Login Register Cart 0
Generic placeholder image

Medicinal Chemistry

Editor-in-Chief

ISSN (Print): 1573-4064
ISSN (Online): 1875-6638

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Unveiling the ESR1 Conformational Stability and Screening Potent Inhibitors for Breast Cancer Treatment

Author(s): Khushboo Sharma, Umesh Panwar, Maddala Madhavi, Isha Joshi, Ishita Chopra, Lovely Soni, Arshiya Khan, Anushka Bhrdwaj, Abhyuday Singh Parihar, Vineeth Pazharathu Mohan, Leena Prajapati, Rashmi Sharma, Shweta Agrawal, Tajamul Hussain, Anuraj Nayarisseri* and Sanjeev Kumar Singh

Volume 20, Issue 3, 2024

Published on: 27 October, 2023

Page: [352 - 368] Pages: 17

DOI: 10.2174/0115734064256978231024062937

Price: $65

Abstract

Background: The current study recognizes the significance of estrogen receptor alpha (ERα) as a member of the nuclear receptor protein family, which holds a central role in the pathophysiology of breast cancer. ERα serves as a valuable prognostic marker, with its established relevance in predicting disease outcomes and treatment responses.

Methods: In this study, computational methods are utilized to search for suitable drug-like compounds that demonstrate analogous ligand binding kinetics to ERα.

Results: Docking-based simulation screened out the top 5 compounds - ZINC13377936, NCI35753, ZINC35465238, ZINC14726791, and NCI663569 against the targeted protein. Further, their dynamics studies reveal that the compounds ZINC13377936 and NCI35753 exhibit the highest binding stability and affinity.

Conclusion: Anticipating the competitive inhibition of ERα protein expression in breast cancer, we envision that both ZINC13377936 and NCI35753 compounds hold substantial promise as potential therapeutic agents. These candidates warrant thorough consideration for rigorous In vitro and In vivo evaluations within the context of clinical trials. The findings from this current investigation carry significant implications for the advancement of future diagnostic and therapeutic approaches for breast cancer.

Keywords: Cancer, ESR1, Erα, virtual screening, docking based simulation, binding affinity, R programming.

