Potent Small Molecules Inhibitors Discovery through Ligand-based Modelling for Effective Treatment Of Parkinson’s Disease

Sani   Najib   Yahaya; Yusuf   Ayipo   Oloruntoyin; Waleed   Abdullah Ahmad   Alananzeh; Amar      Ajmal; Sulaiman      Shams; Abdul      Wadood; Mohd   Nizam   Bn Mordi

doi:10.2174/1570180820666230822094954

Abstract

Background: Parkinson’s disease (PD) is a chronic neurodegenerative disease affecting mostly aged people. The disease's symptoms develop gradually over time and include tremors, bradykinesia, rigidity, and postural instability. Current treatment options for PD are only symptom-targeted. Prolyl oligopeptidase (POP) is a serine protease enzymes implicated in PD pathogenesis via an increase in the aggregation of α-synuclein protein in the brain.

Aim: This study aims to identify potent anti-PD ligands with inhibitory potential against POP Methods: Ligand-based pharmacophore modeling, Glide extra precision (XP) docking, and postsimulation analysis methods were used.

Results: The adopted ligand-based (LB) modeling generated pharmacophoric features, including 1 hydrophobic group, 1 positive ionizable group, 2 aromatic rings, and 2 hydrogen bond acceptors. A total of 23 hits with a Gunner-Henry score of 0.7 and an enrichment factor of 30.24 were obtained as validation protocols, making it an ideal model. The LB model retrieved 177 hit compounds from the 69,543 natural screening ligands available in the Interbioscreen database. Interestingly, ligands 1, 2, 3, 4, and 5 orderly demonstrated higher binding affinities with Glide XP docking of -9.0, -8.8, -8.7, -8.7, -8.7 kcal/mol compared to reference drugs, GSK552 and ZPP with -8.2, and -6.8 kcal/mol respectively. Similarly, their MM/GBSA values were recorded as -54.4, -51.3, -58.4, -49.3, - 33.5, & -32.5 kJ/mol respectively. Further, MD analysis indicated that ligands had higher favorable binding and stability to the receptor.

Conclusion: Overall, the study paves the way for developing potential anti-PD therapeutics. The ligands are recommended as adjuvant/single candidate as anti-PD candidates upon further experiment.

« Previous Next »

[1]
Bachovchin, D.A.; Cravatt, B.F. The pharmacological landscape and therapeutic potential of serine hydrolases. Nat. Rev. Drug Discov.,  2012, 11(1), 52-68.
 [http://dx.doi.org/10.1038/nrd3620]

[2]
Ahuja, V.; Chou, C.H. Novel therapeutics for diabetes: Uptake, usage trends, and comparative effectiveness. Curr. Diab. Rep.,  2016, 16(6), 47.
 [http://dx.doi.org/10.1007/s11892-016-0744-4] [PMID:  27076180]

[3]
Tokai, S.; Bito, T.; Shimizu, K.; Arima, J. Effect of oxidation of the non-catalytic β-propeller domain on the substrate specificity of prolyl oligopeptidase from Pleurotus eryngii. Biochem. Biophys. Res. Commun.,  2017, 487(2), 356-361.
 [http://dx.doi.org/10.1016/j.bbrc.2017.04.064] [PMID:  28414130]

[4]
Kumar, R.; Bavi, R.; Jo, M.G.; Arulalapperumal, V.; Baek, A.; Rampogu, S.; Kim, M.O.; Lee, K.W. New compounds identified through In silico approaches reduce the α-synuclein expression by inhibiting prolyl oligopeptidase in vitro. Sci. Rep.,  2017, 7(1), 10827.
 [http://dx.doi.org/10.1038/s41598-017-11302-0] [PMID:  28883518]

[5]
Tenorio-Laranga, J.; Coret-Ferrer, F.; Casanova-Estruch, B.; Burgal, M.; García-Horsman, J.A. Prolyl oligopeptidase is inhibited in relapsing-remitting multiple sclerosis. J. Neuroinflammation,  2010, 7(1), 23.
 [http://dx.doi.org/10.1186/1742-2094-7-23] [PMID:  20370893]

