Deciphering the Electrostatic and Structural dynamics due to point Mutation in DNA gyrase leading to acquired Quinolone resistance in Mycobacterium tuberculosis

Deciphering the Electrostatic and Structural dynamics due to point Mutation in DNA gyrase leading to acquired Quinolone resistance in Mycobacterium tuberculosis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Deciphering the Electrostatic and Structural dynamics due to point Mutation in DNA gyrase leading to acquired Quinolone resistance in Mycobacterium tuberculosis Sumit Kumar Rai, Dev Bukhsh Singh, Satendra Singh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5778481/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The structural bioinformatics analysis approach provides valuable information regarding the protein’s structure and function by analyzing the contribution of each and every amino acid residue present in its active site. Residue substitution in the active site has a profound effect on the protein’s shape, stability, binding affinity, charge distribution, etc. We inserted a mutation in the DNA gyrase protein's A chain (3ILW_wild) to understand the structural and electrical alternations, resulting in the formation of the 3ILW_G88A, 3ILW_G88C, 3ILW_D94G, and 3ILW_D94H mutant proteins. The molecular docking approach was applied to screen the best-interacting fourth-generation quinolone antibiotics and to elucidate their stability, binding affinity, and interaction pattern with the wild protein. The results of molecular docking studies suggested that delafloxacin (dfx) had the highest binding affinity with the DNA gyrase A chain and fits best at the active site. Mutant proteins were again docked with delafloxacin to monitor the effect of residue change on the protein’s properties. The results of the molecular docking approach were further validated by molecular dynamic simulation and binding free energy calculation studies. Molecular dynamics simulations over 100 ns were carried out for five protein systems. Parameters like RMSD, RMSF, radius of gyration, H-bond, and solvent-accessible area obtained from MD simulation studies revealed that the mutant proteins experienced greater rigidity and lesser structural fluctuations than the wild protein. Electrostatic investigation and comparison of BFE revealed that the electrostatic interactions were reduced, and this reduction directly affected the binding affinity of proteins and ligand molecules. Per-residue BFE decomposition and hydrogen bond analysis indicated that the reduced interaction was due to loss or gain of hydrophilic/hydrophobic or positively/negatively charged residues. It is worth noting that mutation at position 94 of DNA gyrase A has a very profound effect as it shows a positive contribution towards increased resistance and reduced binding affinity with delafloxacin. DNA gyrase A Quinolone Mutation Binding affinity Electrostatic interaction Drug Resistance Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1. Introduction Tuberculosis (TB) remains a significant threat to public health globally. According to a WHO report, around 10 million TB cases were detected worldwide in 2023; among them, 1.4 million died due to tuberculosis (Williams et. al. 2024 ). 450,000 new patients with multi-drug-resistant tuberculosis (MDR) were reported with few cases of extensively resistant tuberculosis (XDR) in 2023 (Castro et. al. 2017 ; Cole et. al. 2020 ). Mycobacterium tuberculosis, an etiologic agent responsible for the disease, has become a severe threat to mankind because of its high contagious nature and lesser symptoms initially. Inadequate medical facilities and unavailability of proper diagnostics for detection also contribute to the disease (Arévalo & Amorim 2022 ; Piton et al. 2010 ). This micro-organism harbors an integral validated target protein, DNA gyrase (a bacterial topoisomerase), which is involved in a variety of functions like DNA supercoiling, DNA cleavage, and catenation activities (McKie et. al. 2021 ; Nagaraja et. al. 2017 ). These bacterial topoisomerase enzymes are divided into two categories based on the catalytic method of DNA breakage and reconnection: type (I) enzymes, which act on single-strand breaks in DNA, and type (II) enzymes, which act on double-strand breaks. According to a number of in vitro and in silico investigations, DNA gyrases are among the scientifically promising and established targets for antibiotic development (Cazzaniga et al. 2021 ; Mathur et al. 2018 ; Setzer et al. 2016 ; Arévalo et al. 2021). DNA gyrase consists of two subunits, namely Gyr A (838 amino acids) and Gyr B (675 amino acids), together forming a hetero-tetrameric A2B2 complex. DNA gyrase A possesses two domains, the N-terminal breakage-reunion domain and the CTD-carboxyterminal domain, while Gyrase B also has the ATPase domain and Toprim domain. DNA gyrase B Toprim and gyrase A breakage-reunion domain belongs to two different subunits, together forming the core of the enzyme DNA gyrase (Aubry et al. 2004 ). The breakage-reunion domain binds a DNA segment termed G-segment at the DNA gate. The ATPase terminal present at the N-terminal dimerizes on ATP attachment, resulting in DNA duplex transportation (T-segment). The breakage-reunion domain begins a transient break in the G-segment to pass the T-segment; resealing the DNA forces the T-segment to pass through a protein gate and C-gate before the enzyme adopts an open clamp conformation (Matrat et al. 2007 ; Nakatani et al. 2012 ; Agrawal et al. 2013 ). Quinolone targets DNA gyrase by interfering with its enzymatic activities. It binds to the DNA-enzyme complex, which stabilizes the covalent DNA-enzyme intermediate, thus preventing the release of DNA from the enzyme after introducing a double-stranded break (Pham et al. 2019 ). Quinolone also inhibits the re-ligation process after DNA break, thereby resulting in fragmented DNA possessing an inability to properly manage DNA supercoiling and repair, leading to the generation of lethal DNA damage. The damage occurred, disrupting critical cellular processes like replication and transcription, leading to bacterial cell stasis or cell death (Al-Saeedi et al. 2017; Tang et al. 2023). Quinolones are considered the most effective second-line drugs in the treatment of multi-drug-resistant tuberculosis (MDR-TB; strains that are found to be resistant to the two main Rifampicin and isoniazid antibiotics) because of their broad-spectrum activity, good bioavailability, and also their ability to penetrate cells and tissues that harbor the Mycobacterium (Bush et al. 2020 ). Incongruous prolonged use of quinolone antibiotics paved the path for the emergence of “acquired resistant” strains that are defined as extensively resistant strain XDR-TB (strains that show resistance to first-line drugs along with fluoroquinolone and aminoglycoside) due to mutations in the gene coding DNA gyrase protein (Bendre et al. 2021 ; Quenard et al. 2017 ). Mutations vested in bacterial resistance against quinolones occur in two unattached segments termed the Quinolone-resistance determining region (QRDR) located in the breakage reunion domain of the DNA gyrase A subunit (QRDR-A; amino acid positions 73 to 113) and less frequently in the Toprim domain of the gyrase B subunit (QRDR-B; amino acid positions 461 to 499) (Collin et al. 2011 ; Drlica et al. 2019 ). Mutations at positions 88, 90, and 94 play a vital role in “acquired resistance” to quinolone. Globally, mutation at position 88 is less profound in comparison to 90 and 94, which share a greater percentage of patients (Arun et al. 2020 ; Kim et al. 2011 ; Chauffour et al. 2021 ). Among 90 and 94 positions, mutation at position 90 is extensively studied, and much information is available. In the present work, we selected an unexplored, less-studied mutation at position 94 with prevalence among the majority of TB patients. Mutation at this position is a result of residue change leading to structural and electrostatic changes. Monitoring these changes will enrich our insight to add information and help in the development of new lead molecules with good potency and efficacy to encounter Mycobacterium. 2. Materials and Methods 2.1 Ligand Structure Retrieval and Preparation The literature review revealed the importance of fourth-generation quinolone antibiotics in the treatment of tuberculosis (Mogle et al. 2018 ; Pranger 2019). Structural data files (in .sdf format) of quinolone antibiotics were obtained from the PubChem database ( https://pubchem.ncbi.nlm.nih.gov/ ) (Atanasov et al. 2021 ). Using the Marvin sketch tool (23.10), structures were modified and cleaned before being saved in the protein data bank (.pdb) format (Rajalakshmi et al. 2021 ). The physiochemical properties like logP, H-bond donor, H-bond acceptor, polar surface area, polarizability, van der Waals surface area, refractivity, etc. were studied and listed (Table 1 ). The pdb files were converted to. pdbqt format after being opened in Auto Dock Vina for structure modification and visualization. Table 1 Physiochemical properties of fourth generation antibiotics (drug) used against Mycobacterium tuberculosis. Antibiotics Mol. weight (g/mol) Log-p H-bond donor H-bond acceptor Polar surface area (Å 2 ) Polarizability (Å 3 ) Van der waal surface area (Å 2 ) Refractivity Clinafloxacin 365.80 1.84 2 7 86.90 34.22 448.12 92.51 Gatifloxacin 375.40 1.81 2 8 82.10 36.65 513.56 98.82 Moxifloxacin 401.40 1.97 2 8 82.10 39.59 544.18 106.22 Sitafloxacin 409.80 2.10 2 8 86.90 36.97 491.80 99.09 Prulifloxacin 461.50 2.89 1 11 99.62 43.32 560.50 127.37 Besifloxacin 393.80 2.81 2 7 86.90 37.87 504.11 101.75 Delafloxacin 440.80 2.56 3 11 120.0 36.25 476.16 101.33 Ozenoxacin 363.40 3.36 2 6 82.53 39.77 505.93 106.11 2.2 Protein Structure Retrieval, Preparation and Docking DNA gyrase subunit A's N-terminal domain's X-ray crystallographic structure (Tretter et al. 2010 ) was acquired using (PDB ID: 3ILW) from the RCSB PDB ( www.rcsb.org ). Using the Discovery Studio and Auto-dock Vina tools, water molecules and other tiny molecules found in the crystal structure were removed. In order to minimize energy and eliminate steric conflicts, the structures were optimized using the Chimera program. Mutation in the protein 3ILW_wild (DNA gyrase A) was inserted at residue positions 88 and 94, respectively, by substituting alanine (A) and cysteine (C) for glycine (G) at position 88 and histidine (H) and glycine (G) against aspartic acid (D) at position 94 in the protein’s sequence using UCSF Chimera (1.16) tool (Pettersen et al. 2004 ). Four models of 3ILW_wild protein were generated: 3ILW_G88A, 3ILW_G88C, 3ILW_D94G, and 3ILW_D94H (Maruri et al. 2012 ). The chimera tool was used for energy minimization. The PROCHECK (Wlodawer 2017 ) module in the SAVES server ( www.saves.mbi.ucla.edu ) was used for Ramachandran plot generation to obtain the accuracy of wild and mutated protein models ( Supplementary Fig. 1 ). The generated protein molecules were provided with optimum Kollman charges, polar hydrogen atoms were added, tautomeric states for all hetero groups at pH 7.0 were designated, and bond order was assigned. The grid box for the proteins was generated using the Auto Dock Vina tool. Utilizing coordinates from a configuration file; center (X = 4.105, Y = -10.989, Z = 85.249), size (X = 86, Y = 112, and Z = 98); energy range: 4, exhaustiveness: 8, a PDBQT file included the organized data were generated for all the protein molecules and were saved in .pdbqt format. Screening for the best-interacting antibiotic was done through flexible docking between the 3ILW_wild protein and prepared ligands using the Auto Dock Vina tool (Ferreira et al. 2017; Guttula et al. 2011 ). To monitor the changes in the structure and interaction with the antibiotic that occurred by inserting mutations, the four mutated protein models (3ILW_G88A, 3ILW_G88C, 3ILW_D94G, and 3ILW_D94H) and the wild protein 3ILW_wild were finally docked with the top ligand obtained from Table 2 . 2.3 MD simulation The GROMACS software tool (v.2023.3; http://www.gromacs.org/ ) was utilized in conjunction with the CHARMM27 force field to assess the structural and electrostatic alterations brought about by insertional mutations in DNA gyrase (Bjelkmar et al. 2010 ). For this investigation, a total of five systems were created. At pH 7.4, each constructed structure was protonated in accordance with the protonation states of every titrable residue. The protonated structures were placed in a dodecahedron box with a space of greater than 1.0 nm between any protein atom and the box wall after being dissolved using the TIP3P water model (Price et al. 2004). In order to replicate physiological conditions, the net charges of both simulation systems were neutralized using a 100 mM concentration of NaCl. The simulation systems were equilibrated by two continuous 10-ns position constraint simulations in the NVT and NPT ensembles with 1000 kl/mol/nm2 harmonic force constants after first being subjected to energy minimization using the steepest descent algorithm. Swiss-param ( http://www.swissparam.ch/ ), an online tool, was used to create the topology of the ligands. The LINC algorithms (Hess et al. 1997 ) were utilized to constrain the bond lengths with an integration time step of 2 fs. The particle-mesh Ewald (PME) algorithm (Essmann et al. 1995 ) calculated long-range electrostatic interactions with an interpolation order of 4, Fourier grid spacing of 1.6 Å, and a Coulomb radius of 1 nm. The v-rescale thermostat (Bussi et al. 2007 ) was employed to couple the temperature of the systems at 310 K with a time constant of 0.1 ps. The Parrinello-Rahman barostat (Parrinello et al. 1981) was utilized to maintain the pressure of the systems at 1 atm with a time constant of 0.5 ps. The snapshots were saved every 10 ps. 2.4 Structural and Geometrical Properties The MD trajectories underwent structural and geometrical analysis using GROMAC tools. Specifically, "gmx rms" was utilized to determine the time-dependent backbone root mean square deviation (RMSD) in relation to the wild structure; "gmx rmsf" was employed to calculate the per-residue Cα root mean square fluctuation (RMSF); ‘gmx gyrate’ was used to measure the compactness (Rg) of the molecule; and the "gmx hbond’ module enhances the understanding of protein structure, folding, function, and ligand binding, as well as other biomolecular interactions (Sang et al. 2022 ). "gmx sasa" was utilized to determine the solvent-accessible surface area (SASA); "gmx sham" was used to calculate the free energy landscape, allowing one to comprehend the energetics and stability of various conformational states. The "hydrogen bond" plugin in VMD was used to compute the hydrogen bonds that formed between the ligand and protein molecules when the donor-acceptor angle was more than 120º and the donor-acceptor distance was less than 3.5 Å (Humphrey et al. 1996 ). Pymol 2.5.2 was used to create the electrostatic surface potential of the structures 3ILW_wild + dfx, 3ILW_G88A + dfx, 3ILW_G88C + dfx, 3ILW_D94G + dfx, and 3ILW_D94H + dfx (Dutta et al. 2013 ). The protein-ligand bond causes a portion of the two molecules' molecular surfaces to be buried. Using the following formula, the buried solvent-accessible surface area (SASA) Area burial was determined: Area burial = (SASA protein + SASA ligand ) - SASA complex where, SASA protein, SASA ligand and SASA complex represent the solvent accessible area (SASA) of Protein (3ILW_wild, 3ILW_G88A, 3ILW_G88C, 3ILW_D94G and 3ILW_D94H), Ligand (delafloxacin) and complex of protein and ligand, respectively. 