Generic placeholder image

Current Enzyme Inhibition

Author(s): Sangavi Pandiyan and Langeswaran Kulanthaivel*

DOI: 10.2174/1573408016666200831171943

DownloadDownload PDF Flyer Cite As
Computational Study on Identification of Potential Elephantiasis Inhibitors Against UDP-Galactopyranose Mutase (UGM)

Page: [57 - 70] Pages: 14

  • * (Excluding Mailing and Handling)

Abstract

Background: Lymphatic filariasis, regularly known as elephantiasis, is a dismissed tropical malady. A filarial parasite causes the disease when it is transmitted to humans through mosquitoes. The World Health Organization distinguished that this is one of the subsequent driving reasons for lasting and long haul inability. Inaccessibility of immunization and medication opposition of a large portion of the ebb and flow hostile to filarial drugs necessitate quest of novel medication that focuses on creating elective medications. UDP-galactopyranose mutase (UGM) is a flavoenzyme that catalyzes the change of UDP-galactopyranose mutase to UDP-galactofuranose, which is a focal response in galactofuranose biosynthesis. This UGM is fundamental for some pathogens however, it is missing in people, makes UGM a potential medication target.

Objective: In the current investigation, UGM from the parasitic nematode Brugia malayi has been considered as an objective during in silico medicate planning of powerful filarial inhibitor.

Methods: Here, we build up the homology model of UGM protein dependent on the gem structure of 4DSG. To break down the quality and unwavering quality of the created model, model approval was performed utilizing the SAVES server. Mixes from Specs, Enamine, and Maybridge databases were screened to recognize a potential ligand that could hinder the action of the UGM protein utilizing Glide HTVS and Glide XP.

Results: Because of the scoring boundaries, the best 6 hit mixes were chosen and exposed to ADME forecast utilizing QikProp module from Schrodinger. To check the security of docked buildings, an atomic element study was completed.

Conclusion: The consequences of this examination give six novel lead mixes to building up an enemy of filarial medication focusing on the UGM protein.

Keywords: UDP-galactopyranose mutase (UDP), Homology modeling, Virtual Screening, MM-GBSA, Molecular dynamics, Lymphatic filariasis.

