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Identification of Potential c-MET Inhibitors from Natural Product Library by Virtual Screening, Molecular Dynamics and MM/PBSA Approach

Year 2026, Volume: 38 Issue: 1, 71 - 84, 20.03.2026
https://doi.org/10.7240/jeps.1740666
https://izlik.org/JA74XH54YE

Abstract

c-MET is a transmembrane receptor tyrosine kinase and an important therapeutic target for anticancer drug development. Dysregulation of the c-MET/HGF pathway has been implicated in various human proliferative disorders. Crizotinib, initially developed as a selective c-MET inhibitor, is now recognized as a multitargeted tyrosine kinase inhibitor (TKI). Natural products remain a valuable source for drug discovery. In this study, a total of 153,019 natural product and natural product-like molecules collected from eight major libraries were screened to identify novel c-MET inhibitors using an integrated workflow combining virtual screening, ADME/toxicity filtering, molecular dynamics (MD) simulations, and MM/PBSA free energy analysis. A total of 30,534 molecules passing the Lipinski, PAINS and Brenk filters in the KNIME workflow were used in the virtual screening. After filtration based on the Veber, Egan, Ghose, and Muegge criteria, 148 molecules exhibited better binding scores than Crizotinib (−10.40 kcal/mol). Molecules with binding energies lower than –10.40 kcal/mol were carried forward for further analyses. Subsequent ProTox-II screening reduced the pool to seven non-toxic candidates. The two molecules among the selected seven that exhibited the highest binding scores were ZINC98363582 and ZINC98365604, each with a value of –10.70 kcal/mol. MD simulations (30 ns) demonstrated that all protein–ligand complexes maintained structural stability, showing average RMSD values between 0.125–0.20 nm, comparable to or better than the Crizotinib complex. MM/PBSA calculations revealed that several candidates exhibited markedly superior binding affinities relative to Crizotinib (−47.648 kJ/mol). Notably, ZINC98063878 displayed the strongest binding free energy (−153.481 kJ/mol), followed by NPA009357 (−112.425 kJ/mol) and NPA032454 (−109.835 kJ/mol). These values indicate significantly enhanced thermodynamic favorability driven by strong van der Waals and electrostatic contributions. Overall, the molecules ZINC98063878, NPA009357, NPA032454, ZINC98363582, and ZINC98365604 emerged as promising natural product-derived c-MET inhibitor candidates, outperforming the clinical reference molecule (Crizotinib) in both stability and predicted binding energy. These findings highlight their potential for further in vitro and in vivo evaluation toward developing next-generation c-MET–targeted anticancer agents.

Thanks

The virtual screening and molecular dynamics simulations calculations reported in this study were performed at TUBITAK ULAKBIM, High Performance and Grid Computing Center (TRUBA).

