Rational design of novel compounds to serve as potential NDM-1 inhibitors using molecular docking, molecular dynamics simulation, and physicochemical studies

doi:10.21203/rs.3.rs-2488439/v1

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Rational design of novel compounds to serve as potential NDM-1 inhibitors using molecular docking, molecular dynamics simulation, and physicochemical studies

https://doi.org/10.21203/rs.3.rs-2488439/v1

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published 11 Jul, 2023

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New Delhi Metallo-β-lactamase enzyme (NDM-1) is an enzyme that hydrolyzes a wide range of β-lactam antibiotics, including most carbapenems, leading to antimicrobial resistance. The development of a novel NDM-1 inhibitor for use in combination with carbapenems may help to combat drug-resistant pathogens. Twenty compounds derived from naphthalene, thiazole, and sulfone derivatives were designed to inhibit bacterial NDM-1 and protect β-lactam antibiotics from enzyme attack. Two- and three-dimensional structures of the designed molecules were sketched using MarvinSketch, and a molecular docking protocol was used to identify potential inhibitor(s) of the NDM-1 target protein using AMDock v 1.5.2. The binding free energy of each compound against NDM-1 was determined and the drug-likeness properties of the designed molecules were assessed using SwissADME. Two compounds with the highest ΔG_binding results, T008 and T016, were selected for further investigation using molecular dynamic (MD) simulations with the GROMACS simulation package (GROMACS 2020.4). The duration of each MD simulation was 100 ns. Both compounds had a significantly higher binding free energy than the positive control and other designed molecules, their MD simulations remained stable, they passed Lipinski’s rule of five, and were shown to have favorable physicochemical properties. The study outcomes can be used to inform synthesis and in vitro testing of the selected molecules.

New Delhi Metallo-β-lactamase-1

carbapenems

binding free energy

drug-likeness properties

molecular docking study

molecular dynamics simulation

Antibacterial resistance is a serious global public health issue due to the spread of drug-resistant bacteria and has become a primary focus within the fields of microbiology, epidemiology, and medicinal chemistry [1]. The expectation that antibiotics would revolutionize the treatment of bacterial infections was rapidly overcome by the emergence of antibiotic resistance [2]. Despite the worldwide appearance and expansion of multidrug-resistant (MDR) bacteria, projects aimed at developing novel antibiotics are waning [3]. In 2017, the World Health Organization (WHO) announced the need to prioritize the development of new antimicrobial agents against Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa and Enterobacter (ESKAPE) [4, 5] because of their resistance to most current antibiotics and empirical therapies [6], including macrolides, fluoroquinolones, lipopeptides, β-lactams, tetracyclines, and β-lactamase–β-lactams combinations.

Carbapenems, the last line of defense against MDR bacteria, remain the most effective treatment for ESKAPE infections and are extensively prescribed [7] because they are more stable against β-lactamase enzymes than other β-lactam antibiotics [8]. Carbapenems have a broader antibacterial spectrum than penicillin antibiotics, cephalosporins, and β-lactam/β-lactamase inhibitor combinations due to their ability to target penicillin-binding proteins (PBP) [9, 10]. However, recent studies have shown a rise in carbapenem-resistant strains [11]. β-lactamase enzymes are required to protect bacteria and prevent antibiotics from reaching the PBP target site by hydrolyzing β-lactam antibiotics. They are grouped according to their structural homology into four classes: A, B, C, and D. The class A, C, and D enzymes are also known as serine β-lactamases (SBLs) because they can hydrolyze the β-lactam bond using serine as a nucleophilic agent, while the class B enzymes are called Metallo-β-lactamases (MBLs) since they can cleave the β-lactam amide bond in antibiotics using Zn^+ 2 to facilitate antibiotic-binding and activation of the Zn^+ 2-bound water/hydroxide required for nucleophilic attack [12].

