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Molecular Docking Studies of Wide Spectrum Targets in Staphylococ
Advanced Techniques in Biology & Medicine

Advanced Techniques in Biology & Medicine
Open Access

ISSN: 2379-1764

+44 1223 790975

Research Article - (2014) Volume 2, Issue 1

Molecular Docking Studies of Wide Spectrum Targets in Staphylococcus aureus - An Aim towards Finding Potent Inhibitors

Balaji SR*, Gupta KK, Anusha P and Raveena P
NTHRYS Biotech labs, Hyderabad, Andhra Pradesh, India
*Corresponding Author: Balaji SR, Director & Research Head, NTHRYS Biotech labs, Hyderabad Andhra Pradesh, India, Tel: 040-276212 48 Email:

Abstract

Methicillin-resistant staphylococcus aureus (MRSA) is a bacterium that is evolving towards adaptive changes to certain antibiotics like methicillin, oxacillin, penicillin, and amoxicillin. It is a communicable and most rapidly spreading disease worldwide. It is reported that MRSA is becoming common among children in intensive care units. This disease comes under Emerging Infectious Disease (EID). In this paper, MRSA proteome screening is done and Drug/vaccine targets are proposed based on its essentiality to the pathogen and non-homology with human proteome. Targets validation is done so that its targeting must not affect human proteome and vital pathways. Those targets which has no structure, structure prediction and validation is done and important epitopes and ligands are proposed on suitable targets.

Keywords: MRSA, EPA, Infection, Targets, Epitope, Drug, Pathways

Introduction

MRSA spreads infection through blood and skin. The former infection is of severe kind. But skin infections are the most common one. Skin infection appears as pustules or boils which generally form at areas of the body covered by hair like back of neck, groin, buttock, armpit, beard area of men. The degree of symptoms depends on the stage of infection. The persons who meet MRSA patients are at Risk of Acquiring MRSA Infections. In hospitals, patients has more risk of getting MRSA through catheters inserted into the skin and those patients who has undergo medical procedures like surgery. According to drug bank only three antibiotics namely Arbekacin, Meticillin and Linezolid, are approved for the treatment of MRSA. Drug targets are limited and there is an urgent need for the discovery of novel drug targets. Apart from drug target, we also need Good ligand formulation that would act as a potent inhibitors to the novel targets without affecting human proteome (Figure 1).

advanced-techniqes-MRSA

Figure 1: MRSA.

Any contaminated surfaces is the source of many kind of infections, MRSA is one of them. Mishandled Hospital procedures can leave immune compromised patients vulnerable to MRSA. Environmental hygienic conditions are the source of good physical and mental health. It is reported that U.S. Environmental Protection Agency (EPA) labeled Cleaners, disinfectants and sanitizers must be used in order to spread all kinds of infections.

Materials and Methods

Staphylococcus aureus proteome screening

Prokaryotic sequence homology analysis tool (PSAT) [1] is used for finding conserved patches in all strains of Staphylococcus aureus and Streptococcus, as both are heavily involved in skin infections. Yersinia Pestis CO 92 is taken as reference strain. Following parameters is considered for finding conserved genes in these selected pathogens: BLAST alignment score thresholds for finding gene homologs: e-value < 10; bit score > 20; % identity > 10

DEG BLAST

All the conserved genes are checked for their key role to pathogens. Database of essential genes (DEG) [2] is used to screen all the conserved genes for their vitality to the selected pathogens. This tool gives hit if query gene is showing any significant similarity with pathogen’s growth, reproduction and survival.

NCBI Homo sapiens (human) protein BLAST

Those conserved genes which are giving hits in DEG blast were further checked in NCBI Homo sapiens (human) Protein BLAST [3]. If we are proposing target on pathogen proteome then those conserved genes must be checked if there is any similarity with human proteome because drug/vaccine targeting must not affect any of the human protein.

Annotation and pathways analysis

The conserved genes which are showing no significant similarity checked in NCBI Homo sapiens (human) Protein BLAST was checked for annotation and their involvement in any crucial pathway. Annotation was done by using CELLO [4] and PSORTB [5]. Pathway analysis was done in KEGG genes database [6].

Target proposal and structure prediction

Based on the pathway study, its essentiality and non-homolog to human proteome property of Targets, they are proposed for drug targets and epitope design. Those targets whose have no protein structure was modeled by Protein Homology/analogy Recognition Engine (PHYRE) [7].

Structure validation

These Target proteins were optimized from KOBAMIN [8] and Galaxy WEB server [9] and validated in Rampage [10] and Erratplot [11], the former check the stereo chemical properties of modeled structures and latter one analyze the non-bonded interactions.

