Journal of Pharmacovigilance and Pharmacotherapeutics (ISSN: 2688-6464)

Article / research article

"Homology Modeling, Docking and Structure Based Virtual Screening Against AcrB Efflux Pump of Multidrug Resistant Salmonella"

 Vijaya Bhaskar Baki1, Muni Chandra Babu Tirumalsetty1, Vijaya Kumar Bathala2, Rajendra Wudayagiri1*

1Division of Bioinformatics, Department of Zoology, Sri Venkateswara University, Tirupati, A.P, India

2Department of Biotechnology, Sri Venkateswara University, Tirupati, A.P, India

*Corresponding author: Rajendra Wudayagiri, Department of Zoology, Sri Venkateswara University, Titurpati-517502, India. Tel: +919849667236; Email: rajendraw2k@yahoo.co.in ; vijaybio08@gmail.com

Received Date: 20 December, 2017; Accepted Date: 11 January, 2018; Published Date:  17 January, 2018

1.       Abstract

AcrB efflux pump is an inner transmembrane protein that belong to the RND transporters family. AcrB plays an important role in the extrusion of diverse structural compounds from the cytoplasm and periplasmic region to the exterior of the gram negative bacterial cell and considered as potential drug target for the discovery of efflux pump inhibitors (EPIs). In the present study, we constructed AcrB model based on the crystal structure of AcrB from E. coli (PDBID: 2j8s). Structural analysis revealed that AcrB is mainly composed of three structural domains such as transmembrane (TM) domain, pore domain and docking domain. TM domain consists of 12 TM helices which traverse the inner lipid bi layer. Pore domain is present in the periplasm region and contains four subdomains viz., PN1, PN2, PC1 and PC2, and formed distal and proximal binding pockets. Molecular docking study documented that tetracycline, cefsulodin, penicillin, ceftazamid, lincomycin, chloramphenicol, meropenum and aminoglycoside have shown significant interactions with distal pocket whereas carbenicillin has explicated towards proximal pocket. Moreover, virtual screening using ZINC and PubChem database against AcrB that enlighten ten potent lead molecules: ZINC28475998 (-11.2), ZINC28476198 (-11.1), ZINC28477171 (-10.9), ZINC28475792 (-10.6), ZINC27182211 (-9.2), CID11143966 (-9.6), CID44265715 (-9.5), CID102503 (-9.1), CID11902980 (-9.0) and CID22845248 (-9.0) with greater binding affinities and displayed marked interactions with decisive residues of proximal and distal binding pockets. Accordingly, these lead molecules could be helpful for the development of efflux pump inhibitors and might restore the conventional antibiotics longer time within the bacteria.

