Pharmacophore Modeling Guided by Conformational Dynamics Reveals Potent Anticancer Agents

Year 2023, Volume: 27 Issue: 1, 51 - 63, 25.04.2023

Nigar Çarşıbaşı

https://doi.org/10.19113/sdufenbed.1121167

Abstract

Targeting the interaction between tumor suppressor p53 and murine double minute 2(MDM2) has been an attractive therapeutic strategy of recent cancer research. There are a few number of MDM2-targeted anticancer drug molecules undergoing clinical trials, yet none of them have been approved so far. In this study, a new approach is employed in which dynamics of MDM2 obtained by elastic network models are used as a guide in the generation of the ligand-based pharmacophore model prior to virtual screening. Hit molecules exhibiting high affinity to MDM2 were captured and tested by rigid and induced-fit molecular docking. The knowledge of the binding mechanism was used while creating the induced-fit docking criteria. Application of Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) method provided an accurate prediction of the binding free energy values. Two leading hit molecules which have shown better docking
scores, binding free energy values and drug-like molecular properties were identified. These hits exhibited extra intermolecular interactions with MDM2, indicating a stable complex formation and hence would be further tested in vitro. Finally, the combined computational strategy employed in this study can be a promising tool in drug design for the discovery of potential new hits.

Keywords

elastic network model, drug repurposing, induced-fit docking, MDM2, p53

Konformasyonel Dinamik Yönlendirmeli Farmakofor Modelleme ile Güçlü Antikanser Ajanlarının Belirlenmesi

Abstract

Tümör baskılayıcı p53 ile Murine Double Minute 2 (MDM2) proteinleri arasındaki etkileşimi hedeflemek, son kanser araştırmalarında öne çıkan bir terapötik strateji olmuştur. Şu anda klinik deneylerde çalışılan birkaç MDM2 hedefli antikanser ilaç molekülü bulunmakla beraber hiçbiri henüz onay alamamıştır. Bu çalışmada, elastik ağ modelleri ile elde edilen MDM2 dinamiklerinin, ligand bazlı farmakofor modelinin oluşturulmasında ardından sanal tarama yürütülerek yeni MDM2 inhibitörlerinin araştırılmasında kılavuz olarak kullanıldığı bir yaklaşım yürütülmüştür. Sanal tarama sonucu elde edilen öncü moleküllerin MDM2'ye afiniteleri sabit ve uyarılmış-uyumlu moleküler yerleştirme (induced-fit docking) metodları ile test edilmiştir. İndüklenmiş yerleştirme kriterleri oluşturulurken
bağlanma mekanizması bilgisi kullanılmıştır. Bağlanma serbest enerji değerlerinin doğru tahminini sağlayan Moleküler Mekanik Generalized Born-Surface Area (MM-GBSA) yönteminin uygulanması ile, yüksek yerleştirme puanları, bağlanma serbest enerjileri ve ilaca benzer fizikokimyasal özelliklere sahip iki adet lider molekül belirlenmiştir. Bu lider moleküller, MDM2 ile ekstra etkileşimler sergilerken kararlı kompleks oluşturmaktadırlar ve sonraki aşamada in vitro çalışmalarda inceleneceklerdir. Sonuç olarak, burada uygulanan kombine bilgisayar destekli strateji, yeni ilaç adaylarının keşfinde başarılı bir yöntem olarak uygulanabilir.

Keywords

elastik ağ modeli, ilaç yeniden konumlandırma, uyarılmış-uyumlu moleküler yerleştirme, MDM2, p53