« Previous
Graphical Abstract

[1]
Musgrove, E.A.; Sutherland, R.L. Biological determinants of endocrine resistance in breast cancer. Nat. Rev. Cancer, 2009, 9(9), 631-643.
[http://dx.doi.org/10.1038/nrc2713] [PMID: 19701242]
[2]
Lupien, M.; Eeckhoute, J.; Meyer, C.A.; Wang, Q.; Zhang, Y.; Li, W.; Carroll, J.S.; Liu, X.S.; Brown, M. FoxA1 translates epigenetic signatures into enhancer-driven lineage-specific transcription. Cell, 2008, 132(6), 958-970.
[http://dx.doi.org/10.1016/j.cell.2008.01.018] [PMID: 18358809]
[3]
Yang, M.; Park, J.Y. DNA methylation in promoter region as biomarkers in prostate cancer. Cancer Epigenetics; Humana Press: Totowa, NJ, 2012, pp. 67-109.
[http://dx.doi.org/10.1007/978-1-61779-612-8_5]
[4]
Sheng, X.; Guo, Y.; Lu, Y. Prognostic role of methylated GSTP1, p16, ESR1 and PITX2 in patients with breast cancer. Medicine, 2017, 96(28), e7476.
[http://dx.doi.org/10.1097/MD.0000000000007476] [PMID: 28700487]
[5]
Stone, A.; Zotenko, E.; Locke, W.J.; Korbie, D.; Millar, E.K.A.; Pidsley, R.; Stirzaker, C.; Graham, P.; Trau, M.; Musgrove, E.A.; Nicholson, R.I.; Gee, J.M.W.; Clark, S.J. DNA methylation of oestrogen-regulated enhancers defines endocrine sensitivity in breast cancer. Nat. Commun., 2015, 6(1), 7758.
[http://dx.doi.org/10.1038/ncomms8758] [PMID: 26169690]
[6]
Martínez-Galán, J.; Torres-Torres, B.; Núñez, M.I.; López-Peñalver, J.; Del Moral, R.; Ruiz De Almodóvar, J.M.; Menjón, S.; Concha, Á.; Chamorro, C.; Ríos, S.; Delgado, J.R. ESR1gene promoter region methylation in free circulating DNA and its correlation with estrogen receptor protein expression in tumor tissue in breast cancer patients. BMC Cancer, 2014, 14(1), 59.
[http://dx.doi.org/10.1186/1471-2407-14-59] [PMID: 24495356]
[7]
Ding, S.; Yu, J.C.; Chen, S.T.; Hsu, G.C.; Hsu, H.M.; Ho, J.Y.; Lin, Y.H.; Chang, C.C.; Fann, C.S.J.; Cheng, C.W.; Wu, P.E.; Shen, C.Y. Diverse associations between ESR1 polymorphism and breast cancer development and progression. Clin. Cancer Res., 2010, 16(13), 3473-3484.
[http://dx.doi.org/10.1158/1078-0432.CCR-09-3092] [PMID: 20570923]
[8]
Osborne, C.K.; Schiff, R. Estrogen-receptor biology: Continuing progress and therapeutic implications. J. Clin. Oncol., 2005, 23(8), 1616-1622.
[http://dx.doi.org/10.1200/JCO.2005.10.036] [PMID: 15755967]
[9]
Lau, W.S.; Chen, W.F.; Chan, R.Y.K.; Guo, D.A.; Wong, M.S. Mitogen-activated protein kinase (MAPK) pathway mediates the oestrogen-like activities of ginsenoside Rg1 in human breast cancer (MCF-7) cells. Br. J. Pharmacol., 2009, 156(7), 1136-1146.
[http://dx.doi.org/10.1111/j.1476-5381.2009.00123.x] [PMID: 19298253]
[10]
Khan, S.A.; Rogers, M.A.M.; Khurana, K.K.; Meguid, M.M.; Numann, P.J. Estrogen receptor expression in benign breast epithelium and breast cancer risk. J. Natl. Cancer Inst., 1998, 90(1), 37-42.
[http://dx.doi.org/10.1093/jnci/90.1.37] [PMID: 9428781]
[11]
Holst, F.; Stahl, P.R.; Ruiz, C.; Hellwinkel, O.; Jehan, Z.; Wendland, M.; Lebeau, A.; Terracciano, L.; Al-Kuraya, K.; Jänicke, F.; Sauter, G.; Simon, R. Estrogen receptor alpha (ESR1) gene amplification is frequent in breast cancer. Nat. Genet., 2007, 39(5), 655-660.
[http://dx.doi.org/10.1038/ng2006] [PMID: 17417639]
[12]
Chen, J.; Wang, J.; Zhu, W. Molecular mechanism and energy basis of conformational diversity of antibody SPE7 revealed by molecular dynamics simulation and principal component analysis. Sci. Rep., 2016, 6(1), 36900.
[http://dx.doi.org/10.1038/srep36900] [PMID: 27830740]
[13]
Bandaru, S.; Sumithnath, T.G.; Sharda, S.; Lakhotia, S.; Sharma, A.; Jain, A.; Hussain, T.; Nayarisseri, A.; Singh, S.K. Helix-Coil transition signatures B-Raf V600E mutation and virtual screening for inhibitors directed against mutant B-Raf. Curr. Drug Metab., 2017, 18(6), 527-534.