2.       Keywords: AcrB Efflux Pump; Antibiotics; Homology Modeling; PaβN; Virtual Screening  

1.       Introduction

Salmonella enterica serovar Typhimurium is gram negative bacteria and belongs to the Enterococci sps which causes serious health problems such as gastroenteritis, bacteremia and typhoid fever. Multidrug resistance Salmonella out breaks had been found in United States [1,2] United Kingdom [3,4] in poultry, beef and swine [5-8]. Prevalence of multidrug resistance to numerous antibiotics reported to occur due to gene mutation in target protein, target modification, drug inactivation, target bypassing, horizontal gene transfers and dysfunction of efflux pumps [9,10]. Efflux pumps are majorly associated with resistance of both gram negative and gram positive bacteria and categorized into five types such as ATP binding cassette (ABC), Resistance Nodulation Division family (RND), Major Facilitator Super family (MFS), Small Multidrug Resistance (SMR) protein family (SMR) and Multidrug and Toxic Compound Extrusion (MATE). Among RND transporters are highly expressed in several gram-negative bacteria such as E. coli, Pseudomonas aeruginosa, Klebsiella pneumonia, Acinetobacter baumanni and Enterococcus sps [11]. However, Complete genome sequencing of Salmonella has shown the existence of nine efflux pumps that belongs to the various transporter family such as ABC (macAB), RND (AcrAB, AcrCD, AcrEF, mdsABC, mdsABC), MFS (EmrAB, mdgA) and MATE (mdtk) [12,13]. RND efflux pump play an important role especially in gram negative bacterial pathogens and to show broad spectrum substrate specificity to a wide array of diverse compounds such as dyes (acriflavin and ethidium), antibiotics (macrolides, fluoroquinolones, β-lactams, tetracycins, chloramphenicol, rifampin, novobiocin, fusidic acid), detergents (bile salts, tritonX-100, SDS) and some organic solvents (hexane, heptanes, octane and nonane or cyclohexane) [14,15]. RND transporter is huge complex that composed with three proteins such as inner transmembrane protein, periplasmic adaptor protein and outer transmembrane protein and it traverse both inner and outer membrane lipid bilayer. Inner transmembrane protein has several binding pockets which allow the binding of numerous diverse compounds by obtaining the energy from the proton translocation motive force, this phenomenon is highly conserved in bacteria. The fabulous function of RND pump clearly indicates that its acts as attractive broad spectrum molecular target for designing of novel efflux pump inhibitors. RND efflux pump may be thwarted in several ways such as altering the regulatory mechanism, inhibiting the functional assembly of multicomponent proteins, inhibiting the proton translocation mechanism, blocking the outer membrane protein, the functional group modification of existing drugs and altered sensitivity to the competitive and non-competitive inhibitors. So far, a little number of Efflux Pump Inhibitors (EPIs) have been discovered to deny the efflux pump activity such as globomycin by blocking of functional assembly of tripartite complex, carbonyl cyanide m-chlorophenyl hydrazone (cccp) and potassium cyanide which collapse the proton motive force of the pump and PAβN itself is a substrate of efflux pumps and act as competitive inhibitor. Generally, PAβN is used as EPI to treat gram negative bacteria and restore the activity of various antibiotics (chloramphenicol, macrolide, oxazolidinones and rifampicin). Keeping in view, the present study is undertaken to explicate the binding properties of various antibiotics and the novel potent EPIs for clinical management of gram negative bacterial infections.

2.       Materials and Methods

4.1.  Prediction of Secondary Structure and TM α-helices

Protein sequence of AcrB (Acc No: Q8ZRA7) was retrieved from SWISSPROT and Physicochemical properties such as Aliphatic index, Grand average of hydropathy (GRAVY) and Theoretical pI value were calculated using Protparam Secondary structural elements such as alpha helix, extended sheets, beta turns and random coils were predicted using different servers Viz., SOPMA [16], GOR4 [17] and Chou & Fosman. TM α-helices were predicted using different servers such as TMHMM [18], TMpred [19], SOSUI [20] and HMMTOP [21] to confirm origin and end of the helices.

4.2.Protein Modeling and Validation

Initially, template structure was selected by performing BLASTp search against Brookhaven Protein Data Bank (PDB) on the basis of sequence identity with high score, less e-value, highest resolution and R-factor. Ensuing, the coordinates for the query structure were assigned from template structure by means of pairwise sequence alignment using ClustalX [22]. Subsequently, 3D structures of AcrB were built by using MODELLER 9.14 [23]. Ensuing, the model has lowest DOPE Score was taken and amended irregular secondary structures such as α-helices, β-strands and superfluous loops by adopting MODLOOP Server [24].  Then, model was energy minimized by applying the force field of GROMOS96 using SPDBV software. The quality of the model was corroborated by calculating the stereo chemical properties, compatibility of the atomic model (3D) with its own amino acid resides (1D), bond lengths, bond angles and side chain planarity using SAVES server. Ramachandran plot calculations to check the stereo chemical quality of protein structure using PROCHECK [25] Environment profile using Verify3D [26] and ERRAT [27]. The residue packing and atomic contact were analyzed using WHATIF and Z Score of Ramachandran plot was calculated using WHATCHECK [28] Root Mean Square Deviation (RMSD) was calculated by superimposition of 3D-Model with template using SPDBV [29]. This final refined model was used for docking studies.

