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Current Computer-Aided Drug Design

Current Computer-Aided Drug Design

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ISSN (Print): 1573-4099
ISSN (Online): 1875-6697

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Research Article

Exploration of Fingerprints and Data Mining-based Prediction of Some Bioactive Compounds from Allium sativum as Histone Deacetylase 9 (HDAC9) Inhibitors

Author(s): Totan Das, Arijit Bhattacharya, Tarun Jha and Shovanlal Gayen*

Volume 21, Issue 3, 2025

Published on: 06 February, 2024

Page: [270 - 284] Pages: 15

DOI: 10.2174/0115734099282303240126061624

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Abstract

Background: Histone deacetylase 9 (HDAC9) is an important member of the class IIa family of histone deacetylases. It is well established that over-expression of HDAC9 causes various types of cancers including gastric cancer, breast cancer, ovarian cancer, liver cancer, lung cancer, lymphoblastic leukaemia, etc. The important role of HDAC9 is also recognized in the development of bone, cardiac muscles, and innate immunity. Thus, it will be beneficial to find out the important structural attributes of HDAC9 inhibitors for developing selective HDAC9 inhibitors with higher potency.

Methods: The classification QSAR-based methods namely Bayesian classification and recursive partitioning method were applied to a dataset consisting of HADC9 inhibitors. The structural features strongly suggested that sulphur-containing compounds can be a good choice for HDAC9 inhibition. For this reason, these models were applied further to screen some natural compounds from Allium sativum. The screened compounds were further accessed for the ADME properties and docked in the homology-modelled structure of HDAC9 in order to find important amino acids for the interaction. The best-docked compound was considered for molecular dynamics (MD) simulation study.

Results: The classification models have identified good and bad fingerprints for HDAC9 inhibition. The screened compounds like ajoene, 1,2 vinyl dithiine, diallyl disulphide and diallyl trisulphide had been identified as compounds having potent HDAC9 inhibitory activity. The results from ADME and molecular docking study of these compounds show the binding interaction inside the active site of the HDAC9. The best-docked compound ajoene shows satisfactory results in terms of different validation parameters of MD simulation study.

Conclusion: This in-silico modelling study has identified the natural potential lead (s) from Allium sativum. Specifically, the ajoene with the best in-silico features can be considered for further in-vitro and in-vivo investigation to establish as potential HDAC9 inhibitors.

Keywords: Histone deacetylase 9 (HDAC9), bayesian classification, recursive partitioning tree, Allium sativum, molecular docking, molecular dynamics simulation.

