Structural modeling, model assessment, validation, and binding site analysis

AlphaFold2 was utilized to predict 3D model of the transmembrane histidine kinase, and the top-ranked model was selected based on the Local Distance Difference Test (IDDT) score. This model was then refined using the ModRefiner server and assessed for quality using the SAVES server. The resulting PROCHECK analysis, which is based on the Ramachandran plot, showed that 93.5% of the residues were located in the core or most favored regions, 6.0% in additional allowed regions, 0.2% in generously allowed regions, and 0.2% in disallowed regions. These findings suggest that the overall quality of the predicted model was good. Furthermore, CASTp and COACH were used to determine the binding side residues in transmembrane histidine kinase. During the analysis, comparable results were obtained for multiple amino acid residues, including Asp325, Asn395, Lys398, Tyr399, Asp423, Ile428, Tyr441, Arg442, Thr443, Gly455, Leu456, Gly457, Ile458, Gly459, Leu460, Thr484, and Phe486. These residues were predicted to be commonly present in the binding pocket using both tools. The amino acid residues Leu321, Asn324, Asp325, Asn328, Val332, Phe357, Leu360, Arg 361, Gln387, Met390, Ile391, Asp394, Asn395, Ala396, Ile397, Lys398, Tyr399, Asp423, Ile428, Ile436, Phe437, Asp438, Tyr441, Arg442, Thr443, Ser446, Gly455, Leu456, Gly457, Ile458, Gly459, Leu460, Ser461, Val462, Thr484, and Phe486 predicted by CASTp were chosen as binding site residues and utilized during molecular docking studies.

Investigating transmembrane histidine kinase inhibitors via structure-based virtual screening

Structure-based virtual screening is a computational method that uses the three-dimensional structure of a molecular drug target to identify potential candidate molecules in drug discovery programs. This technique involves the use of molecular docking to predict the binding energy between a set of ligands and receptors. The study demonstrated the use of the ZINC database's natural compounds subset (n = 224,205) to perform a virtual screening against transmembrane histidine kinase, in order to identify potential hits. Furthermore, molecular docking of selected antibiotics and histidine kinase (HK) inhibitors was performed as a control to compare the results with those of natural compounds. To identify potential hits for further evaluation, the binding free energy of each compound in the study was assessed. Typically, a protein–ligand complex with low binding energy indicates high binding affinity. As a result, the top ten natural compounds that showed the lowest binding energy with the transmembrane histidine kinase were selected as potential lead compounds (ranging from − 14.4 to − 13.6 kcal/mol). Table 1 lists the ZINC ID, binding free energy, type of interaction, and interacting amino acid residues for these top ten natural compounds. Tables 2 and 3 list the predicted binding energy of the selected antibiotics and HK inhibitors along with interaction type and interacting residues.

Table 1 Top ten screened natural compounds, their corresponding binding free energies, types of interactions, and the amino acid residues in the transmembrane histidine kinase involved in interactionsTable 2 Binding free energy of docked antibiotics with transmembrane histidine kinase, types of interactions, and interacting amino acid residuesTable 3 Binding free energy of reported histidine kinase inhibitors with transmembrane histidine kinase, types of interactions, and interacting amino acid residues obtained through molecular dockingEvaluation and visualization of top-screened docked complexes of natural compounds, antibiotics and HK inhibitors