4.3.  Retrieval of Ligands

Antibiotics such as tetracycline, penicillin, chloramphenicol, aminoglycoside, meropenem, cefsulodin, carbepenem, lincomycin, and ceftazamide, efflux pump inhibitor (PaβN) and its analogues were downloaded from PubChem (http://www.ncbi.nlm.nih.gov/pccompound) and ZINC database (https://docking.org/). 

4.4.  Structure Based Virtual Screening and Docking

Molecular docking simulations and virtual screening was carried out by using AUTODOCK VINA 4.0 [30] with PyRx [31] interface tool. Initially, energy of all the ligands were minimized by applying the Universal Force Field (UFF) using conjugate-gradient algorithm with 200 run iterations and converted into PDBQT format. Subsequently, virtual screening was employed by using Lamarckian genetic algorithm and parameters were set with 150 Number of individual population, 25000 Max number of energy evaluation, 27000 Max number of generation, one among the top individuals to survive to the next generation, Gene mutation rate of 0.02, Crossover rate of 0.8, Cauchy beta of 1.0 and GA window size of 10.0. The grid was set to pore region of efflux pump at X=29.3901, Y= -42.745, Z= -51.82 and dimensions (Å) at X= 90.000, Y= 105.7097, Z= 104.2448 with exhaustiveness 8. The best docked ligand conformations were sorted out and scrutinized the bond angle, lengths and, binding interactions using PyMol [32]. 

3.       Results and Discussion

5.1.  Assessment of Primary, Secondary and TM α-helices

Analysis of physico-chemical properties was found to be aliphatic index (105.02), grand average of hydropath city (0.273), theoretical PI (5.47), extinction coefficients (8.0345) and the instability index (31.13). Secondary structure analysis has shown 76.1% alpha helix with 789 residues using the method of Chou & Fasman, 44.74% with 464 residues using SOPMA, 42.62% with 442 residues using GOR4. Extended strands showed 55.2% with 572 residues by Chou & Fasman, 19.77% with 205 residues by SOPMA and 15.14% with 157 residues by GOR4. Random coil confers 42.24% with 438 residues by GOR4, 27.39% with 284 residues and 9.6% with 100 residues by Chou & Fasman. Beta turn exhibits 5.2% with residues 84 by SOPMA and GOR4 whereas Chou & Fasmon was failed to provide beta turns (Figure 1). Twelve TM α-helices were predicted and shown in Table 1. Helix-I is started at residues position 9 and end at the 111 residues portion. Helix-II (158-432), Helix-III (366-459), Helix-IV (392-489), Helix-V (438-543), Helix-VI (470-580), Helix-VII (541-639), Helix-VIII (688-966), Helix-IX (898-977), Helix-X (925-1081), Helix-XI (974-1068) and Helix-XII (1006-1101). Besides, Helix-XIII (1088-1109) was identified by TMpred while HMMTOP, SOUSI and TMHMM were failed to found Helix- XIII and SOUSI also failed to predict Helix10.