Epitope design

Transmembrane region of proposed target was predicted with TMHMM [12]. After that, Prediction of B cell epitopes with BCpred (cutoff 0.8, 20-mer epitopes) [13]. Generally B cell epitope sequences are surface-exposed of corresponding proteins. T cell epitopes from propred [14], propred 1[15], MHCpred [16] and T epitope designer [17]. Those T cell epitopes were considered that is part of B cell epitopes and lies at Transmembrane region according to TMHMM. Predict those epitope's antigenicity from vaxijen [18]. Finally, epitopes that bound more than 13 MHC molecules in ProPred and ProPred-I with less than 100 nM IC50 for DRB1*0101 in MHCPred v2.0 and that bound >=80% of HLA molecules in T-epitope designer were selected. Proteins that were antigenic according to Vaxijen (threshold=0.4, ACC output) score above than 0.5 in VaxiJen were selected.

Virtual screening of ligands against target proteins

All the target proteins were screened with 12 million drug-like ZINC12 + 6507 DrugBank drugs from FINDSITE-COMB [19]. Less characterized ZINC DATABASE molecules were selected for druglikeness analysis from FAF- Drugs2 [20]. FAF results were cross checked with Osiris property explorer [21]. We modified most of the ZINC ID ligands to satify druglikeness and ADME properties. We proposed those novel ligands for each target which is following all the drug-likeness properties.

Molecular docking studies of ADMET following ligands with target proteins

ADMET following ligands were docked with their corresponding target in particular coordinate predicted by Pocket-Finder [22] using Molegro Virtual Docker [23]. All the docking parameters and images were finalized for each ligand-protein docking.

Results

Six targets are finalized for further study (Table 1). Protein accession number, locus tags are the unique identifier of insilico discovered targets. All are essential for pathogens as inferred from DEG Blast results. kdpA, opp-1B and icac structures are not available. Therefore, they must be modeled for further molecular docking studies. Pathways and Cellular localization results are shown in Table 2.

Locus Tag Protein ID Target name Structure/ Related (yes/no) DEG hits (yes/no)
SAR2165 YP_041527.1 kdpA no yes
SAR2553 YP_041904.1 Opp-1B no yes
SAR2750 YP_042088.1 icac no yes
SAR0118 YP_039582.1 sirA Yes (Related: pdbid: 3MWF) yes
SAR2537 YP_041888.1 opuCB Yes (Related: pdbid: 3D31 C) yes
SAS0639 YP_042767.1 Hypothetical protein Yes (Related: pdbid: 3MLV L) yes

Table 1: Final Targets based on subtractive proteome screening.

Target name CELLO PSORTB Pathway
kdpA Membrane Cytoplasmic membrane Two-component system
Opp-1B Membrane Cytoplasmic membrane ABC transporters
icac Membrane Cytoplasmic membrane No pathway
sirA Periplasmic Cytoplasmic membrane ABC transporters
opuCB Inner membrane Cytoplasmic membrane ABC transporters
Hypothetical protein Membrane Cytoplasmic membrane No pathway

Table 2: Cellular localization and pathways of target proteins.

All the target proteins are suitable for epitope design as all are predicted to be localized in Membrane from CELLO and PSORTB. They can also be considered for potent drug target. Function of Target Protein is very important for Target validation. Functional annotation is done in Table 3. Epitope design on the target proteins are given in Table 4. The final epitopes following all the criteria from target proteins are given in Table 5. The structural information of refined modeled target proteins from erratplot and rampage are shown in Tables 6 and 7. The coordinates of best pockets for each target proteins are given in Table 8.

Target name Function Molecular Function
kdpA One of the components of the high-affinity ATP-driven potassium transport (or KDP) system, which catalyzes the hydrolysis of ATP coupled with the exchange of hydrogen and potassium ions ATP binding, potassium-transporting ATPase activity
Opp-1B Oligopeptide transporter putative membrane permease domain transporter activity
icac Presumably involved in the export of the biofilm adhesin polysaccharide poly-beta-1,6-N-acetyl-D-glucosamine (PNAG, also referred to as PIA) across the cell membrane transferase activity, transferring acyl groups other than amino-acyl groups
sirA Iron-regulated ABC transporter siderophore-binding protein SirA Iron-regulated ABC transporter siderophore-binding protein SirA
opuCB Probable glycine betaine/carnitine/choline ABC transporter opuCB transporter activity
Hypothetical protein Uncharacterized protein conserved in bacteria [Function unknown] Function unknown

Table 3: Function annotation of target proteins.