[6]
Momeni, N.; Yoshimoto, T.; Ryberg, B.; Sandberg-wollheim, M.; Grubb, A. Factors influencing analysis of prolyl endopeptidase in human blood and cerebrospinal fluid: Increase in assay sensitivity. Scand. J. Clin. Lab. Invest.,  2003, 63(6), 387-396.
 [http://dx.doi.org/10.1080/00365510310001951] [PMID:  14594319]

[7]
Komatsu, Y. GABAB receptors, monoamine receptors, and postsynaptic inositol trisphosphate-induced Ca2+ release are involved in the induction of long-term potentiation at visual cortical inhibitory synapses. J. Neurosci.,  1996, 16(20), 6342-6352.
 [http://dx.doi.org/10.1523/JNEUROSCI.16-20-06342.1996] [PMID:  8815913]

[8]
Connolly, B.S.; Lang, A.E. Pharmacological treatment of Parkinson disease: A review. JAMA,  2014, 311(16), 1670-1683.
 [http://dx.doi.org/10.1001/jama.2014.3654] [PMID:  24756517]

[9]
Dokleja, L.; Hannula, M.J.; Myöhänen, T.T. Inhibition of prolyl oligopeptidase increases the survival of alpha-synuclein overexpressing cells after rotenone exposure by reducing alpha-synuclein oligomers. Neurosci. Lett.,  2014, 583, 37-42.
 [http://dx.doi.org/10.1016/j.neulet.2014.09.026] [PMID:  25240592]

[10]
Spillantini, M.G.; Schmidt, M.L.; Lee, V.M.Y.; Trojanowski, J.Q.; Jakes, R.; Goedert, M. α-Synuclein in lewy bodies. Nature,  1997, 388(6645), 839-840.
 [http://dx.doi.org/10.1038/42166] [PMID:  9278044]

[11]
Kocadag Kocazorbaz, E.; Zihnioglu, F. Purification, characterization and the use of recombinant prolyl oligopeptidase from Myxococcus xanthus for gluten hydrolysis. Protein Expr. Purif.,  2017, 129, 101-107.
 [http://dx.doi.org/10.1016/j.pep.2016.09.016] [PMID:  27693621]

[12]
Williams, R.S.B. Pharmacogenetics in model systems: Defining a common mechanism of action for mood stabilisers. Prog. Neuropsychopharmacol. Biol. Psychiatry,  2005, 29(6), 1029-1037.
 [http://dx.doi.org/10.1016/j.pnpbp.2005.03.020] [PMID:  15950352]

[13]
Fernández-Atucha, A.; Echevarría, E.; Larrinaga, G.; Gil, J.; Martínez-Cengotitabengoa, M.; González-Pinto, A.M.; Irazusta, J.; Seco, J. Plasma peptidases as prognostic biomarkers in patients with first-episode psychosis. Psychiatry Res.,  2015, 228(2), 197-202.
 [http://dx.doi.org/10.1016/j.psychres.2015.04.027] [PMID:  25997998]

[14]
Jambunathan, K.; Watson, D.S.; Endsley, A.N. Comparative analysis of the substrate preferences of two postproline cleaving endopeptidases, prolyl oligopeptidase and fibroblast activation protein α. FEBS Lett.,  2012, 586, 2507-2512.
 [http://dx.doi.org/10.1016/j.febslet.2012.06.015] [PMID:  22750443]

[15]
García-Horsman, J.A.; Männistö, P.T.; Venäläinen, J.I. On the role of prolyl oligopeptidase in health and disease. Neuropeptides,  2007, 41(1), 1-24.
 [http://dx.doi.org/10.1016/j.npep.2006.10.004] [PMID:  17196652]

[16]
Cahlíková, L.; Hulová, L.; Hrabinová, M.; Chlebek, J.; Hošťálková, A.; Adamcová, M.; Šafratová, M.; Jun, D.; Opletal, L.; Ločárek, M.; Macáková, K. Isoquinoline alkaloids as prolyl oligopeptidase inhibitors. Fitoterapia,  2015, 103, 192-196.
 [http://dx.doi.org/10.1016/j.fitote.2015.04.004] [PMID:  25863351]