2.5 Binding Free Energy (BFE) Calculation The most rigorous BFE computational approach is the free-energy perturbation (FEP), which may estimate the difference in free energy between two states by gradually switching one state to another through a series of nonphysical intermediate states. However, this method takes a very long time. The two most widely used approximations for computing BFE are the molecular mechanics generalized Born surface area (MM/GBSA) and the molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) (Tuccinardi 2021 ). The MM-PBSA method (Homeyer et al. 2012) was employed in this investigation to determine the BFE between the proteins (3ILW_wild, 3ILW_G88A, 3ILW_G88C, 3ILW_D94G, and 3ILW_D94H) and delafloxacin (dfx). A well-known endpoint technique called MM-PBSA may calculate the protein-ligand BFE only from the structure or structural ensemble of the bound complex, ignoring any non-physical or physical intermediates (Wang et al. 2018 ). The BFE of the protein and ligand in MM-PBSA is described as follows: ΔG binding = ΔG complex – (ΔG protein + ΔG ligand ) where the change in free energy is denoted by ΔG. Each subunit's free energy, G, can be shown as follows: G = E MM + G sol -TS The average molecular mechanical potential energy (E MM ) in vacuum is made up of the electrostatic (E ele ) and van der Waals (E vdw ) interactions, and it can be expressed as follows: E MM = E ele + E vdw The solvation free energy, or Gsol, can be described as follows. It is divided into polar (G polar ) and non-polar (G non−polar ) components. G sol = G polar + G non−polar T stands for temperature, S for entropy, and TS for the entropy contribution to the free energy. It is important to note that while comparing the relative BFEs, the TS component is insignificant. Per-residue contribution was also studied for the QRDR region in wild and mutated proteins (Genheden et al. 2015; Tian et al. 2022 ). To calculate the MM-PBSA, the single trajectory method included in gmx_MMPBSA 1.5.7 was employed (Valdés-Tresanco et al. 2021 ). Using the gmx_MMPBSA default parameters, the BFE between the protein and ligand was computed for every simulation system. 3. Results and Discussion 3.1 Visualization and Analysis of protein-ligand interaction To identify the interacting residues, type, and nature of bond formation with the antibiotics in the active site, the molecular docking was performed between the 3ILW_wild protein and fourth-generation antibiotics. Table 2 depicts the interacting antibiotics, their binding free energies, and residues in the active site of the protein actively participating in the interaction. Among all the eight antibiotics, Delafloxacin was found to be the most promising candidate with the lowest binding free energy of -8.6 kcal/mol. Residues Trp 103 , Pro 119 and Val 278 were found to be involved in H-bond interaction. Other interactions, like van der Waals and pi-alkyl, were also observed. Table 2 Protein DNA gyrase A (wild) interaction with fourth generation antibiotics. The amino acid residues shown in bold are involved in hydrogen-bonding interactions. Antibiotics Binding free energy (Kcal/mol) Amino acid residues involved in interactions via different types of bonding Delafoxacin -8.6 Arg 98 , Gln 101 , Trp 103 , Ser 104 , Gly 117 , Ser 118 , Pro 119 , Gly 120 , Asn 121 , Asp 122 , Pro 124 , Ile 181 , Ala 182 , Gln 277 , Thr 230 , Val 278 , Asn 279 , His 280 Prulifloxacin -8.4 Ala 90 , Tyr 93 , Asp 94 , Val 97 , Arg 98 , Gln 101 , Trp 103 , Ser 104 , Ser 118 , Pro 123 , Pro 124 , Gln 277 , Asn 279 Moxifloxacin -7.2 Pro 102 , Trp 103 , Ser 118 , Pro 119 , Thr 230 , Ala 231 , Thr 272 , Glu 273 , Leu 274 , Ser 306 , Asp 308 Ozenoxacin -7.1 Trp 103 , Gly 117 , Ser 118 , Pro 119 , Thr 230 , Ala 231 , Thr 272 , Glu 273 , Leu 274 , His 280 , Ser 306 , Ser 307 , Asp 308 , Gly 311 , Leu 312 Sitafloxacin -7.1 Asn 115 , Phe 116 , Gly 117 , Ser 118 , Pro 119 , Gly 120 , Asp 122 , His 280 , Asp 281 , Ile 284 , Asp 304 , Ser 306 , Ser 307 , Gly 311 , Leu 312 , Ile 314 Gatifloxacin -6.9 Arg98, Gln 101 , Trp 103 , Ser 104 , Phe 116 , Gly 117 , Ser 118 , Pro 119 , Gly 120 , Asn 121 , Asp 122 , Pro 124 , Gly 180 , Ile 181 , Asn 182 , Met 185 , Gln 277 , Asn 279 Besifloxacin -6.8 Pro 102 , Trp 103 , Ser 118 , Pro 119 , Gly 120 , Asn 121 , Asp 122 , His 280 , Asp 281 , Asp 304 , Ser 306 , Gly 311 , Leu 312 Clinafloxacin -6.8 Pro 102 , Trp 103 , Ser 118 , Pro 119 , Pro 229 , Thr 230 , Glu 273 , His 280 , Ser 306 , Gly 311 , Leu 312 , Arg 495 To understand the change in interaction pattern of residues with the antibiotics leading to the structural change in the active site, again a molecular docking approach was applied. The wild protein 3ILW_wild, along with mutated proteins 3ILW_G88A, 3ILW_G88C, 3ILW_D94G, and 3ILW_D94H, were docked with delafloxacin (dfx) obtained from previous docking studies. The binding free energy during docking studies varied from − 7.0 kcal/mol to -9.0 kcal/mol (Table 3 ). 3ILW_wild, 3ILW_G88A, and 3ILW_D94G had the same binding free energy of -8.6 kcal/mol, showing no effect of mutation on binding free energy. Residues Trp 103 , Pro 119 , Asp 122 and Val 278 were active in protein 3ILW_wild. In mutated proteins 3ILW_G88A and 3ILW_D94G, the residues that actively participated in hydrogen bonding were Ser 82 , Pro 83 , Gly 84 , Asp 86 , Val 242 among them, Pro 83 and Val 242 were common. Mutated proteins 3ILW_G88C and 3ILW_D94H showed variable binding free energy of -7.5 kcal/mol and − 8.3 kcal/mol. Lower binding free energy probably may be due to a change in the nature of residues on mutation. Table 3 binding free energies of wild and mutant proteins on interaction with delafloxacin antibiotics. The amino acid residues shown in bold are involved in hydrogen-bonding interactions. S.NO. Protein Binding free energy (Kcal/mol) Amino acid residues involved in interactions via different types of bonding 1. 3ILW_wild -8.6 Arg 98 , Gln 101 , Trp 103 , Ser 104 , Gly 117 , Ser 118 , Pro 119 , Gly 120 , Asn 121 , Asp 122 , Pro 124 , Ile 181 , Ala 182 , Gln 277 , Thr 230 , Val 278 , Asn 279 , His 280 , 2. 3ILW_G88C -7.5 Pro 66 , Trp 67 , Pro 83 , Pro 193 , Thr 194 , Ala 195 , Thr 236 , Glu 237 , Leu 238 , His 244 , Leu 276 , Arg 459 , 3. 3ILW_G88A -8.6 Arg 62 , Gln 65 , Trp 67 , Ser 68 , Gly 81 , Ser 82 , Pro 83 , Gly 84 , Asn 85 , Asp 86 , Pro 88 , Ile 145 , Ala 146 , Thr 194 , Gln 241 , Val 242 , Asn 243 , His 244 , 4. 3ILW_D94G -8.6 Arg 62 , Gln 65 , Trp 67 , Ser 68 , Gly 81 , Ser 82 , Pro 83 , Gly 84 , Asn 85 , Asp 86 , Pro 87 , Pro 88 , Ile 145 , Ala 146 , Thr 194 , Gln 241 , Val 242 , Asn 243 , His 244 5. 3ILW_D94H -8.3 Trp 103 , Pro 119 , Thr 272 , Glu 273 , Leu 274 , His 280 , Asp 308 , Val 310 , Gly 311 , Leu 312 Halogen (fluorine), van der Waals, h bond, pi-sigma, amide-pi stacked etc. interaction were observed in Fig. 1 . Hydrogen bonding, van der Waals and pi-alkyl interactions were seen in 3ILW_wild. In addition, halogen, pi-cation, amide pi stacked interactions were also observed in mutated proteins complexes 3ILW_G88A + dfx, 3ILW_G88C + dfx, 3ILW_D94G + dfx, and 3ILW_D94H + dfx. Hydrogen bond interaction was maximum in 3ILW_D94H + dfx complex with six hydrogen bond formation while rest 3ILW_wild + dfx, 3ILW_G88A + dfx, 3ILW_G88C + dfx and 3ILW_D94G + dfx formed 4,4,3 and 3 hydrogen bonds. 3.2 Analysis of Structural stability and flexibility during MD simulation 3.2.1 Root Mean Square Deviation (RMSD) The structural stability was examined by monitoring the time-dependent backbone RMSD trajectories of five simulation systems. It was used to determine the protein’s proximity with the ligand molecule, which reflects the protein's stability and conformational change. The RMSD curves of the wild 3ILW_wild + dfx complex, along with mutant protein complexes, i.e., 3ILW_G88A + dfx, 3ILW_G88C + dfx, 3ILW_D94H + dfx, and 3ILW_D94G + dfx, were shown in Figs. 2 (a) & 2(b). RMSD for five systems was between 0.26 nm and 0.20 nm, indicating that all the systems were stable and had a very low probability of conformational change during the 100 ns simulation (Table 4 ). The mutated protein complex 3ILW_D94H + dfx experienced the highest 0.26 nm deviation as the wild protein 3ILW_wild, showing no effect of residue substitution and hence the stability with delafloxacin. Rest mutated proteins 3ILW_D94G, 3ILW_G88C, and 3ILW_G88A experienced 0.20 nm, 0.23 nm, and 0.22 nm deviations, indicating greater stability. All the mutated protein complexes showed greater stability than the wild protein complex 3ILW_wild + dfx. 3.2.2 Root Mean Square Fluctuation (RMSF) In order to compare the structural flexibility of wild and mutated proteins complexed with delafloxacin, we calculated the per-residue Cα atom root mean square fluctuation values. It provides insights into the dynamic regions of proteins, such as flexible loops, active sites, and binding regions, and can indicate the importance of specific residues in conformational changes or protein-ligand interactions. According to Figs. 3 (a) & 3(b), the molecular fluctuations were seen highest in the QRDR region, especially around 85–100 amino acid residues. The complexed mutated protein 3ILW_D94H + dfx and the wild protein 3ILW_wild + dfx showed the highest structural fluctuation of 0.14 nm, indicating no effect of mutation due to residue substitution in the DNA gyrase A sequence, and it counts for greater flexibility and binding affinity at this position. Similarly, the other mutant protein complexes, 3ILW_D94G + dfx and 3ILW_G88A + dfx, experienced the least fluctuation of 0.11 nm, indicating rigid conformation or structure. 3ILW_G88C had an intermediate value of 0.12 nm. Since the structural fluctuation seems to get reduced, indicating lesser flexibility and greater rigidity, it may increase the binding ability of proteins with the delafloxacin molecule due to formation of stable secodary structures like α-helices and β-sheets. 3.2.3 Radius of Gyration (Rg) The compactness or the rigidity of the protein-ligand complexes, both wild and mutated, was evaluated by Radius of Gyration (Rg). A smaller Rg value indicates a more compact or folded structure, while a larger value suggests more extended or flexible conformations during MD simulation. According to Figs. 4(a) and 4(b), the gyration value for the wild protein complex 3ILW_wild + dfx was found to be 3.0 nm, and for the mutated protein complexes 3ILW_G88A + dfx, 3ILW_G88C + dfx, 3ILW_D94G + dfx, and 3ILW_D94H + dfx were 2.97 nm, 2.99 nm, 2.96 nm, and 2.98 nm, respectively (Table 4 ). The Rg value suggested that 3ILW_wild + dfx complex had the most extended or unfolded structure, and the compactness in the mutated proteins has increased on amino acid substitution. The increase in compactness probably may be due to changed interacting residues and bonding patterns in the mutated protein complexes. Among all the five systems, it was observed that 3ILW_D94G + dfx was the most compact complex, showing greater stability or rigidity. The binding affinity of the mutant proteins does not change much, as indicated by the Rg value in the table. Table 4 Comparative analysis of Stability, Flexibility, Compactness and Surface properties of Wild and Mutated protein complexes over 100 ns MD simulation. Complexes RMSD (nm) RMSF (nm) Rg (nm) SASA-COMPLEX (Å 2 ) SASA-PROTEIN (Å 2 ) SASA-LIGAND (Å 2 ) 3ILW_wild + dfx 0.26 0.14 3.00 235.31 234.43 6.13 3ILW_G88A + dfx 0.22 0.11 2.97 229.31 231.10 6.05 3ILW_G88C + dfx 0.23 0.12 2.99 234.40 234.99 6.04 3ILW_D94G + dfx 0.20 0.11 2.96 234.10 234.66 6.04 3ILW_D94H + dfx 0.26 0.14 2.98 234.44 235.68 6.11 3.3 Analysis of Interfacial Interaction between Protein and Ligand 3.3.1 Electrostatic Surface Potential The electrostatic surface potential (ESPs) of the protein reflects the binding affinity of the protein with a ligand molecule. Blue, white, and red color coding on the surface of the protein signifies the positive, neutral, and negative electrostatic surface potential. The interfacial ESPs of wild and mutated proteins varied between − 75 kcal/mol and + 75 kcal/mol due to positive, negative, or neutral regions present in the protein molecule, as depicted in Fig. 5 . The gain of positively charged residues or the loss of negatively charged residues increases the binding affinity. The wild protein has the largest positive charged region (red) as compared to mutant proteins except 3ILW_G88C, which shows comparable electrostatic potential as depicted by ΔE ele term of binding free energy value. A larger blue region was observed in the 3ILW_D94H mutant protein, which shows its higher binding affinity for delafloxacin among all the mutant molecules. 3.3.2 Hydrogen Bonding (HB) For investigation of the binding affinity of delafloxacin towards DNA gyrase A protein, the trajectories of the complex were analyzed, and H bonds between the ligand and proteins were calculated over the 100 ns simulation and plotted (Fig. 6 ). The total H-bonds formed between the docked wild protein 3ILW_wild and dfx were 3, but only two of them were stable over the simulation studies with 5.31% occupancy. Mutated protein complexed with delafloxacin (3ILW_G88A + dfx) exhibited a maximum of 4 H-bonds, and during simulation studies, it was found that five H-bonds were stable for 100 ns with 46.8% occupancy. Complexed mutated protein 3ILW_G88C + dfx formed 2 H-bonds, which were stable during MD studies with 14.31% occupancy. 