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Frontiers in Catalysis ·

Volume 6 - 2026 | https://doi.org/10.3389/fctls.2026.1665232

The phosphorylated tyrosine as a gatekeeper for topoisomerase catalytic activity: a molecular dynamics simulation study

  • Muralidhar1*corresponding author
  • Rakesh Kumar Tiwari1*corresponding author
  • Vipin Kumar Mishra2
  • K. M. Vandana1
  • Vinayak Pandey1
  1. 1Department of Physics, Deen Dayal Upadhyay Gorakhpur University Gorakhpur, Gorakhpur, India
  2. 2Chemistry Division School of Advanced science and languages VIT Bhopal University, Bhopal, India
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Abstract

DNA topoisomerase-IA is an essential enzyme that relaxes supercoiled DNA by introducing transient single-strand breaks through a covalent phosphorylated tyrosine (PTR) intermediate. This cleavage occurs when the active-site tyrosine of dTopo-IA forms a covalent bond with the DNA phosphate backbone, resulting in PTR formation. Although dTopo-IA is believed to mediate strand passage via an enzyme-induced DNA gate, the actual opening of this gate has not been demonstrated experimentally or theoretically. To address this gap, we employed 200-nanosecond (ns) molecular dynamics (MD) simulations using AMBER18 to explore the catalytic mechanism and conformational dynamics of dTopo-IA. Important parameters like RMSD, RMSF, the number of hydrogen bonds, hydrogen bond distances, the radius of gyration (RoG), binding free energy, solvent-accessible surface area (SASA), and per-residue pair-wise decomposition energy were analyzed. Our simulations revealed that the bond between PTR and nucleotide acts as a gatekeeper, regulating the opening and closing of the DNA gate critical for strand passage. MD trajectories clearly demonstrate that gate opening and strand passage occur only after the formation of the covalent bond between PTR and the C5′ atom of the DNA strand. Additionally, we investigated how topoisomerase selectively binds single-stranded DNA in the presence of double-stranded DNA to initiate its catalytic function. The enzymatic roles of residues Gln-223, Arg-533, and Lys-117 were also elucidated in the process. This provides a novel and deeper understanding of the enzyme’s mechanism, which has been challenging to capture through experimental techniques alone, and potentially aids the development of targeted anticancer therapies by disrupting DNA replication in cancer cells.

Keywords

1 Introduction

DNA topoisomerase enzymes (dTopoE) are cellular enzymes that play vital roles in various aspects of biological cell growth, such as DNA replication, transcription, chromosome segregation, and recombination (Burgers, 1998; Wang, 1985). These enzymes act as tools to alter the topological state of DNA, which is crucial for their central dogmatic functions (Sutormin et al., 2021). The first DNA topoisomerase enzymes were discovered in bacteria and were referred to as “ω” proteins (Wang, 1971). Based on their catalytic mechanisms, there are two types of topoisomerase enzymes: DNA topoisomerase I (dTopo-I) and II (dTopo-II) (McKie et al., 2021).