References

  • Cooper C.S., Park M., Blair D.G., Tainsky M.A., Huebner K., Croce C.M., et al. (1984). Molecular cloning of a new transforming gene from a chemically transformed human cell line. Nature, 311, 29–33.
  • Schmidt, C., Bladt, F., Goedecke, S., Brinkmann, V., Zschiesche, W., Sharpe, M., Gherardi, E., & Birchmeier, C. (1995). Scatter factor/hepatocyte growth factor is essential for liver development. Nature, 373(6516), 699–702.
  • Bottaro, D. P., Rubin, J. S., Faletto, D. L., Chan, A. M., Kmiecik, T. E., Vande Woude, G. F., & Aaronson, S. A. (1991). Identification of the hepatocyte growth factor receptor as the c-met proto-oncogene product. Science (New York, N.Y.), 251(4995), 802–804.
  • Gherardi E., Birchmeier W., Birchmeier C., Vande Woude G. (2012). Targeting MET in cancer: rationale and progress. Nat Rev Cancer, 12, 89–103.
  • Wang R., Ferrell L. D., Faouzi S., Maher J. J., Bishop J. M. (2001). Activation of the Met receptor by cell attachment induces and sustains hepatocellular carcinomas in transgenic mice. J Cell Biol, 153, 1023–34.
  • Suzuki M., Shiraha H., Fujikawa T., Takaoka N., Ueda N., Nakanishi Y., et al. (2005). Des-gamma-carboxy prothrombin is a potential autologous growth factor for hepatocellular carcinoma. J Biol Chem, 280, 6409–15.
  • Zhang, J., Jiang, X., Jiang, Y., Guo, M., Zhang, S., Li, J., He, J., Liu, J., Wang, J., & Ouyang, L. (2016). Recent advances in the development of dual VEGFR and c-Met small molecule inhibitors as anticancer drugs. European journal of medicinal chemistry, 108, 495–504.
  • Asaoka, Y., Tada, M., Ikenoue, T., Seto, M., Imai, M., Miyabayashi, K., Yamamoto, K., Yamamoto, S., Kudo, Y., Mohri, D., Isomura, Y., Ijichi, H., Tateishi, K., Kanai, F., Ogawa, S., Omata, M., & Koike, K. (2010). Gastric cancer cell line Hs746T harbors a splice site mutation of c-Met causing juxtamembrane domain deletion. Biochemical and biophysical research communications, 394(4), 1042–1046.
  • Giordano, S., & Columbano, A. (2014). Met as a therapeutic target in HCC: facts and hopes. Journal of hepatology, 60(2), 442–452.
  • Ou S. H. (2011). Crizotinib: a novel and first-in-class multitargeted tyrosine kinase inhibitor for the treatment of anaplastic lymphoma kinase rearranged non-small cell lung cancer and beyond. Drug design, development and therapy, 5, 471–485.
  • Cui, J. J., Tran-Dubé, M., Shen, H., Nambu, M., Kung, P. P., Pairish, M., Jia, L., Meng, J., Funk, L., Botrous, I., McTigue, M., Grodsky, N., Ryan, K., Padrique, E., Alton, G., Timofeevski, S., Yamazaki, S., Li, Q., Zou, H., Christensen, J., … Edwards, M. P. (2011). Structure based drug design of crizotinib (PF-02341066), a potent and selective dual inhibitor of mesenchymal-epithelial transition factor (c-MET) kinase and anaplastic lymphoma kinase (ALK). Journal of medicinal chemistry, 54(18), 6342–6363.
  • Curran M.P. (2012) Crizotinib: in locally advanced or metastatic non-small cell lung cancer. Drugs, 72, 99–107.
  • Katz, L., & Baltz, R. H. (2016). Natural product discovery: past, present, and future. Journal of industrial microbiology & biotechnology, 43(2-3), 155–176.
  • Mushtaq, S., Abbasi, B. H., Uzair, B., & Abbasi, R. (2018). Natural products as reservoirs of novel therapeutic agents. EXCLI journal, 17, 420–451.
  • Atanasov, A. G., Zotchev, S. B., Dirsch, V. M., & Supuran, C. T. (2021). International natural product sciences taskforce. Nat. Rev. Drug Discov, 20(3), 200-216.
  • Naeem, A., Hu, P., Yang, M., Zhang, J., Liu, Y., Zhu, W., & Zheng, Q. (2022). Natural Products as Anticancer Agents: Current Status and Future Perspectives. Molecules (Basel, Switzerland), 27(23), 8367.
  • Stanzione, F., Giangreco, I., & Cole, J. C. (2021). Use of molecular docking computational tools in drug discovery. Progress in medicinal chemistry, 60, 273–343.
  • Rollinger, J. M., Stuppner, H., & Langer, T. (2008). Virtual screening for the discovery of bioactive natural products. Progress in drug research. Fortschritte der Arzneimittelforschung. Progres des recherches pharmaceutiques, 65, 211–249.
  • Cerqueira, N. M., Gesto, D., Oliveira, E. F., Santos-Martins, D., Brás, N. F., Sousa, S. F., Fernandes, P. A., & Ramos, M. J. (2015). Receptor-based virtual screening protocol for drug discovery. Archives of biochemistry and biophysics, 582, 56–67.
  • Irwin, J. J., Sterling, T., Mysinger, M. M., Bolstad, E. S., & Coleman, R. G. (2012). ZINC: a free tool to discover chemistry for biology. Journal of chemical information and modeling, 52(7), 1757–1768.
