
==== Front
Emerg Microbes Infect
Emerg Microbes Infect
Emerging Microbes & Infections
2222-1751
Taylor & Francis

39193648
2396877
10.1080/22221751.2024.2396877
Version of Record
Drug Resistance and Novel Antimicrobial Agents
Research Article
Discovery of novel BfmR inhibitors restoring carbapenem susceptibility against carbapenem-resistant Acinetobacter baumannii by structure-based virtual screening and biological evaluation
Emerging Microbes & Infections
Y. Yao et al.
Yao Yue abc
Lei Tailong abcd
Gao Junbo d
Xu Qingye abc
Xu Lei e
Zhao Buhui fg
Qin Shangshang fg
Yu Yunsong abc
https://orcid.org/0000-0001-8215-916X
Hua Xiaoting abc
a Department of Infectious Diseases, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, People’s Republic of China
b Key Laboratory of Microbial Technology and Bioinformatics of Zhejiang Province, Hangzhou, People’s Republic of China
c Regional Medical Center for National Institute of Respiratory Diseases, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, People’s Republic of China
d Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, People’s Republic of China
e Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, People’s Republic of China
f School of Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, People’s Republic of China
g Key Laboratory of Advanced Drug Preparation Technologies, Ministry of Education, Zhengzhou University, Zhengzhou, People’s Republic of China
CONTACT Yunsong Yu yvys119@zju.edu.cn Department of Infectious Diseases, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, No. 3 Qingchun East Road, Hangzhou 310016, Zhejiang, People's Republic of China
Xiaoting Hua xiaotinghua@zju.edu.cn Department of Infectious Diseases, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, No. 3 Qingchun East Road, Hangzhou 310016, Zhejiang, People's Republic of China
Yue Yao and Tailong Lei are co-first authors of the article. They contribute equally to the article.

Supplemental data for this article can be accessed online at https://doi.org/10.1080/22221751.2024.2396877.

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https://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.

ABSTRACT

The emergence and spread of Acinetobacter baumannii pose a severe threat to public health, highlighting the urgent need for the next generation of therapeutics due to its increasing resistance to existing antibiotics. BfmR, a response regulator modulating virulence and antimicrobial resistance, shows a promising potential as a novel antimicrobial target. Developing BfmR inhibitors may propel a new therapeutic direction for intractable infection of resistant strains. In this study, we conducted a structure-based hierarchical virtual screening pipeline combining molecular docking, molecular dynamic simulation, and MM/GBSA calculation to sift the Specs chemical library and finally discover three novel potential BfmR inhibitors. The three hits can reduce the MIC of meropenem for the carbapenem-resistant Acinetobacter baumannii (CRAB) strain ZJ06. Similar to the BfmR knockout strain, Cmp-98 was demonstrated to downregulate the expression of K locus genes, indicating it as a BfmR inhibitor. Bacteria underwent harmful morphological changes after treatment with these inhibitors. Molecular dynamic simulations found that all the hits tend to dynamically bind to different positions of the phosphorylation site of BfmR. Wherein we identified a potential inhibitory-binding cleft, beside a possible activated binding cleft at the edge of the phosphorylation site. Restraining the ligand binding poses may help exert inhibitory effects. This study reports a group of new scaffold BfmR inhibitors, offering new insights for novel antibiotic therapeutics against CRAB.

KEYWORDS

BfmR
inhibitor
carbapenem-resistant Acinetobacter baumannii
virtual screening
molecular simulation
activity evaluation
National Natural Science Foundation of China 10.13039/501100001809 U22A20338 82304377 Natural Science Foundation of Zhejiang Province 10.13039/501100004731 LQ21H300001 This study was supported by the National Natural Science Foundation of China (U22A20338, 82304377) and the Natural Science Foundation of Zhejiang Province (LQ21H300001).
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pmcIntroduction

Acinetobacter baumannii is a formidable/intractable Gram-negative pathogen prevalent in hospital-related infections. The emergence and spread of carbapenem-resistant A. baumannii (CRAB) poses a grave threat to public health. Among the molecular participants in A. baumannii's pathogenic arsenal, BfmR (i.e. rstA) stands out as a pivotal response regulator within the BfmRS two-component system [1]. In 2008, the bfmR gene was initially identified its crucial involvement in biofilm formation, fimbriae production, and bacterial morphogenesis for A. baumannii's adaptive strategies [2]. Later extensive research has elucidated the multifaceted roles of BfmR in modulating virulence and antimicrobial resistance. BfmR has been demonstrated to control virulence, capsular exopolysaccharide production, and biofilm formation, facilitating escape from antibiotic exposure with a feedback interaction in A. baumannii [3]. Moreover, BfmR confers increased resistance to critical antimicrobials such as colistin, imipenem, and rifampin [3,4]. BfmR knockout studies showed diminished meropenem resistance in BfmR-deficient strains of multidrug-resistant A. baumannii (MDRAB) [5]. These characteristics of BfmR have propelled it into the spotlight as a potential antimicrobial target, and are further supported by structural studies indicating its druggability [5].