Type IA topoisomerase enzymes (dTopo-IA) are polypeptides that bind to single strands of DNA and are composed of multiple domains often labeled as domains “I” through “IV.” Domain I (D-I) is a Toprim domain, which is a Rossmann fold. Both domains III (D-III) and IV (D-IV) contain a helix-turn-helix (HTH) motif, with the catalytic tyrosine located on the HTH of D-III (Lima et al., 1994). Domain II (D-II) plates up as a bendable linker connecting domains III and IV (Changela et al., 2001). The final structure of type IA topoisomerase looks like a lock, with domains I, III, and IV forming the base of structure. Structural analysis of dTopo-III bound to single-stranded DNA illustrates how the HTH and Toprim domains interact with the DNA (Champoux and Dulbecco, 1972). These enzymes introduce a transient single-stranded break by forming a phosphorylated tyrosine (PTR) intermediate bond between the active domain tyrosine (Tyr) in the enzyme and a 5′-phosphate in the DNA (Wang, 2002) (Figures 1A,B). The DNA strand where the intermediate bond forms and breaks is referred to as the “gate-strand” (G-strand), which allows the passage of another DNA strand. The uncut DNA strand that passes through the gate is called the “transport-strand” (T-segment) due to its transport activity (Chen et al., 2013). The T-segment must pass through the gate via the strand passage (Sutormin et al., 2021; Vos et al., 2011), followed by the ligation of the G-strands (Brown and Cozzarelli, 1981). This process involves a conformational change in the enzymes to open the DNA gate and permit T-segment transfer, altering the DNA’s linking number by adding or subtracting one during DNA relaxation (Wang et al., 1996). In bacteria, the topoisomerase I part of the dTopo-IA subfamily plays a critical role in relieving negative supercoiling that arises behind the RNA polymerase during transcription (Dekker et al., 2002). This action prevents the formation of R-loops, which are DNA–RNA hybrid structures that are known to interfere with the transcription process (Chen et al., 2013). In addition to its role in relaxing negatively supercoiled DNA and preventing excessive supercoiling, type IA topoisomerase also participates in resolving DNA entanglements by decatenation, replication intermediates, and untangling single-stranded DNA circles or nicked duplex DNA (Cairns, 1963; Champoux, 2001). All living organisms possess at least one type IA topoisomerase that helps resolve topological challenges, such as those encountered during replication and recombination, as well as other complex DNA conformations. These enzymes enable the passage of one DNA strand through a transient break in another, facilitating essential topological changes (Champoux and Dulbecco, 1972). In recent years, a combination of experimental and computational approaches has been used to explore the catalytic functions, topological roles, and conformational dynamics of these enzymes, particularly in relation to single-stranded DNA (Stivers et al., 1997). This study aims to elucidate the molecular mechanism of gate opening following the formation of an intermediate complex, specifically in the context of double-stranded DNA (dsDNA) a process that remains difficult to capture through experimental observation (Baker et al., 2009; Kikuchi and Asai, 1984). In addition, the conformational changes that occur before and after the formation of the intermediate covalent bond in the complex are studied, which are not experimentally possible.

Structure of dTopo-IA, highlighting its distinct domains. **(A)** All four domains along with the reactive amino acid Tyr-318. **(B)** The same domains after the formation of the phosphorylated tyrosine (PTR). Domains are color-coded as follows. domain D-I (green): DNA binding domain; domain II (red) acts as a flexible linker between domains III and IV; domain III (yellow): reactive domain; domain IV (blue): structural base and linker of the protein.
Figure 1. Structure of dTopo-IA, highlighting its distinct domains. (A) All four domains along with the reactive amino acid Tyr-318. (B) The same domains after the formation of the phosphorylated tyrosine (PTR). Domains are color-coded as follows. domain D-I (green): DNA binding domain; domain II (red) acts as a flexible linker between domains III and IV; domain III (yellow): reactive domain; domain IV (blue): structural base and linker of the protein.

2 Computational details

In order to computationally study DNA dTopo-IA enzymes, we employed a three-step calculation protocol.

2.1 System setup

To explore how the gate-opening mechanism occurs when an intermediate bond forms in the residence of dsDNA, the two experimental coordinates of dTopo-IA and dTopo-I, obtained via X-ray crystallography, were taken from the Protein Data Bank (PDB ID: 1MW8 and 3PX7) (Perry and Mondragón, 2003; Zhang et al., 2011). Missing residues in the protein part were modeled using the Chimera MODELLER program, and the best-scoring models of 3PX7.pdb (zDOPE = −0.98) and 1MW8.pdb (zDOPE = −1.23) were selected for further simulations (Šali and Blundell, 1993) (Supplementary Tables S1A,B). The template of both enzymes is given in Supplementary Table S2A. The DNA sequence was converted into double-stranded DNA using UCSF Chimera Tools (Couch et al., 2006). To convert active-site tyrosine into phosphorylated tyrosine (PTR) in dTopo-IA, the necessary parameter for this residue (Supplementary Table S2B) was generated with the Gaussian 09 package (Frisch, 1995), using the density functional theory (DFT) method with the B3LYP/6-31++G (2d, 2p) level of theory. For the artificial cleavage in the double-stranded DNA, one strand of DNA was broken and the corresponding nucleotide was modified as thymine (MDT5) (Pavlin et al., 2023; Tiwari, 2018) (MDT5.lib file given in Supplementary Table S2C). A bond between PTR residue and the DNA nucleotide was made using the xleap bond command. The dTopo-IA complex with covalent intermediate and dTopo-I without covalent bond was then taken to compare the action in the enzyme with and without the intermediate covalent bond (Bates et al., 2011) (Figures 2A,B).The entire modified complex molecule was input into the xleap program of the AMBER18 package. The protein was treated with the ff14SB force field (Raguette et al., 2020), and the DNA with DNA.OL15 (Raguette et al., 2024). For covalent bond phosphorylated tyrosine, leaprc.phosaa10 of the Generalized Amber Force Field (GAFF2) was used (Wang et al., 2004). Partial atomic charges and missing parameters for PTR were obtained with the RESP charge fitting method (Wang et al., 2004) at the B3LYP/6-31++G (2d, 2p) level of theory (Bayly et al., 1993; Cornell et al., 2002) using an optimized geometry methodology level of theory with the Gaussian 09 program (Becke, 1988).

