|Year : 2017 | Volume
| Issue : 2 | Page : 99-105
Homology modeling of Leishmania donovani enolase and its molecular interaction with novel inhibitors
Jay Prakash Mahato1, Sindhuprava Rana1, Maneesh Kumar2, Surendra Sarsaiya2
1 Department of Biotechnology, Sri Satya Sai University of Technology and Medical Sciences, Bhopal, Madhya Pradesh, India
2 Department of Biotechnology, College of Commerce, Arts and Science (Magadh University, Bodh Gaya), Patna, Bihar, India
|Date of Web Publication||23-Jun-2017|
Department of Biotechnology, Sri Satya Sai University of Technology and Medical Sciences, Bhopal, Madhya Pradesh
Source of Support: None, Conflict of Interest: None
| Abstract|| |
Introduction: The treatment of Indian tropical disease such as kala-azar is likely to be troublesome to the clinicians as AmpB- and miltefosine-resistant Leishmania donovani has been reported. The rationale behind designed a novel inhibitors of model of L. donovani enolase and performing a binding study with its inhibitors to gain details of the interaction between protein residues and ligand molecules. Methods and Materials: The L. donovani enolase model consists of two typical domains. The N-terminal one contains three-stranded antiparallel β-sheets, followed by six α-helices. The C-terminal domain composes of eleven-stranded mixed α/β-barrel with connectivity. The first α-helix within the C-terminal domain, H7, and the second β-strand, S7, of the barrel domain was arranged in an antiparallel fashion compared to all other α-helices and β-strands. The root-mean-square deviation between predicted model and template is 0.4 Å. The overall conformation of L. donovani enolase model is similar to those of Trypanosoma cruzi enolase and Streptococcus pneumoniae enolase crystal structures. Result: The key amino acid residues within the docking complex model involved in the interaction between model enolase structure and ligand molecule are Lys70, Asn165, Ala168, Asp17, and Asn213. Conclusion: Our theoretical prediction may lead to the establishment of prophylactic and therapeutic approaches for the treatment of kala-azar. This biomedical informatics analysis will help us to combat future kala-azar.
Keywords: Homology modeling, Leishmania donovani enolase, molecular docking, visceral leishmaniasis
|How to cite this article:|
Mahato JP, Rana S, Kumar M, Sarsaiya S. Homology modeling of Leishmania donovani enolase and its molecular interaction with novel inhibitors. J Pharm Bioall Sci 2017;9:99-105
|How to cite this URL:|
Mahato JP, Rana S, Kumar M, Sarsaiya S. Homology modeling of Leishmania donovani enolase and its molecular interaction with novel inhibitors. J Pharm Bioall Sci [serial online] 2017 [cited 2018 Mar 24];9:99-105. Available from: http://www.jpbsonline.org/text.asp?2017/9/2/99/208898
| Introduction|| |
Visceral leishmaniasis (VL) is also known as kala-azar, a systemic protozoan disease which has been caused by Leishmania donovani under the effect of phlebotomine sand flies., The serious VL infections are being occurred in both young adults and children that frequently need proper treatment. This pathogen causes chronic fever that leads to severe weight loss and anemia. It infects not only our pulmonary system of infected person but other organ systems also affected simultaneously., At some conditions, extrapulmonary complications may occur in association with L. donovani infection as a result of direct invasion, especially central nervous system (CNS) during encephalitis. Encephalitis manifestations are greater severity and have more clinical importance than the primary respiratory infection. Over the past decade, the incidence of this organism that invades CNS has increased dramatically., Patients who suffer from persistent L. donovani infection are a continuous transmission source to others with in community., In fact, a number of recently developed therapies are no longer effective in treatments due to resistance from previously existed drugs.,
Recently, there have been a number of reports indicating that enolase enhances the virulence of some pathogens. The enolase is a cytoplasmic metalloenzyme which participates in glycolytic pathways. It is also termed as phosphopyruvate hydratase. It belongs to lyases family. The multifunctional protein basically serves as a plasminogen receptor over many epithelial, endothelial, and hematopoietic cells. It has a great role in invasive autoimmune disorder which was found recently in humans. There are several different enolase isozymes present within all organisms. Structurally, the enzyme consists of dimers of three different polypeptide chains, i.e., α, β, and γ.,, The dimer of αα isozyme expresses in many tissues whereas ββ isozymes found exclusively in the muscular tissues. The γγ dimer is present in the neuroendocrine tissues and also in neurons and exclusively termed as neuron-specific enolase.,