3ILW_D94G, when docked with dfx, formed 4 H-bonds; among them, only three were found to be stable with 34.25% occupancy during simulation. Moreover, 3ILW_D94H + dfx formed the maximum number of H-bonds with 21.69% occupancy during simulation studies. With reference to wild protein, mutant proteins 3ILW_G88A and 3ILW_D94G showed the most stable H-bonds during simulation studies. 3.3.3 Solvent Accessible Area (SASA) Solvent accessible Area representing the ability of water accessibility at the binding pocket of target protein molecules (wild and mutated), delafloxacin (ligand) and protein-ligand complex was calculated ( Table 4 ). The results of average SASA calculations for proteins, ligands, and protein complexes are shown in Table 4 . The average SASA value for wild and mutated protein complex, wild and mutated protein, and only ligand ranged between 229.31 nm 2 to 235.31 nm 2 , 231.10 nm 2 to 235.68 nm 2 , and 6.04 nm 2 to 6.05 nm 2 , respectively. According to Figs. 7 (a) & 7(b) , the highest average SASA value was observed in the 3ILW_wild + dfx complex. This may be due to slight deviation of the residues forms the binding pocket. The lowest value was observed in 3ILW_G88A + dfx. Rest mutated protein complexes do not show much variation in SASA values, probably due to hydrophobic residue substitutions, and experienced greater repulsion so that only a few residues were in contact with water molecules. The buried surface area is the measurement of the interface at which the protein and ligand form the complex. The size of the interface reflects the strength of overall nonbonded intermolecular interactions. In the present study, the buried total, polar (hydrophilic) BSAs and non-polar (hydrophobic) BSAs were calculated for proteins (wild and mutated) and delafloxacin complexes. These BSA findings were used to highlight or reveal the strength of Vander Waals, hydrophobic, and electrostatic interactions, respectively. As shown in Fig. 8 , almost all average values (marked in black line) of the BSAs in 3ILW_wild + dfx are lower than the mutant proteins revealing that nonbonding interactions are more enhanced due to mutation. The mutant proteins 3ILW_D94G + dfx and 3ILW_d94H + dfx has lower BSAs during hydrophilic interactions indicating the lower binding affinity. The difference in the Total and polar BSAs between wild and mutant proteins and ligand are significantly higher than that of hydrophobic BSAs indicating their stronger contribution in binding affinity between proteins and ligand. 3.4.1 Gibbs free energy landscape Gibbs' free energy landscape represents the free energy as a function of two reaction coordinates, PC1 and PC2, obtained through Principal component analysis of molecular dynamic simulation for 100ns. The color coding signifies the protein-ligand complex's thermodynamic stability, conformational change, partial flexibility, etc. In the Fig. 9 , the shifting of color from red to blue indicates the stability of complex. All the complex molecules represented lesser red region indicating significant stability. Figure 9 (a) and Fig. 9 (e) representing cluster along the PC2 representing distinct conformational states probably due to structural transition or localized motion between the protein-ligand complex. The variation of distance between the two clusters in 3ILW_wild + dfx (Fig. 9 (a)) and 3ILW_D94H + dfx (Fig. 9 (e)) represents the energy barrier between the conformational states. The complex 3ILW_D94H + dfx harbor larger energy barrier indicating greater conformational movements. Complex 3ILW_G88A + dfx, 3ILW_G88C + dfx and 3ILW_D94G + dfx show very less movement between protein and ligand molecules (Fig. 9 (b) , Fig. 9 (c) and Fig. 9 (d)) , and these mutant complexes had gradual change in color from red to blue indicating uniform energy distribution. This study clearly indicate that mutants 3ILW_G88A, 3ILW_G88C and 3ILW_D94G forms an altered structural complex with dfx than the Wild type 3ILW_wild. 3.4.2 Analysis of Binding free energy Change The binding free energies for all five systems were calculated for 100 ns of MD trajectories. Wild and mutated protein complexes showed negative binding free energies (Table 4 ), indicating that all complexes were stable. Mutated protein 3ILW_D94H showed the highest free binding energy (-22.49 ± 9.71 kcal/mol), followed by 3ILW_D94G (-22.31 ± 7.50 kcal/mol), 3ILW_G88A (-21.74 ± 7.07 kcal/mol), 3ILW_wild (-16.65 ± 6.0 kcal/mol), and 3ILW_G88C (− 9.82 ± 4.35 kcal/mol), respectively, when complexed with delafloxacin. Table 5 Binding free energy of wild and mutated protein calculated using MM-PBSA method. a a 3ILW_wild (Kcal/mol) 3ILW_G88A (Kcal/mol) 3ILW_G88C (Kcal/mol) 3ILW_D94G (Kcal/mol) 3ILW_D94H (Kcal/mol) ΔE ele -3.55 ± 4.27 -35.16 ± 4.68 -5.02 ± 2.43 -23.82 ± 4.99 -24.62 ± 7.36 ΔE vdw -38.53 ± 2.76 -30.22 ± 3.74 -24.32 ± 2.20 -38.54 ± 3.95 -36.41 ± 3.48 ΔG MM -42.08 ± 5.08 -65.38 ± 5.99 -29.34 ± 3.28 -62.36 ± 6.36 -61.03 ± 8.14 ΔG polar 30.06 ± 3.20 48.22 ± 3.75 22.33 ± 2.85 45.03 ± 3.98 43.11 ± 5.28 ΔG nonpolar -4.63 ± 0.30 -4.58 ± 0.34 − 2.81 ± 0.29 − 4.98 ± 0.31 − 4.57 ± 0.35 Δ G sol 25.43 ± 3.21 43.64 ± 3.77 19.52 ± 2.86 40.05 ± 3.99 38.54 ± 5.29 Δ G bind -16.65 ± 6.0 -21.74 ± 7.07 − 9.82 ± 4.35 -22.31 ± 7.50 -22.49 ± 9.71 a Note: ΔG bind= ΔG MM + ΔG sol ; ΔG MM= ΔE ele + ΔE vdw ; ΔG sol= ΔG polar + ΔG nonpolar . Energy components like van der Waals (ΔE vdw ), electrostatics (ΔE ele ), polar (ΔG polar ) and non-polar (ΔG non−polar ) were also studied and showed attractive results. However, the component (ΔG non−polar ) comes up with low values for the total free energies, probably due to the shielding of inhibitors from solvent. The electrostatic contribution was negative, ranging from low to medium. The (ΔE ele ) for 3ILW_wild complex protein was highest (-3.55 ± 4.27 kcal/mol), while it was lowest for 3ILW_G88A (-35.16 ± 4.68 kcal/mol). The polar solvation free energy (ΔG polar ) was positive for all the systems, indicating unfavorable binding of wild and mutated proteins with delafloxacin. The van der Waals (ΔE vdw ) interaction seems to be the main bond for all systems having less difference in energy values. Among the wild and mutated proteins, 3ILW_D94G showed the lowest ΔE vdw energy. Rest 3ILW_G88A + dfx, 3ILW_G88C + dfx and 3ILW_D95H + dfx complexes had the ΔE vdw energy values ranging between − 24.32 ± 2.20 kcal/mol to -36.41 ± 3.48 kcal/mol. 3ILW_D94G showed the highest binding affinity, while 3ILW_G88C showed the least binding affinity than wild protein. Contribution of residues towards binding affinity of protein in QRDR region for delafloxacin was analyzed by per residue contribution of each residue for wild and mutated proteins was estimated by applying mmpbsa.py program. The per-residue contributions of each mutation site to BFE values were calculated and compared (Fig. 10 ). It is important to note that if the per-residue BFE value in mutant proteins is lower than in wild proteins, this suggests that the mutation at this location contributes favorably to increasing the protein's binding affinity for delafloxacin. The BFE value in protein 3ILW_wild was found to be at position 88 for glycine (6.05 kcal/mol) and at position 94 for aspartic acid (-104.04 kcal/mol). On mutating the protein with alanine and cystine residue at position 88, the BFE values were found to be 17.67 kcal/mol and 19.38 kcal/mol. While for position 94, the mutated residues glycine and histidine, the BFE values calculated were 5.02 kcal/mol and − 16.13 kcal/mol. The results suggest that mutations lowered the binding affinity at positions 88 and 94. Among all the four mutated proteins, 3ILW_D94H showed lower binding affinity than 3ILW_wild protein. 4. Conclusion The main aim of the study was to discover the changes that occurred in proteins' active binding sites and in the structure of DNA gyrase on mutation by using computational techniques. The computational study resulted in five protein systems: one wild and four mutant proteins with good structural stability. The virtual screening techniques resulted in a lead compound, delafloxacin with an excellent stability and binding affinity and having good binding interactions with the amino acid residues at the active binding site of the 3ILW_wild protein. Further, the delafloxacin molecule was docked with mutant proteins 3ILW_G88A, 3ILW_G88C, 3ILW_D94G and 3ILW_D94H to estimate stability, binding affinity and to observe the binding interaction at the active site. During the study, it was noted that the mutant 3ILW_D94G had the lowest RMSD value of 0.20 nm, while 3ILW_D94H and the wild protein had the highest. Residue fluctuation among mutants 3ILW_G88A and 3ILW_D94G was lower than in the wild protein. All the mutants had a gyration value lower than the wild protein of 3.0 nm; among them, 3ILW_D94G showed the lowest value. Solvent accessibility was highest at 235.31 Ų in the wild protein and the lowest value was 229.31 Ų; the second lowest value of 234.10 Ų was observed in 3ILW_D94G. The binding affinity of all the mutant protein molecules was greater than that of the wild protein molecules, but among them, 3ILW_D94G and 3ILW_D94H showed good results. The study revealed that despite the good binding affinity of all the mutant molecules, they showed resistance against the drug, probably due to good structural stability. The results obtained during the study may be utilized in the development of new lead molecules that would have good draggability and efficacy. Declarations Acknowledgement: The authors are grateful to the Department of Computational Biology and Bioinformatics, JIBB, Sam Higginbottom University of Agriculture Technology and Sciences, Prayagraj (Allahabad) 211007, UP, India and Department of Biotechnology, Siddharth University, Kapilvastu, Siddharth Nagar-272202, U.P., India for providing the facilities and support to complete this research work. Author contributions Methodology: DBS and SKR; Software: DBS and SKR; Formal analysis: DBS and SS; Investigation: SKR and SS; Resources: DBS and SS; Writing—original draft: SKR; Editing: DBS and SS All authors have read and agreed to the published version of the manuscript. Competing interests The authors declare that they have no competing interests. References Williams PM, Pratt RH, Walker WL, Price SF, Stewart RJ, Feng PI (2024) Tuberculosis — United States, 2023. MMWR Morb Mortal Wkly Rep 73:265–270 Castro KG, Marks SM, Chen MP et al (2017) Estimating tuberculosis cases and their economic costs averted in the United States over the past two decades. Int J Tuberc Lung Dis 20:926–933 Cole B, Nilsen DM, Will L, Etkind SC, Burgos M, Chorba T (2020) Essential components of a public health tuberculosis prevention, control, and elimination program: recommendations of the Advisory Council for the Elimination of Tuberculosis and the National Tuberculosis Controllers Association. 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J Chem Theory Comput 17:6281–6291 Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigure1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5778481","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":398899267,"identity":"e88f01fe-8e3d-4cdc-bfea-7c8bce06bf69","order_by":0,"name":"Sumit Kumar Rai","email":"","orcid":"","institution":"Sam Higginbottom University of Agriculture Technology and Sciences","correspondingAuthor":false,"prefix":"","firstName":"Sumit","middleName":"Kumar","lastName":"Rai","suffix":""},{"id":398899268,"identity":"f73887f4-29d7-49bc-ac07-f6de8b1276e7","order_by":1,"name":"Dev Bukhsh Singh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYBADxgYG5gNAWkKGFC1sCSAtPKRo4TEAMQhr4Z/d/kzi4x4G2X6JnM+vbtRY8DCwHz66AZ8WiTtnzCRnPGMwnjkjd5t1zjGgw3jS0m7gteZGDtttngMMiRvOnN1mnMMG1CLBY4ZXi/yN9Ge3/wC17D9z5plxzj8itBjcSDC7zQCyhb2H+XFuGxFaDG/kmP/sOSBhPON4mxlzbp8EDxshv8jdSH9s8OOAjWx/M/Pjzznf6uT42Q8fw+99CJAAEWwQkgjlcMD8gRTVo2AUjIJRMHIAAJWFSWUZxgY3AAAAAElFTkSuQmCC","orcid":"","institution":"Siddharth University","correspondingAuthor":true,"prefix":"","firstName":"Dev","middleName":"Bukhsh","lastName":"Singh","suffix":""},{"id":398899269,"identity":"26ea3f19-8e95-4541-9434-5e51fd19e785","order_by":2,"name":"Satendra Singh","email":"","orcid":"","institution":"Sam Higginbottom University of Agriculture Technology and Sciences","correspondingAuthor":false,"prefix":"","firstName":"Satendra","middleName":"","lastName":"Singh","suffix":""}],"badges":[],"createdAt":"2025-01-07 06:38:51","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5778481/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5778481/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73393561,"identity":"e14ebfe0-e81e-4c52-a303-6784e1359799","added_by":"auto","created_at":"2025-01-09 13:13:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":283566,"visible":true,"origin":"","legend":"\u003cp\u003e2D representation of binding interaction between wild and mutated proteins with delafloxacin (dfx) \u003cstrong\u003e(a)\u003c/strong\u003e 3ILW_wild+dfx \u003cstrong\u003e(b)\u003c/strong\u003e 3ILW_G88A+dfx \u003cstrong\u003e(c)\u003c/strong\u003e3ILW_G88C+dfx \u003cstrong\u003e(d)\u003c/strong\u003e 3ILW_D94H+dfx \u003cstrong\u003e(e)\u003c/strong\u003e 3ILW_D94G+dfx.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5778481/v1/1ce4b6d264f9fe1c92691492.png"},{"id":73393560,"identity":"31c9fe95-5d8f-48ba-8b89-e204f5ec115f","added_by":"auto","created_at":"2025-01-09 13:13:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":170621,"visible":true,"origin":"","legend":"\u003cp\u003eRMSD value obtained over100ns simulation for complexes \u003cstrong\u003e(a) \u003c/strong\u003e3ILW_wild+dfx (wild), 3ILW_G88A+dfx and 3ILW_G88C+dfx (mutants) \u003cstrong\u003e(b)\u003c/strong\u003e 3ILW_wild+dfx (wild), 3ILW_D94G+dfx and 3ILW_D94H+dfx (mutants)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5778481/v1/ccae4ff3ff099a7919c6e5ed.png"},{"id":73393936,"identity":"88102180-774a-4d3c-b1a6-77fd12b50938","added_by":"auto","created_at":"2025-01-09 13:21:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":154110,"visible":true,"origin":"","legend":"\u003cp\u003eRMSF value obtained over100ns MD simulation for complexes \u003cstrong\u003e(a)\u003c/strong\u003e3ILW_wild+dfx (wild), 3ILW_G88A+dfx and 3ILW_G88C+dfx (mutants) \u003cstrong\u003e(b)\u003c/strong\u003e3ILW_wild+dfx (wild), 3ILW_D94G+dfx and 3ILW_D94H+dfx (mutants)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5778481/v1/5ceb4ce1fe745eadab6f9184.png"},{"id":73393571,"identity":"4951156a-5e21-43a7-b507-82485faf28d5","added_by":"auto","created_at":"2025-01-09 13:13:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":190867,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical representation of radius of gyration (Rg) values obtained over100ns MD simulation for complexes \u003cstrong\u003e(a)\u003c/strong\u003e 3ILW_wild+dfx (wild), 3ILW_G88A+dfx and 3ILW_G88C+dfx (mutants) \u003cstrong\u003e(b) \u003c/strong\u003e3ILW_wild+dfx (wild), 3ILW_D94G+dfx and 3ILW_D94H+dfx (mutants).