[PMID: 28472910]
[14]
Grasso, G.; Deriu, M.A.; Tuszynski, J.A.; Gallo, D.; Morbiducci, U.; Danani, A. Conformational fluctuations of the AXH monomer of Ataxin-1. Proteins, 2016, 84(1), 52-59.
[http://dx.doi.org/10.1002/prot.24954] [PMID: 26522012]
[15]
Basak, S. C.; Nayarisseri, A.; González-Díaz, H.; Bonchev, D. Editorial (Thematic issue: Chemoinformatics models for pharmaceutical design, Part 1). Curr Pharm Des., 2016, 22(33), 5041-5042.
[16]
Swain, S.S.; Paidesetty, S.K.; Dehury, B.; Sahoo, J.; Vedithi, S.C.; Mahapatra, N.; Hussain, T.; Padhy, R.N. Molecular docking and simulation study for synthesis of alternative dapsone derivative as a newer antileprosy drug in multidrug therapy. J. Cell. Biochem., 2018, 119(12), 9838-9852.
[http://dx.doi.org/10.1002/jcb.27304] [PMID: 30125973]
[17]
Pochetti, G.; Mitro, N.; Lavecchia, A.; Gilardi, F.; Besker, N.; Scotti, E.; Aschi, M.; Re, N.; Fracchiolla, G.; Laghezza, A.; Tortorella, P.; Montanari, R.; Novellino, E.; Mazza, F.; Crestani, M.; Loiodice, F. Structural insight into peroxisome proliferator-activated receptor γ binding of two ureidofibrate-like enantiomers by molecular dynamics, cofactor interaction analysis, and site-directed mutagenesis. J. Med. Chem., 2010, 53(11), 4354-4366.
[http://dx.doi.org/10.1021/jm9013899] [PMID: 20462215]
[18]
Soares Rodrigues, G.C.; dos Santos Maia, M.; Muratov, E.N.; Scotti, L.; Scotti, M.T. Quantitative structure–activity relationship modeling and docking of monoterpenes with insecticidal activity against Reticulitermes chinensis Snyder and Drosophila melanogaster. J. Agric. Food Chem., 2020, 68(16), 4687-4698.
[http://dx.doi.org/10.1021/acs.jafc.0c00272] [PMID: 32251592]
[19]
Shelley, J.C.; Cholleti, A.; Frye, L.L.; Greenwood, J.R.; Timlin, M.R.; Uchimaya, M. Epik: A software program for pK a prediction and protonation state generation for drug-like molecules. J. Comput. Aided Mol. Des., 2007, 21(12), 681-691.
[http://dx.doi.org/10.1007/s10822-007-9133-z] [PMID: 17899391]
[20]
Baby, K.; Maity, S.; Mehta, C.H.; Suresh, A.; Nayak, U.Y.; Nayak, Y. Targeting SARS-CoV-2 main protease: A computational drug repurposing study. Arch. Med. Res., 2021, 52(1), 38-47.
[PMID: 32962867]
[21]
Gahlawat, A.; Kumar, N.; Kumar, R.; Sandhu, H.; Singh, I.P.; Singh, S.; Sjöstedt, A.; Garg, P. Structure-based virtual screening to discover potential lead molecules for the SARS-CoV-2 main protease. J. Chem. Inf. Model., 2020, 60(12), 5781-5793.
[http://dx.doi.org/10.1021/acs.jcim.0c00546] [PMID: 32687345]
[22]
Xu, X.; Mao, L.; Xu, W.; Tang, W.; Zhang, X.; Xi, B.; Xu, R.; Fang, X.; Liu, J.; Fang, C.; Zhao, L.; Wang, X.; Jiang, J.; Hu, P.; Zhao, H.; Zhang, L. AC0010, an irreversible EGFR inhibitor selectively targeting mutated EGFR and overcoming T790M-induced resistance in animal models and lung cancer patients. Mol. Cancer Ther., 2016, 15(11), 2586-2597.
[http://dx.doi.org/10.1158/1535-7163.MCT-16-0281] [PMID: 27573423]
[23]
Khandelwal, R.; Chauhan, A.P.S.; Bilawat, S.; Gandhe, A.; Hussain, T.; Hood, E.A.; Nayarisseri, A.; Singh, S.K. Structure-based virtual screening for the identification of high-affinity small molecule towards STAT3 for the clinical treatment of osteosarcoma. Curr. Top. Med. Chem., 2019, 18(29), 2511-2526.
[http://dx.doi.org/10.2174/1568026618666181115092001] [PMID: 30430945]
[24]
Khandekar, N.; Singh, S.; Shukla, R.; Tirumalaraju, S.; Bandaru, S.; Banerjee, T.; Nayarisseri, A. Structural basis for the in vitro known acyl-depsipeptide 2 (ADEP2) inhibition to Clp 2 protease from Mycobacterium tuberculosis. Bioinformation, 2016, 12(3), 92-97.
[http://dx.doi.org/10.6026/97320630012092] [PMID: 28149041]