The enzyme enolase which participates in crucial biological metabolisms, especially glycolysis and gluconeogenesis, is highly conserved. It has overall similar fold and identical catalytic residues in archaea, bacteria, and eukaryotes. The ubiquitous presences of metabolic enzyme enolase under conserved manner in different phyla clearly indicate the existence of enolase gene as common in ancestor. The gene has been potentially diversified by speciation of organisms and smartly duplicated within organisms. The enolase enzyme is actively engaged in such biological mechanisms in L. donovani to cause their virulent effect in humans. It is generally found at cell membrane of Leishmania that effectively plays infectious role host cells.Leishmania oligopeptidase B initially infects the host macrophages and then regulates an enolase level that further facilitates the parasite to enter into the macrophages. Enolase is a key enzyme, a part of novel class of surface protein responsible for the reversible conversion of 2-phosphoglycerate and phosphoenolpyruvate in glycolysis and gluconeogenesis for vital cellular function. The microbial enolase is captured by inhibitors of known compounds, and its subsequent conversion to plasmin provides a mechanism to augment virulence, favoring host tissue invasion.,,
For such action, enolase protein must be located on the surface of microbial pathogens., In light of the above findings, this work is an attempt to predict molecular interaction of L. donovani enolase with inhibitors of known compounds which would be useful for further investigation of the mechanism of L. donovani invasion to human brain.
| Materials and Methods|| |
The identification of the protein sequence
The primary protein sequence of L. donovani enolase sequence was obtained from GenBank (accession number: P75189). This protein sequence has been further entertained for computational analysis, molecular modeling, and predicted effective protein–ligand interaction with suitable ligand inhibitors. Modeling template was searched using BLAST. To analyze modeled complex of protein–protein interaction, sequence identity of 40%–50% between target and template may be required.
Multiple sequence alignment analysis
Multiple sequence alignment was performed using ClustalW. The ClustalW is a dynamic program widely used in identifying the sequence similarities between nucleotides and protein sequences. The atomic coordinates of Escherichia More Details coli enolase were retrieved from Protein Data Bank (PDB ID: 1E9I), which was most suitable for our work.
Three-dimensional structure prediction, model prediction, and protein stimulation
Molecular structures of Mycoplasma pneumoniae enolase were modeled using restraint-based modeling implemented in the program MODELLER. Several models were generated and then energy minimized using the molecular dynamics and simulation procedure CHARMM  in program MODELLER. The structural quality and stereochemistry evaluation was carried out by Ramachandran Plot through using the program PROCHECK., The structure was also analyzed by RAMPAGE that further checks the allowed and disallowed regions of amino acid residues in Ramachandran plot. The final model was selected based on stereochemical quality. The main-chain conformations for 98.80% amino acid residues were within the favored or allowed regions of the Ramachandran plot, and the overall G-factor was 0.11, indicating that molecular geometry of the model is of good quality. The selected model was then added Mg 2+ and further refined by energy minimization by the NAMD program (http://www.ks.uiuc.edu/Research/namd/) by 2,000 steps of conjugate gradient minimization until the energy gradient root-mean-square (RMS) was <0.05 kcal (mol Å)-1. Structural models were visualized by PyMol™ Molecular Graphics System version 0.97 (DeLano Scientific LLC, San Carlos, CA, USA, http://www.pymol.org). The VMD program was used to superimpose structure of L. donovani enolase model with crystal structures of enolases from Trypanosoma cruzi (PDB ID: 1E9I) and Streptococcus pneumoniae (PDB ID: 1W6T).L. donovani enolase model was docked to inhibitors of known compounds using Hex 4.5 (http://www.csd.abdn. ac.uk/hex/). The atomic coordinates of inhibitors of known compounds were retrieved from PDB (PDB ID: 1B2I). Automate energy minimization was applied to each docking solution. The Chimera program (http://www.cgl.ucsf.edu/chimera/) was exploited to identify hydrogen bonds using default parameters and geometric criteria described previously., Mark Gerstein's calc-surface program, which is implemented in the program Chimera, was used to calculate the solvent accessibility at the interface of L. donovani enolase and inhibitors of known compounds before and after docking.
