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Adoption of an in-silico analysis approach to assess the functional and structural impacts of rpoB-encoded protein mutations on Chlamydia pneumoniae sensitivity to antibiotics

BMC Microbiology volume 25, Article number: 157 (2025) Cite this article

Abstract

Background

Antibiotics are frequently used to treat infections caused by Chlamydia pneumoniae; an obligate intracellular gram-negative bacterium commonly associated with respiratory diseases. However, improper or overuse of these drugs has raised concerns about the development of antibiotic resistance, which poses a significant global health challenge. Previous studies have revealed a link between mutations in the rpoB-encoded protein of C. pneumoniae and antibiotic resistance. This study assessed these mutations via various bioinformatics tools to predict their impact on function, structural stability, antibiotic binding, and, ultimately, their effect on bacterial sensitivity to antibiotics.

Results

Eight mutations in the rpoB-encoded protein (R421S, F450S, L456I, S454F, D461E, S476F, L478S, and S519Y) are associated with resistance to rifampin and rifalazil. These mutations occur in conserved regions of the protein, leading to decreased stability and affecting essential functional sites of RNA polymerase, the target of these antibiotics. Although the structural differences between the native and mutant proteins are minimal, notable changes in local hydrogen bonding have been observed. Despite similar binding energies, variations in hydrogen bonds and hydrophobic interactions in certain mutants (for instance, D461E for rifalazil and S476F for rifampin) indicate that these changes may diminish ligand affinity and specificity. Furthermore, protein-protein network analysis demonstrated a strong correlation between wild-type rpoB and ten C. pneumoniae proteins, each fulfilling specific functional roles. Consequently, some of these mutations can reduce the bacterium’s sensitivity to rifampin and rifalazil, thereby contributing to antibiotic resistance.

Conclusion

The findings of this study indicate that mutations in the rpoB gene, which encodes the beta subunit of RNA polymerase, are pivotal in the resistance of C. pneumoniae to rifampin and rifalazil. Some of these mutations may result in reduced protein stability and changes in the structure, function, and antibiotic binding. As a consequence, the efficacy of these drugs in inhibiting RNA polymerase is compromised, allowing the bacteria to persist in transcription and replication even in the presence of antibiotics. Overall, these insights enhance our understanding of the resistance mechanisms in C. pneumoniae and could guide the development of strategies to address this challenge.

Clinical trial number

Not applicable.

Peer Review reports

Background

Chlamydia is a gram-negative, obligate intracellular bacterium that causes various infections in humans and animals [1, 2]. The Chlamydiaceae family includes many species, and those pathogenic to humans are Chlamydia trachomatis and Chlamydia pneumoniae, in addition to several Candidatus species [1], as well as the recently described Chlamydiifrater genus, C. phoenicopteri, and C. volucris [3]. C. pneumoniae is primarily associated with respiratory tract infections. Untreated or prolonged C. pneumoniae infection can lead to several conditions, including lung cancer, Alzheimer’s disease, asthma, atherosclerosis, multiple sclerosis, temporal arteritis, stroke, and macular degeneration [4, 5]. The first line of treatment for Chlamydia infections typically involves antibiotics, depending on the specific Chlamydia species [5]. However, several factors, such as improper prescription, overuse, or misuse of these drugs, can lead to antibiotic resistance, making treatment ineffective [6, 7]. Antibiotic resistance is a global public health issue that has progressed faster than ever since the discovery of the first antibiotic [8, 9]. Mutations in specific genes are related to the development of antibiotic resistance [5, 7, 10, 11]. In the case of C. pneumoniae, mutations in the rpoB-encoded protein are linked to resistance to rifampin or rifalazil [11,12,13]. In silico analysis of gene mutations is a valuable tool that helps enhance experimental research by providing insights into how changes in the genetic code affect the stability, structure, and antibiotic binding. This, in turn, determines the protein’s function and the bacterium’s sensitivity to antibiotic treatment [14, 15]. While this method has been used to some extent, its application in the study of gene alterations in C. pneumoniae is still limited. Notably, Benamri and colleagues reviewed the genes and mutations related to antibiotic resistance in Chlamydia species [11]. The study focused on specific mutations in the rpoB gene of C. trachomatis and C. pneumoniae that are associated with resistance to rifamycins, such as rifampin. It also identified mutations in the gyrA gene linked to resistance against fluoroquinolones. Additionally, C. trachomatis displayed resistance to macrolides (due to mutations in 23 S rRNA, rplD, and rplV), tetracyclines (rpoB), fosfomycin (murA), and MDQA (secY). C. psittaci demonstrated resistance to aminoglycosides (mutations in 16 S rRNA and rpoB) and macrolides (23 S rRNA). On the other hand, C. suis exhibited resistance to tetracyclines (tet(C)), while C. caviae developed resistance to macrolides (23 S rRNA). These mutations affect mechanisms such as protein synthesis inhibition and transcription, contributing to antibiotic resistance across different species of Chlamydia [11]. Furthermore, they performed an in-silico analysis of rpoB mutations to understand their impact on C. trachomatis susceptibility to rifamycin [16]. However, despite these significant contributions, a comprehensive understanding of gene mutations and antibiotic resistance across Chlamydia species is still relatively lacking, especially in C. pneumoniae. In this study, we performed an in-silico analysis of mutant proteins encoded by the rpoB gene in C. pneumoniae to understand the functional, structural and antibiotic binding impacts of the mutations on the bacterium’s resistance to antibiotic treatment.