**(A)** Modeled system of dTopo-I enzymes, showing the double-stranded DNA without any intermediate bonding. In contrast **(B)** is the modeled dTopo-IA system, featuring double-stranded DNA with an intermediate covalent bond between phosphorylated tyrosine (PTR) residue and nucleotide residue with bond length 4.2 Å. This is the starting structure, where this covalent bond has been artificially created, hence its great length, which is reduced during the initial stages of the simulations.
Figure 2. (A) Modeled system of dTopo-I enzymes, showing the double-stranded DNA without any intermediate bonding. In contrast (B) is the modeled dTopo-IA system, featuring double-stranded DNA with an intermediate covalent bond between phosphorylated tyrosine (PTR) residue and nucleotide residue with bond length 4.2 Å. This is the starting structure, where this covalent bond has been artificially created, hence its great length, which is reduced during the initial stages of the simulations.

Since the covalent PTR linkage is not parameterized in standard AMBER force fields, custom charge derivation was required. Although ff14SB and DNA.OL15 are historically calibrated using RESP charges at the HF/6-31G level, this approach can over-polarize highly charged phosphate groups. Therefore, RESP charges derived from B3LYP/6-31++G (2d, 2p) electrostatic potentials were employed to obtain a balanced description of phosphate charge distribution and P–O bond polarity relevant to the covalent intermediate while maintaining compatibility with the non-polarizable AMBER framework through RESP charge restraints.

To neutralize the protein–DNA complex, 14 Na+ ions in dTopo-I and 19 Na+ ions in dTopo-IA were added according to their charges. The whole system was solvated in an octahedral box of the TIP3P model, extended to a minimum cutoff of 12.0 Å from the protein boundary (Dubey and Shaik, 2019).

2.2 Molecular dynamics (MD) simulation setup

After generating the required coordinate and topology files, minimization was performed up to 10,000 steps in two parts using the steepest descent (5000 steps) and coupled gradient (5,000 steps) methods (Muralidhar and Tiwari, 2024; Sahoo et al., 2021). In the first case, the complex molecules were restrained at a weight of 500 kcal/mol-Å2, while the water and neutralizing ions were free (Xiong et al., 2008). In the second step, the entire system was minimized without any restraint.

The system was gradually heated to reach 300 K under the NVT ensemble over a period of 50 ps (Dubey et al., 2013). This step was followed by a 1 ns density equilibration under the NPT ensemble, maintaining a temperature of 300 K and a pressure of 1 atm, regulated using a Langevin thermostat (Ka et al., 2005) and a Berendsen barostat (Izaguirre et al., 2001), with a collision frequency of 2 ps and pressure relaxation time of 1 ps (Berendsen et al., 1984). Subsequently, a 3 ns equilibration phase without restraints was performed prior to the 200 ns molecular dynamics (MD) production run. The production run utilized a Monte Carlo barostat (Case et al., 2005) for all complex systems. Long-range electrostatic interactions were treated using the particle mesh Ewald (PME) method (Åqvist et al., 2004) with a cutoff distance of 10 Å, and the SHAKE (Darden et al., 1993) algorithm was applied to constrain bonds involving hydrogen atoms. All MD simulations were executed using the AMBER18 package (Ryckaert et al., 1977).