| Results And Discussion|| |
Template identification and model quality crystal structures of enolases from many organisms, including those from bacteria, have already been determined and available in PDB., Based on sequence similarity analysis, L. donovani enolase shows 79% amino acid sequence identity with T. cruzi enolase [Table 1]. The high degree of sequence identities between the three-dimensional (3D) coordinate structure of 1E9I_Chain_A of enolase from T. cruzi enolase and other enolases with known structures was studied to find the suitable modeled structure of enolase of L. donovani. Practically, at this level of sequence identity, it is good enough to use crystallographic structures of T. cruzi enolase as a template, to obtain high-quality alignment for structure prediction by homology modeling. A T. cruzi enolase crystal structure 4G7F_A  was specifically selected on the basis of BLAST result and was utilized as a template for L. donovani enolase structure modeling. Structural models for L. donovani enolase were built by dynamic MODELLER program  based on their atomic coordinates of 1E9I and were then energy minimized with the help of other computational programs.
|Table 1: Different 3D coordinate structure of PDB, with Maximum dock scores, query cover, E-value and sequence identities|
Click here to view
The model with the lowest discrete optimized protein energy  score (−348.7318), which was considered as the best one, was selected and subjected to quality evaluation. The PROCHECK Ramachandran plot analysis shows that the main-chain conformations for 98.80% of amino acid residues are within the most favored, 1.4% allowed regions and 0.2% outlier [Figure 1]. The G-factors, indicating the quality of covalent and bond-angle distance, were −0.07 for dihedrals, −0.22 for covalent, and overall −0.11. The overall main- and side-chain parameters, as evaluated by PROCHECK, are all very favorable. The comparable Ramachandran plot characteristics and G-factors confirm the quality of predicted model. VERIFY 3D of model protein is 90.44% of the residues which had an average 3D–1D score >=0.2 which pass at least 80% of the amino acids that have scored >=0.2 in the 3D/1D profile. Overall quality factor of 94.537 of ERRAT plot [Figure 2] and ProSA result have shown that the model has a good quality (Z-Score: −9.78) [Figure 3]. The L. donovani enolase model consists of typical two domains. The N-terminal one contains three-stranded antiparallel β-sheets, followed by six α-helices. The C-terminal domain composes of eleven-stranded mixed α/β-barrel with connectivity. The first α-helix within the C-terminal domain, H7, and the second β-strand, S7, of the barrel domain was arranged in an antiparallel fashion compared to all other α-helices and β-strands [Figure 4]. The RMS deviation between predicted model and template is 0.4 Å (Angstrom). The overall conformation of L. donovani enolase model is similar to those of T. cruzi enolase crystal structure , as observed by the superposition analysis (0.04Å). The structural model of L. donovani enolase is shown in [Figure 4]. The secondary structure was predicted and shown in [Figure 5]. The Mg 2+, a metal ion cofactor, was encircled by Asp256, Glu310, and Asp337 which located in active site of enolase [Figure 6]. Although Mg 2+ is required for catalytic activity of the enzyme, it may also play a role in stabilizing enolase conformation.
|Figure 1: Ramachandran Plots of model protein showing, number of residues in favored region: 419 (98.4%); number of residues in allowed region: 6 (1.4%); number of residues in outlier region: 1 (0.2%)|
Click here to view
|Figure 6: The best-scoring compounds which show the pindolol structure of highest scoring compounds|
Click here to view
Interaction of Leishmania donovani enolase and inhibitors of known compounds
Based on rigid-body docking using HEX 4.5, both proteins were analyzed for shape complementary, hydrophobic effects resulting from a decrease in the solvent accessible surface, and electrostatic interactions. The homology model of the hypothetical protein (enolase) shows alpha (α), beta (β) and flexible loops. [Figure 4].
These residues were determined based on intermolecular hydrogen bond lengths of amino acid residues interacted between inhibitors of known compounds and L. donovani enolase. All hydrogen bond lengths appear to be shorter than 3.4 Å. This suggests that hydrogen bonds can be plausibly formed. The docking result indicated that the complex could be stabilized by hydrogen bonding. Electrostatic potential surface area showed that ten amino acid residues of L. donovani enolase appeared to be available for making contact with pindolol [Figure 6] and [Figure 7].