Methods

In this study, we conducted an in-silico analysis utilizing various bioinformatics tools. Our approach included predicting the functional impact and pathogenicity of genetic variants, assessing changes in thermodynamic stability, analyzing evolutionary conservation, performing structural comparisons of wild-type and mutant models, examining protein-ligand interactions through docking analysis, and investigating protein-protein interactions through network analysis (Fig. 1).

Fig. 1
figure 1

Study steps fowchart

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Mutations and native protein sequence retrieval

To identify all rpoB gene mutations associated with C. pneumoniae antibiotic resistance, we performed a comprehensive literature search targeting databases such as PubMed, ScienceDirect, Google Scholar, and Web of Science, using the following keywords: Chlamydia pneumoniae, resistance gene, rpoB gene, mutations, antibiotic, and sensitivity to antibiotics. The C. pneumoniae rpoB-encoded protein sequence and its 3D structure were obtained from UniProt (https://www.uniprot.org/) and the AlphaFold Protein Structure Database (https://alphafold.ebi.ac.uk/), respectively.

Functional impact and pathogenicity prediction of variants

To predict the functional impact of amino acid substitutions and their pathogenicity, we used two web-based tools: MutPred2 (http://mutpred.mutdb.org/#qform) [18] and PredictSNP1.0 (https://loschmidt.chemi.muni.cz/predictsnp1/) [19]. MutPred2 is a machine-learning tool that predicts the impact of amino acid substitutions on protein function and pathogenicity. It classifies substitutions as pathogenic or benign, forecasting effects on over 50 protein properties. Integrating genetic and molecular data generates a pathogenicity score from 0 (likely benign) to 1 (likely pathogenic) and identifies affected molecular processes with associated scores [18]. On the other hand, PredictSNP1.0 incorporates nine different bioinformatics tools: SIFT [20], PolyPhen-1 [21], PolyPhen-2 [22], MAPP [23], PhD-SNP [24], SNAP [25], PANTHER [26], PredictSNP [27], and nsSNPAnalyzer [28]. This tool displays the confidence scores from each tool and provides an overall prediction expressed as percentages based on their accuracy levels. We classified mutations as deleterious when the results of five of the nine tools were determined to be deleterious [16].

Prediction of changes in protein thermodynamic stability

Protein stability is crucial for maintaining a protein’s structure and function. It is influenced by factors such as amino acid residue interactions, covalent bonds, and environmental conditions such as temperature, pH, and solute concentration [29]. The stability of a protein can be measured by the difference in the Gibbs free energy of unfolding between its native and denatured states (∆∆G = ∆Gmutant − ∆Gwild–type). The sign of ∆∆G determines if a variation increases (∆∆G > 0) or decreases (∆∆G < 0) protein stability [16, 29]. Numerous computational approaches have been developed to predict changes in the thermodynamic stability of proteins due to mutations [30]. This study carefully selected two tools, Dynamut2 (https://biosig.lab.uq.edu.au/dynamut2/submit_prediction_mm) [31] and SAAFEC-SEQ (http://compbio.clemson.edu/SAAFEC-SEQ/index.php#started) [32], to perform this analysis.