Target name TMHMM Bcpred
Position-Bcell epitope (confidance value)
Propred 1
(Sorted in descending score)
Propred MHCpred
kdpA Outside:
1-3
86-126
192-244
303-324
373-375
438-483
549-558
TMhelix:
4-26
63-85
127-149
169-191
245-267
280-302
325-347
354-372
376-398
415-437
484-506
526-548
462-AAANNGSGFEGLKDDTTFWN (0.97)
335-
FTVITTAFTTGSVNNMHDSL
(0.92)
77-LLIVQQWLFLNPNHNLNQSI(0.91)
434
AFMIPGASESITNPSFHGIS (0.89)
LIVQQWLFL
NNGSGFEGL
MIPGASESI
AAANNGSGF
LLIVQQWLF
IPGASESIT
FTVITTAFT
TVITTAFTT
FMIPGASES
ITTAFTTGS
VITTAFTTG
ANNGSGFEG
IVQQWLFLN
FMIPGASES (10/51) FMIPGASES - 72% MHC alleles  are Binding.  
Opp-1B outside
32-106
162-175
257-275
TMhelix:
9-31
107-126
139-161
176-193
234-256
276-298
80- NFGTSYITGDPVAERIGPAF (0.99) 41- AQGTPNVTPELIAETNEKYG (0.91) GTPNNTPEL
TPNVTPELI
TSYITGDPV
GTSYITGDP
AQGTPNVTP
FGTSYITGD
NFGTSYITG
QGTPNVTPE
YITGDPV (8/51) YITGDPV -100% MHC alleles are Binding.
Icac outside 30-43
102-115
168-186
234-242
292-305
Tmhelix:
7-29
44-66
79-101
116-138
145-167
187-204
211-233
243-262
269-291
306-328
165- YFTNNTAFHDTVLHYYPLSE (0.7) NNTAFHDTV
FTNNTAFHD
TNNTAFHDT
YFTNNTAFH (14/51) VLHYYPLSE (8/51) FTNNTAFH
100% MHC alleles are Binding.
sirA Outside:
Whole protein
20-GCSGNSNKQSSDSKDKETTS (0.99)
70-LGVKPVGAVESWTQKPKFEY (0.99)
149-KDTTKLMGKALGKEKEAEDL (0.97)
269-LVKKTESEWTSSKEWKNLDA (0.97)
43-AMGTTEIKGKPKRVVTLYQG (0.96)
91-KNDLKDTKIVGQEPAPNLEE (0.94)
177-AAFQKDAKAKYKDAWPLKAS (0.84)
KNDLKDTKI
TTKLMGKAL
FQKDAKAKY
LGVKPVGAV
AAFQKDAKA
KPVGGAVESW
NDLKDTKIV
TTEIKGKPK
KDTTKLMGK
KKTESEWTS
LVKKTESEW
NDLKDTKIV
CSGGNSNKQS
KTESEWTSS
GCSGGNSNKQ
SGNSNNQSS
DTTKLMGKA
MGTTEIKGKP
AFQKDAKAK
GVKPGAVE
DLKDTKIVG
VKPAVES
AMGTTEIKG
GTTEIKGKP
QKDAKAKYK
VKKTESEWT
GNSNKQSSD
TKLMGKALG
LGVKPVGAV (4/51) VKPVGAVES (10/51) FQKDAKAKY (8/51)   LGVKPVGAV
68% MHC alleles are Binding. VKPVGAVES
72% MHC alleles are Binding. FQKDAKAKY
80% MHC alleles are Binding.
opuCB TMHelix 114  135               
115  134               
144  170               
145  168               
174  189               
175  188               
223  248               
225  235               
238  243               
268  292               
269   290
155- YVGAGGLGDFIFNGLNLYDP (0.9)   YVGAGGLGD
VGAGGLGDF
AGGLGDFIF
GAGGLGDFI
YVGAGGLGD (8/51) YVGAGGLGD 72% MHC alleles are Binding.
Hypothetical protein TMHelix 64 102  
65 102  
113 131
115 129
150 181
151 180
194 222
195 202
205 221
No B cell epitope predicted. No common T cell epitope No common T cell epitope No epitope binding affinity prediction.

Table 4: Epitope designing on target proteins.

Target name Final Predicted epitopes Vaxijen score (>0.5)
kdpA FMIPGASES 0.4000
Opp-1B YITGDPV 1.2802
icac FTNNTAFH 0.3954
sirA LGVKPVGAV VKPVGAVES FQKDAKAKY 0.0443
0.0286
1.9244
opuCB YVGAGGLGD 1.5458
Hypothetical protein No predicted epitope No predicted epitope

Note: red color indicates non promising peptides whereas green color indicates promising one.

Table 5: Final Epitopes on target proteins.