Traditional drug discovery endeavours are lengthy, arduous, and resource-intensive, whereas structure-based virtual screening offers a promising avenue to expedite the screening of putative BfmR inhibitors from millions of compounds. The high-resolution structure model of BfmR unveiled in a previous study shedding light on the details of its DNA-binding mechanism, is a pivotal starting step in rational drug design [6]. Similar to other OmpR/PhoB family response regulators, BfmR consists of a CheY-like receiver domain and a winged-helix DNA-binding domain. The N-terminal receiver domain contains a phosphorylation site dephosphorylated by histidine kinases BfmS [7], and the C-terminal domain takes charge of binding to promoters of downstream target genes. BfmR usually undergoes autophosphorylation by small phosphate donors such as acetyl phosphate and then binds directly to the promoter and initiates transcription, reprograms the A. baumannii transcriptome [1].

Despite these advancements, the translation of BfmR inhibition into clinically viable therapeutics remains a great challenge. Early studies identified several 2-aminoimidazoles bound to the hinge region between the two domains of BfmR through molecular docking and thermal shift assays [8,9]. Siph-1 was identified as a potential inhibitor of BfmR due to its in vitro activity and virtual inverse docking to the phosphorylation cavity of BfmR [10]. An early virtual screening study identified a series of hits bound to the phosphorylation cavity and the α4-β5-α5 dimerization face [11]. In a recent extensive virtual screening study, several hits, scoring strong binding affinity to BfmR, were obtained from 8450 phytomolecules through molecular docking, ADMET filtering, and molecular dynamic simulation [12]. Among them, three hits, namely calystegine B3, 7,7 A-diepialexine, and α-methyl noradrenaline, warrant further wet validation of their inhibitory activity. However, these studies have reported compounds with various scaffolds to inhibit BfmR, yet further activity validation was seldom carried out [12]. In this study, a high-throughput hierarchical virtual screening approach was established to search for novel BfmR inhibitors, yielding three promising BfmR inhibitors through antibacterial activity validation. Among these, cmp-98 exhibited potentiation of meropenem activity, and demonstrated superior performance in biochemical experiments, offering a glimmer of hope for combating CRAB strains.

Methods

Bacterial strains, chemicals, and MIC determination

CRAB strain ZJ06, belonging to global clone 2 (GC2), was isolated as previously described [13]. Other CRAB strains were isolated, as previously described [14], and carbapenem-resistant Pseudomonas aeruginosa (CRPA) strains were isolated from patients in the ICU of our hospital in Hangzhou, China (Table S1). Hinton (MH) Broth or Mueller Hinton (MH) Agar (Oxoid, UK) served as the culture media throughout this study. Compounds procured from the Specs company (http://www.specs.net) were dissolved in DMSO at the concentration of 32 μg/mL. The minimum inhibitory concentrations (MIC) of meropenem and compounds were determined using the broth microdilution method as per the Clinical and Laboratory Standards Institute (CLSI) guidelines [15].

Structure-based virtual screening for BfmR inhibitors

Small molecule Compound-530 [11] was docked to the apo-state crystal structure of the BfmR receiver domain (PDB ID: 5E3J) [5] at the Asp58 phosphorylation site and the α4-β5-α5 dimerization interface site respectively. The binding free energy (ΔG) of the various docking poses was calculated using the MM/GBSA method to identify the optimal protein–ligand complex pose for two distinct binding sites. Subsequently, every solvated complex was heated uniformly from 0 to 300 K during 50 ps, and then equilibrated density 50 ps with a weak restraint of 2.0 kcal·mol−1·Å−2 on the complex. After 500 ps system equilibration in the standard condition (300 K and 1.01325 bar) NPT ensemble, the 5 ns conventional molecular dynamic (cMD) simulation was performed for two distinct binding sites using the NPT ensemble [16-18]. The snapshots were captured every 2 ps. All the cMD conformations of each binding site were then clustered into five groups by the k-means algorithm based on the pairwise root-mean-square deviations (RMSDs) between any two of the extracted conformations, and one representative conformation was finally chosen for each binding site [16–18]. The ΔG was calculated on the last 250 snapshots extracted from the 5 ns cMD trajectory and then decomposed into per residue-ligand contributions to determine key interaction residues [16–18]. For the representative conformations of the two sites, the standard precision (SP) scoring mode of Glide was used to evaluate their docking capabilities respectively using the previously reported ligands [11], and then the optimal virtual screening model of the inhibitory conformation was achieved for each site.