Analysis of conserved amino acid residues

The amino acid sequence of a protein provides insight into its structure and function. A comparison of homologous sequences revealed which amino acids are essential for this function. The Consurf server (https://consurf.tau.ac.il) was utilized to identify the locations of amino acids based on evolutionary conservation [33,34,35]. The conservation scores range from 1 to 9, with 1 being the least conserved region and 9 being the most conserved region.

Site-directed mutagenesis analysis

Hydrogen bonds play a crucial role in stabilizing protein structures and influencing their functional properties. Therefore, understanding how mutations affect hydrogen bonding is essential for evaluating their impact on protein function [36]. In this study, we introduced site-directed mutations in the rpoB-encoded protein using Swiss PDB Viewer software version 4.1.0 [37]. This allowed us to transform the 3D structure from its native form into mutant forms, enabling us to examine the hydrogen bonding patterns in both the native and mutant structures of the protein.

Protein 3D mutant structure modeling and validation

To evaluate the impact of each mutation on protein structure, we constructed 3D models of the mutant proteins encoded by rpoB gene using SWISS-MODEL (https://swissmodel.expasy.org/) [38]. This tool employs a homology modeling algorithm to generate 3D structures based on sequence alignment with established templates. We chose the model of highest quality based on sequence identity and the Global Model Quality Estimation (GMQE) [38]. To verify the reliability of the models, we created Ramachandran plots using MolProbity version 4.4 [39]. Following the modeling process, we conducted energy minimization with UCSF Chimera v.1.18 using the steepest descent algorithm [40, 41]. This step optimizes the molecular arrangement by minimizing steric clashes and achieving a more stable, energetically favorable conformation.

Comparison of the structures of wild-type and mutant models

We compared the structures of the native and mutated proteins by calculating the Root-Mean-Square Deviation (RMSD) between the wild-type and mutated structures using PyMol software version 3.0.3. A smaller RMSD value indicates a greater structural similarity [42].

Molecular docking analysis

To understand how structural changes resulting from mutations can affect antibiotic sensitivity, we conducted a molecular docking analysis using AutoDock4 (version 4.2.6) [43,44,45]. This analysis simulates the binding of antibiotics to both wild-type and mutant protein structures, allowing us to predict how mutations may alter binding affinity. We determined the grid box coordinates and dimensions in the x, y, and z directions using CavityPlus 2022 (http://www.pkumdl.cn:8000/cavityplus#/) tool with ligand mode whenever possible [46]. Additionally, we utilized BIOVIA Discovery Studio v.24.1 to create 2D visualizations that highlight key aspects of the protein-ligand interactions [47].

Prediction of protein-protein interactions

To investigate the interaction of the rpoB-encoded protein with various proteins, the STRING server (version 12.0) (https://string-db.org) [48] was used. This allowed us to contextualize the rpoB gene within its broader protein interaction network, helping to explore its functional relationships with other proteins within the C. pneumonia system [48, 49].

Results

Retrieved and collected data

A literature search revealed eight mutations in the rpoB-encoded protein related to the antibiotic resistance of C. pneumoniae to rifampin or rifalazil. These mutations were identified in studies by Kutlin et al. in 2004 [12] and Rothstein et al. in 2008 [13] (Table 1). The sequence data for the wild-type rpoB-encoded protein were obtained in FASTA format from the UniProt database (UniProt ID: Q9Z9A0). The predicted 3D structure was retrieved from the AlphaFold database (AlphaFold DB: AF-Q9Z9A0-F1, https://alphafold.ebi.ac.uk/search/text/AF-Q9Z9A0-F1), with an average quality score (pLDDT) of 88.5. This score indicates that the predicted 3D structure of the wild-type protein is of high quality.