MBLs were first identified in non-pathogenic bacteria in the 1960s [13], but were not considered a threat to human health [14, 15] until the 1990s when plasmid-mediated imipenemase (IMP) and verona integron-borne MBL (VIM) were identified in the Gram-negative pathogens (Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacteriaceae). This confirmed that MBLs were contributing to global antibiotic resistance [16, 17]. More recently, New Delhi MBL (NDM-1) was also identified as an MBL enzyme [18]. Bacteria carrying these gene products are superbugs that were initially discovered in India and are currently found worldwide [19]. The NDM-1 protein is expressed in diverse Gram-negative strains, such as Klebsiella pneumonia, Escherichia coli, Acinetobacter baumannii, Enterobacteriaceae, and Pseudomonas aeruginosa [20]. NDM-1-producing bacteria are a threat to human health because this enzyme can hydrolyze all β-lactam antibiotics, including carbapenems, and create MDR bacteria [21]. The development of NDM-1 inhibitors is considered a promising treatment approach to overcome carbapenem resistance [22, 23]. Various molecules are shown to inhibit NDM-1, including boric acid derivatives, thiol compounds, captopril, Aspergillomarasmine, some natural products, and sulfanilamide. These molecules inhibit NDM-1 using different mechanisms, such as producing a covalent bond with enzyme pocket residues like Cys208 and confiscating the Zinc ions. However, no molecules to date are recommended for clinical use due to efficacy and safety obstacles [23–26].

Structurally, the NDM-1 enzyme consists of a single polypeptide chain of 270 residues with a signal peptide of 28 amino acids at the N-terminus. The enzyme can move to the periplasmic space and even the outer membrane of Gram-negative bacteria to hydrolyze β-lactam antibiotics [27–29]. The protein monomer is composed of five α helices and twelve, including four twisted β sheets. The first two sheets are bridged by an α helix and the second two are flanked by a fifth α helix, which forms a αβ/βα sandwich structure. The L3 (Leu65–Val73), L7 (Thr119–Met126), and L10 (Cys208–Leu221) loops produce the primary components of the active site, while the two positively charged zinc ions (Zn1 and Zn2) are positioned at the bottom of the pocket and cause hydrolysis of the β-lactam rings [30, 31]. Zn1 produces tightly bound zinc with His120, His122, His189, and a water/hydroxyl group to form a tetrahedron, which is known as the histidine site. In contrast, Zn2 is a loosely coordinated zinc that binds to Asp124, Cys208, and His250 to form a triangular conical structure, which is known as the cysteine site. Zn1 and Zn2 interact through the Asp124 side chain [32, 33]. The NDM-1 sequence differs significantly from other MBLs, sharing the closest sequence homology with VIM-4 (38%), IMP-1, and VIM-2 (32%). NDM-1 also has a remarkably longer N-terminus and more hydrophobic L3, which favors binding to the inhibitor molecules (Fig. 1) [31, 32, 34].

A current drawback of carbapenems is NDM-1-mediated hydrolysis and no approved NDM-1 inhibitors are currently available in the clinic to combine with carbapenems as an alternative treatment option [35]. The proposed mechanism for carbapenem inactivation through NDM-1 begins with the formation of a Michaelis complex when a water/hydroxyl group connects both Zn1 and Zn2 and gains a nucleophilic property required to attack the carbonyl carbon atom and cleave the carbapenem C7–N4 amide bond. After opening the β-lactam ring, an anionic intermediate is produced through the interactions of Zn1 with the carboxylic acid group on the β-lactam ring and Zn2 with the amide nitrogen and the β-lactam ring-fused carboxylic acid (C3). The final step involves hydronation of the intermediate (Fig. 2) [12, 36, 37].

Computer-aided drug design (CADD) has contributed to early drug discovery projects by assisting in the design and development of new molecules. The computational approaches needed to design the MBL inhibitors against the NDM-1 protein can improve project success and reduce the cost of developing novel lead molecules [38]. They are primarily performed using molecular docking studies and molecular dynamic (MD) simulations. Molecular docking analysis is an essential approach of structure-based drug design (SBDD) that realizes and explores the binding interactions between a ligand and receptor by predicting the preferred pose of the ligand within the receptor binding pocket and calculating the docking score [39]. MD simulation is a theoretical method used to predict the physical movement of the ligand-protein complex, inspect conformational changes of the docked molecules in the protein binding site, and investigate the time-dependent behavior of molecular systems during drug discovery projects [39, 40].