References

• [1] Ferlay, J., Colombet, M., Soerjomataram, I., et al. 2019. Estimating the global cancer incidence and mortality in 2018: Globocan sources and methods. International Journal of Cancer, 144 (8), 1941-1953.
• [2] Siegel, R. L., Miller, K. D., Jemal, A. 2016. Cancer statistics. CA Cancer Journal for Clinicians 2016, 66, 7-30.
• [3] Singh, S., Sharma, B., Kanwar, S., Kumar, A. 2016. Lead phytochemicals for anticancer drug development. Frontiers in Plant Science, 7, 1667.
• [4] Choudri, A. S., Mandave, P. C., Deshpande, M., Ranjekar, P., Prakash, O. 2020. Phytochemicals in cancer treatment: From preclinical studies to clinical practice. Frontiers in Pharmacology, 10, 1614.
• [5] Khoo, K. H., Verma, C. S., Lane, D. P. 2014.Drugging the p53 pathway: understanding the route to clinical efficacy. Nature Reviews Drug Discovery,13, 217-236.
• [6] Lane, D. P. 1992. P53, guardian of the genome. Nature, 358, 15–16.
• [7] Skalniak, L., Surmiak, E., Holak, T. A. 2019. A therapeutic patent overview of MDM2/X-targeted therapies (2014-2018). Expert Opinion on Therapeutic Patents, 29 (3), 151-170.
• [8] Momand, J. G., Zambetti, P., Olson, D. C., Donna, G., George, D., Levine, A. J. 1992. The MDM2 oncogene product forms a complex with the p53 protein and inhibits p53 mediated transactivation. Cell, 69 (7), 1237-1245.
• [9] Roth, J., Dobbelstein, M., Freedman, D., Shenk, T., Levine, A. J. 1998. Nucleo-cytoplasmic shuttling of the hdm2 oncoprotein regulates the levels of the p53 protein via a pathway used by the human immunodeficiency virus rev protein, Embo Journal, 17, 554-564.
• [10] Vassilev, L. T., Vu, B. T., Graves, B., Carvajal, D., Podlaski, F., et al. 2001. In vivo activation of the p53 pathway by small-molecule antagonists of MDM2. Science, 303, 844-8.
• [11] Vu, B., Wovkulich, P., Pizzolato, G., Lovey, A., Ding, Q., Jiang, N., et al. 2013. Discovery of RG7112: a small-molecule MDM2 inhibitor in clinical development. ACS Medicinal Chemistry Letters, 4, 466−9.
• [12] Ding, Q., Zhang, Z., Liu, J. J., Jiang, N., Zhang, J., Ross, T. M., et al. 2013. Discovery of RG7388, a potent and selective p53-MDM2 inhibitor in clinical development. Journal of Medicinal Chemistry, 56, 5979-83.
• [13] Bill, K. L. J., Garnett, J., Meaux. I., Creighton, C. J., Bolshakov, S., Barriere, C., et al. 2016. SAR405838: a novel and potent inhibitor of the MDM2:p53 Axis for the treatment of dedifferentiated liposarcoma. Clinical Cancer Research, 22, 1150-60.
• [14] Sun, D., Li, Z., Rew, Y., Gribble, M., Bartberger, M. D., Beck, H. P. et al. 2014. Discovery of AMG 232, a potent, selective, and orally bioavailable MDM2-p53 inhibitor in clinical development. Journal of Medicinal Chemistry, 57, 1454-72.
• [15] Holzer, P., Masuya, K., Furet, P., Kallen, J., Valat-Stachyra, T., Ferretti, S., Berghausen. J., et al. 2015. Discovery of a dihydroisoquinolinone derivative (NVP-CGM097): a highly potent and selective MDM2 inhibitor undergoing phase 1 clinical trials in p53wt tumors. Journal of Medicinal Chemistry, 58, 6348-58.
• [16] Stachyra-Valat, T., Baysang, F., D'Alessandro, A. C., Dirk, E., Furet, P., et al. 2016. HDM201: biochemical and biophysical profile of a novel highly potent and selective PPI inhibitor of p53-Mdm2. Cancer Research, 76, 1239.
• [17] Talevi, A., Bellera, C. L. 2020. Challenges and opportunities with drug repurposing: finding strategies to find alternative uses of therapeutics. Expert Opinion in Drug Discovery, 15(4), 397-401.