  • van Santen, J. A., Jacob, G., Singh, A. L., Aniebok, V., Balunas, M. J., Bunsko, D., Neto, F. C., Castaño-Espriu, L., Chang, C., Clark, T. N., Cleary Little, J. L., Delgadillo, D. A., Dorrestein, P. C., Duncan, K. R., Egan, J. M., Galey, M. M., Haeckl, F. P. J., Hua, A., Hughes, A. H., Iskakova, D., … Linington, R. G. (2019). The Natural Products Atlas: An Open Access Knowledge Base for Microbial Natural Products Discovery. ACS central science, 5(11), 1824–1833.
  • Zhao, H., Yang, Y., Wang, S., Yang, X., Zhou, K., Xu, C., Zhang, X., Fan, J., Hou, D., Li, X., Lin, H., Tan, Y., Wang, S., Chu, X. Y., Zhuoma, D., Zhang, F., Ju, D., Zeng, X., & Chen, Y. Z. (2023). NPASS database update 2023: quantitative natural product activity and species source database for biomedical research. Nucleic acids research. 51(D1). D621–D628.
  • Pettersen, E. F., Goddard, T. D., Huang, C. C., Couch, G. S., Greenblatt, D. M., Meng, E. C., & Ferrin, T. E. (2004). UCSF Chimera--a visualization system for exploratory research and analysis. Journal of computational chemistry, 25(13), 1605–1612.
  • Berthold MR, Cebron N, Dill F, Gabriel TR,Kötter T, Meinl T, et al. (2009). KNIME-the Konstanz information miner: Version 2.0 and beyond, SIGKDD Explor. 11(1). 26-31.
  • Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (2001). Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced drug delivery reviews, 46(1-3), 3–26.
  • Baell, J. B., & Holloway, G. A. (2010). New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. Journal of medicinal chemistry, 53(7), 2719–2740.
  • O'Boyle, N. M., Banck, M., James, C. A., Morley, C., Vandermeersch, T., & Hutchison, G. R. (2011). Open Babel: An open chemical toolbox. Journal of cheminformatics, 3, 33.
  • Trott, O., & Olson, A. J. (2010). AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of computational chemistry, 31(2), 455–461.
  • BIOVIA. Dassault Systèmes. Discovery Studio 2020 Client. (2020). San Diego: Dassault Systèmes.
  • Daina, A., Michielin, O., & Zoete, V. (2017). SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Scientific reports, 7, 42717.
  • Drwal, M. N., Banerjee, P., Dunkel, M., Wettig, M. R., & Preissner, R. (2014). ProTox: a web server for the in silico prediction of rodent oral toxicity. Nucleic acids research, 42(Web Server issue), W53–W58.
  • Pronk, S., Páll, S., Schulz, R., Larsson, P., Bjelkmar, P., Apostolov, R., Shirts, M. R., Smith, J. C., Kasson, P. M., van der Spoel, D., Hess, B., & Lindahl, E. (2013). GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics (Oxford, England), 29(7), 845–854.
  • Wang, J., Cieplak, P., & Kollman, P. A. (2000). How well does a restrained electrostatic potential (RESP) model perform in calculating conformational energies of organic and biological molecules?. Journal of computational chemistry, 21(12), 1049-1074.
  • Sousa da Silva, A. W., & Vranken, W. F. (2012). ACPYPE - AnteChamber PYthon Parser interfacE. BMC research notes, 5, 367.
  • Humphrey, W., Dalke, A., & Schulten, K. (1996). VMD: visual molecular dynamics. Journal of molecular graphics, 14(1), 33–28.
  • Kumari, R., Kumar, R., Open Source Drug Discovery Consortium, & Lynn, A. (2014). g_mmpbsa--a GROMACS tool for high-throughput MM-PBSA calculations. Journal of chemical information and modeling, 54(7), 1951–1962.
  • Damghani, T., Sedghamiz, T., Sharifi, S., & Pirhadi, S. (2020). Critical c-Met-inhibitor interactions resolved from molecular dynamics simulations of different c-Met complexes. Journal of Molecular Structure, 1203, 127456.
  • Veber, D. F., Johnson, S. R., Cheng, H. Y., Smith, B. R., Ward, K. W., & Kopple, K. D. (2002). Molecular properties that influence the oral bioavailability of drug candidates. Journal of medicinal chemistry, 45(12), 2615–2623.
  • Liu, K., Watanabe, E., & Kokubo, H. (2017). Exploring the stability of ligand binding modes to proteins by molecular dynamics simulations. Journal of computer-aided molecular design, 31(2), 201–211.
  • Fu, Y., Zhao, J., & Chen, Z. (2018). Insights into the Molecular Mechanisms of Protein-Ligand Interactions by Molecular Docking and Molecular Dynamics Simulation: A Case of Oligopeptide Binding Protein. Computational and mathematical methods in medicine, 2018, 3502514.
  • Dhiman, A., & Purohit, R. (2023). Identification of potential mutational hotspots in serratiopeptidase to address its poor pH tolerance issue. Journal of biomolecular structure & dynamics, 41(18), 8831–8843.
  • Lobanov, M. Y., Bogatyreva, N. S., & Galzitskaya, O. V. (2008). Radius of gyration as an indicator of protein structure compactness. Molecular Biology, 42, 623-628.