All MD simulations were performed using the CPU version of AMBER18 version. For each system, three independent replicas of 200 ns were generated for each system, and convergence of key structural and dynamical metrics (RMSD, RMSF, hydrogen-bonding, and PCA) was observed within the simulated timescale.

2.3 Trajectory analysis

Trajectory analyses were performed using the CPPTRAJ module (Salomon-Ferrer et al., 2013), which is available in AMBER Tools. The analysis included calculating various essential parameters, such as root mean square deviation (RMSD), root mean square fluctuation (RMSF), number of hydrogen bonds (Hbond), hydrogen bond distance variation, radius of gyration (RoG), and solvent-accessible surface area (SASA). To visualize and render the figure, VMD and PyMOL were employed (Roe et al., 2013). We utilized VMD’s visualization capabilities to study the system’s hydrogen bond dynamics in addition to its structural properties (Humphrey et al., 1996). This required computing polar bond formation and occupancies using stringent criteria, such as a donor–acceptor distance of less than 3.0 Å and an angle cutoff of 20°. The results demonstrated the critical intermolecular interactions that underlie the system’s behavior, thus exposing the intricate network of the hydrogen bonds that support its stability and functionality. Furthermore, we explored the system’s molecular architecture by examining decomposition-free energy per residue, providing deeper insights into the conformational transitions and structural transformations that were occurring.

3 Results and discussion

3.1 Molecular stability and dynamic behavior

After completing a 200 ns molecular dynamics (MD) simulation, an in-depth evaluation of the structural stability and dynamic flexibility of the protein-DNA complex was performed using several key parameters. The RMSD of residues 1 to 621, including the DNA strands, was meticulously monitored, revealing valuable insights into the conformational stability of the system. Additionally, the RMSF, RoG, and SASA were calculated using the CPPTRAJ module of the AMBER package, providing a thorough analysis of the system’s stability and flexibility throughout the simulation.

From the RMSD calculation for enzymes residues as well as dsDNA residues, we found that all systems exhibited an initial equilibration phase during the first 20–30 ns, followed by a clear stabilization period for the remainder of the simulation.

For the dTopo-IA complex, the enzyme backbone RMSD (black line) gradually increased during equilibration and stabilized at approximately 3.5–4.0 Å, indicating that the overall fold of the enzyme remained stable throughout the 200 ns trajectory. The corresponding DNA RMSD (red line) remained lower, fluctuating between 2.0 and 2.8 Å, suggesting that the DNA duplex experienced only moderate conformational adjustments while maintaining structural integrity. After 30 ns, the RMSD of the complex molecule stabilized, indicating that after the formation of the PTR intermediate bond, the DNA strands and topoisomerase enzyme molecules adjusted to a stable orientation, with a minimum deviation of approximately 3.2 Å, while for the system without cleavage, this deviation was approximately 4.5 Å. A detailed inspection of the DNA RMSD indicated that its variation was mainly due to the contribution of the double-stranded DNA bases (Figure 3). The RMSD of the system with covalent complex oscillated at approximately 3.2 Å. In contrast, the DNA bases showed a relatively high RMSD (nearly 3.0 Å), suggesting that the deviations observed in the crystal study were consistent with our findings. The corresponding snapshot is shown in Supplementary Figure S1A-C.

Root mean square deviations (RMSD) vs. simulation time for the systems dTopo-IA and dTopo-I during the 200 ns MD simulations.
Figure 3. Root mean square deviations (RMSD) vs. simulation time for the systems dTopo-IA and dTopo-I during the 200 ns MD simulations.

Both enzyme systems attained stable conformations overall, according to the RMSD analysis; however, the dTopo-IA complex showed significantly lower RMSD values for both protein and DNA, which is consistent with improved stabilization after PTR formation. The non-cleavage (dTopo-I) system’s higher RMSD indicates that the DNA–enzyme interface is less rigid and more flexible.