[17]
Shishido, Y.; Furushiro, M.; Tanabe, S.; Shibata, S.; Hashimoto, S.; Yokokura, T. Effects of prolyl endopeptidase inhibitors and neuropeptides on delayed neuronal death in rats. Eur. J. Pharmacol.,  1999, 372(2), 135-142.
 [http://dx.doi.org/10.1016/S0014-2999(99)00185-5] [PMID:  10395093]

[18]
Khan, A.; Waqas, M.; Khan, M.; Halim, S.A.; Rehman, N.U.; Al-Harrasi, A. Identification of novel prolyl oligopeptidase inhibitors from resin of Boswellia papyrifera (Del.) Hochst. and their mechanism: Virtual and biochemical studies. Int. J. Biol. Macromol.,  2022, 213, 751-767.
 [http://dx.doi.org/10.1016/j.ijbiomac.2022.06.001] [PMID:  35679958]

[19]
Ayipo, Y.O.; Ahmad, I.; Alananzeh, W.; Lawal, A.; Patel, H.; Mordi, M.N. Computational modelling of potential Zn-sensitive non-β-lactam inhibitors of imipenemase-1 (IMP-1). J. Biomol. Struct. Dyn.,  2022, 0, 1-21.
 [http://dx.doi.org/10.1080/07391102.2022.2153168] [PMID:  36476097]

[20]
Prachayasittikul, V.; Worachartcheewan, A.; Shoombuatong, W.; Songtawee, N.; Simeon, S.; Prachayasittikul, V.; Nantasenamat, C. Computer-aided drug design of bioactive natural products. Curr. Top. Med. Chem.,  2015, 15(18), 1780-1800.
 [http://dx.doi.org/10.2174/1568026615666150506151101] [PMID:  25961523]

[21]
Kamzolova, S.G.; Sivozhelezov, V.S.; Sorokin, A.A.; Dzhelyadin, T.R.; Ivanova, N.N.; Polozov, R.V. RNA polymerase--promoter recognition. Specific features of electrostatic potential of “early” T4 phage DNA promoters. J. Biomol. Struct. Dyn.,  2000, 18(3), 325-334.
 [http://dx.doi.org/10.1080/07391102.2000.10506669] [PMID:  11149509]

[22]
Hošt’álková, A.; Opletal, L.; Kuneš, J.; Novák, Z.; Hrabinová, M.; Chlebek, J.; Čegan, L.; Cahlíková, L. Alkaloids from Peumus boldus and their acetylcholinesterase, butyrylcholinesterase and prolyl oligopeptidase inhibition activity. Nat. Prod. Commun.,  2015, 10(4), 1934578X1501000.
 [http://dx.doi.org/10.1177/1934578X1501000410] [PMID:  25973480]

[23]
Hostalkova, A.; Marikova, J.; Opletal, L.; Korabecny, J.; Hulcova, D.; Kunes, J.; Novakova, L.; Perez, D.I.; Jun, D.; Kucera, T.; Andrisano, V.; Siatka, T.; Cahlikova, L. Isoquinoline alkaloids from berberis vulgaris as potential lead compounds for the treatment of alzheimer’s disease. J. Nat. Prod.,  2019, 82(2), 239-248.
 [http://dx.doi.org/10.1021/acs.jnatprod.8b00592] [PMID:  30701972]

[24]
Bhanukiran, K. T A, G.; Krishnamurthy, S.; Singh, S.K.; Hemalatha, S. Discovery of multi-target directed 3-OH pyrrolidine derivatives through a semisynthetic approach from alkaloid vasicine for the treatment of Alzheimer’s disease. Eur. J. Med. Chem.,  2023, 249, 115145.
 [http://dx.doi.org/10.1016/j.ejmech.2023.115145] [PMID:  36706620]

[25]
Cai, C.Z.; Zhou, H.F.; Yuan, N.N.; Wu, M.Y.; Lee, S.M.Y.; Ren, J.Y.; Su, H.X.; Lu, J.J.; Chen, X.P.; Li, M.; Tan, J.Q.; Lu, J.H. Natural alkaloid harmine promotes degradation of alpha-synuclein via PKA-mediated ubiquitin-proteasome system activation. Phytomedicine,  2019, 61, 152842.
 [http://dx.doi.org/10.1016/j.phymed.2019.152842] [PMID:  31048127]