[25]
Shiau, A.K.; Barstad, D.; Loria, P.M.; Cheng, L.; Kushner, P.J.; Agard, D.A.; Greene, G.L. The structural basis of estrogen receptor/coactivator recognition and the antagonism of this interaction by tamoxifen. Cell, 1998, 95(7), 927-937.
[http://dx.doi.org/10.1016/S0092-8674(00)81717-1] [PMID: 9875847]
[26]
Kelotra, S.; Jain, M.; Kelotra, A.; Jain, I.; Bandaru, S.; Nayarisseri, A.; Bidwai, A. An in silico appraisal to identify high affinity anti-apoptotic synthetic tetrapeptide inhibitors targeting the mammalian caspase 3 enzyme. Asian Pac. J. Cancer Prev., 2015, 15(23), 10137-10142.
[http://dx.doi.org/10.7314/APJCP.2014.15.23.10137] [PMID: 25556438]
[27]
Pang, X.; Fu, W.; Wang, J.; Kang, D.; Xu, L.; Zhao, Y.; Liu, A.L.; Du, G.H. Identification of estrogen receptor α antagonists from natural products viain vitro and in silico approaches. Oxid. Med. Cell. Longev., 2018, 2018, 1-11.
[http://dx.doi.org/10.1155/2018/6040149] [PMID: 29861831]
[28]
Nayarisseri, A.; Moghni, S. M.; Yadav, M.; Kharate, J.; Sharma, P.; Chandok, K. H.; Shah, K. P. In silico investigations on HSP90 and its inhibition for the therapeutic prevention of breast cancer. J. Pharm. Res., 2013, 7(2), 150-156.
[29]
Panwar, U.; Singh, S.K. In silico virtual screening of potent inhibitor to hamper the interaction between HIV-1 integrase and LEDGF/p75 interaction using E-pharmacophore modeling, molecular docking, and dynamics simulations. Comput. Biol. Chem., 2021, 93, 107509.
[http://dx.doi.org/10.1016/j.compbiolchem.2021.107509] [PMID: 34153658]
[30]
Ranganathan, S.; Ilavarasi, A.V.; Palaka, B.K.; Kuppusamy, D.; Ampasala, D.R. Cloning, functional characterization and screening of potential inhibitors for Chilo partellus chitin synthase A using in silico, in vitro and in vivo approaches. J. Biomol. Struct. Dyn., 2022, 40(3), 1416-1429.
[http://dx.doi.org/10.1080/07391102.2020.1827034] [PMID: 33000693]
[31]
Panwar, U.; Singh, S. K. Identification of novel pancreatic lipase inhibitors using in silico studies. Endocrine, Metabolic & Immune Disorders-Drug Targets (Formerly Current Drug Targets-Immune, Endocrine & Metabolic Disorders), 2019, 19(4), 449-457.
[32]
Protein preparation wizard, schrodinger LLC; New York, NY, 2021.
[33]
Vasudevan, A.; Kesavan, D.K.; Wu, L.; Su, Z.; Wang, S.; Ramasamy, M.K. In silico and in vitro screening of natural compounds as broad-spectrum β-lactamase inhibitors against Acinetobactor baumannii New Delhi metallo-β-lactomase 1 (NDM-1). Biomed. Res. Int., 2022, 2022.
[34]
Wang, Y.; Wang, L.F.; Zhang, L.L.; Sun, H.B.; Zhao, J. Molecular mechanism of inhibitor bindings to bromodomain-containing protein 9 explored based on molecular dynamics simulations and calculations of binding free energies. SAR QSAR Environ. Res., 2020, 31(2), 149-170.
[http://dx.doi.org/10.1080/1062936X.2019.1701075] [PMID: 31851834]
[35]
Wang, L.F.; Wang, Y.; Yang, Z.Y.; Zhao, J.; Sun, H.B.; Wu, S.L. Revealing binding selectivity of inhibitors toward bromodomain-containing proteins 2 and 4 using multiple short molecular dynamics simulations and free energy analyses. SAR QSAR Environ. Res., 2020, 31(5), 373-398.
[http://dx.doi.org/10.1080/1062936X.2020.1748107] [PMID: 32496901]
[36]
Panwar, U.; Murali, A.; Khan, M.A.; Selvaraj, C.; Singh, S.K. Virtual Screening Process: A Guide in Modern Drug Designing. Computational Drug Discovery and Design; Springer US: New York, NY, 2023, pp. 21-31.
[37]
Opo, F.A.D.M.; Rahman, M.M.; Ahammad, F.; Ahmed, I.; Bhuiyan, M.A.; Asiri, A.M. Structure based pharmacophore modeling, virtual screening, molecular docking and ADMET approaches for identification of natural anti-cancer agents targeting XIAP protein. Sci. Rep., 2021, 11(1), 4049.