Sanal Tarama, Moleküler Dinamik ve MM/PBSA Yaklaşımı ile Doğal Ürün Kütüphanesinden Potansiyel c-MET İnhibitörlerinin Belirlenmesi

Year 2026, Volume: 38 Issue: 1, 71 - 84, 20.03.2026
https://doi.org/10.7240/jeps.1740666
https://izlik.org/JA74XH54YE

Abstract

c-MET, transmembran bir reseptör tirozin kinaz olup antikanser ilaç geliştirme için önemli bir terapötik hedeftir. c-MET/HGF yolaklarının düzensizliği çeşitli insan proliferatif bozukluklarıyla ilişkilendirilmiştir. Başlangıçta seçici bir c-MET inhibitörü olarak geliştirilen Crizotinib günümüzde çok hedefli bir tirozin kinaz inhibitörü (TKI) olarak bilinmektedir. Doğal ürünler, ilaç keşfinde hâlâ değerli bir kaynak oluşturmaktadır. Bu çalışmada, sekiz büyük kütüphaneden toplanan toplam 153.019 doğal ürün ve doğal ürün benzeri molekül, sanal tarama, ADME/toksisite filtreleme, moleküler dinamik (MD) simülasyonları ve MM/PBSA serbest enerji analizini birleştiren entegre bir iş akışı ile taranarak yeni c-MET inhibitörlerinin belirlenmesi amaçlanmıştır. KNIME iş akışında Lipinski, PAINS ve Brenk filtrelerinden geçen toplam 30.534 molekül sanal taramada kullanılmıştır. Veber, Egan, Ghose ve Muegge kriterlerine göre yapılan filtrasyonun ardından 148 molekül, Crizotinib’ten (−10.40 kcal/mol) daha iyi bağlanma skorları göstermiştir. Bağlanma enerjisi –10.40 kcal/mol’den daha düşük olan moleküller ileri analizlere taşınmıştır. Ardından yapılan ProTox-II taraması, havuzu yedi toksik olmayan adaya düşürmüştür. Seçilen yedi molekül arasında en yüksek bağlanma skoruna sahip olan iki molekül, her biri –10.70 kcal/mol değeri ile ZINC98363582 ve ZINC98365604 olmuştur. MD simülasyonları (30 ns), tüm protein–ligand komplekslerinin yapısal kararlılığını koruduğunu ve ortalama RMSD değerlerinin 0.125–0.20 nm arasında olduğunu göstermiştir; bu değerler Crizotinib kompleksine eşdeğer veya daha iyidir. MM/PBSA hesaplamaları, bazı adayların Crizotinib’e (−47.648 kJ/mol) kıyasla belirgin şekilde daha yüksek bağlanma afiniteleri gösterdiğini ortaya koymuştur. Özellikle ZINC98063878 en güçlü bağlanma serbest enerjisini (−153.481 kJ/mol) göstermiştir; bunu NPA009357 (−112.425 kJ/mol) ve NPA032454 (−109.835 kJ/mol) izlemiştir. Bu değerler, güçlü van der Waals ve elektrostatik katkılarla yönlendirilen belirgin şekilde artmış termodinamik elverişliliğe işaret etmektedir. Genel olarak, ZINC98063878, NPA009357, NPA032454, ZINC98363582 ve ZINC98365604 molekülleri hem kararlılık hem de tahmin edilen bağlanma enerjisi açısından klinik referans molekül olan Crizotinib'i geride bırakarak umut vadeden doğal ürün türevli c-MET inhibitörü adayları olarak öne çıkmıştır. Bu bulgular, bu moleküllerin yeni nesil c-MET hedefli antikanser ajanların geliştirilmesi amacıyla ileri in vitro ve in vivo değerlendirmeler için potansiyel taşıdığını göstermektedir.