|Figure 7: Ligplus two-dimensional structure visualization and final docked structure (pindolol with enolase structure)|
Click here to view
These also include Lys99 and Glu94 and around the binding site are Glu 352, Val 325, Cys 93, Thr 98, and Lys 90 [Figure 8]. The positive charge residues located at the opposite end of the binding pocket. Considering L. donovani enolase, a significant change of accessible surface area of Lys 90 occurred. Noticeably, hydrogen bonding was observed on this residue. For inhibitors of known compounds in the complex, the model showed large decrease in the accessible surface area involving residues Lys 99 and Glu 94. Some of these appeared to form hydrogen bonds with corresponding residues of L. donovani enolase. The results suggest that model of the interaction complex between L. donovani enolase and inhibitors of known compounds can be practicable. The 2D view of enolase with pindolol gives the suitable information about the hydrogen bond interaction [Figure 8]. In addition, the proposed interaction between inhibitors of known compounds and L. donovani enolase agreed with previous experimental investigations.,, The interaction between library of inhibitors of known compounds and L. donovani enolase proposed in this study is useful for understanding the possible mechanism used by L. donovani to invade human brain tissue. For instance, the interaction between inhibitors of known compounds and L. donovani enolase might provide a vehicle for targeting cells. This line of work may lead to insight into host-pathogen interaction and provide valuable information for prophylactic strategies in combating infections at a very early stage.
|Figure 8: Two-dimensional structure interaction studies between best-scoring compound and three-dimensional enolase protein structure of Leishmania donovani|
Click here to view
| Conclusion|| |
The rationale in building a L. donovani enolase model and performing a binding study with inhibitors of known compounds is to gain details of interaction between the ligand and protein. L. donovani enolase modeling was conducted using homology modeling. Comparison of the obtained model with experimentally derived crystal structures of T. cruzi enolase and S. pneumoniae enolase revealed that they were all basically similar. The docking studies revealed the important residues involving in the interaction of L. donovani enolase with inhibitors of known compounds. Analyses of the interaction model between pindolol and L. donovani enolase, based on distances of hydrogen bonds, changes of solvent accessible surface, and electrostatic potentials, showed that this binding complex was reliable. Our theoretical prediction may lead to the establishment of prophylactic and therapeutic approaches.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Croft SL, Sundar S, Fairlamb AH. Drug resistance in leishmaniasis. Clin Microbiol Rev 2006;19:111-26.
Pereira BA, Silva FS, Rebello KM, Marín-Villa M, Traub-Cseko YM, Andrade TC, et al
. In silico
predicted epitopes from the COOH-terminal extension of cysteine proteinase B inducing distinct immune responses during leishmania (leishmania) amazonensis experimental murine infection. BMC Immunol 2011;12:44.
Daifalla NS, Bayih AG, Gedamu L. Immunogenicity of Leishmania donovani
iron superoxide dismutase B1 and peroxidoxin 4 in BALB/c mice: The contribution of Toll-like receptor agonists as adjuvant. Exp Parasitol 2011;129:292-8.
Sahoo GC, Dikhit MR, Rani M, Das P
. Homology modeling and functional analysis of LPG2 protein of Leishmania strains
. J Proteomics Bioinformatics 2009;2:32-50.
Desjeux P. Leishmaniasis. Public health aspects and control. Clin Dermatol 1996;14:417-23.
Downing T, Imamura H, Decuypere S, Clark TG, Coombs GH, Cotton JA, et al.
Whole genome sequencing of multiple Leishmania donovani
clinical isolates provides insights into population structure and mechanisms of drug resistance. Genome Res 2011;21:2143-56.
Coelho M, Leite A, Revés A, Miranda C, Serra I, Brandão T, et al. Mycoplasma pneumoniae
causing nervous system lesion and SIADH in the absence of pneumonia. Clin Neurol Neurosurg 2004;106:129-31.
Bitnun A, Ford-Jones EL, Petric M, MacGregor D, Heurter H, Nelson S, et al.
Acute childhood encephalitis and Mycoplasma pneumoniae
. Clin Infect Dis 2001;32:1674-84.
Suzuki S, Yamazaki T, Narita M, Okazaki N, Suzuki I, Andoh T, et al.
Clinical evaluation of macrolide-resistant Mycoplasma pneumoniae
. Antimicrob Agents Chemother 2006;50:709-12.
Jayaswal PK, Rani M, Yadav CP, Dikhit MR, Sahoo GC, Das P, et al
. Molecular modeling of cathepsin B protein in different Leishmania strains
. J Integr OMICS 2011;1:115-23.
Pancholi V, Chhatwal GS. Housekeeping enzymes as virulence factors for pathogens. Int J Med Microbiol 2003;293:391-401.
Pancholi V. Multifunctional alpha-enolase: Its role in diseases. Cell Mol Life Sci 2001;58:902-20.