5.2.  Protein Modeling and Validation

As results of BLASTp showed nine proteins such as 1IWG, 1OY6, 2J8S, 2GIF, 3NOC, 2HQG, 1T9T, 2HQD from E. coli and 2V50 from Pseudomonas aeruginosa which have highest identity of 97%, query coverage of 98% and low e-value. Despite all proteins showed highest identity with AcrB, 2J8S was selected as template due to lowest resolution of 2.5Å and R–factor 0.22. Sequence alignment was carried out between template and query sequence, and hundred models were generated using MODELLER 9.14 (Figure 2). The assessment of final model demonstrated that stereo chemical property was elucidated using PROCHECK that Ramachandran plot exhibited that 94.5% with 856 residues were aligned within the most favored regions (A, B, L), 5.1% with 46 residues were located within additional allowed region, 0.2% with 2 residues were located within generously allowed region, no residues were aligned within disallowed region (Figure 3A). WHATCHECK program showed Z-score of 1.193 for the 2nd generation packing quality, 0.386 for the Ramachandran plot appearance and 0.347 for the chi1/chi2 rotamer normality. The bond length, bond angles, omega angle restraints, planarity of side chain, improper dihedral distribution and inside/outside distribution were found to be 0.98, 1.175, 0.625, 0.277, 0.866 and 1.066. The overall quality factor of 90.12% was observed by using ERRAT environment profile (Figure 3B). Verify3D showed that 67.8% residues had an average 3D-1D score above 2 indicated that the model was highly reliable (Figure 3C)

5.3.  Architecture of AcrB Efflux Pump

AcrB pump is composed of three major domains such as TM domain, pore domain and docking domain (Figure 4a). TM domain contains twelve TM α-helices, six TM α-helices such as Helix-I, Helix-II, Helix-III, Helix-IV, Helix-V and Helix-VI are arranged as symmetrically at N-terminal to six TM α-helices such as Helix-VII, Helix-VIII, Helix-IX, Helix-X, Helix-XI and Helix-XII at C-terminal. TM α-helices traverse the inner lipid bilayer and exhibit a pseudo two-fold symmetry, and play an important role in the proton translocation across lipid bilayer that catalyzed by three conserved residues of Asn407 and Asp480 of TM Helix-IV and Lys940 of TM Helix-X (Figures 4b, 4c). Pore domain consists of four sub domains such as PN1, PN2, PC1 and PC2, PN1 and PN2 domains are formed with TM Helix-I and TM helix-II at N-terminal whereas PC1 and PC2 are formed with TM helix-VII and TM helix-VIII at C-terminal (Figure 4d). Moreover, each sub domain consists of two β-strands-α-helix-β-strand structural motif and sandwiched with each other. Docking domain composed of two sub domains such as DN and DC, each sub domain has four β-sheets in which two antiparallel β-strands are parallel to hair pin structure. In addition, a beak like long hairpin loop structure (Gln210-Pro243) protrudes from DN and a vertical hairpin that composed of two small beta sheets linked to second motif of PN2 by short connecting loop (Gly272-Val278). DC domain is extended from the first motif of PC2 sub domain through the connecting loop (Val730-Gln732) and has vertical hairpin which linked to second motif of PC2 by connecting loop (Gly811-Arg814).

5.4.  Docking Simulation of Antibiotics 

Docking simulation results showed that tetracycline, cefsulodin, penicillin, carbenicilin, ceftazamide, lincomycin, aminoglycosides, meropenem and chloramphenicol have shown interaction with distal pocket whereas carbenicilin has shown interactions with proximal pocket (Figure 5a: Table 2). Tetracycline has exhibited highest binding energy of -8.9 kcal/mol and formed nine interactions such as two bonds with OH group of Thr48, two bonds with Gly51, two bonds with Ala85 and Ala273, three bonds with polar amide of Asn274 and Gln619. Cefsulodin displayed nine interactions Viz., three bonds with OH of Thr49, Ser46 and Ser87, five bonds with polar amide of Asn274, Gln176 and Gln619, one bond with Ala85 and showed binding energy of -8.4 kcal/mol. Penicillin displayed four interactions, two bonds with polar amide of Gln34, OH group of Thr34 and two bonds with Ile39 and Ala39, and conferred binding affinity of -8.3 kcal/mol. Ceftazidime has shown binding energy of -8.2 kcal/mol and explicated six bonds such as  five bonds with OH group of Ser44, Thr89 and Thr91, polar amide of Gln176 and, aromatic ring of Phe616. Lincomycin showed binding energy of -7.6 kcal/mol and formed three bonds such as one bond with polar amide of Gln619, one bond with amino group of Lys769 and one bond with OH of Tyr771. Aminoglycoside showed binding energy of -7.2 kcal/mol and exhibited four binding interactions, two bonds with OH of Thr48 and two bonds with polar amide of Gln125. Meropenem has shown binding energy of -7.0 kcal/mol and conferred four bonds, two interactions with polar amide of Gln125 and Asn274, two bonds with OH of Thr771. Chloramphenicol showed lowest binding energy of -6.8 kcal/mol and formed five bonds, one bond with OH of Thr48, two bonds with amino group of Arg185, one bond with COO- of Glu273 and one bond with Gly754. Carbenicillin has affinity of -8.2 kacl/mol for proximal pocket and displayed two bonds viz., one bond with COO- of Gln576 and Gly719. PAβN is efflux pump inhibitor that bound in close proximity to proximal pocket with binding energy of -8.7 kcal/mol and formed interactions with OH group of Tyr467 and Thr560, polar amide of Asn922 and Gln927 (Figure 5b).