Table 1 rpoB-encoded protein mutations related to antibiotic resistance in C. pneumoniae
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Functional impact of variants and their pathogenicity

The eight mutations previously collected were analysed with both MutPred2 and PredictSNP1.0 tools. Pathogenic mutations (R421S, F450S, S454F, D461E, S476F, L478S, and S519Y) consistently show harmful predictions across both tools (Table 2), leading to alterations in critical motifs and multiple molecular modifications that disrupt protein structure and function (Table 3). The benign mutation L456I (MutPred2 score < 0.5), despite being predicted to be deleterious by PredictSNP1.0, shows no affected motifs or molecular alterations. Similarly, although the D461E mutation is expected to be harmful, it does not involve modifications to specific molecular mechanisms.

Table 2 Prediction of the functional impact and the pathogenicity of variants via the MutPred2 and PredictSNP1.0 tools
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Table 3 Results of the molecular mechanism predictions of amino acid substitutions via MutPred2
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Prediction of changes in protein thermodynamic stability

The prediction of changes in protein stability for the eight mutations within the rpoB-encoded protein was conducted via two distinct tools, SAAFECSEQ and Dynamut2. The mutations were predicted to decrease the stability of the rpoB-encoded protein in C. pneumoniae strains (∆∆G < 0) (Fig. 2).

Fig. 2
figure 2

Prediction of changes in protein stability via the SAAFECSEQ and Dynamut2 tools

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Analysis of conserved amino acid residues

The Consurf tool results suggest that the rpoB-encoded protein’s specific residues are conserved. Each of the 8 residues had a high conservation score (9). More specifically, 3 positions were located on surface-exposed residues (F), and 5 positions were buried within the protein (S) (Table 4).

Table 4 Evolutionary conservation analysis of amino acids in the RpoB gene-encoded protein
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Site-directed mutagenesis results

Using Swiss PDB Viewer version 4.1.0, we converted the 3D structure of the rpoB-encoded protein from its native form to its mutant form. The Comparison of the native and mutant structures (Fig. 3a and b) revealed significant changes in the positioning of hydrogen bonds.

3D modeling of mutant proteins and validation

All mutant models were generated using the SWISS-MODEL, achieving a sequence identity of 79.55% with the template and a GMQE score of 0.89, indicating high confidence in the reliability and quality of the predicted 3D structures (Table 5). The Ramachandran favorable region percentages are consistently high, ranging from 96.14 to 97.35%, reflecting well-optimized structural geometries (Fig. 4).

Table 5 Assessment of the structure of 3D models of mutant proteins generated by SWISS-MODEL
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Fig. 3
figure 3

[a] Comparison of rpoB-encoded protein hydrogen bonding, from the native to the mutant structure. a Wild type R and mutant S residues at 421th position (R421S). b Wild type F and mutant S residues at 450th position (F450S). c Wild type S and mutant F residues at 454th position (S454F). d Wild type L and mutant I residues at 456th position (L456I). [b] Comparison of Hydrogen Bonding in the rpoB-Encoded Protein Between the Native and Mutant Structures. e Wild type D and mutant E residues at 461th position (D461E). f Wild type S and mutant F residues at 476th position (S4767). g Wild type L and mutant S residues at 478th position (L478S). h Wild type S and mutant Y residues at 519th position (S519Y)

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Fig. 4
figure 4

Three-dimensional structures of the predicted mutant proteins, along with Ramachandran favorable regions and corresponding plots generated by MolProbity

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Structural comparison between mutant and wild-type proteins

To evaluate the structural impact of mutations, we superimposed the 3D structures of the native and mutant forms of the rpoB-encoded protein (Fig. 5). The analysis revealed minimal structural deviations, with RMSD values ranging from 0.022 Å to 0.024 Å for all the studied mutations (Table 6).

Fig. 5
figure 5

The superimposed structures of the mutant and wild-type amino acids of the rpoB-encoded protein at different positions. The native amino acid is red, and the mutant amino acid is green

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Table 6 RMSD values of the rpoB-encoded protein between the native and mutant structures
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Binding site alterations analysis

The analysis of the binding sites for both the wild-type and mutant forms revealed significant reductions in binding pocket volume and surface area across all mutants compared to the wild-type. The wild-type protein had a larger pocket volume of 7940.62 ų and a surface area of 2553.50 Ų. In contrast, the mutants consistently exhibited reduced volumes, ranging from 5830.62 ų to 5905.62 ų, with surface areas approximately between 1885 Ų and 1896 Ų (Table 7).