Rider CC, Taylor CB. Evidence for a new form of enolase in rat brain. Biochem Biophys Res Commun 1975;66:814-20.
Rider CC, Taylor CB. Enolase isoenzymes. II. Hybridization studies, developmental and phylogenetic aspects. Biochim Biophys Acta 1975;405:175-87.
Marangos PJ, Schmechel DE. Neuron specific enolase, a clinically useful marker for neurons and neuroendocrine cells. Annu Rev Neurosci 1987;10:269-95.
Marangos P, Polak J, Pearse A. Neuron-specific enolase: A probe for neurons and neuroendocrine cells. Trends Neurosci 1982;5:193-6.
Day IN, Peshavaria M, Quinn GB. A differential molecular clock in enolase isoprotein evolution. J Mol Evol 1993;36:599-601.
Avilán L, Gualdrón-López M, Quiñones W, González-González L, Hannaert V, Michels PA, et al.
Enolase: A key player in the metabolism and a probable virulence factor of trypanosomatid parasites-perspectives for its use as a therapeutic target. Enzyme Res 2011;2011:932549.
Ghosh AK, Jacobs-Lorena MM. Surface-expressed enolases of Plasmodium and other pathogens. Memorias Inst Oswaldo Cruz 2011;106:85-90.
Swenerton RK, Zhang S, Sajid M, Medzihradszky KF, Craik CS, Kelly BL, et al.
The oligopeptidase B of Leishmania regulates parasite enolase and immune evasion. J Biol Chem 2011;286:429-40.
Bergmann S, Rohde M, Chhatwal GS, Hammerschmidt S. Alpha-enolase of Streptococcus pneumoniae
is a plasmin(ogen)-binding protein displayed on the bacterial cell surface. Mol Microbiol 2001;40:1273-87.
Fox D, Smulian AG. Plasminogen-binding activity of enolase in the opportunistic pathogen Pneumocystis carinii
. Med Mycol 2001;39:495-507.
Ge J, Catt DM, Gregory RL. Streptococcus mutans
surface alpha-enolase binds salivary mucin MG2 and human plasminogen. Infect Immun 2004;72:6748-52.
Pancholi V, Fischetti VA. Alpha-enolase, a novel strong plasmin(ogen) binding protein on the surface of pathogenic streptococci. J Biol Chem 1998;273:14503-15.
Thompson JD, Gibson TJ, Higgins DG. Multiple sequence alignment using ClustalW and ClustalX. Curr Protoc Bioinformatics [Chapter 2:Unit 2.3].
Sali A, Potterton L, Yuan F, van Vlijmen H, Karplus M. Evaluation of comparative protein modeling by MODELLER. Proteins 1995;23:318-26.
Sali A, Blundell T. Comparative protein modelling by satisfaction of spatial restraints. J Mol Bio 1993;234:779-815.
Morris AL, MacArthur MW, Hutchinson EG, Thornton JM. Stereochemical quality of protein structure coordinates. Proteins 1992;12:345-64.
Laskowski RA, Rullmannn JA, MacArthur MW, Kaptein R, Thornton JM. AQUA and PROCHECK-NMR: Programs for checking the quality of protein structures solved by NMR. J Biomol NMR 1996;8:477-86.
Kühnel K, Luisi BF. Crystal structure of the Escherichia coli
RNA degradosome component enolase. J Mol Biol 2001;313:583-92.
Marti DN, Schaller J, Llinás M. Solution structure and dynamics of the plasminogen kringle 2-AMCHA complex: 31-helix in homologous domains. Biochemistry 1999;38:15741-55.
Mills JE, Dean PM. Three-dimensional hydrogen-bond geometry and probability information from a crystal survey. J Comput Aided Mol Des 1996;10:607-22.
Jamal QM, Dhasmana A, Lohani M, Firdaus S, Ansari MY, Sahoo GC, Haque S. Binding pattern elucidation of NNK and NNAL cigarette smoke carcinogens with NER pathway enzymes: An onco- informatics study. Asian Pac J Cancer Prev 2015;16:5311-7.
Shen MY, Sali A. Statistical potential for assessment and prediction of protein structures. Protein Sci 2006;15:2507-24.
Redlitz A, Fowler BJ, Plow EF, Miles LA. The role of an enolase-related molecule in plasminogen binding to cells. Eur J Biochem 1995;227:407-15.
[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6], [Figure 7], [Figure 8]