References

  • Cooper C.S., Park M., Blair D.G., Tainsky M.A., Huebner K., Croce C.M., et al. (1984). Molecular cloning of a new transforming gene from a chemically transformed human cell line. Nature, 311, 29–33.
  • Schmidt, C., Bladt, F., Goedecke, S., Brinkmann, V., Zschiesche, W., Sharpe, M., Gherardi, E., & Birchmeier, C. (1995). Scatter factor/hepatocyte growth factor is essential for liver development. Nature, 373(6516), 699–702.
  • Bottaro, D. P., Rubin, J. S., Faletto, D. L., Chan, A. M., Kmiecik, T. E., Vande Woude, G. F., & Aaronson, S. A. (1991). Identification of the hepatocyte growth factor receptor as the c-met proto-oncogene product. Science (New York, N.Y.), 251(4995), 802–804.
  • Gherardi E., Birchmeier W., Birchmeier C., Vande Woude G. (2012). Targeting MET in cancer: rationale and progress. Nat Rev Cancer, 12, 89–103.
  • Wang R., Ferrell L. D., Faouzi S., Maher J. J., Bishop J. M. (2001). Activation of the Met receptor by cell attachment induces and sustains hepatocellular carcinomas in transgenic mice. J Cell Biol, 153, 1023–34.
  • Suzuki M., Shiraha H., Fujikawa T., Takaoka N., Ueda N., Nakanishi Y., et al. (2005). Des-gamma-carboxy prothrombin is a potential autologous growth factor for hepatocellular carcinoma. J Biol Chem, 280, 6409–15.
  • Zhang, J., Jiang, X., Jiang, Y., Guo, M., Zhang, S., Li, J., He, J., Liu, J., Wang, J., & Ouyang, L. (2016). Recent advances in the development of dual VEGFR and c-Met small molecule inhibitors as anticancer drugs. European journal of medicinal chemistry, 108, 495–504.
  • Asaoka, Y., Tada, M., Ikenoue, T., Seto, M., Imai, M., Miyabayashi, K., Yamamoto, K., Yamamoto, S., Kudo, Y., Mohri, D., Isomura, Y., Ijichi, H., Tateishi, K., Kanai, F., Ogawa, S., Omata, M., & Koike, K. (2010). Gastric cancer cell line Hs746T harbors a splice site mutation of c-Met causing juxtamembrane domain deletion. Biochemical and biophysical research communications, 394(4), 1042–1046.
  • Giordano, S., & Columbano, A. (2014). Met as a therapeutic target in HCC: facts and hopes. Journal of hepatology, 60(2), 442–452.
  • Ou S. H. (2011). Crizotinib: a novel and first-in-class multitargeted tyrosine kinase inhibitor for the treatment of anaplastic lymphoma kinase rearranged non-small cell lung cancer and beyond. Drug design, development and therapy, 5, 471–485.
  • Cui, J. J., Tran-Dubé, M., Shen, H., Nambu, M., Kung, P. P., Pairish, M., Jia, L., Meng, J., Funk, L., Botrous, I., McTigue, M., Grodsky, N., Ryan, K., Padrique, E., Alton, G., Timofeevski, S., Yamazaki, S., Li, Q., Zou, H., Christensen, J., … Edwards, M. P. (2011). Structure based drug design of crizotinib (PF-02341066), a potent and selective dual inhibitor of mesenchymal-epithelial transition factor (c-MET) kinase and anaplastic lymphoma kinase (ALK). Journal of medicinal chemistry, 54(18), 6342–6363.
  • Curran M.P. (2012) Crizotinib: in locally advanced or metastatic non-small cell lung cancer. Drugs, 72, 99–107.
  • Katz, L., & Baltz, R. H. (2016). Natural product discovery: past, present, and future. Journal of industrial microbiology & biotechnology, 43(2-3), 155–176.