Table 7 Binding site properties of wild-type and mutant rpoB-encoded proteins predicted by CavityPlus
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Molecular docking results

The docking results indicate that the binding energies for both the wild-type and mutant proteins are comparable. Notably, rifalazil shows a slightly stronger binding affinity in the wild-type (-10.56 kcal/mol) compared to rifampin (-9.79 kcal/mol) (Fig. 6). Mutants such as D461E, F450S, and R421S exhibit binding energies similar to those of the wild-type, while others, including S476F and L478S, demonstrate marginally weaker binding affinities (Fig. 6).

Mutations significantly alter the interaction patterns of both rifalazil and rifampin compared to the wild-type protein (Table 8). In the wild-type, rifalazil forms a strong binding interaction through a hydrogen bond with ASN:45 and has extensive hydrophobic interactions with residues such as ILE:46 and GLU:528 (Table 8). In contrast, rifampin forms a single hydrogen bond with SER:398 and has fewer hydrophobic interactions. When mutations occur, rifalazil completely loses its hydrogen bonding (e.g., in D461E), with its interactions shifting solely to hydrophobic contacts involving residues like TYR:166 and LYS:28 (Table 8). For rifampin, mutations such as F450S and R421S enhance hydrogen bonding (e.g., with ASN:45 and ARG:337), increasing its polar interactions. However, in other mutants, such as S476F and L456I, rifampin loses its native hydrogen bonds and relies heavily on hydrophobic interactions with residues like ILE:36 and VAL:76 (Table 8).

While the binding energies remain similar across different protein variants, the distinct patterns of interactions, particularly regarding hydrogen bonds and hydrophobic contacts, suggest that these variations may influence the ligand’s affinity and specificity (Figs. 7 and 8).

Table 8 Docking analysis of wild-type and mutant rpoB proteins withrifalazil and rifampin: interactions involving conventional hydrogen bonds and hydrophobic interactions (Pi-Sigma & Pi-Alkyl, alkyl and Amide-Pi Stacked)
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Fig. 6
figure 6

Binding energy (Kcal /Mol) trends for wild-type and mutant proteins

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Fig. 7
figure 7

2D representations of interactions between mutant models of rpoB-encoded protein and rifalazil and rifampin

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Fig. 8
figure 8

2D docking interaction representations of the wild-type rpoB -encoded protein with rifampin (a) and rifalazil (b)

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Protein-protein interactions

Analysis of protein-protein interactions has revealed insights into the activities and connections of several important proteins in the bacteria C. pneumoniae (Fig. 9 ). The protein encoded by the rpoB gene interacts with various proteins, including RpoC, RpoA, RpIK, RpIF, RpIC, RpIV, RpIJ, RpsE, RpIL, and RpsC, as indicated by the scores (Table 9). Each protein has a unique function that is crucial for cellular activities such as transcription and translation. These interactions are supported by high confidence values, highlighting their reliability.

Fig. 9
figure 9

Overview of rpoB-encoded protein network construction via the STRING server. The evidence view and confidence view are given

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Table 9 Prediction of the molecular interactions of the rpoB-encoded protein with other proteins via the STRING server
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Discussion

