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
2.1. 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.
2.2. 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.
2.3. 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/).
2.4. 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
3.1. 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.
3.2. 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)
3.3. 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).
3.4. 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).
3.5. 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.
3.6. 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 |
|
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 |
|
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 |
|
22OC-----Gln34 14HO-----Thr37 22OC-----Ile38 15HO-----Ala39 |
3.1 2.3 3.5 3.0 |
-8.3 |
Ceftazidime |
|
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 |
|
32OH-----Gln619 30OH-----Lys769 30OH-----Tyr771 |
3.2 3.5 2.8 |
-7.6 |
Aminoglycoside |
|
18HO-----Thr48 22HO-----Thr48 14HO-----Gln125 18HO-----Gln125
|
2.3 2.8 2.6 2.6
|
-7.2 |
Meropenem |
|
17HN-----Gln125 22HO-----Asn274 28HO-----Tyr771 29HO-----Tyr771 |
3.5 2.9 3.0 3.1 |
-7.0 |
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 |
|
24OH-----Ser642 34OC-----Gly993 19OC-----Arg636 25OH-----Gly638
|
3.2 1.9 1.4 2.9
|
-8.2 |
PAβN |
|
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 |
(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 |
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 |
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 |
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
|
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 |
(3S,6R)-3,6-dibenzyl-4-naphthalen-2-ylpiperazin-2-one |
Phe616, Phe665, Phe663, Leu661, Gln576, Ser134, Ser135 |
- |
-9.6 |
|||
CID44265715
|
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 |
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 |
[(2S)-1-[(2S)-2-(naphthalen-2-ylcarbamoyl)pyrrolidin-1-yl]-1-oxo-3-phenylpropan-2-yl]azanium |
29HN-----Leu661 |
2.5 |
-9.0 |
|||
CID22845248 |
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.
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