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Graphical Abstract

Graphical abstract illustrating key findings of the study

[1]
Brancolini, C.; Di Giorgio, E.; Formisano, L.; Gagliano, T. Quis custodiet ipsos custodes (Who controls the controllers)? Two decades of studies on HDAC9. Life, 2021, 11(2), 90.
[http://dx.doi.org/10.3390/life11020090] [PMID: 33513699]
[2]
Hu, S.; Cho, E.H.; Lee, J.Y. Histone deacetylase 9: Its role in the pathogenesis of diabetes and other chronic diseases. Diabetes Metab. J., 2020, 44(2), 234-244.
[http://dx.doi.org/10.4093/dmj.2019.0243] [PMID: 32347025]
[3]
Bhattacharya, A.; Amin, S.A.; Kumar, P.; Jha, T.; Gayen, S. Exploring structural requirements of HDAC10 inhibitors through comparative machine learning approaches. J. Mol. Graph. Model., 2023, 123, 108510.
[http://dx.doi.org/10.1016/j.jmgm.2023.108510] [PMID: 37216830]
[4]
Martin, M.; Kettmann, R.; Dequiedt, F. Class IIa histone deacetylases: Conducting development and differentiation. Int. J. Dev. Biol., 2009, 53(2-3), 291-301.
[http://dx.doi.org/10.1387/ijdb.082698mm] [PMID: 19412888]
[5]
Chen, Y.H.; Yeh, F.L.; Yeh, S.P.; Ma, H.T.; Hung, S.C.; Hung, M.C.; Li, L.Y. Myocyte enhancer factor-2 interacting transcriptional repressor (MITR) is a switch that promotes osteogenesis and inhibits adipogenesis of mesenchymal stem cells by inactivating peroxisome proliferator-activated receptor γ-2. J. Biol. Chem., 2011, 286(12), 10671-10680.
[http://dx.doi.org/10.1074/jbc.M110.199612] [PMID: 21247904]
[6]
Zhou, X.; Marks, P.A.; Rifkind, R.A.; Richon, V.M. Cloning and characterization of a histone deacetylase, HDAC9. Proc. Natl. Acad. Sci., 2001, 98(19), 10572-10577.
[http://dx.doi.org/10.1073/pnas.191375098] [PMID: 11535832]
[7]
Haberland, M.; Arnold, M.A.; McAnally, J.; Phan, D.; Kim, Y.; Olson, E.N. Regulation of HDAC9 gene expression by MEF2 establishes a negative-feedback loop in the transcriptional circuitry of muscle differentiation. Mol. Cell. Biol., 2007, 27(2), 518-525.
[http://dx.doi.org/10.1128/MCB.01415-06] [PMID: 17101791]
[8]
Yang, C.; Croteau, S.; Hardy, P. Histone deacetylase (HDAC) 9: Versatile biological functions and emerging roles in human cancer. Cell. Oncol., 2021, 44(5), 997-1017.
[http://dx.doi.org/10.1007/s13402-021-00626-9] [PMID: 34318404]
[9]
Clocchiatti, A.; Florean, C.; Brancolini, C. Class IIa HDACs: From important roles in differentiation to possible implications in tumourigenesis. J. Cell. Mol. Med., 2011, 15(9), 1833-1846.
[http://dx.doi.org/10.1111/j.1582-4934.2011.01321.x] [PMID: 21435179]
[10]
Yan, K.; Cao, Q.; Reilly, C.M.; Young, N.L.; Garcia, B.A.; Mishra, N. Histone deacetylase 9 deficiency protects against effector T cell-mediated systemic autoimmunity. J. Biol. Chem., 2011, 286(33), 28833-28843.
[http://dx.doi.org/10.1074/jbc.M111.233932] [PMID: 21708950]
[11]
Londhe, V.P.; Gavasane, A.T.; Nipate, S.S.; Bandawane, D.D.; Chaudhari, P.D. Role of garlic (Allium sativum) in various diseases: An overview. Angiogenesis, 2011, 12(13), 129-134.
[12]
Shang, A.; Cao, S.Y.; Xu, X.Y.; Gan, R.Y.; Tang, G.Y.; Corke, H.; Mavumengwana, V.; Li, H.B. Bioactive compounds and biological functions of garlic (Allium sativum L.). Foods, 2019, 8(7), 246.
[http://dx.doi.org/10.3390/foods8070246] [PMID: 31284512]
[13]
Shrivastava, S.R.; Shrivastava, P.S.; Ramasamy, J. Mainstreaming of ayurveda, yoga, naturopathy, unani, siddha, and homeopathy with the health care delivery system in India. J. Tradit. Complement. Med., 2015, 5(2), 116-118.
[http://dx.doi.org/10.1016/j.jtcme.2014.11.002] [PMID: 26151021]
[14]
Capasso, A. Antioxidant action and therapeutic efficacy of Allium sativum L. Molecules, 2013, 18(1), 690-700.
[http://dx.doi.org/10.3390/molecules18010690] [PMID: 23292331]
[15]