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5778481/v1/dc5f3a8c968aeaa8e592057f.png"},{"id":73393960,"identity":"02a161f6-de40-40d5-8cf0-e98123e52fd5","added_by":"auto","created_at":"2025-01-09 13:21:33","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":261258,"visible":true,"origin":"","legend":"\u003cp\u003eDNA gyrase A wild and mutant proteins binding surface electrostatic surface potentials (ESPs) with the delafloxacin (dfx\u003cstrong\u003e). (a)\u003c/strong\u003e 3ILW_wild+dfx \u003cstrong\u003e(b)\u003c/strong\u003e3ILW_G88A+dfx \u003cstrong\u003e(c)\u003c/strong\u003e 3ILW_G88C+dfx \u003cstrong\u003e(d)\u003c/strong\u003e 3ILW_D94G+dfx \u003cstrong\u003e(e)\u003c/strong\u003e3ILW_D94H+dfx.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5778481/v1/51f00bcb821c54f0272ffaf7.png"},{"id":73393573,"identity":"9b768755-5eb9-4627-84ec-745ed43c3f86","added_by":"auto","created_at":"2025-01-09 13:13:31","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":97888,"visible":true,"origin":"","legend":"\u003cp\u003eGraph representing number of hydrogen bonds obtained over100ns MD simulation for complexes \u003cstrong\u003e(a) \u003c/strong\u003e3ILW_wild+dfx (wild), 3ILW_G88A+dfx and 3ILW_G88C+dfx (mutants) \u003cstrong\u003e(b)\u003c/strong\u003e 3ILW_wild+dfx (wild), 3ILW_D94G+dfx and 3ILW_D94H+dfx (mutants)\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5778481/v1/32771b874accdcb5aea8382f.png"},{"id":73393938,"identity":"f94c1a81-998f-429a-a141-04f956bf27f4","added_by":"auto","created_at":"2025-01-09 13:21:31","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":194182,"visible":true,"origin":"","legend":"\u003cp\u003eSolvent accessible surface area (SASA) representation over100ns MD simulation for complexes \u003cstrong\u003e(a)\u003c/strong\u003e 3ILW_wild+dfx (wild), 3ILW_G88A+dfx and 3ILW_G88C+dfx (mutants) \u003cstrong\u003e(b)\u003c/strong\u003e 3ILW_wild+dfx (wild), 3ILW_D94G+dfx and 3ILW_D94H+dfx (mutants).\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-5778481/v1/80ad4cec54db907dd5a656ca.png"},{"id":73393939,"identity":"b862cf67-768f-4bec-948d-bea12e4942fb","added_by":"auto","created_at":"2025-01-09 13:21:31","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":73892,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplot of the Buried total, hydrophobic (non-polar) and hydrophilic (polar) binding surface areas (BSAs) of the DNA gyrase wild and mutated proteins.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-5778481/v1/1d71b10240cd50f7757bd4d1.png"},{"id":73393959,"identity":"1714ec84-5587-4039-ae2b-a85e43802d5e","added_by":"auto","created_at":"2025-01-09 13:21:32","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":129652,"visible":true,"origin":"","legend":"\u003cp\u003eGibbs free energy landscape representing the energy distribution between protein and ligand complexes over 100ns MD simulation. \u003cstrong\u003e(a)\u003c/strong\u003e 3ILW_wild+dfx \u003cstrong\u003e(b)\u003c/strong\u003e3ILW_G88A+dfx \u003cstrong\u003e(c)\u003c/strong\u003e 3ILW_G88C+dfx \u003cstrong\u003e(d)\u003c/strong\u003e 3ILW_D94G+dfx \u003cstrong\u003e(e)\u003c/strong\u003e3ILW_D94H+dfx.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-5778481/v1/c9d441b9aa83ac85076c692b.png"},{"id":73393579,"identity":"4481799b-c0d3-4051-bf74-8504cf0e3b31","added_by":"auto","created_at":"2025-01-09 13:13:31","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":109939,"visible":true,"origin":"","legend":"\u003cp\u003ePer-residue contribution of QRDR region to binding free energy (BFE) of DNA gyrase wild and mutated proteins. The values were calculated using MM-PBSA method.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-5778481/v1/8d77dc4269696de7f0381240.png"},{"id":73576559,"identity":"99ae150d-1586-42e9-813c-0f4962bd0de4","added_by":"auto","created_at":"2025-01-11 21:31:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3144717,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5778481/v1/26ac24fd-8b23-4b3e-a154-381655617a49.pdf"},{"id":73393602,"identity":"d0069328-33c4-4cbb-b45f-7addd42b9165","added_by":"auto","created_at":"2025-01-09 13:13:32","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1303788,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5778481/v1/fccab8ade291a34a68a3932a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Deciphering the Electrostatic and Structural dynamics due to point Mutation in DNA gyrase leading to acquired Quinolone resistance in Mycobacterium tuberculosis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eTuberculosis (TB) remains a significant threat to public health globally. According to a WHO report, around 10\u0026nbsp;million TB cases were detected worldwide in 2023; among them, 1.4\u0026nbsp;million died due to tuberculosis (Williams et. al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). 450,000 new patients with multi-drug-resistant tuberculosis (MDR) were reported with few cases of extensively resistant tuberculosis (XDR) in 2023 (Castro et. al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Cole et. al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Mycobacterium tuberculosis, an etiologic agent responsible for the disease, has become a severe threat to mankind because of its high contagious nature and lesser symptoms initially. Inadequate medical facilities and unavailability of proper diagnostics for detection also contribute to the disease (Ar\u0026eacute;valo \u0026amp; Amorim \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Piton et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). This micro-organism harbors an integral validated target protein, DNA gyrase (a bacterial topoisomerase), which is involved in a variety of functions like DNA supercoiling, DNA cleavage, and catenation activities (McKie et. al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Nagaraja et. al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). These bacterial topoisomerase enzymes are divided into two categories based on the catalytic method of DNA breakage and reconnection: type (I) enzymes, which act on single-strand breaks in DNA, and type (II) enzymes, which act on double-strand breaks. According to a number of in vitro and in silico investigations, DNA gyrases are among the scientifically promising and established targets for antibiotic development (Cazzaniga et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mathur et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Setzer et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ar\u0026eacute;valo et al. 2021). DNA gyrase consists of two subunits, namely Gyr A (838 amino acids) and Gyr B (675 amino acids), together forming a hetero-tetrameric A2B2 complex. DNA gyrase A possesses two domains, the N-terminal breakage-reunion domain and the CTD-carboxyterminal domain, while Gyrase B also has the ATPase domain and Toprim domain. DNA gyrase B Toprim and gyrase A breakage-reunion domain belongs to two different subunits, together forming the core of the enzyme DNA gyrase (Aubry et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The breakage-reunion domain binds a DNA segment termed G-segment at the DNA gate. The ATPase terminal present at the N-terminal dimerizes on ATP attachment, resulting in DNA duplex transportation (T-segment). The breakage-reunion domain begins a transient break in the G-segment to pass the T-segment; resealing the DNA forces the T-segment to pass through a protein gate and C-gate before the enzyme adopts an open clamp conformation (Matrat et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Nakatani et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Agrawal et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eQuinolone targets DNA gyrase by interfering with its enzymatic activities. It binds to the DNA-enzyme complex, which stabilizes the covalent DNA-enzyme intermediate, thus preventing the release of DNA from the enzyme after introducing a double-stranded break (Pham et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Quinolone also inhibits the re-ligation process after DNA break, thereby resulting in fragmented DNA possessing an inability to properly manage DNA supercoiling and repair, leading to the generation of lethal DNA damage. The damage occurred, disrupting critical cellular processes like replication and transcription, leading to bacterial cell stasis or cell death (Al-Saeedi et al. 2017; Tang et al. 2023).\u003c/p\u003e \u003cp\u003eQuinolones are considered the most effective second-line drugs in the treatment of multi-drug-resistant tuberculosis (MDR-TB; strains that are found to be resistant to the two main Rifampicin and isoniazid antibiotics) because of their broad-spectrum activity, good bioavailability, and also their ability to penetrate cells and tissues that harbor the Mycobacterium (Bush et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Incongruous prolonged use of quinolone antibiotics paved the path for the emergence of \u0026ldquo;acquired resistant\u0026rdquo; strains that are defined as extensively resistant strain XDR-TB (strains that show resistance to first-line drugs along with fluoroquinolone and aminoglycoside) due to mutations in the gene coding DNA gyrase protein (Bendre et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Quenard et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Mutations vested in bacterial resistance against quinolones occur in two unattached segments termed the Quinolone-resistance determining region (QRDR) located in the breakage reunion domain of the DNA gyrase A subunit (QRDR-A; amino acid positions 73 to 113) and less frequently in the Toprim domain of the gyrase B subunit (QRDR-B; amino acid positions 461 to 499) (Collin et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Drlica et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Mutations at positions 88, 90, and 94 play a vital role in \u0026ldquo;acquired resistance\u0026rdquo; to quinolone. Globally, mutation at position 88 is less profound in comparison to 90 and 94, which share a greater percentage of patients (Arun et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kim et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Chauffour et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Among 90 and 94 positions, mutation at position 90 is extensively studied, and much information is available.\u003c/p\u003e \u003cp\u003eIn the present work, we selected an unexplored, less-studied mutation at position 94 with prevalence among the majority of TB patients. Mutation at this position is a result of residue change leading to structural and electrostatic changes. Monitoring these changes will enrich our insight to add information and help in the development of new lead molecules with good potency and efficacy to encounter Mycobacterium.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Ligand Structure Retrieval and Preparation\u003c/h2\u003e\n \u003cp\u003eThe literature review revealed the importance of fourth-generation quinolone antibiotics in the treatment of tuberculosis (Mogle et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Pranger 2019). Structural data files (in .sdf format) of quinolone antibiotics were obtained from the PubChem database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov/\u003c/span\u003e\u003c/span\u003e) (Atanasov et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Using the Marvin sketch tool (23.10), structures were modified and cleaned before being saved in the protein data bank (.pdb) format (Rajalakshmi et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). The physiochemical properties like logP, H-bond donor, H-bond acceptor, polar surface area, polarizability, van der Waals surface area, refractivity, etc. were studied and listed (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The pdb files were converted to. pdbqt format after being opened in Auto Dock Vina for structure modification and visualization.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePhysiochemical properties of fourth generation antibiotics (drug) used against Mycobacterium tuberculosis.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAntibiotics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMol. weight\u003c/p\u003e\n \u003cp\u003e(g/mol)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLog-p\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eH-bond donor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eH-bond acceptor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePolar surface area (\u0026Aring;\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePolarizability (\u0026Aring;\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVan der waal surface area\u003c/p\u003e\n \u003cp\u003e(\u0026Aring;\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRefractivity\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClinafloxacin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e365.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e448.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGatifloxacin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e375.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e82.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e513.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMoxifloxacin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e401.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e82.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e544.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e106.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSitafloxacin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e409.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e491.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e99.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrulifloxacin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e461.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e99.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e560.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e127.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBesifloxacin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e393.