5.5.  Virtual Screening and Docking

In order to explicate the selective EPIs, virtual screening was performed using ZINC and PubChem database against AcrB efflux pump and performed docking simulation that revealed ten best lead compounds with different scaffolds (Figure 6: Table 3). Nine compounds have shown best binding affinity for proximal pocket whereas ZINC28477171 has shown significant binding affinity to distal pocket. ZINC28475998 and ZINC28476198 compounds exert highest binding affinities of -11.2 and -11.1 kcal/mol. ZINC28475998 formed three H-bonds such as 30NH, 32NH and 29NH groups with COO- of Glu682 and Glu825, OH of Ser823 and one arene-arene interaction between napthalene and benzene of Phe616. ZINC28476198 formed six interactions such as 23HN, 32HN, 30HN and 30HN with OH of Ser79 and Ser823, COO- of Glu682 and Glu825, 48OC and 29OC with OH of Thr91 and COO- of Glu864. ZINC28475792 has binding energy of -10.6 kcal/mol and formed five interactions viz., 32HN, 30HN, 34HN and 24HN with COO- of Glu682, Arg817, COO- of Glu825, COO- of Glu825 and two hydrophobic interactions with Phe616 and Phe665. ZINC27182211 has shown binding energy of -9.2 Kcal/mol and formed two bonds, OH49 formed one bond with NH2 of Arg714 and NH42 formed one bond with polar amide of Asn718, and two arene-arene interactions with Phe616 and Phe665. CID11143966 exhibited binding energy of -9.6 kcal/mol and displayed hydrophobic interactions with Phe616, Phe665 and Phe663. CID44265715 and CID102503 have shown binding affinities of -9.2 kcal/mol and -9.1 kcal/mol, CID44265715 displayed five interactions, 29HN, 30HN, 30HN, 29HN and 26HN groups formed bonds with COO- of Gln576, OH of Ser615 and Ser617, polar amide of Asn718 and CID102503 formed six bonds, 23HN, 25HN and 27HN formed three bonds with polar amide and OH of Gln576, Ser615, Ser617 and Asn718 and three arene-arene interactions with Phe616 and Phe663. CID11902980 and CID22845248 have shown similar binding energies of -9.0 kcal/mol, CID11902980 made one polar interaction with Leu661 and non-polar interactions with Phe665 and Phe616 and CID22845248 formed three interactions, 31HN and 29HN formed two bonds with Pro717 and Asn718. ZINC28477171 has shown binding affinity of -10.9kcal/mol for proximal pocket and displayed nine polar interactions such as 60O=C, 58O=C, 57O=C formed four bonds with OH of Ser44 and Thr91, 35HN, 25HN, 31HN and 33HN formed four bonds with COO- of Glu130, Asp174 and Glu825, and one arene-arene interaction with Phe616.