Mahfouz Omer, S.M.; Mohamed, D.A.A.; Ali Abdel Latif, R.M. Comparative evaluation of the antibacterial effect of Allium sativum, calcium hydroxide and their combination as intracanal medicaments in infected mature anterior teeth: A randomized clinical trial. Int. Endod. J., 2022, 55(10), 1010-1025.
[http://dx.doi.org/10.1111/iej.13801] [PMID: 35852013]
[16]
Hall, A.; Troupin, A.; Londono-Renteria, B.; Colpitts, T. Garlic organosulfur compounds reduce inflammation and oxidative stress during dengue virus infection. Viruses, 2017, 9(7), 159.
[http://dx.doi.org/10.3390/v9070159] [PMID: 28644404]
[17]
Sasi, M.; Kumar, S.; Kumar, M.; Thapa, S.; Prajapati, U.; Tak, Y.; Changan, S.; Saurabh, V.; Kumari, S.; Kumar, A.; Hasan, M.; Chandran, D. Radha; Bangar, S.P.; Dhumal, S.; Senapathy, M.; Thiyagarajan, A.; Alhariri, A.; Dey, A.; Singh, S.; Prakash, S.; Pandiselvam, R.; Mekhemar, M. Garlic (Allium sativum L.) bioactives and its role in alleviating oral pathologies. Antioxidants, 2021, 10(11), 1847.
[http://dx.doi.org/10.3390/antiox10111847] [PMID: 34829718]
[18]
Brogi, S.; Ramalho, T.C.; Kuca, K.; Medina-Franco, J.L.; Valko, M. In silico methods for drug design and discovery. Front Chem., 2020, 8(8), 612.
[http://dx.doi.org/10.3389/fchem.2020.00612] [PMID: 32850641]
[19]
Sardar, S. Jyotisha; Amin, S.A.; Khatun, S.; Qureshi, I.A.; Patil, U.K.; Jha, T.; Gayen, S. Identification of structural fingerprints among natural inhibitors of HDAC1 to accelerate nature-inspired drug discovery in cancer epigenetics. J. Biomol. Struct. Dyn., 2023, 19, 1-15.
[http://dx.doi.org/10.1080/07391102.2023.2227710]
[20]
Pan, Z.; Li, X.; Wang, Y.; Jiang, Q.; Jiang, L.; Zhang, M.; Zhang, N.; Wu, F.; Liu, B.; He, G. Discovery of thieno [2, 3-d] pyrimidine-based hydroxamic acid derivatives as bromodomain-containing protein 4/histone deacetylase dual inhibitors induce autophagic cell death in colorectal carcinoma cells. J. Med. Chem., 2020, 63(7), 3678-3700.
[http://dx.doi.org/10.1021/acs.jmedchem.9b02178] [PMID: 32153186]
[21]
Yang, Z.; Shen, M.; Tang, M.; Zhang, W.; Cui, X.; Zhang, Z.; Pei, H.; Li, Y.; Hu, M.; Bai, P.; Chen, L. Discovery of 1,2,4-oxadiazole-Containing hydroxamic acid derivatives as histone deacetylase inhibitors potential application in cancer therapy. Eur. J. Med. Chem., 2019, 178, 116-130.
[http://dx.doi.org/10.1016/j.ejmech.2019.05.089] [PMID: 31177073]
[22]
Mehndiratta, S.; Lin, M.H.; Wu, Y.W.; Chen, C.H.; Wu, T.Y.; Chuang, K.H.; Chao, M.W.; Chen, Y.Y.; Pan, S.L.; Chen, M.C.; Liou, J.P. N-alkyl-hydroxybenzoyl anilide hydroxamates as dual inhibitors of HDAC and HSP90, downregulating IFN-γ induced PD-L1 expression. Eur. J. Med. Chem., 2020, 185, 111725.
[http://dx.doi.org/10.1016/j.ejmech.2019.111725] [PMID: 31655430]
[23]
Lee, H.Y.; Nepali, K.; Huang, F.I.; Chang, C.Y.; Lai, M.J.; Li, Y.H.; Huang, H.L.; Yang, C.R.; Liou, J.P. (N-Hydroxycarbonylbenylamino) quinolines as selective histone deacetylase 6 inhibitors suppress growth of multiple myeloma in vitro and in vivo. J. Med. Chem., 2018, 61(3), 905-917.
[http://dx.doi.org/10.1021/acs.jmedchem.7b01404] [PMID: 29304284]
[24]
Luckhurst, C.A.; Aziz, O.; Beaumont, V.; Bürli, R.W.; Breccia, P.; Maillard, M.C.; Haughan, A.F.; Lamers, M.; Leonard, P.; Matthews, K.L.; Raphy, G.; Stott, A.J.; Munoz-Sanjuan, I.; Thomas, B.; Wall, M.; Wishart, G.; Yates, D.; Dominguez, C. Development and characterization of a CNS-penetrant benzhydryl hydroxamic acid class IIa histone deacetylase inhibitor. Bioorg. Med. Chem. Lett., 2019, 29(1), 83-88.
[http://dx.doi.org/10.1016/j.bmcl.2018.11.009] [PMID: 30463802]
[25]
Chao, S.W.; Chen, L.C.; Yu, C.C.; Liu, C.Y.; Lin, T.E.; Guh, J.H.; Wang, C.Y.; Chen, C.Y.; Hsu, K.C.; Huang, W.J. Discovery of aliphatic-chain hydroxamates containing indole derivatives with potent class I histone deacetylase inhibitory activities. Eur. J. Med. Chem., 2018, 143, 792-805.