To ensure the stability and reproducibility of the trajectories, three independent MD simulations were performed for each system and their corresponding RMSD plots (Supplementary Figure S2A–D). Figure 3 depicts a representative MD replica chosen for early convergence to illustrate the conformational behavior.

The RMSF versus residue number of both complexes is depicted in Figure 4A. The highest fluctuated residue ranges of different domains were from domains D-I (27–126), D-II (205–305), and D-III (306–500), while D-IV (501-621) remained stable.

**(A)** RMSF plot per residue of dTopo-IA compared to dTopo-I. **(B)** Structural orientations of domain I between initial model (green) and MD-simulated structure (yellow), showing a shift in domain orientation (indicated by blue arrows and black dashed lines). **(C)** Overlay of domain II from initial (green) and MD-refined (yellow) structures, highlighting a conformational movement. **(D)** Conformational changes occurring between residues present in domain II throughout the simulations. **(E)** Conformational alterations of critical residues Pro-255 and Ser-256 (circled) along with adjacent residues. Insets emphasize the structural movements in Ala-259 and Glu-296, underscoring their contribution to domain dynamics.
Figure 4. (A) RMSF plot per residue of dTopo-IA compared to dTopo-I. (B) Structural orientations of domain I between initial model (green) and MD-simulated structure (yellow), showing a shift in domain orientation (indicated by blue arrows and black dashed lines). (C) Overlay of domain II from initial (green) and MD-refined (yellow) structures, highlighting a conformational movement. (D) Conformational changes occurring between residues present in domain II throughout the simulations. (E) Conformational alterations of critical residues Pro-255 and Ser-256 (circled) along with adjacent residues. Insets emphasize the structural movements in Ala-259 and Glu-296, underscoring their contribution to domain dynamics.

The highest fluctuations were found in the DNA residues (1–26) of dTopo-IA. These indicate that during gate opening, when the active domain III (306–430) allows the passage of DNA T-strands, these residues experience repulsion, resulting in such fluctuations. This phenomenon originates from the dynamic motion of domain I (residues 27–179) within the complex dTopo-IA (Supplementary Figure S3). Upon domains II and III aligning, domain I exhibits a downward displacement of approximately 4.5 Å (black dotted line), followed by a backward shift of approximately 5.5 Å (black dotted line) (Figures 4B,C). This movement closely correlates with structural observations from studies (Bugreev and Nevinsky, 2009). However, the study protocols elucidate the specific conformations of key residues such as Pro-255, Ser-256, Ala-259, and Glu-296, which may contribute to this domain mobility (Schoeffler and Berger, 2008; Gunn et al., 2017). The conformational interactions of the residues are illustrated in Figures 4D,E. For more clarity, the domain-wise deviations and corresponding snapshots are shown in Supplementary Figures S4A–D.

Figure 5A provides information about the formation of a number of hydrogen bonds during the simulation time. Figure 5B depicts the variation in donor–acceptor distances vs. simulation time. Initially, the distance between the O3P of PTR-345 and the NH2 group of Arg-347 extends up to 8.5 Å, and the distance involving the OH of PTR-345 and NH2 of Arg-347 reaches nearly 8.0 Å. After 10 ns, when the enzyme–dsDNA complex became stable and the gate closes, PTR-345 and Arg-347 moved closer, and their interaction distance stabilized at 3.7 Å, forming weak donor–acceptor interactions. Concurrently, the weak hydrogen bond between the oxygen of PTR-345 and the nitrogen of Thr-348 stabilized at approximately 2.6 Å. A notable shift occurred as the hydrogen bond between the OH of PTR-345 and the NH2 of Arg-347 contracted to 2.5 Å. Throughout the simulation, the weak hydrogen bond between the oxygen in PTR-345 and the nitrogen in Thr-348 remained steady at 2.8 Å. However, after 170 ns, the donor–acceptor distance between O3P in PTR-345 and NH2 of Arg-347 re-stabilized at 4.5 Å, exceeding the typical hydrogen bond cutoff; this should be interpreted as a donor–acceptor contact rather than a hydrogen bond. The molecular interaction of PTR-345 to other molecules is shown in Figure 5C. The highly favorable hydrogen bond occurrence for the two complexes is given in Supplementary Tables S3A,B.

