Steered Molecular Dynamics Investigation of Chitosan Nano-Carrier Mediated siRNA Translocation Across Biological Membranes

Steered Molecular Dynamics Investigation of Chitosan Nano-Carrier Mediated siRNA Translocation Across Biological Membranes | 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 Article Steered Molecular Dynamics Investigation of Chitosan Nano-Carrier Mediated siRNA Translocation Across Biological Membranes Parvin Jalali, Shokoufeh Rahmani, Mohsen Shahlaei, Zahra Asvar, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8062956/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 12 You are reading this latest preprint version Abstract The delivery of nucleic acids based therapeutics such as siRNA into cells remains a major challenge in gene therapy, primarily due to their hydrophilicity and intrinsic negative charge, which hinder membrane translocation. Chitosan, a biocompatible and biodegradable cationic polysaccharide, has emerged as a promising carrier for gene delivery applications. Nevertheless, the optimal stoichiometric ratio between chitosan and siRNA required to achieve efficient membrane penetration has not been fully elucidated. In this study, molecular dynamics simulations were employed to examine the formation of chitosan-RNA complexes and their interactions with a model lipid bilayer. The results indicate that the chitosan-to-RNA ratio has a pronounced impact on the biophysical properties of the complexes, affecting both their structural stability and dynamic interactions with the membrane. Specifically, a 1:3 RNA-to-chitosan ratio (R1C3) was found to facilitate more efficient membrane translocation, whereas a 2:2 ratio (R2C2) produced more stable complexes overall. The findings in this study offer valuable insights for the rational design of chitosan-based siRNA delivery systems and underscore the importance of balancing nucleic acid and carrier content to achieve both stability and effective transmembrane delivery. Biological sciences/Biochemistry Biological sciences/Biological techniques Biological sciences/Biotechnology Physical sciences/Nanoscience and technology Molecular dynamic simulation drug delivery molecular modeling gene delivery nanomedicine Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Nucleic acid therapy has gained considerable attention as a next-generation therapeutic strategy aimed at correcting or modulating gene function at its molecular origin. Unlike conventional treatments that primarily alleviate symptoms, gene therapies offer the potential to address the underlying genetic and regulatory defects responsible for a wide spectrum of diseases [ 1 ]. The concept of gene therapy first emerged in the 1970s, coinciding with the rapid expansion of knowledge about the human genome [ 2 ]. Despite its considerable promise, the clinical translation of gene therapy faces persistent challenges, many of which stem from unfavorable pharmacokinetic properties. Naked nucleic acids are highly susceptible to hydrolysis and enzymatic degradation by endo and exonucleases in circulation, and they also exhibit limited capacity to traverse biological barriers such as the cell membrane. This limitation arises partly from their hydrophilic nature, which hinders passage through the hydrophobic lipid bilayer, as well as their substantial molecular weight and the negative charge of their phosphate backbone, which collectively impede cellular uptake [ 3 ]. Consequently, the design of effective delivery carriers is a critical determinant of gene transfer success. Recent multidisciplinary advances have highlighted the pivotal role of biocompatible nanomaterials and smart delivery systems in overcoming the barriers related to variety of delivery systems and enhancing their therapeutic efficacy. Therefore, it is essential to employ delivery platforms that can not only protect nucleic acids from degradation but also enhance cellular translocation by reducing effective particle size and modulating surface charge [ 4 – 6 ]. Gene delivery into cells can be accomplished using either viral or non-viral vectors. Viral vectors have been widely utilized due to their high transduction efficiency; however, their clinical application is often limited by concerns regarding toxicity, immunogenicity, and challenges associated with large-scale production and repeated administration [ 7 ]. In contrast, non-viral vectors provide a safer and more versatile alternative, offering benefits such as improved biocompatibility, reduced immunogenicity, and simpler, more cost-effective manufacturing [ 8 ]. Ongoing research continues to focus on the design and optimization of these systems to enhance the efficiency, specificity, and safety of gene transfer for therapeutic applications. Among non-viral delivery systems, cationic polymers have received particular attention. The use of cationic polymers for gene transfer was first demonstrated by Wu et al. [ 8 ]. Chitosan, a natural cationic polysaccharide, is one of the most extensively studied biopolymers in this context due to its biocompatibility, biodegradability, low toxicity, and minimal immunogenicity. Structurally, chitosan consists of randomly distributed β(1→4)-linked N-acetyl-D-glucosamine and D-glucosamine units [ 9 – 11 ]. At slightly acidic pH, the abundance of deacetylated amino groups imparts a high positive charge density, enabling strong electrostatic interactions with negatively charged nucleic acids and efficient gene condensation. This positive surface charge facilitates binding to the negatively charged cell membrane and promotes cellular uptake of nucleic acid cargo. In