Graphical Abstract

[1]
Melrose WD. Lymphatic filariasis: new insights into an old disease. Int J Parasitol 2002; 32(8): 947-60.
[http://dx.doi.org/10.1016/S0020-7519(02)00062-0] [PMID: 12076624]
[2]
Turner HC, Bettis AA, Chu BK, et al. The health and economic benefits of the global programme to eliminate lymphatic filariasis (2000-2014). Infect Dis Poverty 2016; 5(1): 54.
[http://dx.doi.org/10.1186/s40249-016-0147-4] [PMID: 27388873]
[3]
Addiss DG. Global elimination of lymphatic filariasis: addressing the public health problem. PLoS Negl Trop Dis 2010; 4(6): e741.
[http://dx.doi.org/10.1371/journal.pntd.0000741] [PMID: 20614015]
[4]
Sanders DA, Staines AG, McMahon SA, McNeil MR, Whitfield C, Naismith JH. UDP-galactopyranose mutase has a novel structure and mechanism. Nat Struct Biol 2001; 8(10): 858-63.
[http://dx.doi.org/10.1038/nsb1001-858] [PMID: 11573090]
[5]
Soltero-Higgin M, Carlson EE, Gruber TD, Kiessling LL. A unique catalytic mechanism for UDP-galactopyranose mutase. Nat Struct Mol Biol 2004; 11(6): 539-43.
[http://dx.doi.org/10.1038/nsmb772] [PMID: 15133501]
[6]
Gowthaman R, Silvester AJ, Saranya K, Kanya KS, Archana NR. Modeling of the potential coiled-coil structure of snapin protein and its interaction with SNARE complex. Bioinformation 2006; 1(7): 269-75.
[http://dx.doi.org/10.6026/97320630001269] [PMID: 17597906]
[7]
Larkin MA, Blackshields G, Brown NP, et al. Clustal W and Clustal X version 2.0. Bioinformatics 2007; 23(21): 2947-8.
[http://dx.doi.org/10.1093/bioinformatics/btm404] [PMID: 17846036]
[8]
Frishman D, Argos P. Knowledge-based protein secondary structure assignment. Proteins 1995; 23(4): 566-79.
[http://dx.doi.org/10.1002/prot.340230412] [PMID: 8749853]
[9]
Kuntal BK, Aparoy P, Reddanna P. EasyModeller: A graphical interface to MODELLER. BMC Res Notes 2010; 3: 226.
[http://dx.doi.org/10.1186/1756-0500-3-226] [PMID: 20712861]
[10]
PyMOL. PyMOL molecular graphics system website http://www.pymol.org2010.
[11]
Anupriya G, Roopa K, Basappa S, Chong YS, Annamalai L. Homology modeling and in silico screening of inhibitors for the substrate binding domain of human Siah2: implications for hypoxia-induced cancers. J Mol Model 2011; 17(12): 3325-32.
[http://dx.doi.org/10.1007/s00894-011-1025-4] [PMID: 21409570]
[12]
Fiser A, Do RK, Sali A. Modeling of loops in protein structures. Protein Sci 2000; 9(9): 1753-73.
[http://dx.doi.org/10.1110/ps.9.9.1753] [PMID: 11045621]
[13]
Deng W, Verlinde CL. Evaluation of different virtual screening programs for docking in a charged binding pocket. J Chem Inf Model 2008; 48(10): 2010-20.
[http://dx.doi.org/10.1021/ci800154w] [PMID: 18821750]
[14]
Jørgensen AM, Topiol S. Driving forces for ligand migration in the leucine transporter. Chem Biol Drug Des 2008; 72(4): 265-72.
[http://dx.doi.org/10.1111/j.1747-0285.2008.00707.x] [PMID: 18844672]
[15]
Vijayalakshmi P, Daisy P. Effective interaction studies for inhibition of DNA ligase protein from Staphylococcus aureus. J Recept Signal Transduct Res 2015; 35(1): 15-25.
[http://dx.doi.org/10.3109/10799893.2014.926924] [PMID: 25055026]
[16]
Friesner RA, Banks JL, Murphy RB, et al. Glide: A new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 2004; 47(7): 1739-49.
[http://dx.doi.org/10.1021/jm0306430] [PMID: 15027865]
[17]
Friesner RA, Murphy RB, Repasky MP, et al. Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. J Med Chem 2006; 49(21): 6177-96.
[http://dx.doi.org/10.1021/jm051256o] [PMID: 17034125]
[18]
Sengupta D, Verma D, Naik PK. Docking mode of delvardine and its analogues into the p66 domain of HIV-1 reverse transcriptase: screening using molecular mechanics-generalized born/surface area and absorption, distribution, metabolism and excretion properties. J Biosci 2007; 32(7): 1307-16.
[http://dx.doi.org/10.1007/s12038-007-0140-y] [PMID: 18202455]
[19]
Mobley DL, Dill KA. Binding of small-molecule ligands to proteins: “what you see” is not always “what you get”. Structure 2009; 17(4): 489-98.
[http://dx.doi.org/10.1016/j.str.2009.02.010] [PMID: 19368882]
[20]
Lyne PD, Lamb ML, Saeh JC. Accurate prediction of the relative potencies of members of a series of kinase inhibitors using molecular docking and MM-GBSA scoring. J Med Chem 2006; 49(16): 4805-8.
[http://dx.doi.org/10.1021/jm060522a] [PMID: 16884290]
[21]
Das D, Koh Y, Tojo Y, Ghosh AK, Mitsuya H. Prediction of potency of protease inhibitors using free energy simulations with polarizable quantum mechanics-based ligand charges and a hybrid water model. J Chem Inf Model 2009; 49(12): 2851-62.
[http://dx.doi.org/10.1021/ci900320p] [PMID: 19928916]
[22]
Phillips JC, Braun R, Wang W, et al. Scalable molecular dynamics with NAMD. J Comput Chem 2005; 26(16): 1781-802.
[http://dx.doi.org/10.1002/jcc.20289] [PMID: 16222654]
[23]
Galeazzi R. Molecular dynamics as a tool in rational drug design: Current status and some major applications. Curr Comput Aided Drug Des 2009; 4: 225-40.
[http://dx.doi.org/10.2174/157340909789577847]
[24]
Maragakis P, Lindorff-Larsen K, Eastwood MP, et al. Microsecond molecular dynamics simulation shows effect of slow loop dynamics on backbone amide order parameters of proteins. J Phys Chem B 2008; 112(19): 6155-8.
[http://dx.doi.org/10.1021/jp077018h] [PMID: 18311962]
[25]
QikProp. Version 33. New York: Schro¨dinger, LLC 2010.
[26]
Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 2001; 46(1-3): 3-26.
[http://dx.doi.org/10.1016/S0169-409X(00)00129-0] [PMID: 11259830]
[27]
Yoon BJ. Sequence alignment by passing messages. BMC Genomics 2014; 15(Suppl. 1): S14.
[http://dx.doi.org/10.1186/1471-2164-15-S1-S14] [PMID: 24564436]
[28]
Kandoi G, Leelananda SP, Jernigan RL, Sen TZ. Predicting protein secondary structure using consensus data mining (CDM) based on empirical statistics and evolutionary information. Methods Mol Biol 2017; 1484: 35-44.
[http://dx.doi.org/10.1007/978-1-4939-6406-2_4] [PMID: 27787818]
[29]
Domicevica L, Biggin PC. Homology modelling of human P-glycoprotein. Biochem Soc Trans 2015; 43(5): 952-8.
[http://dx.doi.org/10.1042/BST20150125] [PMID: 26517909]
[30]
Reinertsen I, Jakola AS, Selbekk T, Solheim O. Validation of model-guided placement of external ventricular drains. Int J CARS 2014; 9(5): 777-84.
[http://dx.doi.org/10.1007/s11548-013-0971-y] [PMID: 24414616]
[31]
Karthik L. Identification of potent inhibitors of udpgalactopyranose. Int J Drug Res Tech 2017; 7(3): 134-56.