**(A)** Number of Hbonds, **(B)** variation of hydrogen bond distances as a function of time, and **(C)** hydrogen bonds formed between enzyme and DNA residues. PTR molecule is in yellow.
Figure 5. (A) Number of Hbonds, (B) variation of hydrogen bond distances as a function of time, and (C) hydrogen bonds formed between enzyme and DNA residues. PTR molecule is in yellow.

In our MD simulation of the protein–DNA complex involving dTopo-IA, the RoG served as a crucial metric for understanding conformational dynamics. The plot of RoG against simulation time showcased intriguing dynamics (Figure 6A). The RoG exhibited significant fluctuations, reaching up to 34.0 Å for the complex without PTR residue, while for the system with PTR residues with covalent bonds, the RoG value was 31.5 Å. After 50 ns, at the average value (32.3±5.1) Å of dTopo-I while at average values (61.9±11.3) of dTopo-IA, both complexes became stable. This variability is attributed to the collective interaction of non-hydrogen atoms aligning along rotational axes. After this period, RoG stabilizes, indicating the system relaxing under the applied conditions. These observations underscore the complex interplay of molecular forces that govern conformational dynamics in the simulated environment, providing valuable insights into the stability and behavior of the protein–DNA complex (Dasgupta et al., 2020; Liu et al., 2017).

**(A)** Radius of gyration of versus time of the two complexes. **(B)** Solvent-accessible surface areas (SASA) of the two complexes as a function of time. **(C)** Snapshot of interactions taken during trajectory analysis.
Figure 6. (A) Radius of gyration of versus time of the two complexes. (B) Solvent-accessible surface areas (SASA) of the two complexes as a function of time. (C) Snapshot of interactions taken during trajectory analysis.

The SASA measures the surface area of an enzyme structure accessible to solvent molecules. This metric provides insights into conformational changes and protein–DNA interactions. Figure 6B illustrates SASA versus simulation time for both complexes. Here the average values for the complexes first were 365.8±23.5) nm2 while, for the other, it was 304.7±17.3) nm2. The stabilization of the accessible area is indicative of conformational adjustments in the complex, which may create a more favorable environment for DNA relegation, as shown in Figure 6C taken as a snapshot during trajectory analysis. However, SASA itself does not drive this process.

3.2 MM/GBSA and decomposition energy calculation

The MM/GBSA calculation provides the binding free energy of the receptor ligand (DNA). The highly negative values of binding free energy between the receptor DNA provide the notion of stronger interactions. Here we calculated a binding free energy molecule for two different simulating systems for last 200 frames (after 175 ns) for the generalized Born model, with igb = 5. In the first simulating system without intermediate bond, the value of binding free energy was −442.1 kcal/mol. Furthermore, in the case of dsDNA with the dTopo-IA enzyme, the binding free energy was −108.6 kcal/mol. This loss of binding energy (B.E) of the complex with the covalent intermediate bond indicates that after the intermediate bond formation, the T-strand of dsDNA became unstable, and thus because of this high energy, the strand passage of T-strands occurs.

The molecular mechanics energy function is a pair-wise additive function that can be broken into per-residue components. Per-residue binding energy decomposition analysis revealed the contribution of various amino acids toward total binding energy (Tiwari et al., 2024). From the decomposition analysis (Figure 7), the contributions of the consistently interacting amino acids were extracted, and their correlation with experimental activity was deduced. Arg-519, Gln-223, Arg-533, Ala-216, Lys-212, Arg-90, Thr-65, Arg-96, Val-201, Gly-202, PTR-345, Lys-41, and Lys-117 showed contributions of energy (in kcal/mol) of −15.9, −17.2, −5.7, −5.9, −5.6, −7.8, −13.5, −11.3, −11.1, −6.8, −24.0, −6.5, and −5.1, respectively (Supplementary Table S6). The energies of Arg-519, Gln-223, Arg-533, Lys-212, Val-201, and Gly-202 provide favorable interactions that show that their energy contributions correlate with stabilizing enzyme−DNA interactions, facilitating gate opening and the passage of T-strands of DNA helix. Among the amino acids, Gln-223, Arg-533, Val-201, and Lys-212 interact with the DNA strands for gate opening. The DNA interaction energy map with amino acids of dTopo-IA enzymes is shown in Figure 7. The remnant MM/GBSA is used to compute the interaction map, which is quite helpful because it identifies non-polar and hydrophobic interactions. It is impossible to examine these interactions in the same way as for salt bridges and hydrogen bonds. Among the amino acids, Gln-223, Arg-533, Val-201, and Lys-212 interact with the DNA strands for gate opening.