Based on the optimal virtual screening conformations of the two sites, the Specs chemical library of over 200 thousand compounds was prepared and screened preliminarily using the Glide SP scoring mode. Then the top 5000 compounds were rescored and ranked by the MM/GBSA method. The remaining 1000 compounds were filtered by Lipinski's rule of five and Oprea's rules, and then the remaining compounds were clustered by the Tanimoto similarity of the MACCS fingerprints. Finally, a total of 206 compounds were purchased for further bioassays.

System preparation and docking were implemented in the Schrödinger 2018 suite (New York, NY, USA). All molecular dynamic simulations and binding free energy calculations were performed using Amber18 software [19] (San Francisco, CA, USA), and the general amber force field (GAFF) was used for ligands, and the ff14SB force field was used for protein.

Bactericidal activity validation

Compounds were screened at a final concentration of 256 mg/L combined with 2 mg/L meropenem in 96-well plates. Overnight cultures of the CRAB strain ZJ06 were adjusted to 0.5 McF in NaCl saline, and diluted 1:20. Every 100 μL of drug solutions was co-incubated with 10 μL bacterial solution at 37°C for 20 h. Compounds were selected with no viable bacteria growth by spotting each well culture solution on MHA plates.

Cell cytotoxicity assay

The cytotoxicity of the Cmp-86, Cmp-97, and Cmp-98 against human alveolar basal epithelial cells (A549) was assessed using the CCK-8 assay. A549 cells were inoculated at a density of 5 × 103 cells/well in 96-well plates at 37℃ with 5% CO2 for 24 h. The cells were then treated with different concentrations of the inhibitors for 24 h. Subsequently, 10 µL of CCK-8 solution was added to each well and incubated for another 1.5 h. The supernatant was discarded, and each well received 160 µL of DMSO. After the formazan crystals had completely dissolved, the absorbance of each well was measured at 450 nm with a microplate reader (M200 pro, Switzerland).

Synergy test by growth determination

Growth of the CRAB and CRPA strains under different drug combination groups was determined using a Bioscreen C Analyser (Oy Growth Curves Ab Ltd., Turku, Finland), as previously described [20]. Three parallel replicates were set up for each treatment.

Synergy test by the checkerboard method

The checkerboard method was used to determine the synergy effect of compounds and meropenem combination on the CRAB strain ZJ06, as previously described [21]. In the tests, compound concentrations were adjusted from 4× MIC to 1/64 and 1/32 MIC according to their MICs. The fractional inhibitory concentration (FIC) index was used to assess interactions between meropenem and inhibitors [FIC = (MIC drug A in combination/MIC drug A alone) + (MIC drug B in combination/MIC drug B alone)]. Combinations with FIC values >4 are antagonistic, >1-4 are addictive or indifferent, >0.5−1 are moderate synergistic, and ≤0.5 are synergistic [22].

Cell morphology examination by SEM

Scanning electron microscopy (SEM) was employed to examine the morphological changes of the CRAB strain ZJ06 upon treatment with putative inhibitors, meropenem, or their combination. Bacteria were cultured in MH broth to mid-log phase, then treated with 4x MIC drugs above for 3 h. Bacteria cells were processed and imaged using Nova NanoSEM 450 (Thermo Fisher, USA), as previously described [23].

Biofilm formation assay

Biofilm formation was quantified using the crystal violet staining method with modifications [24]. Overnight bacterial suspension was diluted in 1:50, and incubated in fresh MH broth containing sub-MIC (16, 32 μg/mL) inhibitors in Corning 96-well plates (Sigma) at 37°C for 48 h. The supernatant was gently mixed to measure the optical density at 600 nm. After that suspended bacteria were washed off three times with sterile distilled water. The biofilm remaining in wells was stained with 1% crystal violet at room temperature for 30 min. After being washed three times with sterile distilled water and dried at 60°C for 20 min, the biofilm was solubilized in 95% ethanol and then the absorbance was measured at 600 nm. OD540/OD600 was used to evaluate biofilm formation ability.