5.6.  Lipinski Rule of Five

Appraisal of pharmacological properties using Lipinski rule of five such as molecular weight, H-bond donors, H-bond acceptors and cLogP reveal that majority of the compounds are found to be satisfied as shown in Table 4. H-bond donors were predicted to be less than five and H-bond acceptors are less than ten. cLogP or partition coefficient plays a major role in accessing the drug in the body which was found to be less than five that indicates good absorption and distribution. Finally, most of the compounds obeyed Lipinski rule of five and could be help full in the development of EPIs for the inhibition of AcrB efflux pump. 

4.       Conclusion

Multidrug resistance in bacteria is most serious impairment to health care, currently no novel antibacterial agents are undertaken in clinical settings. In the present investigation, 3D structure of AcrB was constructed on account of unavailability and docking analysis observed that tetracycline, cefsulodin, penicillin, ceftazidime, lincomycin, aminoglycosides, meropenem and chloramphenicol confers significant interactions with distal binding pocket residues and carbenicillin was bound at proximal binding pocket. Virtual screening and docking revealed potent lead compounds such as ZINC28475998, ZINC27182211, ZINC28475792, ZINC28477171, ZINC28476198, CID22845248, CID11143966, CID44265715, CID11902980 and CID102503 with greater specificity and binding affinities en route for distal and proximal binding pockets. Consequently, these compounds have shown relevant drug likeness properties and could inhibit the efflux pump by competing with antibiotics and increase the residence time of antibiotics.

5.       Acknowledgment

BVB is extending thanks to University Grant Commission, India for providing financial assistance in the form of RGNF. Authors are grateful to the Coordinator, Bioinformatics center, Department of Zoology, Sri Venkateswara University, Tirupati for providing bioinformatics facilities.

6.       Conflict of Interest

All the authors declared that there is no conflict of interest.


Figure 1: Secondary Structure of AcrB efflux pump of Salmonella.



Figure 2: Pair wise sequence alignment of AcrB efflux pump of Salmonella with AcrB efflux pump of E. coli.



Figures 3(A-C): A). Ramachandran plot, B). Statistics of non-bonded interactions of AcrB efflux pump was calculated by ERRAT, C). 3D model compatibility of AcrB efflux pump was adopted by using Verify 3D.






Figures 5(a-b): a). Binding pose of antibiotics and b). PAβN within the distal and proximal pocket of periplasmic domain of AcrB efflux pump.





Figures 6(a-j): Binding pose of a). ZINC28475998, b). ZINC28476198, c). ZINC28477171, d). ZINC28475792, e). ZINC2718221, f). CID11143966, g). CID44265715, h). CID102503, i). CID11902980 and j). CID22845248 within the binding pocket of efflux pump.



 

TM Helix

HMMTOP

SOSUI

TMHMM

TMPred

Start

End

Start

End

Start

End

Start

End

Helix1

82

101

83

105

9

31

92

111

Helix2

413

432

408

430

337

359

158

177

Helix3

439

458

437

459

366

388

427

445

Helix4

467

486

467

489

392

414

453

473

Helix5

515

534

514

536

438

460

521

543

Helix6

547

570

549

571

470

492

553

580

Helix7

614

631

613

635

541

563

623

639

Helix8

945

964

944

966

872

891

688

713

Helix9

971

990

969

991

898

920

959

977

Helix10

1001

1018

-

-

925

947

979

1000

Helix11

1047

1064

1046

1068

974

996

1007

1028

Helix12

1077

1101

1079

1101

1006

1028

1056

1077

Helix13

-

-

-

-

-

-

1088

1109

 

Table 1: Prediction of TM helices by using different servers.