  • Mushtaq, S., Abbasi, B. H., Uzair, B., & Abbasi, R. (2018). Natural products as reservoirs of novel therapeutic agents. EXCLI journal, 17, 420–451.
  • Atanasov, A. G., Zotchev, S. B., Dirsch, V. M., & Supuran, C. T. (2021). International natural product sciences taskforce. Nat. Rev. Drug Discov, 20(3), 200-216.
  • Naeem, A., Hu, P., Yang, M., Zhang, J., Liu, Y., Zhu, W., & Zheng, Q. (2022). Natural Products as Anticancer Agents: Current Status and Future Perspectives. Molecules (Basel, Switzerland), 27(23), 8367.
  • Stanzione, F., Giangreco, I., & Cole, J. C. (2021). Use of molecular docking computational tools in drug discovery. Progress in medicinal chemistry, 60, 273–343.
  • Rollinger, J. M., Stuppner, H., & Langer, T. (2008). Virtual screening for the discovery of bioactive natural products. Progress in drug research. Fortschritte der Arzneimittelforschung. Progres des recherches pharmaceutiques, 65, 211–249.
  • Cerqueira, N. M., Gesto, D., Oliveira, E. F., Santos-Martins, D., Brás, N. F., Sousa, S. F., Fernandes, P. A., & Ramos, M. J. (2015). Receptor-based virtual screening protocol for drug discovery. Archives of biochemistry and biophysics, 582, 56–67.
  • Irwin, J. J., Sterling, T., Mysinger, M. M., Bolstad, E. S., & Coleman, R. G. (2012). ZINC: a free tool to discover chemistry for biology. Journal of chemical information and modeling, 52(7), 1757–1768.
  • van Santen, J. A., Jacob, G., Singh, A. L., Aniebok, V., Balunas, M. J., Bunsko, D., Neto, F. C., Castaño-Espriu, L., Chang, C., Clark, T. N., Cleary Little, J. L., Delgadillo, D. A., Dorrestein, P. C., Duncan, K. R., Egan, J. M., Galey, M. M., Haeckl, F. P. J., Hua, A., Hughes, A. H., Iskakova, D., … Linington, R. G. (2019). The Natural Products Atlas: An Open Access Knowledge Base for Microbial Natural Products Discovery. ACS central science, 5(11), 1824–1833.
  • Zhao, H., Yang, Y., Wang, S., Yang, X., Zhou, K., Xu, C., Zhang, X., Fan, J., Hou, D., Li, X., Lin, H., Tan, Y., Wang, S., Chu, X. Y., Zhuoma, D., Zhang, F., Ju, D., Zeng, X., & Chen, Y. Z. (2023). NPASS database update 2023: quantitative natural product activity and species source database for biomedical research. Nucleic acids research. 51(D1). D621–D628.
  • Pettersen, E. F., Goddard, T. D., Huang, C. C., Couch, G. S., Greenblatt, D. M., Meng, E. C., & Ferrin, T. E. (2004). UCSF Chimera--a visualization system for exploratory research and analysis. Journal of computational chemistry, 25(13), 1605–1612.
  • Berthold MR, Cebron N, Dill F, Gabriel TR,Kötter T, Meinl T, et al. (2009). KNIME-the Konstanz information miner: Version 2.0 and beyond, SIGKDD Explor. 11(1). 26-31.
  • Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (2001). Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced drug delivery reviews, 46(1-3), 3–26.
  • Baell, J. B., & Holloway, G. A. (2010). New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. Journal of medicinal chemistry, 53(7), 2719–2740.