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e504.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e101.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDelafloxacin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e440.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e120.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e476.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e101.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOzenoxacin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e363.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e82.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e505.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e106.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Protein Structure Retrieval, Preparation and Docking\u003c/h2\u003e\n \u003cp\u003eDNA gyrase subunit A\u0026apos;s N-terminal domain\u0026apos;s X-ray crystallographic structure (Tretter et al. \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e) was acquired using (PDB ID: 3ILW) from the RCSB PDB (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.rcsb.org\u003c/span\u003e\u003c/span\u003e). Using the Discovery Studio and Auto-dock Vina tools, water molecules and other tiny molecules found in the crystal structure were removed. In order to minimize energy and eliminate steric conflicts, the structures were optimized using the Chimera program. Mutation in the protein 3ILW_wild (DNA gyrase A) was inserted at residue positions 88 and 94, respectively, by substituting alanine (A) and cysteine (C) for glycine (G) at position 88 and histidine (H) and glycine (G) against aspartic acid (D) at position 94 in the protein\u0026rsquo;s sequence using UCSF Chimera (1.16) tool (Pettersen et al. \u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e). Four models of 3ILW_wild protein were generated: 3ILW_G88A, 3ILW_G88C, 3ILW_D94G, and 3ILW_D94H (Maruri et al. \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). The chimera tool was used for energy minimization. The PROCHECK (Wlodawer \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e) module in the SAVES server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.saves.mbi.ucla.edu\u003c/span\u003e\u003c/span\u003e) was used for Ramachandran plot generation to obtain the accuracy of wild and mutated protein models (\u003cstrong\u003eSupplementary Fig.\u0026nbsp;1\u003c/strong\u003e). The generated protein molecules were provided with optimum Kollman charges, polar hydrogen atoms were added, tautomeric states for all hetero groups at pH 7.0 were designated, and bond order was assigned. The grid box for the proteins was generated using the Auto Dock Vina tool. Utilizing coordinates from a configuration file; center (X\u0026thinsp;=\u0026thinsp;4.105, Y = -10.989, Z\u0026thinsp;=\u0026thinsp;85.249), size (X\u0026thinsp;=\u0026thinsp;86, Y\u0026thinsp;=\u0026thinsp;112, and Z\u0026thinsp;=\u0026thinsp;98); energy range: 4, exhaustiveness: 8, a PDBQT file included the organized data were generated for all the protein molecules and were saved in .pdbqt format. Screening for the best-interacting antibiotic was done through flexible docking between the 3ILW_wild protein and prepared ligands using the Auto Dock Vina tool (Ferreira et al. 2017; Guttula et al. \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e). To monitor the changes in the structure and interaction with the antibiotic that occurred by inserting mutations, the four mutated protein models (3ILW_G88A, 3ILW_G88C, 3ILW_D94G, and 3ILW_D94H) and the wild protein 3ILW_wild were finally docked with the top ligand obtained from Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 MD simulation\u003c/h2\u003e\n \u003cp\u003eThe GROMACS software tool (v.2023.3; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.gromacs.org/\u003c/span\u003e\u003c/span\u003e) was utilized in conjunction with the CHARMM27 force field to assess the structural and electrostatic alterations brought about by insertional mutations in DNA gyrase (Bjelkmar et al. \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e). For this investigation, a total of five systems were created. At pH 7.4, each constructed structure was protonated in accordance with the protonation states of every titrable residue. The protonated structures were placed in a dodecahedron box with a space of greater than 1.0 nm between any protein atom and the box wall after being dissolved using the TIP3P water model (Price et al. 2004). In order to replicate physiological conditions, the net charges of both simulation systems were neutralized using a 100 mM concentration of NaCl. The simulation systems were equilibrated by two continuous 10-ns position constraint simulations in the NVT and NPT ensembles with 1000 kl/mol/nm2 harmonic force constants after first being subjected to energy minimization using the steepest descent algorithm. Swiss-param (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.swissparam.ch/\u003c/span\u003e\u003c/span\u003e), an online tool, was used to create the topology of the ligands. The LINC algorithms (Hess et al. \u003cspan class=\"CitationRef\"\u003e1997\u003c/span\u003e) were utilized to constrain the bond lengths with an integration time step of 2 fs. The particle-mesh Ewald (PME) algorithm (Essmann et al. \u003cspan class=\"CitationRef\"\u003e1995\u003c/span\u003e) calculated long-range electrostatic interactions with an interpolation order of 4, Fourier grid spacing of 1.6 \u0026Aring;, and a Coulomb radius of 1 nm. The v-rescale thermostat (Bussi et al. \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e) was employed to couple the temperature of the systems at 310 K with a time constant of 0.1 ps. The Parrinello-Rahman barostat (Parrinello et al. 1981) was utilized to maintain the pressure of the systems at 1 atm with a time constant of 0.5 ps. The snapshots were saved every 10 ps.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4 Structural and Geometrical Properties\u003c/h2\u003e\n \u003cp\u003eThe MD trajectories underwent structural and geometrical analysis using GROMAC tools. Specifically, \u0026quot;gmx rms\u0026quot; was utilized to determine the time-dependent backbone root mean square deviation (RMSD) in relation to the wild structure; \u0026quot;gmx rmsf\u0026quot; was employed to calculate the per-residue C\u0026alpha; root mean square fluctuation (RMSF); \u0026lsquo;gmx gyrate\u0026rsquo; was used to measure the compactness (Rg) of the molecule; and the \u0026quot;gmx hbond\u0026rsquo; module enhances the understanding of protein structure, folding, function, and ligand binding, as well as other biomolecular interactions (Sang et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). \u0026quot;gmx sasa\u0026quot; was utilized to determine the solvent-accessible surface area (SASA); \u0026quot;gmx sham\u0026quot; was used to calculate the free energy landscape, allowing one to comprehend the energetics and stability of various conformational states.\u003c/p\u003e\n \u003cp\u003eThe \u0026quot;hydrogen bond\u0026quot; plugin in VMD was used to compute the hydrogen bonds that formed between the ligand and protein molecules when the donor-acceptor angle was more than 120\u0026ordm; and the donor-acceptor distance was less than 3.5 \u0026Aring; (Humphrey et al. \u003cspan class=\"CitationRef\"\u003e1996\u003c/span\u003e). Pymol 2.5.2 was used to create the electrostatic surface potential of the structures 3ILW_wild\u0026thinsp;+\u0026thinsp;dfx, 3ILW_G88A\u0026thinsp;+\u0026thinsp;dfx, 3ILW_G88C\u0026thinsp;+\u0026thinsp;dfx, 3ILW_D94G\u0026thinsp;+\u0026thinsp;dfx, and 3ILW_D94H\u0026thinsp;+\u0026thinsp;dfx (Dutta et al. \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe protein-ligand bond causes a portion of the two molecules\u0026apos; molecular surfaces to be buried. Using the following formula, the buried solvent-accessible surface area (SASA) Area burial was determined:\u003c/p\u003e\n \u003cp\u003eArea \u003csub\u003eburial\u003c/sub\u003e = (SASA \u003csub\u003eprotein\u003c/sub\u003e + SASA \u003csub\u003eligand\u003c/sub\u003e) - SASA \u003csub\u003ecomplex\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003ewhere, SASA \u003csub\u003eprotein,\u003c/sub\u003e SASA \u003csub\u003eligand\u003c/sub\u003e and SASA \u003csub\u003ecomplex\u003c/sub\u003e represent the solvent accessible area (SASA) of Protein (3ILW_wild, 3ILW_G88A, 3ILW_G88C, 3ILW_D94G and 3ILW_D94H), Ligand (delafloxacin) and complex of protein and ligand, respectively.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5 Binding Free Energy (BFE) Calculation\u003c/h2\u003e\n \u003cp\u003eThe most rigorous BFE computational approach is the free-energy perturbation (FEP), which may estimate the difference in free energy between two states by gradually switching one state to another through a series of nonphysical intermediate states. However, this method takes a very long time. The two most widely used approximations for computing BFE are the molecular mechanics generalized Born surface area (MM/GBSA) and the molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) (Tuccinardi \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). The MM-PBSA method (Homeyer et al. 2012) was employed in this investigation to determine the BFE between the proteins (3ILW_wild, 3ILW_G88A, 3ILW_G88C, 3ILW_D94G, and 3ILW_D94H) and delafloxacin (dfx).\u003c/p\u003e\n \u003cp\u003eA well-known endpoint technique called MM-PBSA may calculate the protein-ligand BFE only from the structure or structural ensemble of the bound complex, ignoring any non-physical or physical intermediates (Wang et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). The BFE of the protein and ligand in MM-PBSA is described as follows:\u003c/p\u003e\n \u003cp\u003e\u0026Delta;G\u003csub\u003ebinding\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;\u0026Delta;G \u003csub\u003ecomplex\u003c/sub\u003e \u0026ndash; (\u0026Delta;G\u003csub\u003eprotein\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;\u0026Delta;G \u003csub\u003eligand\u003c/sub\u003e)\u003c/p\u003e\n \u003cp\u003ewhere the change in free energy is denoted by \u0026Delta;G. Each subunit\u0026apos;s free energy, G, can be shown as follows:\u003c/p\u003e\n \u003cp\u003eG\u0026thinsp;=\u0026thinsp;E \u003csub\u003eMM\u003c/sub\u003e + G \u003csub\u003esol\u003c/sub\u003e -TS\u003c/p\u003e\n \u003cp\u003eThe average molecular mechanical potential energy (E\u003csub\u003eMM\u003c/sub\u003e) in vacuum is made up of the electrostatic (E \u003csub\u003eele\u003c/sub\u003e) and van der Waals (E \u003csub\u003evdw\u003c/sub\u003e) interactions, and it can be expressed as follows:\u003c/p\u003e\n \u003cp\u003eE\u003csub\u003eMM\u003c/sub\u003e = E\u003csub\u003eele\u003c/sub\u003e + E\u003csub\u003evdw\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003eThe solvation free energy, or Gsol, can be described as follows. It is divided into polar (G\u003csub\u003epolar\u003c/sub\u003e) and non-polar (G\u003csub\u003enon\u0026minus;polar\u003c/sub\u003e) components.\u003c/p\u003e\n \u003cp\u003eG\u003csub\u003esol =\u003c/sub\u003e G\u003csub\u003epolar\u003c/sub\u003e + G\u003csub\u003enon\u0026minus;polar\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003eT stands for temperature, S for entropy, and TS for the entropy contribution to the free energy. It is important to note that while comparing the relative BFEs, the TS component is insignificant. Per-residue contribution was also studied for the QRDR region in wild and mutated proteins (Genheden et al. 2015; Tian et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eTo calculate the MM-PBSA, the single trajectory method included in gmx_MMPBSA 1.5.7 was employed (Vald\u0026eacute;s-Tresanco et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Using the gmx_MMPBSA default parameters, the BFE between the protein and ligand was computed for every simulation system.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Visualization and Analysis of protein-ligand interaction\u003c/h2\u003e\n \u003cp\u003eTo identify the interacting residues, type, and nature of bond formation with the antibiotics in the active site, the molecular docking was performed between the 3ILW_wild protein and fourth-generation antibiotics. Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e depicts the interacting antibiotics, their binding free energies, and residues in the active site of the protein actively participating in the interaction. Among all the eight antibiotics, Delafloxacin was found to be the most promising candidate with the lowest binding free energy of -8.6 kcal/mol. Residues Trp\u003csup\u003e103\u003c/sup\u003e, Pro\u003csup\u003e119\u003c/sup\u003e and Val\u003csup\u003e278\u003c/sup\u003e were found to be involved in H-bond interaction. Other interactions, like van der Waals and pi-alkyl, were also observed.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eProtein DNA gyrase A (wild) interaction with fourth generation antibiotics. The amino acid residues shown in bold are involved in hydrogen-bonding interactions.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAntibiotics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBinding free energy\u003c/p\u003e\n \u003cp\u003e(Kcal/mol)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAmino acid residues involved in interactions via different types of bonding\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDelafoxacin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArg\u003csup\u003e98\u003c/sup\u003e, Gln\u003csup\u003e101\u003c/sup\u003e, \u003cstrong\u003eTrp\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e103\u003c/strong\u003e\u003c/sup\u003e, Ser\u003csup\u003e104\u003c/sup\u003e, Gly\u003csup\u003e117\u003c/sup\u003e, Ser\u003csup\u003e118\u003c/sup\u003e, \u003cstrong\u003ePro\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e119\u003c/strong\u003e\u003c/sup\u003e, Gly\u003csup\u003e120\u003c/sup\u003e, Asn\u003csup\u003e121\u003c/sup\u003e, \u003cstrong\u003eAsp\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e122\u003c/strong\u003e\u003c/sup\u003e, Pro\u003csup\u003e124\u003c/sup\u003e, Ile\u003csup\u003e181\u003c/sup\u003e, Ala\u003csup\u003e182\u003c/sup\u003e, Gln \u003csup\u003e277\u003c/sup\u003e, Thr\u003csup\u003e230\u003c/sup\u003e, \u003cstrong\u003eVal\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e278\u003c/strong\u003e\u003c/sup\u003e, Asn \u003csup\u003e279\u003c/sup\u003e, His\u003csup\u003e280\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrulifloxacin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-8.