[http://dx.doi.org/10.1038/s41598-021-83626-x] [PMID: 33603068]
[38]
Molla, M.H.R.; Asseri, A.H.; Islam, M.S. Integrated structure model-based virtual screening approaches identified anti-cancer agents against prostate cancer by targeting MAOB protein. Egypt. J. Med. Hum. Genet., 2023, 24(1), 51.
[http://dx.doi.org/10.1186/s43042-023-00431-z]
[39]
Nakkala, S.; Modak, C.; Bathula, R.; Lanka, G.; Somadi, G.; Sreekanth, S.; Jain, A.; Potlapally, S.R. Identification of new anti-cancer agents against CENTERIN: Structure-based virtual screening, AutoDock and binding free energy studies. J. Mol. Struct., 2022, 1270, 133952.
[http://dx.doi.org/10.1016/j.molstruc.2022.133952]
[40]
Jokinen, E.M.; Niemeläinen, M.; Kurkinen, S.T.; Lehtonen, J.V.; Lätti, S.; Postila, P.A.; Pentikäinen, O.T.; Niinivehmas, S.P. Virtual screening strategy to identify retinoic acid-related orphan receptor γt modulators. Molecules, 2023, 28(8), 3420.
[http://dx.doi.org/10.3390/molecules28083420] [PMID: 37110655]
[41]
Klesse, G.; Rao, S.; Tucker, S.J.; Sansom, M.S.P. Induced polarization in molecular dynamics simulations of the 5-HT3 receptor channel. J. Am. Chem. Soc., 2020, 142(20), 9415-9427.
[http://dx.doi.org/10.1021/jacs.0c02394] [PMID: 32336093]
[42]
Gudala, S.; Khan, U.; Kanungo, N.; Bandaru, S.; Hussain, T.; Parihar, M.S.; Nayarisseri, A.; Mundluru, H.P. Identification and pharmacological analysis of high efficacy small molecule inhibitors of EGF-EGFR interactions in clinical treatment of non-small cell lung carcinoma: A computational approach. Asian Pac. J. Cancer Prev., 2016, 16(18), 8191-8196.
[http://dx.doi.org/10.7314/APJCP.2015.16.18.8191] [PMID: 26745059]
[43]
Gutlapalli, V.R.; Sykam, A.; Nayarisseri, A.; Suneetha, S.; Suneetha, L.M. Insights from the predicted epitope similarity between Mycobacterium tuberculosis virulent factors and its human homologs. Bioinformation, 2015, 11(12), 517-524.
[http://dx.doi.org/10.6026/97320630011517] [PMID: 26770024]
[44]
Selvaraj, C.; Panwar, U.; Dinesh, D.C.; Boura, E.; Singh, P.; Dubey, V.K.; Singh, S.K. Microsecond MD simulation and multiple-conformation virtual screening to identify potential anti-COVID-19 inhibitors against SARS-CoV-2 main protease. Front Chem., 2021, 8, 595273.
[http://dx.doi.org/10.3389/fchem.2020.595273] [PMID: 33585398]
[45]
Panwar, U.; Singh, S.K. Structure-based virtual screening toward the discovery of novel inhibitors for impeding the protein-protein interaction between HIV-1 integrase and human lens epithelium-derived growth factor (LEDGF/p75). J. Biomol. Struct. Dyn., 2018, 36(12), 3199-3217.
[http://dx.doi.org/10.1080/07391102.2017.1384400] [PMID: 28948865]
[46]
Bhrdwaj, A.; Abdalla, M.; Pande, A.; Abdalla, M.; Madhavi, M.; Chopra, I.; Soni, L.; Vijayakumar, N.; Panwar, U.; Khan, M.A.; Prajapati, L.; Gujrati, D.; Belapurkar, P.; Albogami, S.; Hussain, T.; Selvaraj, C.; Nayarisseri, A.; Sanjeev, K.S. Structure-based virtual screening, molecular docking, molecular dynamics simulation of EGFR for the clinical treatment of glioblastoma. Appl BiochemBiotechnol; Springer Nature, 2023, pp. 1-26.
[47]
Bathini, R.; Sivan, S.K.; Fatima, S.; Manga, V. Molecular docking, MM/GBSA and 3D-QSAR studies on EGFR inhibitors. J. Chem. Sci., 2016, 128(7), 1163-1173.
[http://dx.doi.org/10.1007/s12039-016-1103-3]
[48]
Pandey, R. K.; Kumbhar, B. V.; Sundar, S.; Kunwar, A.; Prajapati, V. K. 2017.
[49]
Shukla, P.; Khandelwal, R.; Sharma, D.; Dhar, A.; Nayarisseri, A.; Singh, S.K. Virtual screening of IL-6 inhibitors for idiopathic arthritis. Bioinformation, 2019, 15(2), 121-130.
[http://dx.doi.org/10.6026/97320630015121] [PMID: 31435158]
[50]