[http://dx.doi.org/10.1016/j.ejmech.2017.11.092] [PMID: 29223096]
[26]
Chen, Y.; Wang, X.; Xiang, W.; He, L.; Tang, M.; Wang, F.; Wang, T.; Yang, Z.; Yi, Y.; Wang, H.; Niu, T.; Zheng, L.; Lei, L.; Li, X.; Song, H.; Chen, L. Development of purine-based hydroxamic acid derivatives: potent histone deacetylase inhibitors with marked in vitro and in vivo antitumor activities. J. Med. Chem., 2016, 59(11), 5488-5504.
[http://dx.doi.org/10.1021/acs.jmedchem.6b00579] [PMID: 27186676]
[27]
Yang, Z.; Wang, T.; Wang, F.; Niu, T.; Liu, Z.; Chen, X.; Long, C.; Tang, M.; Cao, D.; Wang, X.; Xiang, W.; Yi, Y.; Ma, L.; You, J.; Chen, L. Discovery of selective histone deacetylase 6 inhibitors using the quinazoline as the cap for the treatment of cancer. J. Med. Chem., 2016, 59(4), 1455-1470.
[http://dx.doi.org/10.1021/acs.jmedchem.5b01342] [PMID: 26443078]
[28]
Yao, Y.; Tu, Z.; Liao, C.; Wang, Z.; Li, S.; Yao, H.; Li, Z.; Jiang, S. Discovery of novel class I histone deacetylase inhibitors with promising in vitro and in vivo antitumor activities. J. Med. Chem., 2015, 58(19), 7672-7680.
[http://dx.doi.org/10.1021/acs.jmedchem.5b01044] [PMID: 26331334]
[29]
Lee, H.Y.; Tsai, A.C.; Chen, M.C.; Shen, P.J.; Cheng, Y.C.; Kuo, C.C.; Pan, S.L.; Liu, Y.M.; Liu, J.F.; Yeh, T.K.; Wang, J.C.; Chang, C.Y.; Chang, J.Y.; Liou, J.P. Azaindolylsulfonamides, with a more selective inhibitory effect on histone deacetylase 6 activity, exhibit antitumor activity in colorectal cancer HCT116 cells. J. Med. Chem., 2014, 57(10), 4009-4022.
[http://dx.doi.org/10.1021/jm401899x] [PMID: 24766560]
[30]
Sekizawa, H.; Amaike, K.; Itoh, Y.; Suzuki, T.; Itami, K.; Yamaguchi, J. Late-stage C-H coupling enables rapid identification of HDAC inhibitors: Synthesis and evaluation of NCH-31 analogues. ACS Med. Chem. Lett., 2014, 5(5), 582-586.
[http://dx.doi.org/10.1021/ml500024s] [PMID: 24900884]
[31]
Tashima, T.; Murata, H.; Kodama, H. Design and synthesis of novel and highly-active pan-histone deacetylase (pan-HDAC) inhibitors. Bioorg. Med. Chem., 2014, 22(14), 3720-3731.
[http://dx.doi.org/10.1016/j.bmc.2014.05.001] [PMID: 24864038]
[32]
Auzzas, L.; Larsson, A.; Matera, R.; Baraldi, A.; Deschênes-Simard, B.; Giannini, G.; Cabri, W.; Battistuzzi, G.; Gallo, G.; Ciacci, A.; Vesci, L.; Pisano, C.; Hanessian, S. Non-natural macrocyclic inhibitors of histone deacetylases: Design, synthesis, and activity. J. Med. Chem., 2010, 53(23), 8387-8399.
[http://dx.doi.org/10.1021/jm101092u] [PMID: 21073160]
[33]
Andrianov, V.; Gailite, V.; Lola, D.; Loza, E.; Semenikhina, V.; Kalvinsh, I.; Finn, P.; Petersen, K.D.; Ritchie, J.W.A.; Khan, N.; Tumber, A.; Collins, L.S.; Vadlamudi, S.M.; Björkling, F.; Sehested, M. Novel amide derivatives as inhibitors of histone deacetylase: Design, synthesis and SAR. Eur. J. Med. Chem., 2009, 44(3), 1067-1085.
[http://dx.doi.org/10.1016/j.ejmech.2008.06.020] [PMID: 18672316]
[34]
Li, X.; Tu, Z.; Li, H.; Liu, C.; Li, Z.; Sun, Q.; Yao, Y.; Liu, J.; Jiang, S. Biological evaluation of new largazole analogues: Alteration of macrocyclic scaffold with click chemistry. ACS Med. Chem. Lett., 2013, 4(1), 132-136.
[http://dx.doi.org/10.1021/ml300371t] [PMID: 24900575]
[35]
Fass, D.M.; Shah, R.; Ghosh, B.; Hennig, K.; Norton, S.; Zhao, W.N.; Reis, S.A.; Klein, P.S.; Mazitschek, R.; Maglathlin, R.L.; Lewis, T.A.; Haggarty, S.J. Short-chain HDAC inhibitors differentially affect vertebrate development and neuronal chromatin. ACS Med. Chem. Lett., 2011, 2(1), 39-42.
[http://dx.doi.org/10.1021/ml1001954] [PMID: 21874153]
[36]
Discovery studio 3.0 (DS 3.0). 2015. Available from: www.accelrys.com
[37]