Determination of gene expression by RT-qPCR

The effect of putative BfmR inhibitors on BfmR downstream-regulated genes gnaA, galU [3], and quorum sensing genes abaR and abaI [25] was determined by quantitative reverse transcription-PCR (RT-qPCR). A single colony of the CRAB strain ZJ06 was cultured overnight in an MH broth and diluted to 1:100 in a fresh broth. The putative inhibitors were added into the exponential-phase culture with a concentration of 0.5x MIC or 1× MIC for 0.5 h treatment. The total RNA extraction, cDNA reversion, and the RT–PCR procedure were performed, as previously described [26] with at least three independent replicates for every treatment group. Primer sequences used in this study are presented in supplementary Table S2. The relative gene expression was calculated as 2-ΔΔCT, using the untreated group as a reference.

Exploring potential binding mechanism by cMD

After the synergy antimicrobial susceptibility tests and other biological evaluations, the optimal inhibitors were identified, and their potential binding mechanism was studied. The tested compounds along with Compound-530 [11] and Siph-1 [10] were re-docked to the optimal virtual screening conformations of BfmR at the two sites and then executed long-time (100 μs) cMD simulation in the NPT ensemble to observe the molecular behaviour. After the complex system was equilibrated after the default system relaxation process, simulations were performed in the standard condition (300 K and 1.01325 bar) in an orthorhombic TIP4P explicit water box with charge neutralized by counter ions. The time step was set to 2 fs. The snapshots were captured every 10 ps. For the Cmp-97 and Cmp-98 systems, a total of three independent MD simulations were conducted, and a total of 600 µs trajectories were generated for the two systems (2 × 3 × 100 μs). The trajectories of each system were carried out the affinity propagation clustering [27], and the geometric and energy-based properties such as RMSD, distance, and secondary structures were analyzed based on all the heavy atom coordinates in the trajectories. The ΔG of the selected representative structures of the clustered metastable macrostates were calculated and analyzed. All the long-time simulations and analyses were done by the Desmond module in Schrödinger on four NVIDIA GeForce RTX 3080Ti GPUs.

ITC assay

ITC measurements were performed using a MicroCal PEAQ-ITC (Malvern Panalytical) at 25 °C. By titrating 2–16 mM putative inhibitors into the solution of 13–36 μM BfmR protein in the buffer (150 mM NaCl, 5% glycerol, and 20 mM tris-HCl pH7.5). Each titration experiment was initiated by a 0.4 μL injection and followed by 18 injections with a 2 μL volume each. The results were analyzed using MicroCal PEAQ-ITC Analysis Software (Malvern Panalytical).

Statistical analysis

Data were analyzed using ANOVA followed by Bonferroni T tests or ordinary ANOVA. GraphPad Prism software (version 8.0; San Diego, CA, USA) was used during statistical analysis and figure drawing. Significance levels were indicated as ****P < 0.0001; ***P < 0.001; **P < 0.01; *P < 0.05; NS, P > 0.05.

Results

Preparation of the optimal inhibitory conformation of BfmR for virtual screening

BfmR exhibits flexible and dynamic behaviours similar to other transcription factors, transitioning between the active and inactive state conformation. However, current crystallographic structures capture either the active state induced by BeF3− or the inactive state resulting from dephosphorylation. The conformation of inhibitor binding remains elusive. Thus, we need to acquire the optimal inhibitor binding conformation to enhance the accuracy of virtual screening efforts.

In this study, a putative inhibitor Compound-530 [11] was applied to generate the protein-inhibitor complexes. MM/GBSA binding energy was calculated for different poses to find the optimal pose at the Asp58 phosphorylation site and the α4-β5-α5 dimerization interface site. The binding energies for all poses are listed in Table S3. Then 5 ns cMD simulations were used to get stable state conformations for the best docking poses of the two sites, respectively. As shown in Figure S1, the MM/GBSA-binding energy of the complexes stabilized after about 1.5 ns (750 frames), with all the simulated systems achieving equilibrium during the 5 ns trajectory.

To identify representative conformations for each site, k-means clustering was performed on all 5 ns cMD conformations, with the centre of the largest cluster designated as the representative conformation. The clustering results are depicted in Figure S2. The energy decomposition on key interaction residues throughout the trajectory is presented in Table S4 and S5 for the two sites, respectively. Furthermore, the validity of the representative conformation for the two sites of BfmR was confirmed through re-docking experiments using previously reported ligands [11], and all the successful docking with the RMSDs of the backbone Cα atoms below 2.0 Å demonstrated the fidelity of the representative conformations. Thus, the representative conformations depicted in Figure S2 were identified as the optimal virtual screening model of the inhibitory conformation of BfmR.