Lagarias, P.; Barkan, K.; Tzortzini, E.; Stampelou, M.; Vrontaki, E.; Ladds, G.; Kolocouris, A. Insights to the binding of a selective adenosine A3 receptor antagonist using molecular dynamic simulations, MM-PBSA and MM-GBSA free energy calculations, and mutagenesis. J. Chem. Inf. Model., 2019, 59(12), 5183-5197.
[http://dx.doi.org/10.1021/acs.jcim.9b00751] [PMID: 31725294]
[51]
Praseetha, S.; Bandaru, S.; Nayarisseri, A.; Sureshkumar, S. Pharmacological analysis of vorinostat analogues as potential anti-tumor agents targeting human histone deacetylases: An epigenetic treatment stratagem for cancers. Asian Pac. J. Cancer Prev., 2016, 17(3), 1571-1576.
[http://dx.doi.org/10.7314/APJCP.2016.17.3.1571] [PMID: 27039807]
[52]
Tang, X.; Wang, Z.; Lei, T.; Zhou, W.; Chang, S.; Li, D. Importance of protein flexibility on molecular recognition: Modeling binding mechanisms of aminopyrazine inhibitors to Nek2. Phys. Chem. Chem. Phys., 2018, 20(8), 5591-5605.
[http://dx.doi.org/10.1039/C7CP07588J] [PMID: 29270587]
[53]
Dunna, N.; Bandaru, S.; Akare, U.; Rajadhyax, S.; Gutlapalli, V.; Yadav, M.; Nayarisseri, A. Multiclass comparative virtual screening to identify novel Hsp90 inhibitors: a therapeutic breast cancer drug target. Curr. Top. Med. Chem., 2015, 15(1), 57-64.
[http://dx.doi.org/10.2174/1568026615666150112113627] [PMID: 25579569]
[54]
Negron, C.; Pearlman, D.A.; del Angel, G. Predicting mutations deleterious to function in beta-lactamase TEM1 using MM-GBSA. PLoS One, 2019, 14(3), e0214015.
[http://dx.doi.org/10.1371/journal.pone.0214015] [PMID: 30889230]
[55]
Paissoni, C.; Spiliotopoulos, D.; Musco, G.; Spitaleri, A. GMXPBSA 2.1: A GROMACS tool to perform MM/PBSA and computational alanine scanning. Comput. Phys. Commun., 2015, 186, 105-107.
[http://dx.doi.org/10.1016/j.cpc.2014.09.010]
[56]
Salas-Burgos, A.; Iserovich, P.; Zuniga, F.; Vera, J.C.; Fischbarg, J. Predicting the three-dimensional structure of the human facilitative glucose transporter glut1 by a novel evolutionary homology strategy: Insights on the molecular mechanism of substrate migration, and binding sites for glucose and inhibitory molecules. Biophys. J., 2004, 87(5), 2990-2999.
[http://dx.doi.org/10.1529/biophysj.104.047886] [PMID: 15326030]
[57]
Jain, D.; Udhwani, T.; Sharma, S.; Gandhe, A.; Reddy, P.B.; Nayarisseri, A.; Singh, S.K. Design of novel JAK3 Inhibitors towards Rheumatoid Arthritis using molecular docking analysis. Bioinformation, 2019, 15(2), 68-78.
[http://dx.doi.org/10.6026/97320630015068] [PMID: 31435152]
[58]
Pandey, R.K.; Narula, A.; Naskar, M.; Srivastava, S.; Verma, P.; Malik, R.; Shah, P.; Prajapati, V.K. Exploring dual inhibitory role of febrifugine analogues against Plasmodium utilizing structure-based virtual screening and molecular dynamic simulation. J. Biomol. Struct. Dyn., 2017, 35(4), 791-804.
[http://dx.doi.org/10.1080/07391102.2016.1161560] [PMID: 26984239]
[59]
Sadhasivam, A.; Vetrivel, U. Identification of potential drugs targeting L,L‐diaminopimelate aminotransferase of Chlamydia trachomatis: An integrative pharmacoinformatics approach. J. Cell. Biochem., 2019, 120(2), 2271-2288.
[http://dx.doi.org/10.1002/jcb.27553] [PMID: 30302805]
[60]
Zakerali, T.; Shahbazi, S. Rational druggability investigation toward selection of lead molecules: Impact of the commonly used spices on inflammatory diseases. Assay Drug Dev. Technol., 2018, 16(7), 397-407.
[http://dx.doi.org/10.1089/adt.2018.853] [PMID: 30106307]
[61]
Kb, S.; Kumari, A.; Shetty, D.; Fernandes, E.; Dv, C.; Jays, J.; Murahari, M. Structure based pharmacophore modelling approach for the design of azaindole derivatives as DprE1 inhibitors for tuberculosis. J. Mol. Graph. Model., 2020, 101, 107718.
[http://dx.doi.org/10.1016/j.jmgm.2020.107718] [PMID: 32949960]
[62]