Decomposition energy per residue variation representing different types of non-covalent interactions.
Figure 7. Decomposition energy per residue variation representing different types of non-covalent interactions.

The DNA strand interacts favorably with Val-201, Glu-223, and Arg-533 through hydrophobic interactions in the presence of an aqueous environment but with PTR-345 covalently bonded. These non-polar and hydrophobic residues make a big difference in the entire positive shift in enthalpy that occurs during the interaction between enzyme residues and DNA interacting residues (Genheden and Ryde, 2015) (Figure 8).

Snapshot showing a molecular different type of non-covalent van der Waals interactions of binding residues to DNA residues during molecular dynamics simulation, providing highly interaction energy.
Figure 8. Snapshot showing a molecular different type of non-covalent van der Waals interactions of binding residues to DNA residues during molecular dynamics simulation, providing highly interaction energy.

To find the global motions of the systems, principal component analysis (PCA) was calculated on the MD trajectories of dTopo-I and dTopo-IA. The resulting projections along the first two principal components (PC1 and PC2) (Figures 9A,B) reveal that the covalent intermediate complex (dTopo-IA) exhibits broader conformational sampling, indicating enhanced structural flexibility compared to the non-covalent dTopo-I system.

Two-dimensional projections plot of first two principal Eigen vectors of **(A)** for the complex with intermediate covalent dTopo-IA and **(B)** for the complex without covalent intermediate bond dTopo-I.
Figure 9. Two-dimensional projections plot of first two principal Eigen vectors of (A) for the complex with intermediate covalent dTopo-IA and (B) for the complex without covalent intermediate bond dTopo-I.

4 Lubricating role of hydrogen bonds

After the intermediate covalent bond is formed between the PTR residue and DNA, the gate opens, allowing interactions between residues of the T-strands of DNA and the water molecules available near the active domain PTR-345, Arg-347, and Met-346. The variations in the interaction distances between T-strand residues DA-5, DA-6, and DG-7 and water molecules are shown in Figure 10A. Notably, the presence of water molecules within the active site facilitates these interactions by acting as a molecular lubricant (Jin et al., 2021; Dhar and Tiwari, 2024). These water molecules help stabilize transient hydrogen-bonding networks that assist the passage of the T-strand through the enzyme. As the T-strand moves through the gate, water molecules are dynamically displaced from the active domain to accommodate the DNA strand. Although direct experimental observation of this process is challenging, molecular dynamics simulations indicate that such a mechanism is feasible. This dynamic interplay between hydrogen bonds and water molecules provides insight into the lubricating role of water in the DNA topoisomerase function. The formation of a transient water channel in this region is illustrated in Figure 10B.

**(A)** Distance variation between water and DNA residues with simulation time. **(B)** Snapshots of the water channel during trajectory analysis.
Figure 10. (A) Distance variation between water and DNA residues with simulation time. (B) Snapshots of the water channel during trajectory analysis.

5 G-strand opening and passes of T-strands

For the deep study of the conformational stability of dTopo-IA during gate opening and closing, we monitored intermolecular distances over the simulation time. In the presence of the covalent intermediate bond complex (Figure 11A), the distances between the PTR-345–DT21 and PTR-345–DT22 distances increased significantly after 100 ns, with the DT21–DT22 separation expanding from 8.0 Å to 18.0 Å—indicating full gate opening. Furthermore, the non-covalent state (Figure 11B) showed distances in Tyr-318–DG9 and Arg-364–DG9 distances, suggesting pre-opening flexibility (Yang et al., 2025; Yang et al., 2022). Water-mediated interactions with DA5, DA6, and DG7 (Figure 11C) stabilized at approximately 50 ns, supporting their role in maintaining the open gate conformation. These studies reveal a sequential mechanism of gate activation involving covalent bond formation and water-mediated stabilization.