addition to this electrostatic mechanism, evidence suggests that chitosan-based nanoparticles may also engage specific endocytic receptors, further enhancing endocytosis and intracellular delivery [ 12 , 13 ]. Cell membranes serve as robust barriers with selective permeability, maintaining homeostasis by regulating the exchange of substances between the intracellular and extracellular environments. This structural feature has a profound impact on drug pharmacokinetics and biodistribution [ 14 – 16 ]. Hydrophilic, high-molecular-weight therapeutics, such as nucleic acid-based drugs employed in gene therapy, encounter significant obstacles in traversing this barrier and therefore typically require modification or incorporation into specialized nanocarriers. These nanoscale delivery systems, often formulated with cationic materials, condense genetic payloads and neutralize their inherent negative charge, thereby enhancing their affinity for the negatively charged cell membrane and facilitating more efficient cellular uptake and intracellular diffusion [ 17 – 19 ]. Computational methods offer valuable insights into the structural and dynamic features of drug–carrier interactions that are often inaccessible through experimental techniques alone. These approaches have been extensively applied to investigate the physicochemical and biological properties of drug delivery systems and their interactions with biological components such as lipid membranes and proteins [ 20 ]. Beyond elucidating molecular-level mechanisms, computational strategies substantially reduce the time and cost associated with experimental research. Among these techniques, molecular dynamics (MD) simulations are particularly advantageous, as they enable the acquisition of detailed, atomistic information about conformational behavior, binding energetics, and transport phenomena, thereby providing a level of resolution and complementarity that is frequently challenging or impractical to achieve experimentally [ 21 , 22 ]. Building upon these considerations and recognizing the widespread use of chitosan as a gene delivery vehicle, this study first investigates the formation of chitosan–siRNA nanocomplex. Subsequently, steered molecular dynamics (SMD) simulations are employed to evaluate the role of chitosan in facilitating siRNA translocation across a lipid bilayer. Complexes with varying chitosan-to-RNA molar ratios (3:1, 2:2, and 1:3) are systematically pulled through the membrane while recording the corresponding force profiles. The objective is to elucidate how compositional ratios and intermolecular interactions govern the structural stability of the complexes and their efficiency in crossing the membrane barrier. 2. Methodology 2.1. Preparation of Molecular Structures and Simulation Systems The initial three-dimensional structures and topologies of the lipid bilayer, chitosan, and RNA molecules were obtained from the CHARMM-GUI server (www.charmm-gui.org). The chitosan polymer was modeled as a linear oligomer of 11 repeating N-Acetylglucosamine units with a degree of deacetylation of approximately 95%. RNA molecules were also constructed as single-stranded oligonucleotides of approximately 11 nucleotides in length. All systems were solvated with TIP3P water molecules applying the Counter-ions of Na⁺ or Cl⁻ to neutralize the total system charge. The model membrane was designed to reflect the compositional complexity of a typical human plasma membrane and included cholesterol, dipalmitoylphosphatidylcholine (DPPC), 1-palmitoyl-2-oleoylphosphatidylcholine (POPC), 1-stearoyl-2-oleoylphosphatidylcholine (SOPC), dipalmitoylphosphatidylethanolamine (DPPE), 1-palmitoyl-2-oleoylphosphatidylethanolamine (POPE), 1-stearoyl-2-oleoylphosphatidylethanolamine (SOPE), stearoyloleoylphosphatidylserine (SOPS), and 1-stearoyl-2-oleoylphosphatidylglycerol (SOPG). The molar ratios and quantities of each lipid species were selected based on existing biophysical literature (Table 1) [23]. Table 1 Percentage of Lipids Forming the Membrane Lipids Outer Layer Inner Layer Cholesterol 30 30 POPE 6 15 DPPE 3 10 SOPE 6 15 POPC 20 8 DPPC 15 5 SOPC 20 7 SOPS 0 9 SOPG 0 1 2.2. Molecular Dynamics and Steered Molecular Dynamics Simulations MD simulations were performed using the GROMACS 2020 software package. Three RNA-to-chitosan molar ratios of 1:3 (R1C3), 2:2 (R2C2), and 3:1 (R3C1) were evaluated so that the complexes possess a positive, neutral and a negative charge respectively. In each case, RNA and chitosan molecules were initially placed in a 10 × 10 × 10 nm³ simulation box under periodic boundary conditions to enable unbiased complex formation. Energy minimization was conducted using the steepest descent algorithm until the maximum force dropped below 100 kJ·mol⁻¹·nm⁻¹. Following minimization, each system underwent equilibration under NVT and NPT ensembles for 100 ps at 310 K and 1 bar using the Nose–Hoover thermostat and Parrinello–Rahman barostat, respectively [24, 25]. Production simulations were then conducted for 100 ns using the leap-frog integrator and a time step of 2 fs [26, 27]. All covalent bonds involving hydrogen atoms were constrained using the LINCS algorithm [28]. Long-range electrostatics were computed using the Particle Mesh Ewald (PME) method, while short-range van der Waals and Coulombic interactions were handled using a cutoff distance of 1.2 nm [29]. The CHARMM36 all-atom additive force field was used for all simulations, providing parameters for lipids, nucleic acids, carbohydrates, and ions. In the second stage, steered molecular dynamics (SMD) simulations were carried out to examine the translocation of RNA-chitosan complexes across the lipid bilayer. The pulling direction was oriented along the membrane normal (z-axis), and the height of the simulation box was adjusted (25 nm) to ensure complete bilayer traversal without interference from periodic boundaries. A harmonic pulling force of 1000 kJ·mol⁻¹·nm⁻² was applied to the center of mass of each complex at a constant velocity of 0.0005 nm·ps⁻¹. A single water molecule located opposite the pulling direction was restrained and served as a reference point for force application [30-32]. 