 

 

Antibiotics

Structure

Binding Interaction

Distance (Å)

Binding energy Kcal/mol ΔG

Tetracycline

 

C:\Users\Vijaya Bhaskar\AppData\Local\Microsoft\Windows\INetCache\Content.Word\tetracyclin.png

29HO-----Thr48

38HO-----Thr48

34HO-----Gly51

36HO-----Gly51

26HO-----la85

23HO-----Ala273

30HO-----Asn274

28HO-----Gln619

30HO-----Gln619

3.3

2.7

3.0

3.3

2.2

2.1

3.2

3.1

3.1

 

-8.9

Cefsulodin

 

penicillin

17OC-----Thr49

39OC-----Ser46

17OC-----Ala85

38OC-----Ser87

32OC-----Gln176

41OC-----sn274

21OC-----Gln619

31OC-----Gln619

34OC-----Gln619

2.4

2.9

2.8

3.3

3.0

3.0

3.1

3.1

3.4

-8.4

Penicilin

cefsulodin

 

22OC-----Gln34

14HO-----Thr37

22OC-----Ile38

15HO-----Ala39

3.1

2.3

3.5

3.0

-8.3

Ceftazidime

ceftazadime

33OC-----Ser44

33OC-----Thr91

32OC-----Thr89

42OC-----Gln176

23OC-----Ser615

34OC-----Phe616

3.0

2.1

1.9

2.4

3.0

-8.2

Lincomycin

lincomyxin

32OH-----Gln619

30OH-----Lys769

30OH-----Tyr771

3.2

3.5

2.8

-7.6

Aminoglycoside

C:\Users\Vijaya Bhaskar\AppData\Local\Microsoft\Windows\INetCache\Content.Word\aminoglycoside.png

 

18HO-----Thr48

22HO-----Thr48

14HO-----Gln125

18HO-----Gln125


 

2.3

2.8

2.6

2.6

 

-7.2

Meropenem

C:\Users\Vijaya Bhaskar\AppData\Local\Microsoft\Windows\INetCache\Content.Word\meropenem.png

17HN-----Gln125

22HO-----Asn274

28HO-----Tyr771

29HO-----Tyr771

3.5

2.9

3.0

3.1

-7.0

Chloramphenicol

chloramphenicol

17ON-----Thr48

14HO-----Pro50

16HO-----Arg185

16HO-----Arg185

23OH-----Glu273

22HN-----Gly754

2.9

3.2

2.9

3.2

2.1

2.3

-6.8

Carbenicillin

carbenicillin

24OH-----Ser642

34OC-----Gly993

19OC-----Arg636

25OH-----Gly638

 

3.2

1.9

1.4

2.9

 

-8.2

PAβN

pabn

43HN-----Thr560

40HN-----yr467

40HN-----Gln927

33OC-----Asn922

2.3

2.4

2.2

3.1

-8.7

 

Table 2:  H-bonds, distance and binding affinities of antibiotics with active site residues of AcrB efflux pump.

 

 

ZINC Compound

Compound Name

Binding Interactions

Distance (∠)

Binding energy (kcal/mol ΔG)

ZINC28475998

27182211

(2S)-1-[(2S)-2-amino-3-(4-hydroxy-2,6-dimethyl-phenyl)propanoyl]-N-[(1S)-1-benzyl-2-(1-naphthylamino

30HN-----Glu682

32HN-----Ser823

29HN-----Glu825

2.5

2.8

2.0

-11.2

ZINC28476198

28473198

2-[3-[(2S,5S,11R,14R)-11-[(4-hydroxyphenyl)methyl]-14-methyl-5-(2-naphthylmethyl)-3,6,9,12,15-pentao

48OC-----Thr91

23HN-----Ser79

30HN-----Glu682

32HN-----Ser823

30HN-----Glu825

29OC-----Glu864

 

3.2

2.5

3.4

2.7

2.4

2.9

-11.1

ZINC28477171

28477171

1-[3-[(2S,5S,11R,14R)-14-(3-guanidinopropyl)-11-[(4-hydroxyphenyl)methyl]-5-(2-naphthylmethyl)-3,6,9