Chem 3D Pro Version 8.0.3 and Chem Draw Ultra Version 8.0.3 are software programs developed by Cambridge Soft Corporation. 2011. Available from: http://www.chemistrysoftware. com/modelling/Chem3D%20Ultra.htm
[38]
Amin, S.A.; Adhikari, N.; Jha, T. Development of decision trees to discriminate HDAC8 inhibitors and non-inhibitors using recursive partitioning. J. Biomol. Struct. Dyn., 2021, 39(1), 1-8.
[http://dx.doi.org/10.1080/07391102.2019.1661876] [PMID: 31530244]
[39]
Amin, S.A.; Nandi, S.; Kashaw, S.K.; Jha, T.; Gayen, S. A critical analysis of urea transporter B inhibitors: Molecular fingerprints, pharmacophore features for the development of next-generation diuretics. Mol. Divers., 2022, 26(5), 2549-2559.
[http://dx.doi.org/10.1007/s11030-021-10353-w] [PMID: 34978011]
[40]
Banerjee, S.; Amin, S.A.; Adhikari, N.; Jha, T. Essential elements regulating HDAC8 inhibition: A classification based structural analysis and enzyme-inhibitor interaction study of hydroxamate based HDAC8 inhibitors. J. Biomol. Struct. Dyn., 2020, 38(18), 5513-5525.
[http://dx.doi.org/10.1080/07391102.2019.1704881] [PMID: 31830865]
[41]
Moinul, M.; Amin, S.A.; Kumar, P.; Patil, U.K.; Gajbhiye, A.; Jha, T.; Gayen, S. Exploring sodium glucose cotransporter (SGLT2) inhibitors with machine learning approach: A novel hope in anti-diabetes drug discovery. J. Mol. Graph. Model., 2022, 111, 108106.
[http://dx.doi.org/10.1016/j.jmgm.2021.108106] [PMID: 34923429]
[42]
Sardar, S.; Bhattacharya, A.; Amin, S.A.; Jha, T.; Gayen, S. Exploring molecular fingerprints of different drugs having bile interaction: A stepping stone towards better drug delivery. Mol. Divers., 2023, 27, 1-3.
[http://dx.doi.org/10.1007/s11030-023-10670-2] [PMID: 37369957]
[43]
Baidya, S.K.; Banerjee, S.; Ghosh, B.; Jha, T.; Adhikari, N. A fragment-based exploration of diverse MMP-9 inhibitors through classification-dependent structural assessment. J. Mol. Graph. Model., 2024, 126, 108671.
[http://dx.doi.org/10.1016/j.jmgm.2023.108671] [PMID: 37976979]
[44]
Baidya, S.K.; Amin, S.A.; Banerjee, S.; Adhikari, N.; Jha, T. Structural exploration of arylsulfonamide-based ADAM17 inhibitors through validated comparative multi-QSAR modelling studies. J. Mol. Struct., 2019, 1185(5), 128-142.
[http://dx.doi.org/10.1016/j.molstruc.2019.02.081]
[45]
Baidya, S.K.; Banerjee, S.; Adhikari, N.; Jha, T. Selective inhibitors of medium-size S1′ pocket matrix metalloproteinases: A stepping stone of future drug discovery. J. Med. Chem., 2022, 65(16), 10709-10754.
[http://dx.doi.org/10.1021/acs.jmedchem.1c01855] [PMID: 35969157]
[46]
Adhikari, N.; Banerjee, S.; Baidya, S.K.; Ghosh, B.; Jha, T. Robust classification-based molecular modelling of diverse chemical entities as potential SARS-CoV-2 3CL pro inhibitors: Theoretical justification in light of experimental evidences. SAR QSAR Environ. Res., 2021, 32(6), 473-493.
[http://dx.doi.org/10.1080/1062936X.2021.1914721] [PMID: 34011224]
[47]
Wang, T.; Sun, J.; Zhao, Q. Investigating cardiotoxicity related with hERG channel blockers using molecular fingerprints and graph attention mechanism. Comput. Biol. Med., 2023, 153, 106464.
[http://dx.doi.org/10.1016/j.compbiomed.2022.106464] [PMID: 36584603]
[48]
Zhao, L.; Xue, Q.; Zhang, H.; Hao, Y.; Yi, H.; Liu, X.; Pan, W.; Fu, J.; Zhang, A. CatNet: Sequence-based deep learning with cross-attention mechanism for identifying endocrine-disrupting chemicals. J. Hazard. Mater., 2024, 465, 133055.
[http://dx.doi.org/10.1016/j.jhazmat.2023.133055] [PMID: 38016311]
[49]
Zhu, Z.; Rahman, Z.; Aamir, M.; Shah, S.Z.A.; Hamid, S.; Bilawal, A.; Li, S.; Ishfaq, M. Insight into TLR4 receptor inhibitory activity via QSAR for the treatment of Mycoplasma pneumonia disease. RSC Advances, 2023, 13(3), 2057-2069.
[http://dx.doi.org/10.1039/D2RA06178C] [PMID: 36712602]