  • O'Boyle, N. M., Banck, M., James, C. A., Morley, C., Vandermeersch, T., & Hutchison, G. R. (2011). Open Babel: An open chemical toolbox. Journal of cheminformatics, 3, 33.
  • Trott, O., & Olson, A. J. (2010). AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of computational chemistry, 31(2), 455–461.
  • BIOVIA. Dassault Systèmes. Discovery Studio 2020 Client. (2020). San Diego: Dassault Systèmes.
  • Daina, A., Michielin, O., & Zoete, V. (2017). SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Scientific reports, 7, 42717.
  • Drwal, M. N., Banerjee, P., Dunkel, M., Wettig, M. R., & Preissner, R. (2014). ProTox: a web server for the in silico prediction of rodent oral toxicity. Nucleic acids research, 42(Web Server issue), W53–W58.
  • Pronk, S., Páll, S., Schulz, R., Larsson, P., Bjelkmar, P., Apostolov, R., Shirts, M. R., Smith, J. C., Kasson, P. M., van der Spoel, D., Hess, B., & Lindahl, E. (2013). GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics (Oxford, England), 29(7), 845–854.
  • Wang, J., Cieplak, P., & Kollman, P. A. (2000). How well does a restrained electrostatic potential (RESP) model perform in calculating conformational energies of organic and biological molecules?. Journal of computational chemistry, 21(12), 1049-1074.
  • Sousa da Silva, A. W., & Vranken, W. F. (2012). ACPYPE - AnteChamber PYthon Parser interfacE. BMC research notes, 5, 367.
  • Humphrey, W., Dalke, A., & Schulten, K. (1996). VMD: visual molecular dynamics. Journal of molecular graphics, 14(1), 33–28.
  • Kumari, R., Kumar, R., Open Source Drug Discovery Consortium, & Lynn, A. (2014). g_mmpbsa--a GROMACS tool for high-throughput MM-PBSA calculations. Journal of chemical information and modeling, 54(7), 1951–1962.
  • Damghani, T., Sedghamiz, T., Sharifi, S., & Pirhadi, S. (2020). Critical c-Met-inhibitor interactions resolved from molecular dynamics simulations of different c-Met complexes. Journal of Molecular Structure, 1203, 127456.
  • Veber, D. F., Johnson, S. R., Cheng, H. Y., Smith, B. R., Ward, K. W., & Kopple, K. D. (2002). Molecular properties that influence the oral bioavailability of drug candidates. Journal of medicinal chemistry, 45(12), 2615–2623.
  • Liu, K., Watanabe, E., & Kokubo, H. (2017). Exploring the stability of ligand binding modes to proteins by molecular dynamics simulations. Journal of computer-aided molecular design, 31(2), 201–211.
  • Fu, Y., Zhao, J., & Chen, Z. (2018). Insights into the Molecular Mechanisms of Protein-Ligand Interactions by Molecular Docking and Molecular Dynamics Simulation: A Case of Oligopeptide Binding Protein. Computational and mathematical methods in medicine, 2018, 3502514.
  • Dhiman, A., & Purohit, R. (2023). Identification of potential mutational hotspots in serratiopeptidase to address its poor pH tolerance issue. Journal of biomolecular structure & dynamics, 41(18), 8831–8843.
  • Lobanov, M. Y., Bogatyreva, N. S., & Galzitskaya, O. V. (2008). Radius of gyration as an indicator of protein structure compactness. Molecular Biology, 42, 623-628.
There are 42 citations in total.