One of the promising CADD applications is the estimation of a lead compound’s physicochemical properties [41, 42], including size, polarity, and lipophilicity, which impacts its binding to receptors. Increasing size and lipophilicity can reduce drug selectivity and accumulation in a specific organ [43, 44]. Lipinski et al. (1997) developed a metric, known as Lipinski’s rule of 5 or Pfizer’s rule of 5 to estimate the pharmacokinetic properties of lead compounds. Based on the guidelines, oral drug molecules must obey the following metrics: ClogP ≤ 5, molecular weight (MW) ≤ 500, hydrogen bond donor ≤ 5, and acceptor ≤ 10 [45, 46]. In silico molecule design to identify MBL inhibitors could be more challenging than the design of SBLs inhibitors because Zn^+ 2 has a more complex shape and participates in chemical bonding interactions due to its d orbitals. In addition, Zn^+ 2 has several coordination numbers and geometries [47]. The current study designed novel NDM-1 inhibitor(s) aimed at protecting carbapenems from MBL hydrolysis and preserving or recovering antibacterial efficacy. This strategy could be used to reduce Gram-negative bacterial resistance.

Ligand and target protein preparation

The 2D structures of the designed molecules were sketched (Fig. 3). The 3D structure of each ligand was generated from the 2D structures since the docking tools require 3D structures that include explicit hydrogen. MarvinSketch from a suite of applications called Marvin was used to draw the 2D structures and the 3D structures were produced by applying MD simulations and energy minimization algorithms using MMFF94 forcefield (Marvin version 22.19, ChemAxon; https://chemaxon.com/products/marvin). PDB files of the designed compounds were generated [48,49]. The NDM-1 enzyme bound with a NO9 ligand was downloaded from the Protein Data Bank website (https://www.rcsb.org; access code 6KXI) [50-52]. Notepad++ version 8.4.6 (Don Ho, https://notepad-plus-plus.org/) [53] was used to remove the ligand, water molecules, and chain A from a dimer of the protein structure (retaining only chain B). The final pure protein structure was saved as a PDB file for molecular docking.

Molecular docking of the designed molecules with NDM-1

NDM-1 was docked with all the designed molecules to estimate the ΔG_binding (binding free energy). The Assisted Molecular Docking (AMDock) program was used to perform the docking by applying AutoDock Vina and AutoDock4. AMDock also has the choice of using the AutoDock4Zn forcefield for metalloproteins and integrates several external programs, such as Open Babel, PDB2PQR, AutoLigand, and ADT scripts to prepare the input structure files and accurately define the search space [54]. The docking poses were generated using the Lamarckian genetic algorithm. While polar hydrogen atoms and Kollman charges were assigned for the target protein, Gasteiger partial charges and non-polar hydrogen atoms were added to the ligands. All the ligand torsion angles were free to rotate throughout the docking experiments. The Lamarckian genetic algorithm combined with an empirical free energy function was used to quickly predict bound conformations and free energy associations [55,56]. The magnitude of the search space was determined using a grid center of X: 20.6, Y: 33.7, and Z: 7.7, and the size in each magnitude was X:39, Y:37, and Z:36 Å. All the docked complexes were assessed using their binding energy values. The mode with the highest binding free energy was selected for further analysis.