[50]
Romano, M.; Contu, G.; Mola, F.; Conversano, C. Threshold-based Naïve Bayes classifier. Adv. Data Anal. Classif., 2023, 1-37.
[51]
Pandey, S.K.; Roy, K. Development of a read-across-derived classification model for the predictions of mutagenicity data and its comparison with traditional QSAR models and expert systems. Toxicology, 2023, 500, 153676.
[http://dx.doi.org/10.1016/j.tox.2023.153676] [PMID: 37993082]
[52]
Lučić, B.; Batista, J.; Bojović, V.; Lovrić, M.; Sović Kržić, A.; Bešlo, D.; Nadramija, D.; Vikić-Topić, D. Estimation of random accuracy and its use in validation of predictive quality of classification models within predictive challenges. Croat. Chem. Acta, 2019, 92(3), 379-391.
[http://dx.doi.org/10.5562/cca3551]
[53]
Batista, J.; Vikić-Topić, D.; Lučić, B. The difference between the accuracy of real and the corresponding random model is a useful parameter for validation of two-state classification model quality. Croat. Chem. Acta, 2016, 89(4), 527-534.
[http://dx.doi.org/10.5562/cca3117]
[54]
Al-Fakih, A.M.; Qasim, M.K.; Algamal, Z.Y.; Alharthi, A.M.; Zainal-Abidin, M.H. QSAR classification model for diverse series of antifungal agents based on binary coyote optimization algorithm. SAR QSAR Environ. Res., 2023, 34(4), 285-298.
[http://dx.doi.org/10.1080/1062936X.2023.2208374] [PMID: 37157994]
[55]
Ramani, J.; Shah, H.; Vyas, V.K.; Sharma, M. A review on the medicinal chemistry of sodium glucose co-transporter 2 inhibitors (SGLT2-I): Update from 2010 to present. Eur. J. Med. Chem. Rep., 2022, 100074.
[56]
Nilar, S.H. Mining big data in drug discovery-Triaging and decision trees. big data; bioinformatics; Data Analyt, 2023, pp. 265-281.
[57]
Yang, C.; Rathman, J.F.; Mostrag, A.; Ribeiro, J.V.; Hobocienski, B.; Magdziarz, T.; Kulkarni, S.; Barton-Maclaren, T. High throughput read-across for screening a large inventory of related structures by balancing artificial intelligence/machine learning and human knowledge. Chem. Res. Toxicol., 2023, 36(7), 1081-1106.
[http://dx.doi.org/10.1021/acs.chemrestox.3c00062] [PMID: 37399585]
[58]
Neal, W.M.; Pandey, P.; Khan, S.I.; Khan, I.A.; Chittiboyina, A.G. Machine learning and traditional QSAR modeling methods: A case study of known PXR activators. J. Biomol. Struct. Dyn., 2023, 1-15.
[http://dx.doi.org/10.1080/07391102.2023.2196701] [PMID: 37059719]
[59]
Béquignon, O.J.M.; Gómez-Tamayo, J.C.; Lenselink, E.B.; Wink, S.; Hiemstra, S.; Lam, C.C.; Gadaleta, D.; Roncaglioni, A.; Norinder, U.; Water, B.; Pastor, M.; van Westen, G.J.P. Collaborative SAR modeling and prospective in vitro validation of oxidative stress activation in human HepG2 cells. J. Chem. Inf. Model., 2023, 63(17), 5433-5445.
[http://dx.doi.org/10.1021/acs.jcim.3c00220] [PMID: 37616385]
[60]
Lv, H.; Zhu, Z.; Qian, C.; Li, T.; Han, Z.; Zhang, W.; Si, X.; Wang, J.; Deng, X.; Li, L.; Fang, T.; Xia, J.; Wu, S.; Zhou, Y. Discovery of isatin-β-methyldithiocarbazate derivatives as New Delhi metallo- β-lactamase-1 (NDM-1) inhibitors against NDM-1 producing clinical isolates. Biomed. Pharmacother., 2023, 166, 115439.
[http://dx.doi.org/10.1016/j.biopha.2023.115439] [PMID: 37673020]
[61]
Banerjee, S.; Kejriwal, S.; Ghosh, B.; Lanka, G.; Jha, T.; Adhikari, N. Fragment-based investigation of thiourea derivatives as VEGFR-2 inhibitors: a cross-validated approach of ligand-based and structure-based molecular modeling studies. J. Biomol. Struct. Dyn., 2023, 1-17.
[http://dx.doi.org/10.1080/07391102.2023.2198039] [PMID: 37029768]
[62]
Elmezayen, A.D.; Yelekçi, K. Homology modeling and In silico design of novel and potential dual-acting inhibitors of human histone deacetylases HDAC5 and HDAC9 isozymes. J. Biomol. Struct. Dyn., 2021, 39(17), 6396-6414.