Structure-based virtual screening of potential BfmR inhibitors

We conducted a hierarchical virtual screening of the Specs library guided by the inhibitory conformation induced by Compound-530 [11]. The Specs compounds were docked to the Asp58 phosphorylation site and the α4-β5-α5 dimerization interface site of BfmR using the Glide SP scoring mode. Subsequently, the top-ranked 5000 compounds were subjected to MM/GBSA rescoring and the refined 1000 compounds were deemed as a condensed library of potential BfmR inhibitors. To further narrow down our selection, a drug-likeness evaluation was conducted, followed by structural clustering and visual inspection of the docked poses. This meticulous approach allowed us to identify 96 potential compounds targeting the Asp58 phosphorylation site and 110 potential compounds targeting the α4-β5-α5 dimerization interface site for subsequent biological evaluation. A schematic representation of the complete virtual screening workflow applied in this study is illustrated in Figure 1. Figure 1. The complete virtual screening workflow for BfmR inhibitors applied in this study. (A) Representative inhibitory binding pose simulation (B) Hierarchical virtual screening workflow.

Putative BfmR inhibitors can restore meropenem susceptibility in CRAB

After the preliminary validation of bactericidal assays, three hits, designated as Cmp-86, Cmp-97, and Cmp-98 (Figure 2(A)), remained for further activity validation. The growth rates of the CRAB strain ZJ06 at different sub-inhibitory concentrations of meropenem and inhibitors were monitored over 20 hours (Figure 2(B)). At the concentrations corresponding to 1/8 MIC of the BfmR inhibitory hits, a discernible reduction in the growth rates of ZJ06 was observed with and without meropenem (Figure 2(C)). Remarkably, nearly no growth was detected in group I when meropenem was combined with either Cmp-86 or Cmp-97. Checkerboard assays were conducted to evaluate the synergistic activity of meropenem and hit combinations against ZJ06. The resulting FIC indexes were 1.00, 0.56, and 0.75 (Figure 2(D)), respectively, indicating moderate synergistic effects between each hit and meropenem. Figure 2. Antibacterial effects of putative BfmR inhibitors. (A) Chemical structures and MICs against ZJ06 of BfmR inhibitors validated by bactericidal assays and meropenem (MEM). (B) The experimental concentration design of the synergistic tests. (C) Growth curves and relative maximum growth rates of ZJ06 at different concentrations, as described in panel B. The results are the mean (±SD) of three independent replicates. *P < 0.05, **P < 0.01, ****P < 0.0001, ns, no significance, compared to the negative control (group A). (D) The synergistic inhibitory effects of meropenem and three hits were tested by the checkerboard assay. The bacteria growth is visualized as a heatmap with corresponding FIC indexes present below.

BfmR is present in other bacterial species as well, to evaluate the broad-spectrum efficacy of BfmR inhibitors and determine whether they are effective against other bacterial species, growth of CRPA, and other CRAB at sub-inhibitory concentrations of meropenem (16 μg/mL for CRAB and 32 μg/mL for CRPA), and inhibitors (32 μg/mL for both CRAB and CRPA) were monitored over 20 hours. The combined drug groups of all strains showed lower levels of growth than the MEM group, once again confirming the synergistic antimicrobial effect of the inhibitor on meropenem (Figure S3).

Moreover, the cytotoxicity of the three inhibitors was evaluated by CCK-8. The IC50 of Cmp-86, Cmp-97, and Cmp-98 against A549 were 10.16, 5.775, and 19.54 μg/mL (Figure S4). It should be noted that these inhibitors were designed as lead candidates at the current stage, a variety of methods can be employed to detoxify and optimize these compounds to achieve better safety (our future work).

Cell morphological aberrations observed after putative BfmR inhibitors’ treatment

To understand how the inhibitors and meropenem affect bacterial cells, we compared ZJ06 morphology before and after treatment via SEM (Figure 3). The interaction between inhibitors and meropenem with ZJ06 all resulted in distinct abnormal alterations in bacterial morphology. Untreated ZJ06 displayed a typical short rod-shaped plumped morphology with a smooth surface, and even division in the middle. After treatment with meropenem alone, many bacteria appeared slightly more sunken and rougher surfaces. In contrast, ZJ06 treated with inhibitors mostly displayed shortened, shrunken, and irregularly shaped morphology, often with a reniform appearance and loss of regular division in the middle. The combination of inhibitors and meropenem resulted in exacerbated damage to ZJ06, further compromising bacterial morphology. Figure 3. Morphological aberrations of ZJ06 under various treatments were examined through SEM. Scale bars are 1 μm. Log-phase ZJ06 bacteria were treated with 4× MIC MEM and inhibitors, both alone and in combination, for 4 h.