C. pneumoniae is a pathogenic bacteria known for causing respiratory tract infections [4, 5]. Antibiotics that target DNA and protein synthesis, such as macrolides, tetracyclines, and quinolones, are effective against C. pneumoniae [4, 50,51,52]. However, with the overuse or misuse of these drugs, this bacterium can develop resistance to specific antibiotics through different mechanisms [6, 10, 53–55]. Mutations in the rpoB-encoded protein of C. pneumoniae are associated with resistance to rifampin and rifalazil antibiotics [11,12,13]. these drugs inhibit bacterial RNA polymerase by binding to its beta-subunit, disrupting RNA synthesis which is critical for bacterial survival [5]. These mutations can alter the protein’s structure, stability, and antibiotic binding, thereby impacting its function and the bacterial response to antibiotics. To investigate these effects, we conducted an in-silico analysis using bioinformatics tools to evaluate the structural and functional implications of these mutations. Each tool provides a different perspective—functional, evolutionary, structural, or interaction-based—regarding how a mutation could influence protein behavior and its subsequent role in bacterial sensitivity to antibiotics. The deleterious effects of the eight identified mutations (Table 1) were predicted via PredictSNP 1.0 [19]. The analysis revealed that all mutations in the rpoB-encoded protein are predicted to be deleterious (Table 2). Using the MutPred2 tool, mutations R421S, F450S, S454F, S476F, L478S, and S519Y were identified as potentially affecting critical functional sites. These sites include those involved in DNA binding, metal binding, and catalytic activity, which may compromise the effectiveness of RNA polymerase— the target for various antibiotics (Table 3). As a result, these alterations could diminish the efficacy of antibiotics and contribute to resistance by obstructing their ability to bind to target proteins. The benign mutation L456I has minimal functional impact with no affected motifs or molecular alterations, as shown in Tables 2 and 3, despite being predicted as deleterious by PredictSNP1.0. Similarly, the D461E mutation was also predicted to be pathogenic across different tools, and lacks affected motifs (Table 2) and specific molecular mechanism alterations (Table 3), suggesting a potentially less severe impact. The discrepancies in the results highlight the need for deeper predictions via new tools. The results from the Consurf server [33,34,35] show that the specific residues of the mutant proteins are conserved, with conservation scores equal to 9 (Table 4). Mutations in these areas can significantly affect the function and stability of the protein, potentially leading to misfolding or loss of function. These areas typically encompass active sites, binding sites, or interaction domains crucial for protein activity. This, in turn, could lead to drug resistance or altered drug efficacy. On the other hand, the use of Dynamut2 [31] and SAAFECSEQ [32] to predict alterations in protein thermodynamic stability revealed negative values of the difference in the Gibbs free energy of unfolding (Fig. 2), indicating that the mutations would likely disrupt the protein structure, potentially resulting in misfolding degradation or abnormal protein aggregation. The predicted three-dimensional structures of the mutant proteins, validated through Ramachandran plots, demonstrate their reliability, with over 90% of residues situated in favored regions (Table 5 ) and (Fig. 4). The minimal RMSD values, ranging from 0.022 to 0.024 Å, indicating that the mutations do not significantly affect the overall architecture of the protein. However, these mutations disrupt or modify crucial hydrogen bonding patterns that are essential for the binding and stability of the protein (Fig. 3. a and b). Although the global structure of the protein remains intact, these localized changes are likely to diminish ligand efficacy, underscoring the impact of specific amino acid substitutions on the development of antibiotic resistance [36]. The docking results indicate that mutations influence the binding affinity and interaction patterns of rifalazil and rifampin, providing valuable insights into potential mechanisms of antibiotic resistance. The comparable binding energies observed between the wild-type and mutant proteins suggest that the overall energy landscape of ligand binding is not significantly altered due to these mutations (Fig. 6, ). However, the changes in interaction patterns indicate structural modifications within the binding pocket that could impact ligand efficacy (Table 8) and (Figs. 7 and 8). In the wild-type protein, rifalazil establishes a strong hydrogen bond with ASN:45, which is likely critical for its binding stability. In mutant forms, rifalazil loses this capability to form a hydrogen bond (as evidenced in D461E) and instead depends solely on hydrophobic interactions (Table 8). This reduction in polar interactions may compromise rifalazil effectiveness, and facilitate the development of antibiotic resistance. Rifampin exhibits fewer interactions within the wild-type protein, suggesting a weaker baseline affinity in comparison to rifalazil (Fig. 6) and (Table 8). However, in certain mutants, such as F450S and R421S, rifampin acquires additional hydrogen bonds (for instance, with ASN:45 and ARG:337), which may enhance its binding stability (Table 8). Conversely, other mutants like S476F and L456I exhibit diminished hydrogen bonding capabilities, resulting in a shift in rifampin interactions towards hydrophobic residues, which may reduce its overall effectiveness (Table 8). Mutations that decrease hydrogen bonding—such as D461E for rifalazil and S476F for rifampin—alter the interaction profile and may subsequently reduce the binding stability of these antibiotics. In contrast, mutations like F450S and R421S, which enhance rifampin hydrogen bonding, may allow for compensation of structural changes and enable the protein to adapt, contributing to partial resistance. Finally, the prediction of protein-protein interactions revealed that the rpoB-encoded protein interacts with 10 different bacterial proteins (Fig. 9). These proteins are essential for ribosome function and RNA polymerase activity (Table 9). The interactions between the rpoB-encoded protein and the RNA polymerase subunits (RpoC and RpoA) underscore the central role of rpoB in transcription—a process targeted by antibiotics such as rifampin and rifalazil. Mutations in the rpoB gene can disrupt these interactions, potentially altering the structure or function of the RNA polymerase complex. Such alterations may result in reduced sensitivity or even resistance to transcription-inhibiting antibiotics. Furthermore, the interactions of rpoB-encoded protein with ribosomal proteins highlight the interconnected nature of transcription and translation, both of which are essential for bacterial survival. Ribosomal proteins like RplC, RplF, and RpsE are known targets for antibiotics, including macrolides and aminoglycosides. These findings suggest that any changes to the rpoB-encoded protein might impact its interactions with these partners and the complex network of molecular activities supporting cellular function. Importantly, some of these tools have inherent errors and biases, such as algorithmic limitations, reliance on potentially outdated or incomplete databases, and an inability to fully capture the complexities of gene expression and interactions.