[http://dx.doi.org/10.1080/07391102.2020.1798812] [PMID: 32715940]
[63]
Soltani, A.; Hashemy, S.I. Homology modeling, virtual screening, molecular docking, and ADME approaches to identify a potent agent targeting NK2R protein. Biotechnol. Appl. Biochem.,, 2023, bab.2533..
[http://dx.doi.org/10.1002/bab.2533] [PMID: 37904319]
[64]
Valanciute, A.; Nygaard, L.; Zschach, H.; Maglegaard Jepsen, M.; Lindorff-Larsen, K.; Stein, A. Accurate protein stability predictions from homology models. Comput. Struct. Biotechnol. J., 2023, 21, 66-73.
[http://dx.doi.org/10.1016/j.csbj.2022.11.048] [PMID: 36514339]
[65]
Webb, B.; Sali, A. Comparative protein structure modeling using modeller. Curr. Protoc. Protein Sci., 2016, 80, 2.9.1-2.9.37.
[66]
Martí-Renom, M.A.; Stuart, A.C.; Fiser, A.; Sánchez, R.; Melo, F.; Šali, A. Comparative protein structure modeling of genes and genomes. Annu. Rev. Biophys. Biomol. Struct., 2000, 29(1), 291-325.
[http://dx.doi.org/10.1146/annurev.biophys.29.1.291] [PMID: 10940251]
[67]
Fiser, A.; Do, R.K.G.; Šali, A. Modeling of loops in protein structures. Protein Sci., 2000, 9(9), 1753-1773.
[http://dx.doi.org/10.1110/ps.9.9.1753] [PMID: 11045621]
[68]
Eramian, M.Y.; Pieper, U.; Sali, A. Comparative protein structure modeling using MODELLER. Curr. Protoc. Bioinformatics, 2006, 5(1-5), 6.
[69]
Selvan, A.S.; Karthikeyan, A.; Udhayavel, S. Structural characterization and biological function annotation of cluster of differentiation 14 gene of crossbred cattle - An In silico approach. J. Anim. Res., 2023, 13(1), 49-56.
[http://dx.doi.org/10.30954/2277-940X.01.2023.6]
[70]
Masjedi, M.N.K.; Sadroddiny, E.; Ai, J.; Balalaie, S.; Asgari, Y. Targeted expression of a designed fusion protein containing BMP2 into the lumen of exosomes. Biochim. Biophys. Acta, Gen. Subj., 2024, 1868(1), 130505.
[http://dx.doi.org/10.1016/j.bbagen.2023.130505] [PMID: 37925035]
[71]
Ram Kumar, A.; Selvaraj, S.; Anthoniammal, P.; Jothi Ramalingam, R.; Balu, R.; Jayaprakash, P.; Sheeja Mol, G.P. Comparison of spectroscopic, structural, and molecular docking studies of 5-nitro-2-fluoroaniline and 2-nitro-5-fluoroaniline: An attempt on fluoroaniline isomers. J. Fluor. Chem., 2023, 270, 110167.
[http://dx.doi.org/10.1016/j.jfluchem.2023.110167]
[72]
Zhang, W.X.; Huang, J.; Tian, X.Y.; Liu, Y.H.; Jia, M.Q.; Wang, W.; Jin, C.Y.; Song, J.; Zhang, S.Y. A review of progress in o-aminobenzamide-based HDAC inhibitors with dual targeting capabilities for cancer therapy. Eur. J. Med. Chem., 2023, 259, 115673.
[http://dx.doi.org/10.1016/j.ejmech.2023.115673] [PMID: 37487305]
[73]
Kumar, S.; Ayyannan, S.R. Identification of new small molecule monoamine oxidase-B inhibitors through pharmacophore-based virtual screening, molecular docking and molecular dynamics simulation studies. J. Biomol. Struct. Dyn., 2023, 41(14), 6789-6810.
[http://dx.doi.org/10.1080/07391102.2022.2112082] [PMID: 35983603]
[74]
Liu, W.; Dai, J.; Chen, X.; Du, N.; Hu, J. Integrated network pharmacology and in-silico approaches to decipher the pharmacological mechanism of dioscorea septemloba thunb in treating gout and its complications. Comb. Chem. High Throughput Screen., 2023, 27.
[http://dx.doi.org/10.2174/0113862073258523231025095117] [PMID: 37957901]
[75]
Zhong, D.Y.; Li, L.; Li, H.J.; Ma, R.M.; Deng, Y.H. Study on the mechanism and molecular docking verification of Buyang Huanwu decoction in treating diabetic foot. World J. Tradit. Chin. Med., 2023, 9(2), 178.
[76]
Hu, C.; Li, S.; Yang, C.; Chen, J.; Xiong, Y.; Fan, G.; Liu, H.; Hong, L. ScaffoldGVAE: Scaffold generation and hopping of drug molecules via a variational autoencoder based on multi-view graph neural networks. J. Cheminform., 2023, 15(1), 91.
[http://dx.doi.org/10.1186/s13321-023-00766-0] [PMID: 37794460]
[77]