Details

Primary Language English
Subjects Molecular Docking, Bioinformatics and Computational Biology (Other)
Journal Section Research Article
Authors

Emre Can Buluz 0000-0002-2491-4347

Submission Date July 11, 2025
Acceptance Date December 22, 2025
Publication Date March 20, 2026
DOI https://doi.org/10.7240/jeps.1740666
IZ https://izlik.org/JA74XH54YE
Published in Issue Year 2026 Volume: 38 Issue: 1

Cite

APA Buluz, E. C. (2026). Identification of Potential c-MET Inhibitors from Natural Product Library by Virtual Screening, Molecular Dynamics and MM/PBSA Approach. International Journal of Advances in Engineering and Pure Sciences, 38(1), 71-84. https://doi.org/10.7240/jeps.1740666
AMA 1.Buluz EC. Identification of Potential c-MET Inhibitors from Natural Product Library by Virtual Screening, Molecular Dynamics and MM/PBSA Approach. JEPS. 2026;38(1):71-84. doi:10.7240/jeps.1740666
Chicago Buluz, Emre Can. 2026. “Identification of Potential C-MET Inhibitors from Natural Product Library by Virtual Screening, Molecular Dynamics and MM PBSA Approach”. International Journal of Advances in Engineering and Pure Sciences 38 (1): 71-84. https://doi.org/10.7240/jeps.1740666.
EndNote Buluz EC (March 1, 2026) Identification of Potential c-MET Inhibitors from Natural Product Library by Virtual Screening, Molecular Dynamics and MM/PBSA Approach. International Journal of Advances in Engineering and Pure Sciences 38 1 71–84.
IEEE [1]E. C. Buluz, “Identification of Potential c-MET Inhibitors from Natural Product Library by Virtual Screening, Molecular Dynamics and MM/PBSA Approach”, JEPS, vol. 38, no. 1, pp. 71–84, Mar. 2026, doi: 10.7240/jeps.1740666.
ISNAD Buluz, Emre Can. “Identification of Potential C-MET Inhibitors from Natural Product Library by Virtual Screening, Molecular Dynamics and MM PBSA Approach”. International Journal of Advances in Engineering and Pure Sciences 38/1 (March 1, 2026): 71-84. https://doi.org/10.7240/jeps.1740666.
JAMA 1.Buluz EC. Identification of Potential c-MET Inhibitors from Natural Product Library by Virtual Screening, Molecular Dynamics and MM/PBSA Approach. JEPS. 2026;38:71–84.
MLA Buluz, Emre Can. “Identification of Potential C-MET Inhibitors from Natural Product Library by Virtual Screening, Molecular Dynamics and MM PBSA Approach”. International Journal of Advances in Engineering and Pure Sciences, vol. 38, no. 1, Mar. 2026, pp. 71-84, doi:10.7240/jeps.1740666.
Vancouver 1.Emre Can Buluz. Identification of Potential c-MET Inhibitors from Natural Product Library by Virtual Screening, Molecular Dynamics and MM/PBSA Approach. JEPS. 2026 Mar. 1;38(1):71-84. doi:10.7240/jeps.1740666