[26]
Singh, S.; Pathak, N.; Fatima, E.; Negi, A.S. Plant isoquinoline alkaloids: Advances in the chemistry and biology of berberine. Eur. J. Med. Chem.,  2021, 226, 113839.
 [http://dx.doi.org/10.1016/j.ejmech.2021.113839] [PMID:  34536668]

[27]
Siatka, T.; Adamcová, M.; Opletal, L.; Cahlíková, L.; Jun, D.; Hrabinová, M.; Kuneš, J.; Chlebek, J. Cholinesterase and prolyl oligopeptidase inhibitory activities of alkaloids from argemone platyceras (Papaveraceae). Molecules,  2017, 22(7), 1181.
 [http://dx.doi.org/10.3390/molecules22071181] [PMID:  28708094]

[28]
Cahlíková, L.; Vrabec, R.; Pidaný, F.; Peřinová, R.; Maafi, N.; Mamun, A.A.; Ritomská, A.; Wijaya, V.; Blunden, G. Recent progress on biological activity of amaryllidaceae and further isoquinoline alkaloids in connection with alzheimer’s disease. Molecules,  2021, 26(17), 5240.
 [http://dx.doi.org/10.3390/molecules26175240] [PMID:  34500673]

[29]
Li, Q.; Lin, J.; Zhang, Y.; Liu, X.; Chen, X.Q.; Xu, M.Q.; He, L.; Li, S.; Guo, N. Differential behavioral responses of zebrafish larvae to yohimbine treatment. Psychopharmacology,  2015, 232(1), 197-208.
 [http://dx.doi.org/10.1007/s00213-014-3656-5] [PMID:  24958231]

[30]
Gomes, N.G.M.; Campos, M.G.; Órfão, J.M.C.; Ribeiro, C.A.F. Plants with neurobiological activity as potential targets for drug discovery. Prog. Neuropsychopharmacol. Biol. Psychiatry,  2009, 33(8), 1372-1389.
 [http://dx.doi.org/10.1016/j.pnpbp.2009.07.033] [PMID:  19666075]

[31]
Wolber, G.; Langer, T. LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters. J. Chem. Inf. Model.,  2005, 45(1), 160-169.
 [http://dx.doi.org/10.1021/ci049885e] [PMID:  15667141]

[32]
Cereto-Massagué, A.; Guasch, L.; Valls, C.; Mulero, M.; Pujadas, G.; Garcia-Vallvé, S. DecoyFinder: An easy-to-use python GUI application for building target-specific decoy sets. Bioinformatics,  2012, 28(12), 1661-1662.
 [http://dx.doi.org/10.1093/bioinformatics/bts249] [PMID:  22539671]

[33]
Ayipo, Y.O.; Alananzeh, W.A.; Yahayaa, S.N.; Mordi, M.N. Molecular modelling and virtual screening to identify new piperazine derivatives as potent human 5-HT1A antagonists and reuptake inhibitors. Comb. Chem. High Throughput Screen.,  2022, 25.
 [http://dx.doi.org/10.2174/1386207325666220524094913] [PMID:  35611784]

[34]
Wang, L.; Pang, X.; Li, Y.; Zhang, Z.; Tan, W. RADER: A RApid DEcoy retriever to facilitate decoy based assessment of virtual screening. Bioinformatics,  2017, 33(8), 1235-1237.
 [http://dx.doi.org/10.1093/bioinformatics/btw783] [PMID:  28011765]

[35]
Pascual, R.; Almansa, C.; Plata-Salamán, C.; Vela, J.M. A new pharmacophore model for the design of sigma-1 ligands validated on a large experimental dataset. Front. Pharmacol.,  2019, 10, 519.
 [http://dx.doi.org/10.3389/fphar.2019.00519] [PMID:  31214020]

[36]
Wallach, I.; Lilien, R. Virtual decoy sets for molecular docking benchmarks. J. Chem. Inf. Model.,  2011, 51(2), 196-202.
 [http://dx.doi.org/10.1021/ci100374f] [PMID:  21207928]