MD simulations of the selected ligands with NDM-1

MD simulations were performed for the two ligand-protein complex structures. The selection of the complexes was based on the binding free energy values of the docking studies and the best ligand poses within the binding pocket. The MD simulations were achieved using the GROMACS simulation package (GROMACS 2020.4) [57]. The simulation period was 100 ns in water using the CHARMM36 forcefield [58] and the trajectory and energy files were written every 10 ps. The system was solvated in a truncated octahedral box containing TIP3P water molecules [59]. The protein was placed in the simulation box with a minimum distance to the box edge of 1 nm to satisfy the minimum image convention. Potassium/chlorine ions were added to the complex to neutralize the overall system [60]. Minimization was carried out for 5000 steps using the Steepest Descent Method and the convergence was achieved using a maximum force <1000 (KJ mol-1 nm-1) to remove any steric clashes. All systems were equilibrated at NVT and NPT ensembles for 100 ps (50,000 steps) and 1000 ps (1,000,000 steps) using 0.2 and 0.1 fs, respectively, at 300 K to ensure a fully converged system for the production run. The production runs for the MD simulations were carried out at a constant temperature of 300 K and a pressure of 1 atm or bar (NPT) using weak coupling velocity-rescaling (modified Berendsen thermostat) Parrinello-Rahman algorithms, respectively [61-63]. Relaxation times were set to τ T = 0.1 ps and τ P = 2.0 ps. All bond lengths involving the hydrogen atom were kept rigid using the Linear Constraint Solver (lincs) algorithm, allowing a time step of 2 fs. The Verlet method was used to determine the non-bonded interactions [64]. Periodic Boundary Conditions (PBC) were used in all x, y, and z directions. Interactions within a short-range cut-off of 1.2 nm were calculated for each time step. Particle Mesh Ewald (PME) was used to calculate the electrostatic interactions and forces to account for a homogeneous medium outside the long-range cut-off. The production runs for the two docked complexes was 100 ns [65].

Visualization and plotting software

AMDock uses PyMOL to assist in setting up the box defining the search space and to visualize and analyze the docking results [54]. The UCSF Chimera 1.6 package [66], PyMOL molecular graphics system [67], and Maestro academic versions [68] were used to visualize interactions between designed ligands and the NDM-1 protein, measuring distances between selected atoms and conformational changes of the protein-ligand complex systems [69]. Protein-ligand interactions, such as hydrophobic and H-bonds, were investigated using Ligplot Plus software (version V.2.2.5, 2022 ) [70] to generate and plot 2D representations of the interactions.

Docking and scoring of the designed compounds

The molecular docking study was carried out to define the binding free energy between the designed ligands and the NDM-1 receptor and to investigate their binding interactions [71]. During the docking process, molecules with the 10 highest binding free energies were selected. The screening results of twenty designed molecules and the positive control used to predict comparative ΔG_binding are shown in Table 1. The T016 compound had the highest calculated binding free energy (-10.64 kcal/mol), T008 had the second highest energy, and T014 had the lowest energy (-7.56 kcal/mol). The designed molecules possessed a higher ΔG_binding than meropenem, except for T011 (-7.90 kcal/mol) and T014 (-7.56 kcal/mol).

Table 1 The chemical formula, binding free energy, ClogP, Molar mass, and the rule of five compliance of the designed ligands and meropenem

Compound	Chemical formula	ΔG_binding (kcal/mol)	CLogP	Rule of 5 criteria	Molar mass (g/mol)
T001	C₁₄H₁₁NOS	-9.39	3.88	passed	241.31
T002	C₁₄H₁₁NS	-9.04	3.89	passed	225.31
T003	C₁₄H₉Cl₂NS	-9.80	5.19	passed	294.20
T004	C₁₄H₉Cl₂NS	-9.80	5.19	passed	294.20
T005	C₁₄H₁₁NOS	-8.96	3.38	passed	241.31
T006	C₁₅H₁₃NO₂S	-9.49	3.39	passed	271.33
T007	C₁₅H₁₃NOS	-9.28	3.90	passed	255.33
T008	C₁₃H₉NO₄S₂	-9.83	1.92	passed	307.34
T009	C₁₄H₁₃NO₂S₂	-9.40	2.95	passed	291.39
T010	C₁₃H₁₁NO₂S₂	-9.60	2.56	passed	277.36
T011	C₁₄H₁₀ClNS	-7.90	4.54	passed	259.75
T012	C₁₄H₁₀N₂O₂S	-8.83	3.80	passed	270.31
T013	C₁₅H₁₂ClNS	-9.50	4.73	passed	273.78
T014	C₁₅H₁₁NS	-7.56	4.47	passed	237.32
T015	C₁₇H₁₃NO₄S	-8.88	2.85	passed	327.35
T016	C₁₇H₁₃NO₄S	-10.64	2.85	passed	327.35
T017	C₁₃H₁₃NOS	-8.29	2.52	passed	231.31
T018	C₁₄H₁₅NO₂S	-8.00	2.53	passed	261.34
T019	C₁₂H₁₁NOS	-8.71	2.13	passed	217.29
T020	C₁₁H₉NO₃S	-8.30	1.94	passed	235.26
Meropenem	C₁₇H₂₅N₃O₅S	-7.94	-0.31	passed	383.46