• [18] Parvathaneni, V., Kulkarni, N. S., Muth, A., Gupta, V. 2019. Drug repurposing: a promising tool to accelerate the drug discovery process. Drug Discovery Today, 24(10), 2076-2085.
• [19] Pushpakom, S., Iorio, I., Eyers, P. A., Escott, K. J., Hopper, S., Wells, A., Doig, A., et al. 2019. Drug repurposing: progress, challanges, and recommendations. Nature Reviews Drug Discovery, 18, 41-58.
• [20] Tiwari, S., Reuter, N. 2018. Conservation of intrinsic dynamics in proteins-what have computational models taught us. Current Opinion in Structural Biology, 50, 75-81.
• [21] Bahar, I., Lezon, T. R., Yang, L. W., Eyal, E. 2010. Global dynamics of proteins: bridging between structure and function. Annual Reviews in Biophysics, 39, 23-42.
• [22] Kantarci-Carsibasi, N., Haliloglu, T., Doruker, P. 2008. Conformational transition pathways explored by monte carlo simulations integrated with collective modes. Biophysical Journal, 95 (12), 5862-5873.
• [23] Haliloglu, T., Bahar, I., Erman, B. 1997. Gaussian Dynamics of folded proteins, Physical Review Letters, 79, 3090-3093.
• [24] Bahar, I., Atilgan, A. R., Demirel, M. C., Erman, B. 1998. Vibrational dynamics of folded proteins: Significance of slow and fast motions in relation to function and stability. Physical Review Letters, 80, 2733-2736.
• [25] Atilgan, A. R., Durell, S. R., Jernigan, R. L., Demirel, M. C., Keskin, O., Bahar, I. 2001. Anisotropy of fluctuation dynamics of proteins with an elastic network model. Biophysical Journal, 80, 505-515.
• [26] Lu, S. H., Wu, J. W., Liu, H. L., Zhao, J. H., et al. 2011. The discovery of potential acetylcholinesterase inhibitors: a combination of pharmacophore modeling, virtual screening, and molecular docking studies. Journal of Biomedical Science, 18 (1), 8.
• [27] Dhanjal, J. K., Sharma, S., Grover, A., Das, A. 2015. Use of ligand-based pharmacophore modeling and docking approach to find novel acetylcholinesterase inhibitors for treating Alzheimer's. Biomedicine Pharmacotherapy, 71, 146-52.
• [28] Malik, R., Mehta, P., Srivastava, S., Choudhary, B. S., Sharma, M. 2017. Structure-based screening, ADMET profiling, and molecular dynamic studies on mGlu2 receptor for identification of newer antiepileptic agents. Journal of Biomolecular Structure and Dynamics, 35(16), 3433-3448.
• [29] Ece, A. 2020. Towards more effective acetylcholinesterase inhibitors: a comprehensive modelling study based on human acetylcholinesterase protein-drug complex. Journal of Biomolecular Structure and Dynamics, 38 (2), 565-572.
• [30] Alamri, M. A., Alamri, M. A. 2019. Pharmacophore and docking-based sequential virtual screening for the identification of novel Sigma 1 receptor ligands. Bioinformation, 15(8), 586-595.
• [31] Moussa, N., Hassan, A., Gharaghani, S. 2021. Pharmacophore model, docking, QSAR, and molecular dynamics simulation studies of substituted cyclic imides and herbal medicines as COX-2 inhibitors. Heliyon, 7(4), e06605.
• [32] Yuce, M., Cicek, E., Inan, T., Dag, A. B., Kurkcuoglu, O., Sungur, F. A. 2021. Repurposing of FDA-approved drugs against active site and potential allosteric drug-binding sites of COVID-19 main protease. Proteins: Structure Function and Bioinformatics, 89(11), 1425-1441.
• [33] Aydin, G. M., Paksoy, N., Orhan, M. D., Avsar, T., Yurtsever, M., Durdagi, S. 2020. Proposing novel MDM2 inhibitors: Combined physics-driven high-throughput virtual screening and in vitro studies. Chemical Biology and Drug Design, 96, 684– 700.
• [34] Chen, J., Wang, J., Zhu, W. A. 2013. Computational analysis of binding modes and conformational changes of MDM2 induced by p53 and inhibitor bindings. Journal of Computer Aided Molecular Design, 27, 965-974.