**(A)** Time evolution of intermolecular distances between the phosphorylated tyrosine residue (PTR-345) and its interacting DNA residues in the covalent intermediate complex (dTopo-IA), highlighting gate-opening events. **(B)** Distance profile between the active-site tyrosine (Tyr-318) and corresponding DNA residues in the non-covalent dTopo-I system, representing pre-cleavage interactions and gate flexibility. **(C)** Temporal variation in distances between the two segments of the cleaved DNA strand (G-strand), illustrating the extent of strand separation and confirming the gate-opening mechanism post-covalent bond formation.
Figure 11. (A) Time evolution of intermolecular distances between the phosphorylated tyrosine residue (PTR-345) and its interacting DNA residues in the covalent intermediate complex (dTopo-IA), highlighting gate-opening events. (B) Distance profile between the active-site tyrosine (Tyr-318) and corresponding DNA residues in the non-covalent dTopo-I system, representing pre-cleavage interactions and gate flexibility. (C) Temporal variation in distances between the two segments of the cleaved DNA strand (G-strand), illustrating the extent of strand separation and confirming the gate-opening mechanism post-covalent bond formation.

6 Conclusion

Our computational and molecular dynamics simulations study provides the central mechanistic role of phosphorylated tyrosine (PTR) in modulating the gate-opening dynamics of DNA topoisomerase-IA (dTopo-IA). Our 200 ns molecular dynamics simulations, supported by key structural and energetic analyses, indicate that covalent bond formation between PTR and DNA is essential for enabling T-strand passage and subsequent enzymatic conformational transitions. The combined insights from structural stability, hydrogen-bonding nature, and energy decomposition collectively suggest a coordinated mechanism driven by PTR and its interacting partners. Notably, residues such as Gln-223, Arg-519, and Lys-117 were found to contribute significantly to complex stability and catalytic function. This study offers an important foundation for future research focused on designing targeted cancer inhibitors that disrupt PTR-mediated gate opening, potentially halting topoisomerase activity in proliferative cancer cells. It may also motivate exploration into resistance-causing mutations, comparative studies with other topoisomerase isoforms, and the engineering of topoisomerase variants for therapeutic or synthetic biology applications. Altogether, this study not only deciphers the structural basis of topoisomerase function but also opens translational opportunities in drug development and molecular design.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fctls.2026.1665232/full#supplementary-material

Supplementary material Supplementary material at the source

Footnotes

Edited by: Hyung Chul Ham, Inha University, Republic of Korea

Reviewed by: Matic Pavlin, National Institute of Chemistry, Slovenia

Luis Ángel Rodríguez Lumbreras, Spanish National Research Council (CSIC), Spain

Statements

Conflict of interest

The authors declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

The author(s) declared that financial support was not received for this work and/or its publication.

Data availability

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Author contributions

Muralidhar: Writing – original draft, Writing – review and editing, Conceptualization, Data curation, Validation, Visualization. RT: Supervision, Writing – review and editing. VM: Formal analysis, Writing – review and editing. KV: Formal analysis, Writing – review and editing. VP: Visualization, Writing – review and editing.

Acknowledgements

The authors express their sincere gratitude to Kshatresh Dutta Dubey, Assistant Professor in the Department of Chemistry at Shiv Nadar University, Noida, Uttar Pradesh, for his outstanding computational support, which was essential in improving the quality of this research. His knowledge and expertise were instrumental throughout this research journey, and his unwavering support is acknowledged.

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Citation

Muralidhar, , et al. (2026). The phosphorylated tyrosine as a gatekeeper for topoisomerase catalytic activity: a molecular dynamics simulation study. Frontiers in Catalysis, Volume 6. https://doi.org/
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6 - 2026

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Copyright © 2026 Muralidhar, Tiwari, Mishra, Vandana and Pandey.

License: https://creativecommons.org/licenses/by/4.0/

*Correspondence: Muralidhar; Rakesh Kumar Tiwari

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