Calculated water-octanol partition coefficient

An initial prediction of the lead molecule’s physicochemical properties is necessary to identify a viable lead during drug discovery projects [72]. LogP is one of the significant evaluators of a lead compound’s physical properties and pharmacokinetic properties [73]. In the current study, Manifold platform (https://postera.ai/mannifold) was used to estimate the calculated LogP (ClogP) and the rule of five criteria for the designed molecules and the positive control molecule. The designed molecules had a similar ClogP to most marketed antibacterial drugs (1.9–5.19) [74]. The highest lipophilic compounds were T003 and T004, followed by T013. However, the most hydrophilic molecules were T008 and T020, with ClogP values of 1.92 and 1.94, respectively, and none of the designed molecules were more hydrophilic than meropenem. Maintaining a balance between hydrophilicity and hydrophobicity was necessary for the designed molecules because the NDM-1 pocket is surrounded by loops that contain hydrophobic residues, such as Leu65, Met67, Phe70, and Val73, that are intended to interact with the ligand’s hydrophobic functional groups. In addition, L10 of NDM-1 includes Cys208, Lys211, and Asn220. While Cys208 coordinates with Zn2, the other residues interact with the substrate polar groups [31]. Thus, the designed ligands require both high lipophilicity and low to moderate hydrophilicity to effectively inhibit the NDM-1 enzyme.

Drug-likeness and Lipinski’s rule of five

Due to their favorable pharmacokinetic properties, small molecules serve as the gold standard for the development of new medications [75]. Drug-likeness properties are applied to small molecules to identify orally bioavailable drugs [76]. These properties can be measured using Lipinski’s rule of five criteria or their variants [77]. In the current study, all the designed ligands were small molecules (MW < 500 Da) and followed the rule of five. The highest MW belonged to compounds T015, T016 (327.35 g/mol), and T008 (307.34 g/mol), when they did not exceed the meropenem MW (383.46 g/mol) (Tables 1 and 2). However, this MW cutoff may not be a strict condition for oral bioavailability given that several antibiotics with MWs up to 900 Da have oral bioavailability while others with small MWs do not. Indeed, when meropenem has a small MW and passes Lipinski’s rule of five, it cannot be administered orally due to its significant hydrophilicity and poor gastrointestinal tract absorption [78].

Table 2 Lipinski’s rule of five criteria of the designed molecules

Compound	Rotatable bonds		HB Donor	HB Acceptor	TPSA
T001		3	0	3	22.12
T002		2	0	2	12.89
T003		2	0	2	12.89
T004		2	0	2	12.89
T005		2	1	3	33.12
T006		3	1	4	42.35
T007		3	0	3	22.12
T008		2	0	6	73.33
T009		3	0	4	47.03
T010		2	0	4	47.03
T011		2	0	2	12.89
T012		3	0	4	56.03
T013		3	0	2	12.89
T014		2	0	2	12.89
T015		3	2	3	97.46
T016		3	2	3	97.46
T017		1	1	3	33.12
T018		2	1	4	42.35
T019		1	1	3	33.12
T020		2	1	5	51.58
Meropenem		5	3	6	110.18

An increase in the MW of orally administered drugs has occurred in recent years [79,80] because some researchers argue that performing drug discovery campaigns beyond the rule of five could allow new medications to be identified [81,82]. Thus, the current study investigated the correlation between the binding free energy and the MW of the designed compounds. A significant P-value (0.0036) and moderate correlation (R²=0.38) were observed between the compound’s weight and binding affinity (Fig. 4), suggesting that increasing the size of the ligands may improve the ligand-receptor binding affinity [83].