• [35] Chene, P. 2004. Inhibition of the p53-MDM2 interaction: targeting a protein-protein interface, Molecular Cancer Research, 2, 20-28.
• [36] Das, P., Mattaparthi, V. 2020. Computational investigation on the p53-MDM2 interaction using the potential of mean force study. ACS Omega, 5, 8449-8462.
• [37] Dasdidar, S. G., Lane, D. P., Verma, C. S. 2009. Modulation of p53 binding to MDM2: computational studies reveal important roles of Tyr100. BMC Bioinformatics, 10(Suppl 15), S6.
• [38] Estrada-Ortiz, N., Neochoritis, C. G., Dömling, A. 2016. How to design a successful p53-MDM2/X interaction inhibitor: a thorough overview based on crystal structures. Chem Med Chem, 1, 757–772.
• [39] Atatreh, N., Ghattas, M. A., Bardaweel, S. K., Rawashdeh, S. A., Sorkhy, M. A. 2018. Identification of new inhibitor of MDM2-p53 interactions via pharmacophore and structure-based virtual screening. Drug Design Development and Therapy, 12, 3741-3752.
• [40] Pantelopus, G. A., Mukherjee, S., Voelz, V. A. 2015. Microsecond simulations of MDM2 and its complex with p53 yield insight into force field accuracy and conformational Dynamics. Proteins, 83, 1665-1676.
• [41] Zhao, P., Cao, H., Chen, Y., Zhu, T. 2019. Insights into the binding mechanisms of inhibitors of MDM2 based on molecular dynamics simulations and binding free energy calculations. Chemical Physics Letters, 728, 94-101.
• [42] Chen, J., Wang, J., Zhu, W. A. 2013. Computational analysis of binding modes and conformational changes of MDM2 induced by p53 and inhibitor bindings. Journal of Computer Aided Molecular Design, 27, 965-74.
• [43] Zou, R., Zhou, Y., Wang, Y., Kuang, G., et al. 2020. Free Energy Profile and Kinetics of Coupled Folding and Binding of the Intrinsically Disordered Protein p53 with MDM2. Journal of Chemical Information and Modeling, 60(3),1551-1558.
• [44] Kantarci-Carsibasi, N. 2021. Elucidation of conformational dynamics of MDM2 and alterations induced upon inhibitor binding using elastic network simulations and molecular docking. Journal of Computational Biophysics and Chemistry, 20 (7), 751-763.
• [45] Schrödinger. 2015. Small-molecule drug discovery suite (version 2015-3). New York, NY: Schrödinger, LLC.
• [46] Schrödinger. 2018. Maestro (version 2018-4). New York, NY: Schrödinger, LLC.
• [47] Sastry, G., Adzhigirey, M., Day, T., Annabhimoju, R., Sherman, W. 2013. Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. Journal of Computer Aided Molecular Design, 27 (3), 221-234.
• [48] Jorgensen, W. L., Tirado-Rives, J. 1988. The OPLS (optimized potentials for liquid simulations) potential functions for protein, energy minimizations for crystals of cyclic peptides, and crambin. Journal of American Chemical Society, 118(45), 1657-1666.
• [49] Shelly, J. C., Cholleti, A., Frye, L. L., Greenwood, J. R., Timlin, M. R., Uchimaya, M. 2007. Epik: a software program for pK a prediction and protonation state generation for drug-like molecules Journal of Computer Aided Molecular Design, 21 (12), 681-691.
• [50] Langer, T., Hoffmann, R., Bachmair, F., Begle, S. 2000. Chemical function based pharmacophore models as suitable filters for virtual screening. Journal of Molecular Structure, 503, 59.
• [51] Wolber, G., Langer, T. 2005. LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters. Journal of Chemical Informationa and Modeling, 45(1), 160–169.
• [52] Halgren, T. A. 1996. Merck molecular force field: Basis, form, scope, parameterization, and performance of MMFF94. Journal of Computational Chemistry, 17 (5–6), 490-519.