Based on virtual screening and molecular docking, the binding energy of natural compounds, antibiotics, and HK inhibitors with transmembrane histidine kinase were predicted, and top-screened docked complexes were sorted for further analysis to evaluate their binding nature and inhibitory potential. The top listed natural compound ZINC000085569031 was found to interact with transmembrane histidine kinase amino acid residue Gln271 and Arg439 with conventional hydrogen bonds; alkyl bonds with Leu336 and Ala469; two pi-anion bonds with Asp438 and one pi-cation bond with Lys464; and four unfavorable positive–positive interactions with Arg268 with binding free energy − 14.4 kcal/mol (Fig. 2A-B). Another top listed natural compound, ZINC000257435291, was found to interact with Tyr265, Gln340, Arg439, and Ser461 with conventional hydrogen bonds, forming carbon hydrogen bond with Gln333. Glu337 was found to interact with a carbon hydrogen bond, unfavorable positive–positive and negative–negative; Arg268 was found to involve in protein–ligand interaction with attractive charges and conventional hydrogen bonding with a binding energy 14.2 kcal/mol (Fig. 2C-D). The details of other top-screened compounds are listed in Table 1. However, the top-screened antibiotic Tetracycline was found to interact with His278, Glu279, Phe440, and Arg447 via a conventional hydrogen bond; it interacts with Leu456 via a conventional hydrogen bond and pi-alkyl bond; and Arg442 with pi-alkyl bond and binding energy was predicted as − 8.4 kcal/mol. The details of other analyzed antibiotics and HK inhibitors are listed in Tables 2 and 3, respectively.

Fig. 2

Key amino acid residues that contribute to the protein–ligand interactions of the top two screened natural compounds with the transmembrane histidine kinase of S. agalactiae depicted through 3D and 2D representations. A-B ZINC000085569031, and (C-D) ZINC000257435291

Analysis of drug-likeness properties

ADMET prediction during the initial stages of drug discovery can be highly advantageous in minimizing the occurrence of clinical trial failures. The ADMET properties were predicted by pkCSM. The molecular properties of top-screened natural compounds were predicted. Molecular weights were predicted in the range of 771.972 to 957.39 Dalton; LogP in the range of 0.4633 to 13.2655; Rotatable bonds in the range of 5 to 15; Hydrogen bond acceptor in the range of 7 to 21; and Hydrogen bond donor in the range of 3 to 11. In absorption prediction, water solubility and intestinal absorption are important parameters, which were predicted to be in the range of − 3.397 to − 2.796 and 3.341 to 100 respectively. In distribution, BBB permeability and CNS permeability were predicted to be in the range of − 3.754 to − 0.742 and − 5.633 to − 1.509 respectively. In metabolism, CYP2D6 substrate, CYP3A4 substrate, and CYP1A2 inhibitor parameters were analyzed. The CYP2D6 substrate and CYP1A2 inhibitor were predicted as ‘No’ for all the selected compounds, whereas the CYP3A4 substrate was predicted as ‘No’ for ZINC000257435291, ZINC000257787835, ZINC000253529638, and ZINC000276935378; it was predicted as ‘Yes’ for ZINC000085569031, ZINC000150351499, ZINC000253530205, ZINC000150359935, ZINC000253531178, and ZINC000085569053. In excretion, the total clearance was predicted in the range of − 2.201 to 0.429. In toxicity, the AMES toxicity and oral rat acute toxicity parameter was analyzed. The AMES toxicity was predicted as none for all compounds. Furthermore, the value of oral rat toxicity was predicted in the range of 2.363 to 2.761 (Supplementary Table 1).

Analyzing the structural and conformational behavior of transmembrane histidine kinase before and after ligand binding

To understand the behavior of transmembrane histidine kinase when bound and unbound to a ligand, we conducted a 100-ns molecular dynamics (MD) simulation. Various parameters, such as RMSD, RMSF, Rg, SASA, H-bond, PCA, FEL, and binding free energy calculations, were used to summarize the obtained results comprehensively.

Stability analysis

During the molecular dynamics simulation of proteins, the Root Mean Square Deviation (RMSD) was utilized to evaluate the stability of their conformational states. The RMSD value quantifies the deviation between the initial protein structure and the subsequent structures during the simulation, and lower RMSD values indicate higher conformational stability. In this study, the RMSD values were computed for 100 ns, and the backbone c-alpha atoms of the transmembrane histidine kinase and its complexes displayed  minimum fluctuations with lower RMSD values after 85 ns (Fig. 3A). Specifically, the transmembrane histidine kinase had an average RMSD of 2.58 nm, and the RMSD values for its complexes, such as transmembrane histidine kinase-Tetracycline, transmembrane histidine kinase-ZINC000085569031, and transmembrane histidine kinase-ZINC000257435291, were 3.32, 2.87, and 2.38 nm, respectively. Based on the RMSD graph, it was inferred that all complexes reached their equilibrium state after 85 ns, and the final 15-ns trajectories were selected for further analysis.