Sukumar, N.; Pask, J.E. Classical and enriched finite element formulations for Bloch-periodic boundary conditions. Int. J. Numer. Methods Eng., 2009, 77(8), 1121-1138.
[http://dx.doi.org/10.1002/nme.2457]
[63]
Di Prinzio, C.L.; Pereyra, R.G. Molecular dynamics simulations of tilt grain boundaries in ice. Model. Simul. Mater. Sci. Eng., 2016, 24(4), 045015.
[http://dx.doi.org/10.1088/0965-0393/24/4/045015]
[64]
Shah, M.; Anwar, M.A.; Yesudhas, D.; Krishnan, J.; Choi, S. A structural insight into the negative effects of opioids in analgesia by modulating the TLR4 signaling: An in silico approach. Sci. Rep., 2016, 6(1), 39271.
[http://dx.doi.org/10.1038/srep39271] [PMID: 27982096]
[65]
Tian, J.; Wang, P.; Gao, S.; Chu, X.; Wu, N.; Fan, Y. Enhanced thermostability of methyl parathion hydrolase from Ochrobactrum sp. M231 by rational engineering of a glycine to proline mutation. FEBS J., 2010, 277(23), 4901-4908.
[http://dx.doi.org/10.1111/j.1742-4658.2010.07895.x] [PMID: 20977676]
[66]
Joshi, T.; Sharma, P.; Joshi, T.; Chandra, S. In silico screening of anti-inflammatory compounds from Lichen by targeting cyclooxygenase-2. J. Biomol. Struct. Dyn., 2020, 38(12), 3544-3562.
[http://dx.doi.org/10.1080/07391102.2019.1664328] [PMID: 31524074]
[67]
Torktaz, I.; Najafi, A.; Golmohamadi, R.; Hassani, S. Molecular dynamics simulation (MDS) analysis of Vibrio cholerae ToxT virulence factor complexed with docked potential inhibitors. Bioinformation, 2018, 14(3), 101-105.
[http://dx.doi.org/10.6026/97320630014101] [PMID: 29785068]
[68]
Raftopoulou, S.; Nicolaides, N.C.; Papageorgiou, L.; Amfilochiou, A.; Zakinthinos, S.G.; George, P.; Vlachakis, D. Structural study of the DNA: Clock/Bmal1 complex provides insights for the role of cortisol, hGR, and HPA axis in stress management and sleep disorders. GeNeDis 2018; Springer: Cham, 2020, pp. 59-71.
[http://dx.doi.org/10.1007/978-3-030-32633-3_10]
[69]
Muthuvel, S.K.; Elumalai, E.; K, G.; K, H. Molecular docking and dynamics studies of 4-anilino quinazolines for epidermal growth factor receptor tyrosine kinase to find potent inhibitor. J. Recept. Signal Transduct. Res., 2018, 38(5-6), 475-483.
[http://dx.doi.org/10.1080/10799893.2019.1590411] [PMID: 31038021]
[70]
Gajendrarao, P.; Krishnamoorthy, N.; Sakkiah, S.; Lazar, P.; Lee, K.W. Molecular modeling study on orphan human protein CYP4A22 for identification of potential ligand binding site. J. Mol. Graph. Model., 2010, 28(6), 524-532.
[http://dx.doi.org/10.1016/j.jmgm.2009.11.010] [PMID: 20079672]
[71]
Alazmi, M.; Motwalli, O. In silico virtual screening, characterization, docking and molecular dynamics studies of crucial SARS-CoV-2 proteins. J. Biomol. Struct. Dyn., 2021, 39(17), 6761-6771.
[http://dx.doi.org/10.1080/07391102.2020.1803965] [PMID: 32762537]
[72]
Nada, H.; Elkamhawy, A.; Lee, K. Identification of 1H-purine-2,6-dione derivative as a potential SARS-CoV-2 main protease inhibitor: molecular docking, dynamic simulations, and energy calculations. PeerJ, 2022, 10, e14120.
[http://dx.doi.org/10.7717/peerj.14120] [PMID: 36225900]
[73]
Cheng, Z.; Bhave, M.; Hwang, S.S.; Rahman, T.; Chee, X.W. Identification of potential p38γ inhibitors via in silico screening, in vitro bioassay and molecular dynamics simulation studies. Int. J. Mol. Sci., 2023, 24(8), 7360.
[http://dx.doi.org/10.3390/ijms24087360] [PMID: 37108523]
[74]
Rampogu, S.; Lemuel, M.R.; Lee, K.W. Virtual screening, molecular docking, molecular dynamics simulations and free energy calculations to discover potential DDX3 inhibitors. Advances in Cancer Biology - Metastasis, 2022, 4, 100022.
[http://dx.doi.org/10.1016/j.adcanc.2021.100022]
[75]