[37]
Ulmer, T.S.; Bax, A.; Cole, N.B.; Nussbaum, R.L. Structure and dynamics of micelle-bound human α-synuclein. J. Biol. Chem.,  2005, 280(10), 9595-9603.
 [http://dx.doi.org/10.1074/jbc.M411805200] [PMID:  15615727]

[38]
Polgar, L.; Szeltner, Z. Structure, function and biological relevance of prolyl oligopeptidase. Curr. Protein Pept. Sci.,  2008, 9(1), 96-107.
 [http://dx.doi.org/10.2174/138920308783565723] [PMID:  18336325]

[39]
Kaushik, S.; Sowdhamini, R. Structural analysis of prolyl oligopeptidases using molecular docking and dynamics: Insights into conformational changes and ligand binding. PLoS One,  2011, 6(11), e26251.
 [http://dx.doi.org/10.1371/journal.pone.0026251] [PMID:  22132071]

[40]
Fülöp, V.; Böcskei, Z.; Polgár, L. Prolyl oligopeptidase. Cell,  1998, 94(2), 161-170.
 [http://dx.doi.org/10.1016/S0092-8674(00)81416-6] [PMID:  9695945]

[41]
Götz, A.W.; Williamson, M.J.; Xu, D.; Poole, D.; Le Grand, S.; Walker, R.C. Routine microsecond molecular dynamics simulations with AMBER on GPUs. 1. generalized born. J. Chem. Theory Comput.,  2012, 8(5), 1542-1555.
 [http://dx.doi.org/10.1021/ct200909j] [PMID:  22582031]

[42]
Roe, D.R.; Cheatham, T.E. III PTRAJ and CPPTRAJ: Software for processing and analysis of molecular dynamics trajectory data. J. Chem. Theory Comput.,  2013, 9(7), 3084-3095.
 [http://dx.doi.org/10.1021/ct400341p] [PMID:  26583988]

[43]
Genheden, S.; Ryde, U. The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin. Drug Discov.,  2015, 10(5), 449-461.

[44]
Miller, B.R., III; McGee, T.D., Jr; Swails, J.M.; Homeyer, N.; Gohlke, H.; Roitberg, A.E. MMPBSA.py: An efficient program for end-state free energy calculations. J. Chem. Theory Comput.,  2012, 8(9), 3314-3321.
 [http://dx.doi.org/10.1021/ct300418h] [PMID:  26605738]

[45]
Wang, E.; Sun, H.; Wang, J.; Wang, Z.; Liu, H.; Zhang, J.Z.H.; Hou, T. End-point binding free energy calculation with MM/PBSA and MM/GBSA: Strategies and applications in drug design. Chem. Rev.,  2019, 119(16), 9478-9508.
 [http://dx.doi.org/10.1021/acs.chemrev.9b00055] [PMID:  31244000]

[46]
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]

[47]
Ni, D.; Song, K.; Zhang, J.; Lu, S. Molecular dynamics simulations and dynamic network analysis reveal the allosteric unbinding of monobody to H-Ras triggered by R135K mutation. Int. J. Mol. Sci.,  2017, 18(11), 2249.
 [http://dx.doi.org/10.3390/ijms18112249] [PMID:  29072601]

[48]
Hou, T.; Wang, J.; Li, Y.; Wang, W. Assessing the performance of the MM/PBSA and MM/GBSA methods. 1. The accuracy of binding free energy calculations based on molecular dynamics simulations. J. Chem. Inf. Model.,  2011, 51(1), 69-82.
 [http://dx.doi.org/10.1021/ci100275a] [PMID:  21117705]

[49]
Swaminathan, S.; Harte, W.E., Jr; Beveridge, D.L. Investigation of domain structure in proteins via molecular dynamics simulation: Application to HIV-1 protease dimer. J. Am. Chem. Soc.,  1991, 113(7), 2717-2721.
 [http://dx.doi.org/10.1021/ja00007a054]

[50]
Junaid, M.; Shah, M.; Khan, A.; Li, C.D.; Khan, M.T.; Kaushik, A.C.; Ali, A.; Mehmood, A.; Nangraj, A.S.; Choi, S.; Wei, D.Q. Structural-dynamic insights into the H. pylori cytotoxin-associated gene A (CagA) and its abrogation to interact with the tumor suppressor protein ASPP2 using decoy peptides. J. Biomol. Struct. Dyn.,  2019, 37(15), 4035-4050.
 [http://dx.doi.org/10.1080/07391102.2018.1537895] [PMID:  30328798]