Fig. 3

Stability analysis (A) RMSD values for the transmembrane histidine kinase-compound complexes. Flexibility analysis (B) RMSF values for the transmembrane histidine kinase-compound complexes over the final 15 ns of the simulations. Compactness (C) Rg, and Solvent accessible surface area analysis D SASA values for the final 15 ns of the simulations

Flexibility analysis

Flexibility is crucial for proteins to maintain their properties, and it can be assessed by analyzing the Root Mean Square Fluctuation (RMSF). In this study, the RMSF analysis was performed on the transmembrane histidine kinase and its complexes during the equilibrated trajectory of the last 15 ns. The results revealed fluctuations in amino acid residues upon the binding of ligands(Fig. 3B). The average RMSF value of the transmembrane histidine kinase was found to be 0.52 nm. Furthermore, the RMSF values of the transmembrane histidine kinase in complexes with Tetracycline, ZINC000085569031, and ZINC000257435291 were 0.20 nm, 0.30 nm, and 0.62 nm, respectively.

Compactness analysis

To investigate the compactness, stability, and folding of protein structures, it is possible to analyze Rg values over time. In this work, the Rg values were examined for transmembrane histidine kinase and its complexes, with the aim of assessing their structural compactness. Specifically, the Rg values for the transmembrane histidine kinase, transmembrane histidine kinase-Tetracycline, transmembrane histidine kinase-ZINC000085569031, and transmembrane histidine kinase-ZINC000257435291 systems were calculated and plotted based on the final 15 ns MD trajectories (Fig. 3C). The average Rg values for these systems were 3.32 nm, 2.84 nm, 2.96 nm, and 4.03 nm, respectively. The findings indicate that the transmembrane histidine kinase-Tetracycline complex demonstrates a more compact structure compared to the other complexes.

Solvent accessible surface area analysis

SASA analysis was conducted from the final 15 ns of simulation; we determined the changes in the solvent-accessible area induced by ligands. The average SASA values for transmembrane histidine kinase, transmembrane histidine kinase-Tetracycline, transmembrane histidine kinase-ZINC000085569031, and transmembrane histidine kinase-ZINC000257435291 were computed as 295.0, 280.6, 278.8, and 289.6 nm2, respectively. Notably, the SASA value for the transmembrane histidine kinase-ZINC000257435291 complex was higher than that of the other  complexes. Furthermore, all systems exhibited a similar pattern, indicating that each compound induced relatively minor changes upon binding (Fig. 3D).

Hydrogen bond analysis

The hydrogen bond is the most crucial binding force that stabilizes protein–ligand interactions. To quantify the hydrogen bonds formed during the interaction between natural compounds and the transmembrane histidine kinase target, we conducted an analysis. Our results showed that the transmembrane histidine kinase-ZINC000257435291 complex had the highest number of hydrogen bonds (0–6) compared to the other estimated complexes. The transmembrane histidine kinase-ZINC000085569031 complex formed 0–3 hydrogen bonds, while the reference molecule transmembrane histidine kinase-Tetracycline complex formed 0–5 hydrogen bonds during the final 15 ns. Therefore, these compounds interacted with the transmembrane histidine kinase to produce stable complexes through the formation of hydrogen bonds (Fig. 4).

Fig. 4

Number of hydrogen bonds in each complex was analyzed based on the data obtained from the last 15 ns of the simulations

Principal component analysis

Principal component analysis (PCA) was employed to detect significant conformational changes upon ligand binding. The protein's global motion is primarily influenced by the first few eigenvectors, and therefore, the first 50 eigenvectors were examined to identify structural variations in the movement. The initial five eigenvectors were used to calculate the percentage of correlated motions, which provided a clear understanding of the changes caused by ligand binding. The correlated motions of the transmembrane histidine kinase, transmembrane histidine kinase-Tetracycline, transmembrane histidine kinase-ZINC000085569031, and transmembrane histidine kinase-ZINC000257435291 complexes were found to be 95.09%, 83.92%, 87.03%, and 94.77%, respectively, with the lowest motion observed in the transmembrane histidine kinase-Tetracycline complex (Fig. 5A).