Analysis of binding interactions between the selected compounds and NDM-1

Several studies have found that ligands with sulfone or sulfamoyl functional groups, including naphthalene and thiazole nuclei, exhibit significant activity and an ability to inhibit MBLs (especially NDM-1) by forming H-bonds and combining with zinc ions in the active site [52,84-86]. Thus, these functional groups were added to the designed compounds. Sulfamoyl is among the most effective chemical groups to coordinate with zinc ions and produce H-bonds with polar residues (Asn220, Asp124, and Gln123) within the active site. Moreover, because the NDM-1 active site has hydrophobic residues adjacent to the zinc center, all the designed molecules had hydrophobic components and were able to form nonpolar interactions with the ligands [87]. Docking was assessed for the crystal structure of a reference complex and two ligand-receptor complexes with a promising outcome (T008 and T016). The main functional groups of T008 were thiazole and naphthalene moieties. T016 also had a naphthalene functional group but possessed a sulfone functional group instead of thiazole (Fig. 3).

Meropenem and NDM-1 binding interactions

In this study, meropenem was considered a positive control to evaluate the activity of the other compounds. The binding interactions between the crystal structure of hydrolyzed meropenem and the NDM-1 complex were assessed, [88] as well as the docking results of meropenem with the protein. Both the crystal structure and docked results confirmed that the ligand was entirely buried in the NDM-1 active site (Fig. 5). In addition, the two positively charged zinc ions in the protein pocket accounted for the hydrolysis of the β-lactam rings and were actively involved in the interactions between the ligand and NDM-1 through a metal coordination bond [89,90]. These results illustrated that various types of binding interactions were available between the ligand and the NDM-1 binding substrate. The crystal structure results showed that three residues were engaged in hydrophobic interactions with meropenem (Val73, Trp93, and Gly219) (Fig. 5A). Meanwhile, the docked results showed that three NDM-1 residues were also involved in hydrophobic interactions with the ligand. Instead of Val73 and Gly219 residues, Gln151 and Asn220 interacted (Fig. 5B). The residues of the NDM-1 active site that interacted with meropenem were similar in both the crystal and docked results, except that the H-bond interactions of the Lys211 side chain in the crystal structure were replaced by main-chain H-bond interactions between Gln123 and meropenem in the docked results. However, binding interactions between several residues and meropenem differed due to changing conformations of the ligand (Figures 5A and 5B).

T016 and NDM-1 binding interactions

T016 could be considered a lead compound because it had the highest binding free energy (-10.64 kcal/mol) and optimum ClogP (2.85) and passed Lipinski’s rule of five (Table 1). According to the docking results, this ligand had several interactions with the receptor’s active site. Its sulfamoyl group interacted with both Asp124 and Asn220 residues on NDM-1 using H-bonds. Moreover, the carbonyl oxygen and hydroxyl atoms of the ligand’s benzoic acid residue formed an H-bond with Asn220 and metal coordination with the zinc ions, respectively. Pi-pi stacking was also observed between the imidazole side chain of His250 and one of the benzene rings of the naphthalene functional group. A second pi-pi interaction (T shaped) belonged to the His120 residue and benzene ring of the ligand’s benzoic acid. Finally, the compound’s hydrophobic naphthalene moiety resulted in a significant increase in binding free energy since it was buried in the sequence of the receptor’s hydrophobic residues (Met67, Phe70, Ala72, Val73, Ala74, and Leu218). Given the availability of hydrophobic residues adjacent to the zinc ions in the NDM-1 active site, the designed ligands may be improved by introducing hydrophobic substituents (Fig. 6) [91].

T008 and NDM-1 binding interactions

T008 had the second highest binding free energy among the designed ligands (9.83 kcal/mol) when the ΔG_binding was significantly higher than the positive control. This compound also had an optimum ClogP (1.94), a small MW (307.34 g/mol), and passed the rule of five (Table 1). Thus, this molecule could also be considered a lead compound. One of the essential binding interactions was the zinc coordination bond between the nitrogen atom of the ligand’s thiazole group and the zinc ion of the receptor pocket (Fig. 7). There was also an H-bond between the carbonyl oxygen of the ligand’s ester functional group and the side chain carboxylic acid of Asp124. In addition, the amide side chain of Gln123 interacted with the sulfonyl functional group of T008. The main hydrophobic contact was a pi-pi stacking between the ligand and the imidazole side chain of the His122. The ligand’s naphthalene functional group formed hydrophobic interactions with NDM-1 residues such as Trp93, Ala121, Met154, and Cys208.