Conclusion

This study used various bioinformatics tools to assess the mutations in the rpoB-encoded protein associated with C. pneumoniae’s resistance to rifampin or rifalazil. The impact of the mutations on function, stability, structure, antibiotic binding and interaction was analysed, considering their location in highly conserved regions. These mutations can affect protein function, structure, and interactions, explaining the observed antibiotic resistance. However, not all mutations result in severe functional impacts. Therefore, the information obtained from this in silico analysis can serve as a valuable resource for further exploration into the mechanisms of C. pneumoniae resistance, ultimately assisting in the management of infections and the prevention of complications.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

C:

Chlamydia

ΔG:

Gibbs free energy of unfolding

ΔΔG:

Change in Gibbs free energy of unfolding

RMSD:

Root means square deviation

rpoB :

RNA polymerase β-subunit gene

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Acknowledgements

Sanae Esskhayry gratefully acknowledges the support of the CNRST (National Centre for Scientific and Technical Research) in Morocco for the PhD Associate Scholarship PASS.

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Authors and Affiliations

  1. Life and Health Sciences Laboratory, Faculty of Medicine and Pharmacy, Abdelmalek Essaâdi University, Tangier, Morocco

    Sanae Esskhayry & Afaf Lamzouri

  2. Department of Medical Genetics and Oncogenetics, Mohammed VI University Hospital Center, Tangier, Morocco

    Sanae Esskhayry, Afaf Lamzouri & Ouafae Kaissi

  3. Systems and Data Engineering Team, National School of Applied Sciences, Abdelmalek Essaâdi University, Tangier, 90 000, Morocco

    Sanae Esskhayry, Ichrak Benamri, Rachida Fissoune & Ahmed Moussa

  4. Laboratory of Information Technology and Modeling, Faculty of Sciences Ben M’Sik, Hassan II University of Casablanca, B. P 7955 Sidi Othmane, Casablanca, Morocco

    Ichrak Benamri

  5. Chlamydiae and Mycoplasma Laboratory, Research Department, Institut Pasteur du Maroc, Casablanca, 20360, Morocco

    Sanae Esskhayry, Ichrak Benamri & Fouzia Radouani

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  1. Sanae Esskhayry
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Contributions

SE: Data extraction, data curation, data analysis, study design, and drafting of the paper. IB: study design and data analysis. FR: Project conception, Study design, Following the study process, Validating the data, and drafting and validating the paper. OK: drafting and revising the paper. AL, RF, and AM: revising the paper. All the authors read and approved the manuscript.

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Correspondence to Fouzia Radouani.

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Esskhayry, S., Benamri, I., Lamzouri, A. et al. Adoption of an in-silico analysis approach to assess the functional and structural impacts of rpoB-encoded protein mutations on Chlamydia pneumoniae sensitivity to antibiotics. BMC Microbiol 25, 157 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12866-025-03860-5

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BMC Microbiology

ISSN: 1471-2180

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