for lead identification of novel coronavirus main protease inhibitors. J Biomol Struct Dyn 40:3655–3667

    Article  CAS  PubMed  Google Scholar 

  • Wang X, Bove AM, Simone G, Ma B (2020) Molecular Bases of VEGFR-2-Mediated Physiological Function and Pathological Role. Front Cell Dev Biol 8:599281

    Article  PubMed  PubMed Central  Google Scholar 

  • Waterhouse A, Bertoni M, Bienert S, Studer G, Tauriello G, Gumienny R, Heer FT, De Beer TAP, Rempfer C, Bordoli L, Lepore R, Schwede T (2018) SWISS-MODEL: homology modelling of protein structures and complexes. Nucl Acids Res 46:W296–W303

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Williams CJ, Headd JJ, Moriarty NW, Prisant MG, Videau LL, Deis LN, Verma V, Keedy DA, Hintze BJ, Chen VB, Jain S, Lewis SM, Arendall WB III, Snoeyink J, Adams PD, Lovell SC, Richardson JS, Richardson DC (2018) MolProbity: more and better reference data for improved all-atom structure validation. Protein Sci 27:293–315

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The authors extend their gratitude toward Professor David A. Case, Department of Chemistry & Chemical Biology, Rutgers University, New Jersey, USA, for granting a licence for Amber 20. NT and NB are also thankful to IIT (BHU) and Ministry of Education (MoE), New Delhi for providing teaching assistantship.

Funding

This work was partially supported by the SERB-CRG Grant (Grant No. CRG/2022/001637).

Author information

Authors and Affiliations

  1. Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, 221005, India

    Nancy Tripathi, Nivedita Bhardwaj & Shreyans K. Jain

  2. Botanical Garden, Plant Conservation and Agro-Technologies Division, CSIR-National Botanical Research Institute, Lucknow, 226001, India

    Bikarma Singh

Authors
  1. Nancy Tripathi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nivedita Bhardwaj
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bikarma Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shreyans K. Jain
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Nancy Tripathi: Conceived, designed, and performed the experiments; Analyzed the data; Writing original draft. Nivedita Bhardwaj: Prepared figures and tables; Paper formatting. Bikarma Singh: Conceived and designed the experiments. Shreyans K. Jain: Conceived and designed the experiments; Supervision; Final paper review and formatting.

Corresponding author

Correspondence to Shreyans K. Jain.

Ethics declarations

Conflict of interest

The authors declare that the research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest.

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 (DOCX 1967 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tripathi, N., Bhardwaj, N., Singh, B. et al. In-silico identification of Coumarin-based natural compounds as potential VEGFR-2 inhibitors. Chem. Pap. (2024). https://doi.org/10.1007/s11696-024-03395-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11696-024-03395-5

Keywords

Search

Navigation