Sub-inhibitory concentrations of Cmp-98 prevented the biofilm formation of ZJ06

Given BfmR's pivotal role in the biofilm formation and maintenance of A. baumannii, we selected sub-inhibitory concentrations of compounds to treat ZJ06 and assessed the biofilm formation levels after 48 h using the crystal violet staining method. Sub-inhibitory concentrations of Cmp-98 led to significant inhibition of the biofilm formation in ZJ06. In contrast, no significant reduction in biofilm formation was observed in the treatment with other compounds (Figure 4). Figure 4. Effects of sub-MIC inhibitors in biofilm formation of A. baumannii. Biofilm was quantified by crystal violet staining, performed on 4 individual replicates, in triplicate. Data are shown as OD540/OD600, mean ± SEM. **P < 0.01, ***P < 0.001, ns, no significance.

Three hits exhibited different effects on BfmR-relative genes

Since BfmR is an integral component of the BfmRS two-component system, and it exerts regulatory control over multiple genes in bacteria, we investigated the impact of BfmR inhibitors on the mRNA expression levels of BfmR-relative genes in ZJ06. Compared to untreated ZJ06, the expression of K locus genes gnaA，and galU exhibited a concentration-dependent increase in response to Cmp-86 and Cmp-97, whereas the expression pattern differed for Cmp-98. Interestingly, while the expression of gnaA and galU initially increased at 0.5x MIC of Cmp-98, it subsequently decreased below the untreated group level at 1x MIC. Similarly, the expression levels of quorum sensing system genes abaI and abaR showed higher expression levels, albeit not significant, compared to the untreated group, generally mirroring the observed trends in K locus genes (Figure S5).

The predicted binding poses of the putative BfmR inhibitors

We first conducted a re-docking analysis with post-docking minimization and Glide SP scoring to explain the findings from mRNA expression and biofilm formation assays, using Compound-530 [11] as the template ligand for docking. Our analysis revealed intriguing insights into the binding modes of the tested compounds.

Specifically, Cmp-98 formed hydrogen bonds with Asp90 of BfmR, just like Compound-530 and Siph-1, adopting a similar pose orientation at the α4-β5-α5 interface (Figure S6A). In contrast, Cmp-97 and Cmp-86 acted similar yet different binding modes than Cmp-98, both curling as a hairpin within the interface groove (Figure S6B). Cmp-98 exhibited the lowest ΔG (−35.95 kcal/mol), while Compound-530 exhibited the highest ΔG (−53.16 kcal/mol), with other compounds ranked as Cmp-97 > Cmp-86 > Siph-1.

At the phosphorylation site, all three screened hits formed salt bridges with Asp58 and Asp15 of BfmR through nitrogen atoms adopting conformations astride the marginal ridge (Figure S6C), but Compound-530 and Siph-1 distinctively bound to Asp15 at an outer cleft beside the marginal ridge (Figure S6D). Siph-1 achieved the lowest ΔG (−34.18 kcal/mol) and Cmp-86 achieved the highest ΔG (−49.46 kcal/mol), with other compounds ranked as Cmp-98 > Cmp-97 > Compound-530. The collective findings suggest that all three hits may exhibit comparable inhibition against the phosphorylation of BfmR. However, it appears that Cmp-98 may exert a stronger inhibitory effect on the dimerization of BfmR. This heightened inhibition could potentially hinder the initiation of transcription downstream genes.

The dynamic interaction patterns of the putative BfmR inhibitors suggest different conformations of the inhibitory and activated state

The bioassay results presented above demonstrated that while the three tested compounds indeed inhibit bacterial growth, they elicit varying transcriptional responses in BfmR downstream genes. To gain deeper insights into the atomic-level interactions between the compounds and BfmR, we examined the compound behaviours through molecular simulation.

First, we inspected all the re-docking poses and observed that small molecules could bind to the α4-β5-α5 dimerization interface site and the Asp58 phosphorylation site, provided they could bind to BfmR at all. Then, Cmp-97 and Cmp-98 as representatives underwent a long-time cMD simulation to explore their dynamic receptor–ligand interaction patterns. These cMD simulations, accounting for solvent effects and structural flexibility of ligands and receptors, aimed to address limitations concerned with rigid docking and empirical scoring protocols. Every fifth frame of all the trajectories was superimposed and calculated the RMSD and one representative frame was picked for each of the 20 clusters from the RMSD matrix. Analysis of trajectory data revealed that all the studied ligands could bind to three main clefts: the A-B cleft and B-C cleft at the Asp58 phosphorylation site, as well as the D cleft at the α4-β5-α5 dimerization interface site (Figure 5). Their binding positions and poses are rather different from the previously reported compounds, showing a completely different binding mode (Figure 6). Furthermore, ligands can undergo significant displacement when binding to BfmR (Figure 5). Even when binding to the same phosphorylation site, ligands are not strictly limited to a certain region, but rather display dynamic states transferring across several adjacent surface grooves (A-B and B-C clefts in Figure 5), leading to conformation changes in the receptor. This observation underscores that the conformation of the BfmR phosphorylation site may be quite responsive to ligand binding. This is consistent with BfmR’s characteristics as a transcription factor capable of activating or inhibiting the transcription of diverse genes. Figure 5. The three typical dynamic receptor-ligand interaction patterns were observed in the 100 μs long-time cMD simulations. The ligand carbons are coloured in green. The surface of the holo BfmR monomer is coloured in residue charges, with red denoting negative charges, and blue denoting positive charges. The ribbon representation of BfmR is coloured grey. Four main binding clefts are labelled as A-D with Asp58 located in the negatively charged bottom of the B cleft.