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAla\u003csup\u003e90\u003c/sup\u003e, Tyr\u003csup\u003e93\u003c/sup\u003e, Asp\u003csup\u003e94\u003c/sup\u003e, Val\u003csup\u003e97\u003c/sup\u003e, \u003cstrong\u003eArg\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e98\u003c/strong\u003e\u003c/sup\u003e, \u003cstrong\u003eGln\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e101\u003c/strong\u003e\u003c/sup\u003e, Trp\u003csup\u003e103\u003c/sup\u003e, \u003cstrong\u003eSer\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e104\u003c/strong\u003e\u003c/sup\u003e, Ser\u003csup\u003e118\u003c/sup\u003e, Pro\u003csup\u003e123\u003c/sup\u003e, Pro\u003csup\u003e124\u003c/sup\u003e, \u003cstrong\u003eGln\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e277\u003c/strong\u003e\u003c/sup\u003e, Asn\u003csup\u003e279\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMoxifloxacin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePro\u003csup\u003e102\u003c/sup\u003e, Trp\u003csup\u003e103\u003c/sup\u003e, Ser\u003csup\u003e118\u003c/sup\u003e, Pro\u003csup\u003e119\u003c/sup\u003e, \u003cstrong\u003eThr\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e230\u003c/strong\u003e\u003c/sup\u003e, Ala\u003csup\u003e231\u003c/sup\u003e, Thr\u003csup\u003e272\u003c/sup\u003e, Glu\u003csup\u003e273\u003c/sup\u003e, Leu\u003csup\u003e274\u003c/sup\u003e, Ser\u003csup\u003e306\u003c/sup\u003e, Asp\u003csup\u003e308\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOzenoxacin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrp\u003csup\u003e103\u003c/sup\u003e, \u003cstrong\u003eGly\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e117\u003c/strong\u003e\u003c/sup\u003e, Ser\u003csup\u003e118\u003c/sup\u003e, Pro\u003csup\u003e119\u003c/sup\u003e, Thr\u003csup\u003e230\u003c/sup\u003e, Ala\u003csup\u003e231\u003c/sup\u003e, \u003cstrong\u003eThr\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e272\u003c/strong\u003e\u003c/sup\u003e, Glu\u003csup\u003e273\u003c/sup\u003e, Leu\u003csup\u003e274\u003c/sup\u003e, His\u003csup\u003e280\u003c/sup\u003e, Ser\u003csup\u003e306\u003c/sup\u003e, Ser\u003csup\u003e307\u003c/sup\u003e, Asp\u003csup\u003e308\u003c/sup\u003e, Gly\u003csup\u003e311\u003c/sup\u003e, Leu\u003csup\u003e312\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSitafloxacin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAsn\u003csup\u003e115\u003c/sup\u003e, Phe\u003csup\u003e116\u003c/sup\u003e, Gly\u003csup\u003e117\u003c/sup\u003e, Ser\u003csup\u003e118\u003c/sup\u003e, Pro\u003csup\u003e119\u003c/sup\u003e, Gly\u003csup\u003e120\u003c/sup\u003e, Asp\u003csup\u003e122\u003c/sup\u003e, His\u003csup\u003e280\u003c/sup\u003e, \u003cstrong\u003eAsp\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e281\u003c/strong\u003e\u003c/sup\u003e, Ile\u003csup\u003e284\u003c/sup\u003e, Asp\u003csup\u003e304\u003c/sup\u003e, \u003cstrong\u003eSer\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e306\u003c/strong\u003e\u003c/sup\u003e, Ser\u003csup\u003e307\u003c/sup\u003e, Gly\u003csup\u003e311\u003c/sup\u003e, Leu\u003csup\u003e312\u003c/sup\u003e, Ile\u003csup\u003e314\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGatifloxacin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eArg98, Gln\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e101\u003c/strong\u003e\u003c/sup\u003e, \u003cstrong\u003eTrp\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e103\u003c/strong\u003e\u003c/sup\u003e, Ser\u003csup\u003e104\u003c/sup\u003e, Phe\u003csup\u003e116\u003c/sup\u003e, Gly\u003csup\u003e117\u003c/sup\u003e, \u003cstrong\u003eSer\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e118\u003c/strong\u003e\u003c/sup\u003e, Pro\u003csup\u003e119\u003c/sup\u003e, \u003cstrong\u003eGly\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e120\u003c/strong\u003e\u003c/sup\u003e, Asn\u003csup\u003e121\u003c/sup\u003e, Asp\u003csup\u003e122\u003c/sup\u003e, Pro\u003csup\u003e124\u003c/sup\u003e, Gly\u003csup\u003e180\u003c/sup\u003e, Ile\u003csup\u003e181\u003c/sup\u003e, Asn\u003csup\u003e182\u003c/sup\u003e, Met\u003csup\u003e185\u003c/sup\u003e, Gln\u003csup\u003e277\u003c/sup\u003e, Asn\u003csup\u003e279\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBesifloxacin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePro\u003csup\u003e102\u003c/sup\u003e, Trp\u003csup\u003e103\u003c/sup\u003e, \u003cstrong\u003eSer\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e118\u003c/strong\u003e\u003c/sup\u003e, Pro\u003csup\u003e119\u003c/sup\u003e, Gly\u003csup\u003e120\u003c/sup\u003e, Asn\u003csup\u003e121\u003c/sup\u003e, Asp\u003csup\u003e122\u003c/sup\u003e, His\u003csup\u003e280\u003c/sup\u003e, Asp\u003csup\u003e281\u003c/sup\u003e, Asp\u003csup\u003e304\u003c/sup\u003e, Ser\u003csup\u003e306\u003c/sup\u003e, Gly\u003csup\u003e311\u003c/sup\u003e, Leu\u003csup\u003e312\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClinafloxacin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePro\u003csup\u003e102\u003c/sup\u003e, Trp\u003csup\u003e103\u003c/sup\u003e, Ser\u003csup\u003e118\u003c/sup\u003e, Pro\u003csup\u003e119\u003c/sup\u003e, \u003cstrong\u003ePro\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e229\u003c/strong\u003e\u003c/sup\u003e, Thr\u003csup\u003e230\u003c/sup\u003e, Glu\u003csup\u003e273\u003c/sup\u003e, His\u003csup\u003e280\u003c/sup\u003e, Ser\u003csup\u003e306\u003c/sup\u003e, Gly\u003csup\u003e311\u003c/sup\u003e, Leu\u003csup\u003e312\u003c/sup\u003e, Arg\u003csup\u003e495\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eTo understand the change in interaction pattern of residues with the antibiotics leading to the structural change in the active site, again a molecular docking approach was applied. The wild protein 3ILW_wild, along with mutated proteins 3ILW_G88A, 3ILW_G88C, 3ILW_D94G, and 3ILW_D94H, were docked with delafloxacin (dfx) obtained from previous docking studies. The binding free energy during docking studies varied from \u0026minus;\u0026thinsp;7.0 kcal/mol to -9.0 kcal/mol (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). 3ILW_wild, 3ILW_G88A, and 3ILW_D94G had the same binding free energy of -8.6 kcal/mol, showing no effect of mutation on binding free energy. Residues Trp\u003csup\u003e103\u003c/sup\u003e, Pro\u003csup\u003e119\u003c/sup\u003e, Asp\u003csup\u003e122\u003c/sup\u003e and Val\u003csup\u003e278\u003c/sup\u003e were active in protein 3ILW_wild. In mutated proteins 3ILW_G88A and 3ILW_D94G, the residues that actively participated in hydrogen bonding were Ser\u003csup\u003e82\u003c/sup\u003e, Pro\u003csup\u003e83\u003c/sup\u003e, Gly\u003csup\u003e84\u003c/sup\u003e, Asp\u003csup\u003e86\u003c/sup\u003e, Val\u003csup\u003e242\u003c/sup\u003e among them, Pro\u003csup\u003e83\u003c/sup\u003e and Val\u003csup\u003e242\u003c/sup\u003e were common. Mutated proteins 3ILW_G88C and 3ILW_D94H showed variable binding free energy of -7.5 kcal/mol and \u0026minus;\u0026thinsp;8.3 kcal/mol. Lower binding free energy probably may be due to a change in the nature of residues on mutation.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ebinding free energies of wild and mutant proteins on interaction with delafloxacin antibiotics. The amino acid residues shown in bold are involved in hydrogen-bonding interactions.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS.NO.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProtein\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBinding free energy\u003c/p\u003e\n \u003cp\u003e(Kcal/mol)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAmino acid residues involved in interactions via different types of bonding\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3ILW_wild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArg\u003csup\u003e98\u003c/sup\u003e, Gln\u003csup\u003e101\u003c/sup\u003e, \u003cstrong\u003eTrp\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e103\u003c/strong\u003e\u003c/sup\u003e, Ser\u003csup\u003e104\u003c/sup\u003e, Gly\u003csup\u003e117\u003c/sup\u003e, Ser\u003csup\u003e118\u003c/sup\u003e, \u003cstrong\u003ePro\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e119\u003c/strong\u003e\u003c/sup\u003e, Gly\u003csup\u003e120\u003c/sup\u003e, Asn\u003csup\u003e121\u003c/sup\u003e, \u003cstrong\u003eAsp\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e122\u003c/strong\u003e\u003c/sup\u003e, Pro\u003csup\u003e124\u003c/sup\u003e, Ile\u003csup\u003e181\u003c/sup\u003e, Ala\u003csup\u003e182\u003c/sup\u003e, Gln \u003csup\u003e277\u003c/sup\u003e, Thr\u003csup\u003e230\u003c/sup\u003e, \u003cstrong\u003eVal\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e278\u003c/strong\u003e\u003c/sup\u003e, Asn \u003csup\u003e279\u003c/sup\u003e, His\u003csup\u003e280\u003c/sup\u003e,\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3ILW_G88C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePro\u003csup\u003e66\u003c/sup\u003e, Trp\u003csup\u003e67\u003c/sup\u003e, Pro\u003csup\u003e83\u003c/sup\u003e, Pro\u003csup\u003e193\u003c/sup\u003e, Thr\u003csup\u003e194\u003c/sup\u003e, Ala\u003csup\u003e195\u003c/sup\u003e, \u003cstrong\u003eThr\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e236\u003c/strong\u003e\u003c/sup\u003e, Glu\u003csup\u003e237\u003c/sup\u003e, \u003cstrong\u003eLeu\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e238\u003c/strong\u003e\u003c/sup\u003e, \u003cstrong\u003eHis\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e244\u003c/strong\u003e\u003c/sup\u003e, Leu\u003csup\u003e276\u003c/sup\u003e, Arg\u003csup\u003e459\u003c/sup\u003e,\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3ILW_G88A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArg\u003csup\u003e62\u003c/sup\u003e, Gln\u003csup\u003e65\u003c/sup\u003e, Trp \u003csup\u003e67\u003c/sup\u003e, Ser\u003csup\u003e68\u003c/sup\u003e, Gly\u003csup\u003e81\u003c/sup\u003e, \u003cstrong\u003eSer\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e82\u003c/strong\u003e\u003c/sup\u003e, \u003cstrong\u003ePro\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e83\u003c/strong\u003e\u003c/sup\u003e, \u003cstrong\u003eGly\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e84\u003c/strong\u003e\u003c/sup\u003e, Asn\u003csup\u003e85\u003c/sup\u003e, Asp\u003csup\u003e86\u003c/sup\u003e, Pro\u003csup\u003e88\u003c/sup\u003e, Ile\u003csup\u003e145\u003c/sup\u003e, Ala\u003csup\u003e146\u003c/sup\u003e, Thr\u003csup\u003e194\u003c/sup\u003e, Gln\u003csup\u003e241\u003c/sup\u003e, \u003cstrong\u003eVal\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e242\u003c/strong\u003e\u003c/sup\u003e, Asn\u003csup\u003e243\u003c/sup\u003e, His\u003csup\u003e244\u003c/sup\u003e,\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3ILW_D94G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArg\u003csup\u003e62\u003c/sup\u003e, Gln\u003csup\u003e65\u003c/sup\u003e, Trp\u003csup\u003e67\u003c/sup\u003e, Ser\u003csup\u003e68\u003c/sup\u003e, Gly\u003csup\u003e81\u003c/sup\u003e, Ser\u003csup\u003e82\u003c/sup\u003e, \u003cstrong\u003ePro\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e83\u003c/strong\u003e\u003c/sup\u003e, Gly\u003csup\u003e84\u003c/sup\u003e, Asn\u003csup\u003e85\u003c/sup\u003e, \u003cstrong\u003eAsp\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e86\u003c/strong\u003e\u003c/sup\u003e, Pro\u003csup\u003e87\u003c/sup\u003e, Pro\u003csup\u003e88\u003c/sup\u003e, Ile\u003csup\u003e145\u003c/sup\u003e, Ala\u003csup\u003e146\u003c/sup\u003e, Thr\u003csup\u003e194\u003c/sup\u003e, Gln\u003csup\u003e241\u003c/sup\u003e, \u003cstrong\u003eVal\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e242\u003c/strong\u003e\u003c/sup\u003e, Asn\u003csup\u003e243\u003c/sup\u003e, His\u003csup\u003e244\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3ILW_D94H\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrp\u003csup\u003e103\u003c/sup\u003e, Pro\u003csup\u003e119\u003c/sup\u003e, \u003cstrong\u003eThr\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e272\u003c/strong\u003e\u003c/sup\u003e, Glu\u003csup\u003e273\u003c/sup\u003e, \u003cstrong\u003eLeu\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e274\u003c/strong\u003e\u003c/sup\u003e, \u003cstrong\u003eHis\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e280\u003c/strong\u003e\u003c/sup\u003e, \u003cstrong\u003eAsp\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e308\u003c/strong\u003e\u003c/sup\u003e, \u003cstrong\u003eVal\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e310\u003c/strong\u003e\u003c/sup\u003e, Gly\u003csup\u003e311\u003c/sup\u003e, \u003cstrong\u003eLeu\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e312\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eHalogen (fluorine), van der Waals, h bond, pi-sigma, amide-pi stacked etc. interaction were observed in \u003cstrong\u003eFig.\u0026nbsp;1\u003c/strong\u003e. Hydrogen bonding, van der Waals and pi-alkyl interactions were seen in 3ILW_wild. In addition, halogen, pi-cation, amide pi stacked interactions were also observed in mutated proteins complexes 3ILW_G88A\u0026thinsp;+\u0026thinsp;dfx, 3ILW_G88C\u0026thinsp;+\u0026thinsp;dfx, 3ILW_D94G\u0026thinsp;+\u0026thinsp;dfx, and 3ILW_D94H\u0026thinsp;+\u0026thinsp;dfx. Hydrogen bond interaction was maximum in 3ILW_D94H\u0026thinsp;+\u0026thinsp;dfx complex with six hydrogen bond formation while rest 3ILW_wild\u0026thinsp;+\u0026thinsp;dfx, 3ILW_G88A\u0026thinsp;+\u0026thinsp;dfx, 3ILW_G88C\u0026thinsp;+\u0026thinsp;dfx and 3ILW_D94G\u0026thinsp;+\u0026thinsp;dfx formed 4,4,3 and 3 hydrogen bonds.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Analysis of Structural stability and flexibility during MD simulation\u003c/h2\u003e\n \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.1 Root Mean Square Deviation (RMSD)\u003c/h2\u003e\n \u003cp\u003eThe structural stability was examined by monitoring the time-dependent backbone RMSD trajectories of five simulation systems. It was used to determine the protein\u0026rsquo;s proximity with the ligand molecule, which reflects the protein\u0026apos;s stability and conformational change. The RMSD curves of the wild 3ILW_wild\u0026thinsp;+\u0026thinsp;dfx complex, along with mutant protein complexes, i.e., 3ILW_G88A\u0026thinsp;+\u0026thinsp;dfx, 3ILW_G88C\u0026thinsp;+\u0026thinsp;dfx, 3ILW_D94H\u0026thinsp;+\u0026thinsp;dfx, and 3ILW_D94G\u0026thinsp;+\u0026thinsp;dfx, were shown in Figs. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cstrong\u003e(a) \u0026amp; 2(b).\u003c/strong\u003e RMSD for five systems was between 0.26 nm and 0.20 nm, indicating that all the systems were stable and had a very low probability of conformational change during the 100 ns simulation (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). The mutated protein complex 3ILW_D94H\u0026thinsp;+\u0026thinsp;dfx experienced the highest 0.26 nm deviation as the wild protein 3ILW_wild, showing no effect of residue substitution and hence the stability with delafloxacin. Rest mutated proteins 3ILW_D94G, 3ILW_G88C, and 3ILW_G88A experienced 0.20 nm, 0.23 nm, and 0.22 nm deviations, indicating greater stability. All the mutated protein complexes showed greater stability than the wild protein complex 3ILW_wild\u0026thinsp;+\u0026thinsp;dfx.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.2 Root Mean Square Fluctuation (RMSF)\u003c/h2\u003e\n \u003cp\u003eIn order to compare the structural flexibility of wild and mutated proteins complexed with delafloxacin, we calculated the per-residue C\u0026alpha; atom root mean square fluctuation values. It provides insights into the dynamic regions of proteins, such as flexible loops, active sites, and binding regions, and can indicate the importance of specific residues in conformational changes or protein-ligand interactions. According to Figs. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e (a) \u0026amp; 3(b), the molecular fluctuations were seen highest in the QRDR region, especially around 85\u0026ndash;100 amino acid residues. The complexed mutated protein 3ILW_D94H\u0026thinsp;+\u0026thinsp;dfx and the wild protein 3ILW_wild\u0026thinsp;+\u0026thinsp;dfx showed the highest structural fluctuation of 0.14 nm, indicating no effect of mutation due to residue substitution in the DNA gyrase A sequence, and it counts for greater flexibility and binding affinity at this position. Similarly, the other mutant protein complexes, 3ILW_D94G\u0026thinsp;+\u0026thinsp;dfx and 3ILW_G88A\u0026thinsp;+\u0026thinsp;dfx, experienced the least fluctuation of 0.11 nm, indicating rigid conformation or structure. 3ILW_G88C had an intermediate value of 0.12 nm. Since the structural fluctuation seems to get reduced, indicating lesser flexibility and greater rigidity, it may increase the binding ability of proteins with the delafloxacin molecule due to formation of stable secodary structures like \u0026alpha;-helices and \u0026beta;-sheets.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.3 Radius of Gyration (Rg)\u003c/h2\u003e\n \u003cp\u003eThe compactness or the rigidity of the protein-ligand complexes, both wild and mutated, was evaluated by Radius of Gyration (Rg). A smaller Rg value indicates a more compact or folded structure, while a larger value suggests more extended or flexible conformations during MD simulation. According to Figs. 4(a) and 4(b), the gyration value for the wild protein complex 3ILW_wild\u0026thinsp;+\u0026thinsp;dfx was found to be 3.0 nm, and for the mutated protein complexes 3ILW_G88A\u0026thinsp;+\u0026thinsp;dfx, 3ILW_G88C\u0026thinsp;+\u0026thinsp;dfx, 3ILW_D94G\u0026thinsp;+\u0026thinsp;dfx, and 3ILW_D94H\u0026thinsp;+\u0026thinsp;dfx were 2.97 nm, 2.99 nm, 2.96 nm, and 2.98 nm, respectively (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). The Rg value suggested that 3ILW_wild\u0026thinsp;+\u0026thinsp;dfx complex had the most extended or unfolded structure, and the compactness in the mutated proteins has increased on amino acid substitution. The increase in compactness probably may be due to changed interacting residues and bonding patterns in the mutated protein complexes.\u003c/p\u003e\n \u003cp\u003eAmong all the five systems, it was observed that 3ILW_D94G\u0026thinsp;+\u0026thinsp;dfx was the most compact complex, showing greater stability or rigidity. The binding affinity of the mutant proteins does not change much, as indicated by the Rg value in the table.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparative analysis of Stability, Flexibility, Compactness and Surface properties of Wild and Mutated protein complexes over 100 ns MD simulation.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eComplexes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRMSD\u003c/p\u003e\n \u003cp\u003e(nm)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRMSF\u003c/p\u003e\n \u003cp\u003e(nm)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRg (nm)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSASA-COMPLEX (\u0026Aring;\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSASA-PROTEIN (\u0026Aring;\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSASA-LIGAND (\u0026Aring;\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3ILW_wild\u0026thinsp;+\u0026thinsp;dfx\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e235.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e234.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3ILW_G88A\u0026thinsp;+\u0026thinsp;dfx\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e229.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e231.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3ILW_G88C\u0026thinsp;+\u0026thinsp;dfx\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e234.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e234.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3ILW_D94G\u0026thinsp;+\u0026thinsp;dfx\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e234.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e234.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3ILW_D94H\u0026thinsp;+\u0026thinsp;dfx\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e234.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e235.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Analysis of Interfacial Interaction between Protein and Ligand\u003c/h2\u003e\n \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.1 Electrostatic Surface Potential\u003c/h2\u003e\n \u003cp\u003eThe electrostatic surface potential (ESPs) of the protein reflects the binding affinity of the protein with a ligand molecule. Blue, white, and red color coding on the surface of the protein signifies the positive, neutral, and negative electrostatic surface potential. The interfacial ESPs of wild and mutated proteins varied between \u0026minus;\u0026thinsp;75 kcal/mol and +\u0026thinsp;75 kcal/mol due to positive, negative, or neutral regions present in the protein molecule, as depicted in Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. The gain of positively charged residues or the loss of negatively charged residues increases the binding affinity. The wild protein has the largest positive charged region (red) as compared to mutant proteins except 3ILW_G88C, which shows comparable electrostatic potential as depicted by \u0026Delta;E \u003csub\u003eele\u003c/sub\u003e term of binding free energy value. A larger blue region was observed in the 3ILW_D94H mutant protein, which shows its higher binding affinity for delafloxacin among all the mutant molecules.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.2 Hydrogen Bonding (HB)\u003c/h2\u003e\n \u003cp\u003eFor investigation of the binding affinity of delafloxacin towards DNA gyrase A protein, the trajectories of the complex were analyzed, and H bonds between the ligand and proteins were calculated over the 100 ns simulation and plotted (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe total H-bonds formed between the docked wild protein 3ILW_wild and dfx were 3, but only two of them were stable over the simulation studies with 5.31% occupancy. Mutated protein complexed with delafloxacin (3ILW_G88A\u0026thinsp;+\u0026thinsp;dfx) exhibited a maximum of 4 H-bonds, and during simulation studies, it was found that five H-bonds were stable for 100 ns with 46.8% occupancy. Complexed mutated protein 3ILW_G88C\u0026thinsp;+\u0026thinsp;dfx formed 2 H-bonds, which were stable during MD studies with 14.31% occupancy. 3ILW_D94G, when docked with dfx, formed 4 H-bonds; among them, only three were found to be stable with 34.25% occupancy during simulation. Moreover, 3ILW_D94H\u0026thinsp;+\u0026thinsp;dfx formed the maximum number of H-bonds with 21.69% occupancy during simulation studies. With reference to wild protein, mutant proteins 3ILW_G88A and 3ILW_D94G showed the most stable H-bonds during simulation studies.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.3 Solvent Accessible Area (SASA)\u003c/h2\u003e\n \u003cp\u003eSolvent accessible Area representing the ability of water accessibility at the binding pocket of target protein molecules (wild and mutated), delafloxacin (ligand) and protein-ligand complex was calculated \u003cstrong\u003e(\u003c/strong\u003eTable \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cstrong\u003e).\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe results of average SASA calculations for proteins, ligands, and protein complexes are shown in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. The average SASA value for wild and mutated protein complex, wild and mutated protein, and only ligand ranged between 229.31 nm\u003csup\u003e2\u003c/sup\u003e to 235.31 nm\u003csup\u003e2\u003c/sup\u003e, 231.10 nm\u003csup\u003e2\u003c/sup\u003e to 235.68 nm\u003csup\u003e2\u003c/sup\u003e, and 6.04 nm\u003csup\u003e2\u003c/sup\u003e to 6.05 nm\u003csup\u003e2\u003c/sup\u003e, respectively. According to Figs. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e\u003cstrong\u003e(a) \u0026amp; 7(b)\u003c/strong\u003e, the highest average SASA value was observed in the 3ILW_wild\u0026thinsp;+\u0026thinsp;dfx complex. This may be due to slight deviation of the residues forms the binding pocket. The lowest value was observed in 3ILW_G88A\u0026thinsp;+\u0026thinsp;dfx. Rest mutated protein complexes do not show much variation in SASA values, probably due to hydrophobic residue substitutions, and experienced greater repulsion so that only a few residues were in contact with water molecules.\u003c/p\u003e\n \u003cp\u003eThe buried surface area is the measurement of the interface at which the protein and ligand form the complex. The size of the interface reflects the strength of overall nonbonded intermolecular interactions. In the present study, the buried total, polar (hydrophilic) BSAs and non-polar (hydrophobic) BSAs were calculated for proteins (wild and mutated) and delafloxacin complexes. These BSA findings were used to highlight or reveal the strength of Vander Waals, hydrophobic, and electrostatic interactions, respectively. As shown in Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e, almost all average values (marked in black line) of the BSAs in 3ILW_wild\u0026thinsp;+\u0026thinsp;dfx are lower than the mutant proteins revealing that nonbonding interactions are more enhanced due to mutation. The mutant proteins 3ILW_D94G\u0026thinsp;+\u0026thinsp;dfx and 3ILW_d94H\u0026thinsp;+\u0026thinsp;dfx has lower BSAs during hydrophilic interactions indicating the lower binding affinity. The difference in the Total and polar BSAs between wild and mutant proteins and ligand are significantly higher than that of hydrophobic BSAs indicating their stronger contribution in binding affinity between proteins and ligand.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\n \u003ch2\u003e3.4.1 Gibbs free energy landscape\u003c/h2\u003e\n \u003cp\u003eGibbs\u0026apos; free energy landscape represents the free energy as a function of two reaction coordinates, PC1 and PC2, obtained through Principal component analysis of molecular dynamic simulation for 100ns. The color coding signifies the protein-ligand complex\u0026apos;s thermodynamic stability, conformational change, partial flexibility, etc. In the Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e, the shifting of color from red to blue indicates the stability of complex. All the complex molecules represented lesser red region indicating significant stability. Figure \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e\u003cstrong\u003e(a)\u003c/strong\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e\u003cstrong\u003e(e)\u003c/strong\u003e representing cluster along the PC2 representing distinct conformational states probably due to structural transition or localized motion between the protein-ligand complex. The variation of distance between the two clusters in 3ILW_wild\u0026thinsp;+\u0026thinsp;dfx (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e \u003cstrong\u003e(a))\u003c/strong\u003e and 3ILW_D94H\u0026thinsp;+\u0026thinsp;dfx (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e \u003cstrong\u003e(e))\u003c/strong\u003e represents the energy barrier between the conformational states. The complex 3ILW_D94H\u0026thinsp;+\u0026thinsp;dfx harbor larger energy barrier indicating greater conformational movements. Complex 3ILW_G88A\u0026thinsp;+\u0026thinsp;dfx, 3ILW_G88C\u0026thinsp;+\u0026thinsp;dfx and 3ILW_D94G\u0026thinsp;+\u0026thinsp;dfx show very less movement between protein and ligand molecules (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e\u003cstrong\u003e(b)\u003c/strong\u003e, Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e\u003cstrong\u003e(c) and\u003c/strong\u003e Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e\u003cstrong\u003e(d))\u003c/strong\u003e, and these mutant complexes had gradual change in color from red to blue indicating uniform energy distribution. This study clearly indicate that mutants 3ILW_G88A, 3ILW_G88C and 3ILW_D94G forms an altered structural complex with dfx than the Wild type 3ILW_wild.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\n \u003ch2\u003e3.4.2 Analysis of Binding free energy Change\u003c/h2\u003e\n \u003cp\u003eThe binding free energies for all five systems were calculated for 100 ns of MD trajectories. Wild and mutated protein complexes showed negative binding free energies (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e), indicating that all complexes were stable. Mutated protein 3ILW_D94H showed the highest free binding energy (-22.49\u0026thinsp;\u0026plusmn;\u0026thinsp;9.71 kcal/mol), followed by 3ILW_D94G (-22.31\u0026thinsp;\u0026plusmn;\u0026thinsp;7.50 kcal/mol), 3ILW_G88A (-21.74\u0026thinsp;\u0026plusmn;\u0026thinsp;7.07 kcal/mol), 3ILW_wild (-16.65\u0026thinsp;\u0026plusmn;\u0026thinsp;6.0 kcal/mol), and 3ILW_G88C (\u0026minus;\u0026thinsp;9.82\u0026thinsp;\u0026plusmn;\u0026thinsp;4.35 kcal/mol), respectively, when complexed with delafloxacin.