60OC-----Ser44

58OC-----Thr91

57OC-----Thr91

60OC-----Thr91

35HN-----Glu130

35HN-----Asp174

25HN-----Phe616

31HN-----Glu825

33HN-----Glu825

2.7

2.6

3.1

2.7

2.2

2.7

1.9

3.3

2.3

-10.9

ZINC28475792

28475792

2-[3-[(2S,5S,11R,14S)-11-[(4-hydroxyphenyl)methyl]-14-methyl-5-(2-naphthylmethyl)-3,6,9,12,15-pentao

32HN-----Glu682

30HN-----Arg817

32HN-----Glu825

34HN-----Glu825

24HN-----Glu825

2.5

2.8

2.7

2.4

2.2

-10.6

ZINC27182211

 

C:\Users\vijay basker\AppData\Local\Microsoft\Windows\INetCache\Content.Word\28475998.png

2-[3-[(2S,5S,11R,14S)-11-[(4-hydroxyphenyl)methyl]-13,14-dimethyl-5-(2-naphthylmethyl)-3,6,9,12,15-p

49HO-----Arg714

43HN-----Asn718

3.4

2.6

-9.2

 

CID11143966

11143966

(3S,6R)-3,6-dibenzyl-4-naphthalen-2-ylpiperazin-2-one

Phe616, Phe665, Phe663,

Leu661, Gln576, Ser134, Ser135

-

-9.6

CID44265715

 

44265715

MK-56-(2S)-5-(diaminomethylideneamino)-N-naphthalen-2-yl-2-(naphthalen-2-ylmethylamino)pentanamide

29HN-----Gln576

30HN-----Gln576

30HN-----Ser615

29HN-----Ser617

26HN----------Asn718

2.0

2.7

2.4

2.7

2.0

-9.5

CID102503

102503

Nalpha-Benzoyl-DL-arginine-2-naphthylamide hydrochloride

23HN-----Gln576

25HN-----Gln576

23HN-----Ser615

23HN-----Ser617

27HN-----Asn718

27HN-----Asn718

2.5

2.6

2.7

2.7

2.2

2.7

-9.1

CID11902980

11902980

[(2S)-1-[(2S)-2-(naphthalen-2-ylcarbamoyl)pyrrolidin-1-yl]-1-oxo-3-phenylpropan-2-yl]azanium

29HN-----Leu661

2.5

-9.0

CID22845248

22845248

Nalpha-Benzoyl-DL-arginine-2-naphthylamide hydrochloride-BANA

31HN-----Pro717

29HN-----Asn718

29HN-----Asn718

2.4

2.4

2.0

-9.0

Table 3:  H-bonds, distance and binding affinities of best lead compounds with active site residues of AcrB efflux pump.

 

Compound Name

Molecular weight

cLog

H-Don

H-Acc

TPSA

Rotatable bonds

ZINC28475998

659

0.33

10

14

223

8

ZINC28476198

645

0.21

11

14

229

9

ZINC28477171

368

2.9

1

5

55

5

ZINC28475792

645

0.21

11

14

229

9

ZINC27182211

579

3.8

6

8

125

9

CID11143966

406

6.4

1

2

32.3

5

CID44265715

439

3.9

4

3

106

9

CID102503

439

2.1

5

3

123

8

CID11902980

388

3.5

2

2

77

5

CID22845248

559

2.6

6

5

181

13


Table 4: Lipinski rule of five of best docked conformations.

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Citation: Baki VB, Tirumalsetty MCB, Bathala VK, Wudayagiri R (2018) Homology Modeling, Docking and Structure Based Virtual Screening Against AcrB Efflux Pump of Multidrug Resistant Salmonella. J Pharmacovigil Pharm Ther: JPPT-125. DOI: 10.29011/JPPT-125. 100025