Figure 6. Two typical interaction patterns when ligands are bound to the Asp58 phosphorylation site. The ligand carbons are coloured in green. The binding surface of the holo BfmR monomer is coloured in residue charges, with red denoting negative charges, and blue denoting positive charges. The ribbon representation of BfmR is coloured in position. Four main binding clefts are labelled as A-D with Asp58 located in the negatively charged bottom of the B cleft.

Setting Siph-1 as a reference inhibitor for its reported BfmR inhibitory activity and binding pose, we noticed that the conformational dynamics of BfmR were less significant when combined with agonists or modulators, but markedly significant when combined with inhibitors (Figure 7). The activated conformation of BfmR typically exhibited relatively stretched (Figure 7), with ligands spanning the A and B clefts, lower ΔG values (Figure 6(F&H)), and weaker DNA-binding ability. Conversely, the inhibitory conformation of BfmR tended to be compact (Figure 7), with ligands spanning the B and C clefts, higher ΔG values (Figure 6(A&E)), and stronger DNA-binding ability. Cmp-98 exhibited much stronger BfmR inhibition, resulting in a greater difference between its inhibitory and activated binding conformations compared to Cmp-97. Figure 7. Binding mode and receptor flexibility comparisons between the supposed inhibitory and activated conformation in the Cmp-98 (A) and Cmp-97 (B) bounded BfmR complexes, and the surface comparisons of the supposed Cmp-98 inhibited (C) and BeF3- activated (D) BfmR. Four main binding clefts are labelled as A-D with Asp58 located in the negatively charged bottom of the B cleft.

Despite variations in ligand behaviour, our simulations consistently demonstrated that all ligands can transfer to the D cleft through continuous conformational changes at some point during the cMD simulation, with typically the lowest ΔG among four clefts that implied the unstable transient binding. Subsequently, ligands always continue to transfer to other clefts following their interaction with the D cleft.

Based on the above phenomena, we speculated that when a ligand binds to the A-B cleft of BfmR, the protein adopts an activated phenotype (Figure 8(A)), while when a ligand binds to the B-C cleft of BfmR, the protein adopts an inhibitory phenotype (Figure 8(B)). When ΔG binding to the B-C cleft is higher and ΔG binding to the A-B cleft is lower, resulting in a greater ΔG difference between the two clefts, the ligand is more likely to exhibit inhibitory properties. This could lead to a stronger inhibition of transcription levels of downstream genes such as gnaA and galU (Figure 8(D)). Ligands with a lower ΔG difference between the two clefts may tend to exhibit activated properties, potentially promoting the transcription of downstream genes. Therefore, in subsequent screening and design efforts of BfmR inhibitors, it may be advantageous to prioritize enhancing ligand binding affinity to the B-C cleft together with reducing ligand binding affinity to the A-B cleft. This approach could facilitate the identification of more potent inhibitors with greater specificity towards inhibiting BfmR activity, as opposed to merely modulating its function. Figure 8. The ΔGMM/GBSA differences (C) between the supposed inhibitory and activated conformations (Cmp-98 in panels A and B as examples) revealed that greater ΔGMM/GBSA difference results iin greater BfmR inhibition against promoting the gene transcription (D). The surface of the holo BfmR monomer is coloured in residue charges, with red denoting negative charges, and blue denoting positive charges. The ribbon representation of BfmR is coloured grey. Four main binding clefts are labelled as A-D with Asp58 located in the negatively charged bottom of the B cleft.

Overall, our molecular simulations offer valuable insights into the dynamic interactions between BfmR and its inhibitors, shedding light on the conformational flexibility and binding mechanisms underlying BfmR inhibition.