Jiang, D.; Zhao, H.; Du, H.; Deng, Y.; Wu, Z.; Wang, J.; Zeng, Y.; Zhang, H.; Wang, X.; Wu, J.; Hsieh, C.Y.; Hou, T. How good are current docking programs at nucleic acid-ligand docking? A comprehensive evaluation. J. Chem. Theory Comput., 2023, 19(16), 5633-5647.
[http://dx.doi.org/10.1021/acs.jctc.3c00507] [PMID: 37480347]
[78]
Lilkova, E.; Petkov, P.; Ilieva, N.; Litov, L. The PyMOL molecular graphics system Version 2.0. 2015, 10919-25.
[79]
Singh, H.; Raja, A.; Prakash, A.; Medhi, B. Gmx_qk: An Automated protein/protein–ligand complex simulation workflow bridged to MM/PBSA, based on gromacs and zenity-dependent GUI for beginners in MD simulation study. J. Chem. Inf. Model., 2023, 63(9), 2603-2608.
[http://dx.doi.org/10.1021/acs.jcim.3c00341] [PMID: 37079775]
[80]
Krishnamoorthy, P.K.P.; Balaraman, A.D.; Priyadharshini, A.; Shanmugam, D.A.S.; Muthukumaran, S.; Kesavamurthy, A.; Revanasiddappa, P.D. Molecular docking and simulation binding analysis of boeravinone B with caspase-3 and EGFR of hepatocellular carcinoma. Lett. Drug Des. Discov., 2023, 20(2), 238-244.
[http://dx.doi.org/10.2174/1570180819666220805163725]
[81]
Mostafavi-Pour, Z.; Jamali, N.; Saffari-Chaleshtori, J.; Samare-Najaf, M. The effect of metformin on bad, bak, and bim pro-apoptotic factors: A molecular dynamic simulation study. Curr. Cancer Ther. Rev., 2023, 19(1), 74-81.
[http://dx.doi.org/10.2174/1573394718666220930143651]
[82]
Pulikkottil, A.A.; Kumar, A.; Jangid, K.; Kumar, V.; Jaitak, V. Structure-based virtual screening and molecular dynamic simulation approach for the identification of terpenoids as potential DPP-4 inhibitors. Curr. Computeraided Drug Des., 2024, 20(4), 416-429.
[http://dx.doi.org/10.2174/1573409919666230515160502] [PMID: 37190809]
[83]
Park, S.J.; Kern, N.; Brown, T.; Lee, J. Im, W. CHARMM-GUI PDB manipulator: Various PDB structural modifications for biomolecular modeling and simulation. J. Mol. Biol., 2023, 435(14), 167995.
[http://dx.doi.org/10.1016/j.jmb.2023.167995] [PMID: 37356910]
[84]
Tamang, J.S.D.; Banerjee, S.; Baidya, S.K.; Ghosh, B.; Adhikari, N.; Jha, T. Employing comparative QSAR techniques for the recognition of dibenzofuran and dibenzothiophene derivatives toward MMP-12 inhibition. J. Biomol. Struct. Dyn., 2023, 20, 1-17.
[http://dx.doi.org/10.1080/07391102.2023.2239923] [PMID: 37498149]
[85]
Kim, H.; Fábián, B.; Hummer, G. Neighbor list artifacts in molecular dynamics simulations. J. Chem. Theory Comput., 2023, 19(23), 8919-8929.
[http://dx.doi.org/10.1021/acs.jctc.3c00777] [PMID: 38035387]
[86]
Costa, R.K.M.; Souza, L.M.P.; Silva, R.S.; Souza, F.R.; Pimentel, A.S. The reconciliation between the experimental and calculated octanol-water partition coefficient of 1,2-dipalmitoyl-sn-glycero-3-phosphatidylcholine using atomistic molecular dynamics: An open question. J. Biomol. Struct. Dyn., 2023, 41(21), 11510-11517.
[http://dx.doi.org/10.1080/07391102.2023.2173298] [PMID: 36715129]
[87]
Ahmad, A. Identification of potential edible spices as EGFR and EGFR mutant T790M/L858R inhibitors by structure-based virtual screening and molecular dynamics. J. Biomol. Struct. Dyn., 2023, 12, 1-8.
[88]
Patil, S.A.; Akki, A.J.; Raghu, A.V.; Kulkarni, R.V.; Akamanchi, K.G. Sugarcane polyphenol oxidase: Structural elucidation using molecular modeling and docking analyses. Process Biochem., 2023, 134(1), 243-249.
[http://dx.doi.org/10.1016/j.procbio.2023.09.013]
[89]
Baidya, A.T.K.; Das, B.; Devi, B.; Långström, B.; Ågren, H.; Darreh-Shori, T.; Kumar, R. Mechanistic insight into the inhibition of choline acetyltransferase by proton pump inhibitors. ACS Chem. Neurosci., 2023, 14(4), 749-765.
[http://dx.doi.org/10.1021/acschemneuro.2c00738] [PMID: 36749117]

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