[51]
Pires, D.E.V.; Blundell, T.L.; Ascher, D.B. pkCSM: Predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. J. Med. Chem.,  2015, 58(9), 4066-4072.
 [http://dx.doi.org/10.1021/acs.jmedchem.5b00104] [PMID:  25860834]

[52]
Secci, D.; Carradori, S.; Bizzarri, B.; Chimenti, P.; De Monte, C.; Mollica, A.; Rivanera, D.; Zicari, A.; Mari, E.; Zengin, G.; Aktumsek, A. Novel 1,3-thiazolidin-4-one derivatives as promising anti- Candida agents endowed with anti-oxidant and chelating properties. Eur. J. Med. Chem.,  2016, 117, 144-156.
 [http://dx.doi.org/10.1016/j.ejmech.2016.04.012] [PMID:  27100030]

[53]
Jade, D.D.; Pandey, R.; Kumar, R.; Gupta, D. Ligand-based pharmacophore modeling of TNF-α to design novel inhibitors using virtual screening and molecular dynamics. J. Biomol. Struct. Dyn.,  2022, 40(4), 1702-1718.
 [http://dx.doi.org/10.1080/07391102.2020.1831962] [PMID:  33034255]

[54]
Zainab, B.; Ayaz, Z.; Alwahibi, M.S.; Khan, S.; Rizwana, H.; Soliman, D.W.; Alawaad, A.; Mehmood Abbasi, A. In-silico elucidation of Moringa oleifera phytochemicals against diabetes mellitus. Saudi J. Biol. Sci.,  2020, 27(9), 2299-2307.
 [http://dx.doi.org/10.1016/j.sjbs.2020.04.002] [PMID:  32884411]

[55]
Vuorinen, A.; Engeli, R.; Meyer, A.; Bachmann, F.; Griesser, U.J.; Schuster, D.; Odermatt, A. Ligand-based pharmacophore modeling and virtual screening for the discovery of novel 17β-hydroxysteroid dehydrogenase 2 inhibitors. J. Med. Chem.,  2014, 57(14), 5995-6007.
 [http://dx.doi.org/10.1021/jm5004914] [PMID:  24960438]

[56]
Yu, W.; Lakkaraju, S.K.; Raman, E.P.; MacKerell, A.D. Jr Site-Identification by Ligand Competitive Saturation (SILCS) assisted pharmacophore modeling. J. Comput. Aided Mol. Des.,  2014, 28(5), 491-507.
 [http://dx.doi.org/10.1007/s10822-014-9728-0] [PMID:  24610239]

[57]
Zeb, A.; Son, M.; Yoon, S.; Kim, J.H.; Park, S.J.; Lee, K.W. Computational simulations identified two candidate inhibitors of Cdk5/p25 to abrogate tau-associated neurological disorders. Comput. Struct. Biotechnol. J.,  2019, 17, 579-590.
 [http://dx.doi.org/10.1016/j.csbj.2019.04.010] [PMID:  31073393]

[58]
Shih, K.C.; Lin, C.Y.; Zhou, J.; Chi, H.C.; Chen, T.S.; Wang, C.C.; Tseng, H.W.; Tang, C.Y. Development of novel 3D-QSAR combination approach for screening and optimizing B-Raf inhibitors In silico. J. Chem. Inf. Model.,  2011, 51(2), 398-407.
 [http://dx.doi.org/10.1021/ci100351s] [PMID:  21182293]

[59]
Shahlaei, M.; Doosti, E. Virtual screening based on pharmacophore model followed by docking simulation studies in search of potential inhibitors for p38 map kinase. Biomed. Pharmacother.,  2016, 80, 352-372.
 [http://dx.doi.org/10.1016/j.biopha.2016.02.041] [PMID:  27133076]

[60]
John, S.; Thangapandian, S.; Sakkiah, S.; Lee, K.W. Potent bace-1 inhibitor design using pharmacophore modeling, In silico screening and molecular docking studies. BMC Bioinformatics,  2011, 12(S1), S28.
 [http://dx.doi.org/10.1186/1471-2105-12-S1-S28] [PMID:  21342558]