Confirming the interaction between inhibitors and BfmR by ITC assay

We conducted ITC experiments to validate the binding and the affinity between the putative inhibitors and BfmR. We determined that Cmp-86, Cmp-97, and Cmp-98 bind to the BfmR protein with equilibrium dissociation constant (Kd) of 824, 543 μM, and 4.83 mM, respectively (Figure S7). Non-S shape curves were obtained which normally means the protein never reaches saturation. A plausible explanation could be the moderate binding affinity between the inhibitors and BfmR [28]. It is important to note that these inhibitors are non-covalent inhibitors, which inherently suggests a less stringent interaction compared to covalent binding. We are currently attempting to design covalent inhibitors targeting BfmR, which could potentially offer greater specificity and potency in their interactions.

Discussion

This study integrates structure-based virtual screening against BfmR with whole-cell-based biochemical assays, ensuring that the resulting compounds possess antibacterial activity against A. baumannii. In silico pharmacology applies computational models to rapidly select compounds from extensive libraries, providing a rather predictable and rational route to drug discovery. With advancements in genomic sequencing and structural bioinformatics, empirical high-throughout drug screening based on biochemical assays does not seem to be precise enough, and therefore, it is transitioning towards structure-based drug discovery.

Although the significance of BfmR’s global regulatory role in A. baumannii has been highlighted in prior studies, only a few mentioned BfmR inhibitors until now [8,10–12]. Milton et al. reported a 2-amino imidazoles library consisting of 26 compounds targeting BfmR, and several of them could enhance susceptibility to carbapenem derivatives and decrease biofilm formation. Ahmad et al. utilized BfmR’s structure to perform a structure-based virtual screening on the Asinex antibacterial library with 8044 natural scaffolds, leading to the discovery of the highest scored Compound-530 but without any wet test [11]. Lokhande et al. conducted similar research on an Indian IMPPAT database with 8450 phytomolecules and discovered five potential hits yet without any wet test validation [12]. Alam et al. discovered that a natural product from sponges (Siph-1) inhibited quorum-sensing systems and biofilm formation, and bound to the phosphorylation site of BfmR’s N-terminal domain with high affinity in the molecular docking and simulation [10]. Our work advances the BfmR inhibitor screening by establishing a comprehensive process that includes in silico screening and in vitro bioactivity validation. This approach provides a precise methodological framework for future studies. Our study benefits from a larger compound library (>200,000 molecules) and precise structure-based screening, ensuring a high screening yield as much as possible. Moreover, our bioactivity results provide empirical evidence for the dose-dependent inhibitory effect of putative BfmR inhibitors. We also identified a better inhibitor Cmp-98 showing lower ΔG than the initial molecule Compound-530, indicating a step forward in the field of BfmR inhibitors [11].

Typically, inhibitors block target protein function by binding to crucial sites. In our study, effective inhibitors may act through BfmR-related and antibacterial-related pathways. For instance, the expression downregulation of K locus gene gnaA following treatment with Cmp-98 may be reasonably explained by the inhibitor-targeting BfmR, leading to phenotypic changes such as aberrated morphology, because gnaA is a downstream gene regulated by BfmR [3] and its disruption affected the morphology, antibiotic susceptibility, and virulence of the pathogen [23].

BfmR knockout could lead to changes in multiple cell characteristics in A. baumannii due to its global transcription control function. Insertional inactivation of BfmR leads to an aberrant cell morphology with long filaments when cultured in a minimal medium [2]. Bacteria lacking BfmRS also experience a different kind of defect cell morphology at low concentrations of imipenem [1], which share similar results with our study. The observed phenotype differences between BfmR knockout strains and inhibitor-treated strains suggest that inhibitors exert unique effects distinct from knockout models but similar to knockdown models.

The predicted binding poses of the identified ligands showed that all the ligands harbour the phosphorylation site though forming hydrogen bonds with Asp15, suggesting that Asp15 may play a crucial role in the strong binding, which comes in accord with the previous studies [5,6,10,11,29,30]. This residue is also crucial for preserving Mg2+ coordination at the active site [5,6], so the future screening efforts of BfmR inhibitors should emphasize Asp15 as another key residue alongside the phosphorylated residue Asp58.

In conclusion, we identified three putative BfmR inhibitors through structure-based hierarchical virtual screening and found that they can restore meropenem susceptibility to CRAB, providing a promising therapeutic strategy for clinical CRAB infection.

Supplementary Material

Supplementary_material_clean.docx

Acknowledgments

We are grateful for the computility support of Alibaba Cloud and the software support of the Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology. We are very grateful to our colleague Hanming Ni for the gift of the Pseudomonas aeruginosa strains.

Disclosure statement

No potential conflict of interest was reported by the author(s).
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