Verma, P.; Tiwari, M.; Tiwari, V. In silico high-throughput virtual screening and molecular dynamics simulation study to identify inhibitor for AdeABC efflux pump of Acinetobacter baumannii. J. Biomol. Struct. Dyn., 2018, 36(5), 1182-1194.
[http://dx.doi.org/10.1080/07391102.2017.1317025] [PMID: 28393677]
[76]
Yadav, M.; Abdalla, M.; Madhavi, M.; Chopra, I.; Bhrdwaj, A.; Soni, L.; Shaheen, U.; Prajapati, L.; Sharma, M.; Sikarwar, M.S.; Albogami, S.; Hussain, T.; Nayarisseri, A.; Singh, S.K. Structure-based virtual screening, molecular docking, molecular dynamics simulation and pharmacokinetic modelling of cyclooxygenase-2 (COX-2) inhibitor for the clinical treatment of colorectal cancer. Mol. Simul., 2022, 48(12), 1081-1101.
[http://dx.doi.org/10.1080/08927022.2022.2068799]
[77]
Mukherjee, S.; Abdalla, M.; Yadav, M.; Madhavi, M.; Bhrdwaj, A.; Khandelwal, R.; Prajapati, L.; Panicker, A.; Chaudhary, A.; Albrakati, A.; Hussain, T.; Nayarisseri, A.; Singh, S.K. Structure-based virtual screening, molecular docking, and molecular dynamics simulation of VEGF inhibitors for the clinical treatment of Ovarian Cancer. J. Mol. Model., 2022, 28(4), 100.
[http://dx.doi.org/10.1007/s00894-022-05081-3] [PMID: 35325303]
[78]
Ibrahim, M.T.; Uzairu, A.; Uba, S.; Shallangwa, G.A. Computational virtual screening and structure-based design of some epidermal growth factor receptor inhibitors. Future J. Pharm. Sci., 2020, 6(1), 55.
[http://dx.doi.org/10.1186/s43094-020-00074-6]
[79]
Daina, A.; Michielin, O.; Zoete, V. SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci. Rep., 2017, 7(1), 42717.
[http://dx.doi.org/10.1038/srep42717] [PMID: 28256516]
[80]
Ziemska, J.; Solecka, J.; Jarończyk, M. In silico screening for novel leucine aminopeptidase inhibitors with 3, 4-dihydroisoquinoline scaffold. Molecules, 2020, 25(7), 1753.
[http://dx.doi.org/10.3390/molecules25071753] [PMID: 32290229]
[81]
Bhrdwaj, A.; Abdalla, M.; Pande, A.; Madhavi, M.; Chopra, I.; Soni, L.; Vijayakumar, N.; Panwar, U.; Khan, M.A.; Prajapati, L.; Gujrati, D.; Belapurkar, P.; Albogami, S.; Hussain, T.; Selvaraj, C.; Nayarisseri, A.; Singh, S.K. Structure-based virtual screening, molecular docking, molecular dynamics simulation of EGFR for the clinical treatment of glioblastoma. Appl. Biochem. Biotechnol., 2023, 195(8), 5094-5119.
[http://dx.doi.org/10.1007/s12010-023-04430-z] [PMID: 36976507]
[82]
Joshi, I.; Bhrdwaj, A.; Khandelwal, R.; Pande, A.; Agarwal, A.; Srija, C.D.; Suresh, R.A.; Mohan, M.; Hazarika, L.; Thakur, G.; Hussain, T. Artificial intelligence, big data and machine learning approaches in genome-wide SNP-based prediction for precision medicine and drug discovery. Big Data Analytics in Chemoinformatics and Bioinformatics; Elsevier, 2023, pp. 333-357.
[http://dx.doi.org/10.1016/B978-0-323-85713-0.00021-9]
[83]
Natchimuthu, V.; Abdalla, M.; Yadav, M.; Chopra, I.; Bhrdwaj, A.; Sharma, K.; Ravi, S.; Ravikumar, K.; Alzahrani, K.J.; Hussain, T.; Nayarisseri, A. Synthesis, crystal structure, hirshfeld surface analysis, molecular docking and molecular dynamics studies of novel olanzapinium 2,5-dihydroxybenzoate as potential and active antipsychotic compound. J. Exp. Nanosci., 2022, 17(1), 247-273.
[http://dx.doi.org/10.1080/17458080.2022.2063278]
[84]
Maia, M.S.; Mendonça-Junior, F.J.B.; Rodrigues, G.C.S.; Silva, A.S.; Oliveira, N.I.P.; Silva, P.R.; Felipe, C.F.B.; Gurgel, A.P.A.D.; Nayarisseri, A.; Scotti, M.T.; Scotti, L. Virtual screening of different subclasses of lignans with anticancer potential and based on genetic profile. Molecules, 2023, 28(16), 6011.
[http://dx.doi.org/10.3390/molecules28166011] [PMID: 37630263]

Rights & Permissions Print Cite
Article Metrics
22
2
© 2024 Bentham Science Publishers | Privacy Policy