[61]
Niu, M.; Qin, J.; Tian, C.; Yan, X.; Dong, F.; Cheng, Z.; Fida, G.; Yang, M.; Chen, H.; Gu, Y. Tubulin inhibitors: Pharmacophore modeling, virtual screening and molecular docking. Acta Pharmacol. Sin.,  2014, 35(7), 967-979.
 [http://dx.doi.org/10.1038/aps.2014.34] [PMID:  24909516]

[62]
Ramírez, D.; Caballero, J. Is it reliable to take the molecular docking top scoring position as the best solution without considering available structural data? Molecules,  2018, 23(5), 1038.
 [http://dx.doi.org/10.3390/molecules23051038] [PMID:  29710787]

[63]
Castro-Alvarez, A.; Costa, A.; Vilarrasa, J. The Performance of several docking programs at reproducing protein-macrolide-like crystal structures. Molecules,  2017, 22(1), 136.
 [http://dx.doi.org/10.3390/molecules22010136] [PMID:  28106755]

[64]
Babkova, K.; Korabecny, J.; Soukup, O.; Nepovimova, E.; Jun, D.; Kuca, K. Prolyl oligopeptidase and its role in the organism: Attention to the most promising and clinically relevant inhibitors. Future Med. Chem.,  2017, 9(10), 1015-1038.
 [http://dx.doi.org/10.4155/fmc-2017-0030] [PMID:  28632451]

[65]
Gajjar, N.D.; Dhameliya, T.M.; Shah, G.B. In search of RdRp and Mpro inhibitors against SARS CoV-2: Molecular docking, molecular dynamic simulations and ADMET analysis. J. Mol. Struct.,  2021, 1239, 130488.
 [http://dx.doi.org/10.1016/j.molstruc.2021.130488] [PMID:  33903778]

[66]
Patel, H.M.; Ahmad, I.; Pawara, R.; Shaikh, M.; Surana, S. In silico search of triple mutant T790M/C797S allosteric inhibitors to conquer acquired resistance problem in non-small cell lung cancer (NSCLC): A combined approach of structure-based virtual screening and molecular dynamics simulation. J. Biomol. Struct. Dyn.,  2021, 39(4), 1491-1505.
 [http://dx.doi.org/10.1080/07391102.2020.1734092] [PMID:  32102624]

[67]
Ahmad, I.; Jadhav, H.; Shinde, Y.; Jagtap, V.; Girase, R.; Patel, H. Optimizing Bedaquiline for cardiotoxicity by structure based virtual screening, DFT analysis and molecular dynamic simulation studies to identify selective MDR-TB inhibitors. In silico Pharmacol.,  2021, 9(1), 23.
 [http://dx.doi.org/10.1007/s40203-021-00086-x] [PMID:  33854869]

[68]
Galzitskaya, O.V.; Garbuzynskiy, S.O. Entropy capacity determines protein folding. Proteins,  2006, 63(1), 144-154.
 [http://dx.doi.org/10.1002/prot.20851] [PMID:  16400647]

[69]
Delaney, J.S. ESOL: Estimating aqueous solubility directly from molecular structure. J. Chem. Inf. Comput. Sci.,  2004, 44(3), 1000-1005.
 [http://dx.doi.org/10.1021/ci034243x] [PMID:  15154768]

[70]
Montanari, F.; Ecker, G.F. Prediction of drug - ABC-transporter interaction - Recent advances and future challenges. Adv. Drug Deliv. Rev.,  2019, 17-26.
 [http://dx.doi.org/10.1016/j.addr.2015.03.001]

Rights & Permissions Print Cite

Explore Articles

Open Access

Open Access Articles

DOI https://dx.doi.org/10.2174/1570180820666230822094954	Print ISSN 1570-1808
Publisher Name Bentham Science Publisher	Online ISSN 1875-628X

Letters in Drug Design & Discovery

Potent Small Molecules Inhibitors Discovery through Ligand-based Modelling for Effective Treatment Of Parkinson’s Disease

Abstract Play Pause

Abstract