• [53] Wishart, D. S., Knox, C., Guo, A. C., et al. 2006. Drug Bank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Research, 34, 668-672.
• [54] Lipinski, C. A. 2000. Drug-like properties and the causes of poor solubility and poor permeability. Journal of Pharmacology Toxicology Methods, 44(1), 235-249.
• [55] Trott, O., Olson, A. J. 2009. AutoDock Vina: Improving the Speed and Accuracy of Docking with a New Scoring Function, Efficient Optimization, and Multithreading. Journal of Computational Chemistry, 31(2), 174-82.
• [56] Friesner, R. A., Banks, J. L., Murphy, R. B., Halgren T. A., et al. 2004. Glide: a new approach for rapid, accurate docking and scoring: 1. method and assessment of docking accuracy. Journal of Medicinal Chemistry, 47 (7), 1739-1749.
• [57] Friesner, R. A., Murphy, R. B., Repasky, M. P., Frye, L. L., Greenwood, J. R., et al. 2006. Extra precision glide: Docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. Journal of Medicinal Chemistry, 49 (21), 6177-6196.
• [58] Clark, A. J., Tiwary, P., Borrelli, K., Feng, S., Miller, E. B., et al. 2016. Prediction of Protein-Ligand Binding Poses via a Combination of Induced Fit Docking and Metadynamics Simulations. Journal of Chemical Theory and Computation, 12 (6), 2990-2998.
• [59] Rastelli, G., Del Rio, A., Degliesposti, G., Sgobba, M. 2010. Fast and accurate predictions of binding free energies using MM-PBSA and MM-GBSA. Journal of Computational Chemistry, 31(4),797-810.
• [60] Hou, T., Wang, J., Li, Y., Wang, W. 2011. Assessing the performance of the molecular mechanics/Poisson Boltzmann surface area and molecular mechanics/generalized Born surface area methods. II. The accuracy of ranking poses generated from docking. Journal of Computational Chemistry, 32(5), 866-77.
• [61] Jianing, L., Abel, R., Zhu, K., Cao, Y., Zhao, S., Friesner, R. A. 2011. The VSGB 2.0 model: A next generation energy model for high resolution protein structure modeling. Proteins, 79 (10), 2794-2812.
• [62] Walter, S. D. 2005. The partial area under the ROC curve. Statistics in Medicine; 24:2025-40.
• [63] Basu, S., Wallner, B. 2016. Finding correct protein-protein docking models using PRoQDock. Bioinformatics, 32 (12), i262-i270.
• [64] Truchon, J. F., Bayly, C. I. 2007. Evaluating virtual screening methods: good and bad metrics for the "early recognition" problem. . Journal of Chemical Information and Modeling, 47(2), 488-508.
• [65] Wang, Y., Xiao, J., Suzek, T.O., Zhang, J., Wang, J., Bryant, S. H. 2009. PubChem: a public information system for analyzing bioactivities of small molecules. Nucleic Acids Research, 37, 623–633.
• [66] Kim, S., Chen, J., Cheng, T., et al. 2021. PubChem in 2021: new data content and improved web interfaces. Nucleic Acids Research, 49(D1), D1388–D1395.
• [67] Overington, J. P., Al-Lazikani, B., Hopkins, A. L. 2006. How many drug targets are there? Nature Reviews Drug Discovery, 5(12), 993-996.
• [68] Tetko, I. V., Gasteiger, J., Todeschini, R., Mauri, A., et al. 2005. Virtual computational chemistry laboratory design and description. Journal of Computer Aided Molecular Design, 19, 453-463.
• [69] Lee, S. K., Lee, I. H., Kim, H. J., Chang, G. S., Chung, J. E., No, K. T. 2003. The PreADME Approach: web-based program for rapid prediction of physicochemical, drug absorption and drug-like properties. Euro QSAR 2002 designing drugs and crop protectants: processes, problems and solutions, Blackwell Publishing, Massachusetts, USA. 418–420.