MD simulation studies of T008 and T016 with NDM-1

MD simulations were used to predict the stability and dynamics of T008 and T016 with NDM-1 under physiological conditions [90]. These ligands were selected because they had the highest ΔG_binding rank of the designed ligands. The root-mean-square deviation (RMSD) of the protein-ligand complexes was used to compute the scalar distance between the Ca backbone of the protein and the ligand’s throughput trajectory to determine its conformational stability. According to the RMSD graph, the protein-ligand complexes fluctuated between ~0.5 Å for the T008-NDM-1 complex and 0.2 Å for the T016-NDM-1 complex after passing 2 ns and stabilizing the complex structures. These fluctuations are a normal behavior of globular proteins and were significantly below the upper limit (2.0 Å) [90]. The results suggested that both compounds, especially T016, formed strong interactions with the NDM-1 active site residues and stable complex systems (Fig. 8B).

The root-mean-square fluctuations (RMSF), which were calculated for both complex systems based on the Ca atoms of NDM-1, showed that the fluctuation intensity remained below 0.2 Å, except for Phe70, Asn176, and Lys216, which were represented as either loop or turn in the protein structure (Fig. 8C). Thus, the RMSF plots were able to identify the stability of the protein structure and the availability of flexible regions needed to acquire the superlative conformations. Since the number of H-bonds between a protein and ligand can influence the stability of a ligand-receptor complex, it was critical to probe these bonding interactions during the MD simulations [92]. The H-bonds created between the ligands and NDM-1 during 100 ns MD simulations are shown in Fig. 8A. The bonds maintained a constant interaction during the MD simulation time with an average of one H-bond for the T008-NDM-1 complex and two H-bonds for the T016-NDM-1.

Carbapenems were previously identified as the most effective β-lactam antibiotics against Gram-positive and Gram-negative pathogens. However, the emergence of MDR bacteria has greatly limited the efficacy of this drug class. As a result, the development of novel therapeutics against MDR bacteria is an urgent global requirement. NDM-1, which can hydrolyze most β-lactam antibiotics, including carbapenems, is the most abundant enzyme produced by a variety of bacterial species. Thus, effective NDM-1 inhibitors could recover the sensitivity of superbugs to β-lactamase antibiotics and address the most significant concerns about bacterial resistance. This study designed and assessed the physicochemical properties and binding affinities of 20 molecules that targeted the NDM-1 enzyme. Docking and MD simulation studies identified two compounds (T008 and T016) with the highest binding free energies (-9.83 and 10.64 kcal/mol, respectively). T016 could be characterized as a lead molecule because it had the highest binding free energy, showing even stronger binding than the positive control. This compound also interacted strongly with the NDM-1 active site using hydrophobic and metal coordination contacts, which are required to inhibit the enzyme’s activity. Moreover, the stability of T016 with the receptor was significant during the MD simulations. The chemical structure of T016, which lacked a β-lactam group and included thiazole, naphthalene, benzoic acid, and sulfone functional groups, was vital to its interactions with the receptor’s active site and the formation of H-bonds and metal coordination. These findings showed that T016 may competitively bind with NDM-1 when combined with carbapenems and protect carbapenem from hydrolysis, helping to improve the activity of this drug against MDR bacteria. Further in vitro and in vivo studies are required to confirm the activity of T016.

Funding information This research received no external funding.

Acknowledgments We appreciate the support from the College of Pharmacy at the University of Sulaimani in Sulaymaniyah, Iraq.

Author contribution Twana Salih and Pshtiwan G. Ali contributed to the study's conception and design. Both authors participated in material preparation and data collection. Pshtiwan G. Ali prepared Figures 2, 3, 6, 7, and 8. Twana Salih prepared Figures 1, 4, and 5. Pshtiwan G. Ali prepared both Tables 1 and 2. Twana Salih performed data analysis and interpretation of the results. The first draft of the manuscript was written by Pshtiwan G. Ali and reviewed by Twana Salih. Both authors read and approved the final manuscript.

Conflicts of Interest The authors declare no conflict of interest.

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Rational design of novel compounds to serve as potential NDM-1 inhibitors using molecular docking, molecular dynamics simulation, and physicochemical studies

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