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBinding free energy of wild and mutated protein calculated using MM-PBSA method.\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e3ILW_wild\u003c/p\u003e\n \u003cp\u003e(Kcal/mol)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e3ILW_G88A\u003c/p\u003e\n \u003cp\u003e(Kcal/mol)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e3ILW_G88C\u003c/p\u003e\n \u003cp\u003e(Kcal/mol)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e3ILW_D94G\u003c/p\u003e\n \u003cp\u003e(Kcal/mol)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e3ILW_D94H\u003c/p\u003e\n \u003cp\u003e(Kcal/mol)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026Delta;E \u003csub\u003eele\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.55\u0026thinsp;\u0026plusmn;\u0026thinsp;4.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-35.16\u0026thinsp;\u0026plusmn;\u0026thinsp;4.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-5.02\u0026thinsp;\u0026plusmn;\u0026thinsp;2.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-23.82\u0026thinsp;\u0026plusmn;\u0026thinsp;4.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-24.62\u0026thinsp;\u0026plusmn;\u0026thinsp;7.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026Delta;E \u003csub\u003evdw\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-38.53\u0026thinsp;\u0026plusmn;\u0026thinsp;2.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-30.22\u0026thinsp;\u0026plusmn;\u0026thinsp;3.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-24.32\u0026thinsp;\u0026plusmn;\u0026thinsp;2.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-38.54\u0026thinsp;\u0026plusmn;\u0026thinsp;3.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-36.41\u0026thinsp;\u0026plusmn;\u0026thinsp;3.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026Delta;G \u003csub\u003eMM\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-42.08\u0026thinsp;\u0026plusmn;\u0026thinsp;5.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-65.38\u0026thinsp;\u0026plusmn;\u0026thinsp;5.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-29.34\u0026thinsp;\u0026plusmn;\u0026thinsp;3.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-62.36\u0026thinsp;\u0026plusmn;\u0026thinsp;6.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-61.03\u0026thinsp;\u0026plusmn;\u0026thinsp;8.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026Delta;G \u003csub\u003epolar\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.06\u0026thinsp;\u0026plusmn;\u0026thinsp;3.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48.22\u0026thinsp;\u0026plusmn;\u0026thinsp;3.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.33\u0026thinsp;\u0026plusmn;\u0026thinsp;2.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45.03\u0026thinsp;\u0026plusmn;\u0026thinsp;3.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43.11\u0026thinsp;\u0026plusmn;\u0026thinsp;5.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026Delta;G \u003csub\u003enonpolar\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;2.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;4.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;4.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026Delta; G \u003csub\u003esol\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.43\u0026thinsp;\u0026plusmn;\u0026thinsp;3.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43.64\u0026thinsp;\u0026plusmn;\u0026thinsp;3.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.52\u0026thinsp;\u0026plusmn;\u0026thinsp;2.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40.05\u0026thinsp;\u0026plusmn;\u0026thinsp;3.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.54\u0026thinsp;\u0026plusmn;\u0026thinsp;5.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026Delta; G \u003csub\u003ebind\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-16.65\u0026thinsp;\u0026plusmn;\u0026thinsp;6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-21.74\u0026thinsp;\u0026plusmn;\u0026thinsp;7.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;9.82\u0026thinsp;\u0026plusmn;\u0026thinsp;4.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-22.31\u0026thinsp;\u0026plusmn;\u0026thinsp;7.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-22.49\u0026thinsp;\u0026plusmn;\u0026thinsp;9.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003eNote: \u0026Delta;G\u003csub\u003ebind=\u003c/sub\u003e \u0026Delta;G\u003csub\u003eMM +\u003c/sub\u003e \u0026Delta;G\u003csub\u003esol ;\u003c/sub\u003e \u0026Delta;G\u003csub\u003eMM=\u003c/sub\u003e\u0026Delta;E\u003csub\u003eele +\u003c/sub\u003e \u0026Delta;E\u003csub\u003evdw ;\u003c/sub\u003e \u0026Delta;G\u003csub\u003esol=\u003c/sub\u003e \u0026Delta;G\u003csub\u003epolar +\u003c/sub\u003e \u0026Delta;G\u003csub\u003enonpolar\u003c/sub\u003e .\u003c/p\u003e\n \u003cp\u003eEnergy components like van der Waals (\u0026Delta;E \u003csub\u003evdw\u003c/sub\u003e), electrostatics (\u0026Delta;E \u003csub\u003eele\u003c/sub\u003e), polar (\u0026Delta;G \u003csub\u003epolar\u003c/sub\u003e) and non-polar (\u0026Delta;G \u003csub\u003enon\u0026minus;polar\u003c/sub\u003e) were also studied and showed attractive results. However, the component (\u0026Delta;G \u003csub\u003enon\u0026minus;polar\u003c/sub\u003e) comes up with low values for the total free energies, probably due to the shielding of inhibitors from solvent. The electrostatic contribution was negative, ranging from low to medium. The (\u0026Delta;E \u003csub\u003eele\u003c/sub\u003e) for 3ILW_wild complex protein was highest (-3.55\u0026thinsp;\u0026plusmn;\u0026thinsp;4.27 kcal/mol), while it was lowest for 3ILW_G88A (-35.16\u0026thinsp;\u0026plusmn;\u0026thinsp;4.68 kcal/mol). The polar solvation free energy (\u0026Delta;G \u003csub\u003epolar\u003c/sub\u003e) was positive for all the systems, indicating unfavorable binding of wild and mutated proteins with delafloxacin. The van der Waals (\u0026Delta;E \u003csub\u003evdw\u003c/sub\u003e) interaction seems to be the main bond for all systems having less difference in energy values. Among the wild and mutated proteins, 3ILW_D94G showed the lowest \u0026Delta;E \u003csub\u003evdw\u003c/sub\u003e energy. Rest 3ILW_G88A\u0026thinsp;+\u0026thinsp;dfx, 3ILW_G88C\u0026thinsp;+\u0026thinsp;dfx and 3ILW_D95H\u0026thinsp;+\u0026thinsp;dfx complexes had the \u0026Delta;E \u003csub\u003evdw\u003c/sub\u003e energy values ranging between \u0026minus;\u0026thinsp;24.32\u0026thinsp;\u0026plusmn;\u0026thinsp;2.20 kcal/mol to -36.41\u0026thinsp;\u0026plusmn;\u0026thinsp;3.48 kcal/mol. 3ILW_D94G showed the highest binding affinity, while 3ILW_G88C showed the least binding affinity than wild protein.\u003c/p\u003e\n \u003cp\u003eContribution of residues towards binding affinity of protein in QRDR region for delafloxacin was analyzed by per residue contribution of each residue for wild and mutated proteins was estimated by applying mmpbsa.py program. The per-residue contributions of each mutation site to BFE values were calculated and compared (Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e). It is important to note that if the per-residue BFE value in mutant proteins is lower than in wild proteins, this suggests that the mutation at this location contributes favorably to increasing the protein\u0026apos;s binding affinity for delafloxacin. The BFE value in protein 3ILW_wild was found to be at position 88 for glycine (6.05 kcal/mol) and at position 94 for aspartic acid (-104.04 kcal/mol). On mutating the protein with alanine and cystine residue at position 88, the BFE values were found to be 17.67 kcal/mol and 19.38 kcal/mol. While for position 94, the mutated residues glycine and histidine, the BFE values calculated were 5.02 kcal/mol and \u0026minus;\u0026thinsp;16.13 kcal/mol. The results suggest that mutations lowered the binding affinity at positions 88 and 94. Among all the four mutated proteins, 3ILW_D94H showed lower binding affinity than 3ILW_wild protein.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThe main aim of the study was to discover the changes that occurred in proteins' active binding sites and in the structure of DNA gyrase on mutation by using computational techniques. The computational study resulted in five protein systems: one wild and four mutant proteins with good structural stability. The virtual screening techniques resulted in a lead compound, delafloxacin with an excellent stability and binding affinity and having good binding interactions with the amino acid residues at the active binding site of the 3ILW_wild protein. Further, the delafloxacin molecule was docked with mutant proteins 3ILW_G88A, 3ILW_G88C, 3ILW_D94G and 3ILW_D94H to estimate stability, binding affinity and to observe the binding interaction at the active site. During the study, it was noted that the mutant 3ILW_D94G had the lowest RMSD value of 0.20 nm, while 3ILW_D94H and the wild protein had the highest. Residue fluctuation among mutants 3ILW_G88A and 3ILW_D94G was lower than in the wild protein. All the mutants had a gyration value lower than the wild protein of 3.0 nm; among them, 3ILW_D94G showed the lowest value. Solvent accessibility was highest at 235.31 \u0026Aring;\u0026sup2; in the wild protein and the lowest value was 229.31 \u0026Aring;\u0026sup2;; the second lowest value of 234.10 \u0026Aring;\u0026sup2; was observed in 3ILW_D94G. The binding affinity of all the mutant protein molecules was greater than that of the wild protein molecules, but among them, 3ILW_D94G and 3ILW_D94H showed good results. The study revealed that despite the good binding affinity of all the mutant molecules, they showed resistance against the drug, probably due to good structural stability. The results obtained during the study may be utilized in the development of new lead molecules that would have good draggability and efficacy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are grateful to the Department of Computational Biology and Bioinformatics, JIBB, Sam Higginbottom University of Agriculture Technology and Sciences, Prayagraj (Allahabad) 211007, UP, India and Department of Biotechnology, Siddharth University, Kapilvastu, Siddharth Nagar-272202, U.P., India for providing the facilities and support to complete this research work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMethodology: DBS and SKR; Software: DBS and SKR; Formal analysis: DBS and SS; Investigation: SKR and SS; Resources: DBS and SS; Writing—original draft: SKR; Editing: DBS and SS\u003c/p\u003e\n\u003cp\u003eAll authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWilliams PM, Pratt RH, Walker WL, Price SF, Stewart RJ, Feng PI (2024) Tuberculosis \u0026mdash; United States, 2023. 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Expert Opin Drug Discov 10:449\u0026ndash;461\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTian M, Li H, Yan X, Gu J, Zheng P, Luo S, Zhangsun D, Chen Q, Ouyang Q (2022) Application of per-Residue Energy Decomposition to Design Peptide Inhibitors of PSD95 GK Domain. Front Mol Biosci 9:848353\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVald\u0026eacute;s-Tresanco MS, Valiente PA, Moreno E, gmx_MMPBSA: (2021) A New Tool to Perform End-State Free Energy Calculations with GROMACS. J Chem Theory Comput 17:6281\u0026ndash;6291\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"DNA gyrase A, Quinolone, Mutation, Binding affinity, Electrostatic interaction, Drug Resistance","lastPublishedDoi":"10.21203/rs.3.rs-5778481/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5778481/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe structural bioinformatics analysis approach provides valuable information regarding the protein\u0026rsquo;s structure and function by analyzing the contribution of each and every amino acid residue present in its active site. Residue substitution in the active site has a profound effect on the protein\u0026rsquo;s shape, stability, binding affinity, charge distribution, etc. We inserted a mutation in the DNA gyrase protein's A chain (3ILW_wild) to understand the structural and electrical alternations, resulting in the formation of the 3ILW_G88A, 3ILW_G88C, 3ILW_D94G, and 3ILW_D94H mutant proteins. The molecular docking approach was applied to screen the best-interacting fourth-generation quinolone antibiotics and to elucidate their stability, binding affinity, and interaction pattern with the wild protein. The results of molecular docking studies suggested that delafloxacin (dfx) had the highest binding affinity with the DNA gyrase A chain and fits best at the active site. Mutant proteins were again docked with delafloxacin to monitor the effect of residue change on the protein\u0026rsquo;s properties. The results of the molecular docking approach were further validated by molecular dynamic simulation and binding free energy calculation studies. Molecular dynamics simulations over 100 ns were carried out for five protein systems. Parameters like RMSD, RMSF, radius of gyration, H-bond, and solvent-accessible area obtained from MD simulation studies revealed that the mutant proteins experienced greater rigidity and lesser structural fluctuations than the wild protein. Electrostatic investigation and comparison of BFE revealed that the electrostatic interactions were reduced, and this reduction directly affected the binding affinity of proteins and ligand molecules. Per-residue BFE decomposition and hydrogen bond analysis indicated that the reduced interaction was due to loss or gain of hydrophilic/hydrophobic or positively/negatively charged residues. It is worth noting that mutation at position 94 of DNA gyrase A has a very profound effect as it shows a positive contribution towards increased resistance and reduced binding affinity with delafloxacin.\u003c/p\u003e","manuscriptTitle":"Deciphering the Electrostatic and Structural dynamics due to point Mutation in DNA gyrase leading to acquired Quinolone resistance in Mycobacterium tuberculosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-09 13:13:25","doi":"10.21203/rs.3.rs-5778481/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7528fde5-f7b2-46a3-9088-d43ac3ec1ba1","owner":[],"postedDate":"January 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-11T21:23:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-09 13:13:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5778481","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5778481","identity":"rs-5778481","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}