Target Name/Protein ID Ramachandran parameters Errat plot quality factor
kdpA/ YP_041527.1 RMSD 2.290 78.46
Clash score 20.9
Poor
rotamers
3.5
Rama favored 90.6
Opp-1B/ YP_041904.1 RMSD 8.941 82.7
Clash score 15.7
Poor
rotamers
0.4
Rama favored 94.2
Icac/ YP_042088.1 RMSD 5.597 93.59
Clash score 17.4
Poor
rotamers
2.2
Rama favored 95.7
sirA/ YP_039582.1 RMSD 4.377 86.00
Clash score 8.6
Poor
rotamers
1.0
Rama favored 98.2
opuCB/ YP_041888.1 RMSD 5.944 94.44
Clash score 10.9
Poor
rotamers
1.2
Rama favored 96.2
Hypothetical protein/ YP_042767.1 RMSD 0.749 77.03
Clash score 17.0
Poor
rotamers
1.0
Rama favored 91.2

Table 6: Structural information of target proteins.

advanced-techniqes-Modeled-proteins

Table 7: Modeled proteins.

advanced-techniqes-Best-pockets

Table 8: Best pockets and its coordinates.

References

  1. Fong C, Rohmer L, Radey M, Wasnick M, Brittnacher MJ (2008) PSAT: a web tool to compare genomic neighborhoods of multiple prokaryotic genomes. BMC Bioinformatics 9: 170.
  2. Zhang R, Lin Y (2009) DEG 5.0, a database of essential genes in both prokaryotes and eukaryotes. Nucleic Acids Res 37: D455-D458.
  3. Yu CS, Lin CJ, Hwang JK (2004) Predicting subcellular localization of proteins for Gram-negative bacteria by support vector machines based on n-peptide compositions. Protein Sci 13: 1402-1406.
  4. Yu NY, Wagner JR, Laird MR, Melli G, Rey S, et al. (2010) PSORTb 3.0: Improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes. Bioinformatics 26: 1608-1615.
  5. Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M (2012) KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res 40: D109-D114.
  6. Kelley LA, Sternberg MJ (2009) Protein structure prediction on the Web: a case study using the Phyre server. Nat Protoc 4: 363-371.
  7. Rodrigues JP, Levitt M, Chopra G (2012) KoBaMIN: a knowledge-based minimization web server for protein structure refinement. Nucleic Acids Res 40: W323-W328.
  8. Ko J, Park H, Heo L, Seok C (2012) GalaxyWEB server for protein structure prediction and refinement. Nucleic Acids Res 40: W294-W297.
  9. Colovos C, Yeates TO (1993) Verification of protein structures: patterns of nonbonded atomic interactions. Protein Sci 2: 1511-1519.
  10. Chen J, Liu H, Yang J, Chou KC (2007) Prediction of linear B-cell epitopes using amino acid pair antigenicity scale. Amino Acids 33: 423-428.
  11. Singh H, Raghava GP (2003) ProPred1: prediction of promiscuous MHC Class-I binding sites. Bioinformatics 19: 1009-1014.
  12. Singh H, Raghava GP (2001) ProPred: prediction of HLA-DR binding sites. Bioinformatics 17: 1236-1237.
  13. Guan P, Doytchinova IA, Zygouri C, Flower DR (2003) MHCPred: bringing a quantitative dimension to the online prediction of MHC binding. Appl Bioinformatics 2: 63-66.
  14. Kangueane P, Sakharkar MK (2005) T-Epitope Designer: A HLA-peptide binding prediction server. Bioinformation 1: 21-24.
  15. Doytchinova IA, Flower DR (2007) VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinformatics 8: 4.
  16. Zhou H, Skolnick J (2013) FINDSITE(comb): a threading/structure-based, proteomic-scale virtual ligand screening approach. J Chem Inf Model 53: 230-240.
  17. Lagorce D, Sperandio O, Galons H, Miteva MA, Villoutreix BO (2008) FAF-Drugs2: free ADME/tox filtering tool to assist drug discovery and chemical biology projects. BMC Bioinformatics 9: 396.
  18. Hendlich M, Rippmann F, Barnickel G (1997) LIGSITE: automatic and efficient detection of potential small molecule-binding sites in proteins. J Mol Graph Model 15: 359-363, 389.
  19. Thomsen R, Christensen MH (2006) MolDock: a new technique for high-accuracy molecular docking. J Med Chem 49: 3315-3321.
Citation: Balaji SR, Gupta KK, Anusha P, Raveena P (2014) Molecular Docking Studies of Wide Spectrum Targets in Staphylococcus aureus - An Aim towards Finding Potent Inhibitors. Adv Tech Biol Med 2:115.

Copyright: © 2014 Balaji SR. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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