Fig. 5

Principal component analysis. A Eigenvalues derived from the final 15 ns of each simulation and used for PCA analysis depicted eigenvalues vs. the first fifty eigenvectors. B The first two eigenvectors depicted the transmembrane histidine kinase motion in space for all the systems

The first few eigenvectors reflect the protein's overall dynamics, as illustrated in the figure, leading to the selection of the first two eigenvectors and their plotting in phase space. The transmembrane histidine kinase-Tetracycline, and transmembrane histidine kinase-ZINC000085569031 complexes clustered together and exhibited higher stability, with low correlated motions compared to the transmembrane histidine kinase and transmembrane histidine kinase-ZINC000257435291 (Fig. 5B).

Gibbs free energy landscape analysis

Gibbs free energy landscape (FEL) calculations were carried out using the first two principal components, and the resulting FEL for all the systems is presented in Fig. 6. The color bar in the figure represents the Gibbs free energies (in units of kJ mol−1), ranging from the lowest energy (shown in blue) to the highest energy (represented by the red color) conformational states. The analysis revealed that the transmembrane histidine kinase-Tetracycline (0–7.36 kJ mol−1) and transmembrane histidine kinase-ZINC000085569031 (0–7.71 kJ mol−1) systems exhibited enriched energy minima indicated by the larger blue area. These systems were found to form a stable cluster compared to transmembrane histidine kinase (0–8.3 kJ mol−1) and transmembrane histidine kinase-ZINC000257435291 (0–8.3 kJ mol−1) systems. Based on the FEL analysis, all the systems gained minimum energy corresponding to the most stable conformations. Therefore, this study demonstrates that the FEL analysis provides insights into the stability and conformational behavior of the protein–ligand complexes.

Fig. 6

Gibbs free energy landscape has been depicted using a color-coded illustration, which is plotted based on PC1 and PC2. The contour map represents various energy states, with the lower energy systems represented by a deeper blue color. A Transmembrane histidine kinase (B) Transmembrane histidine kinase-Tetracycline (C) Transmembrane histidine kinase-ZINC000085569031,and (D) Transmembrane histidine kinase-ZINC000257435291

Calculation of binding free energy and residual binding energy analysis

To evaluate the strength of the binding affinities between simulated complexes, an MM-PBSA method was utilized to calculate their binding free energy. The binding free energies were determined based on the last 10 ns of the molecular dynamics simulation trajectories. The resulting values for the transmembrane histidine kinase-Tetracycline, transmembrane histidine kinase-ZINC000085569031, and transmembrane histidine kinase-ZINC000257435291 complexes were − 67.697, − 230.136, and − 125.662 kJ mol−1, respectively. The calculated values for the various energy contributions, such as van der Waals, electrostatic, polar solvation, SASA, and binding free energies are presented in Table 4. Furthermore, a residual binding energy analysis was performed to identify the key amino acid residues that are important for ligand binding. All of the selected compounds were observed to significantly interact with amino acid residues of the transmembrane histidine kinase, which suggests that there is potential for transmembrane histidine kinase inhibitors. Furthermore, amino acid residues from positions 280 to 470 were found to be more involved in the protein–ligand interactions for inhibition of transmembrane histidine kinase activities (Fig. 7).

Table 4 Affinities of top-two screened natural compounds and reference Tetracycline with transmembrane histidine kinase (van der Waals and electrostatic forces, polar solvation, SASA, and binding free energy in kJ mol-1)Fig. 7

Amino acid residues in transmembrane histidine kinase that participate in the interactions with reference Tetracycline and natural compounds ZINC000085569031 and ZINC000257435291