2.3. Analysis of Simulation Trajectories To quantify intermolecular interactions, the binding energies between RNA and chitosan were estimated using the Molecular Mechanics Poisson–Boltzmann Surface Area (MMPBSA) approach. Visual molecular dynamics (VMD) were used to monitor conformational behavior throughout the simulations [33, 34]. 3. Results and discussion 3.1. Complex Formation and Stability 3.1.1. Molecular Dynamics and Structural Stability The lipid membrane is widely recognized as the most significant structural barrier preventing drug molecules and pharmaceutical formulations from reaching their intracellular sites of action. This barrier is particularly restrictive for highly charged, hydrophilic, high-molecular-weight therapeutics such as nucleic acids. Chitosan-based carriers have demonstrated the capability to transport nucleic acids across biological barriers. Fig. 1 illustrates the results of molecular dynamics (MD) simulations of siRNA-chitosan complexes at the end of the production phase for the three examined ratios. All systems formed stable complexes, with the R2C2 system appearing the most compact and symmetric. Analysis of the root-mean-square deviation (RMSD) provided valuable insights into the stability of the complexes throughout the simulations. In molecular modeling, RMSD quantifies the average distance between corresponding atoms in a structure relative to a reference configuration, typically their initial positions [35]. Higher RMSD values indicate greater deviations from the starting structure. The time-dependent RMSD plots of RNA-chitosan complexes in the different systems are presented in Fig. 2 and highlight the dynamic stability of each configuration. The results demonstrate that the RNA-chitosan ratio significantly influences complex stability. At the onset of the simulations, a rapid increase in RMSD was observed, reflecting initial structural relaxation. Subsequently, RMSD values stabilized around a mean value, albeit with notable fluctuations. This stabilization indicates that the complexes achieved relatively stable configurations. In systems with excess chitosan (R1C3), the high density of positive charges surrounding the complexes induced electrostatic repulsion among chitosan molecules, driving them away from the nanoparticle surface and resulting in increased RMSD fluctuations. A comparable phenomenon was observed in systems with elevated RNA content (R3C1), although in this case, the mean value of the RMSD is higher than the other systems. In contrast, the medium value of RMSD and fluctuations observed in the system containing 1:1 ratio of RNA and chitosan (R2C2). This may be because of better intermolecular association between RNA and chitosan, ultimately leading to the formation of a more stable complex. 3.1.2. Intermolecular Contacts The stability of the complexes was investigated by analyzing the number of contacts and interaction energies between RNA and chitosan (Fig. 3, Table 2). This analysis provides a more detailed understanding of the molecular interactions that governing the stability of the complexes. The R2C2 system exhibited the highest number of contacts, with a value nearly twice that observed in the other systems. This suggests that a greater number of intermolecular interactions occur in this configuration. Notably, the time required to reach the maximum number of contacts in the R2C2 system was longer compared to the other two systems. This delay may reflect the necessity for molecules to rearrange and optimize their interactions prior to forming a stable complex. In contrast, the R1C3 and R3C1 systems showed fewer interactions, with the R3C1 system exhibiting slightly lower contact numbers than the R1C3 system. The reduced number of contacts in the R1C3 system aligns with its lower interaction energy, suggesting the formation of a less stable molecular complex. This observation is consistent with the RMSD results, which showed higher fluctuations in the R1C3 configuration. 3.1.3. Implications for Complex Stability The interaction energies of the complexes during the MD simulation were calculated using the MMPBSA method, and the results are reported in Table 1. The analysis reveals that the R2C2 system has the lowest total energy, with a value of -32,816 kJ/mol. This suggests that this system is the most energetically favorable complex, which is consistent with the higher number of contacts observed in the same configuration. The lower total energy of the R2C2 system indicates that the interactions between RNA and chitosan are more favorable, leading to a more stable complex. In contrast, the R1C3 and R3C1 systems exhibited higher energy values, with the R3C1 system being the least favorable in terms of energy. This is consistent with the lower number of contacts observed in this system, suggesting that the interactions between RNA and chitosan are less favorable. The R1C3 system demonstrated an intermediate energy value, indicating that the interactions between RNA and chitosan are less favorable than in the R2C2 system but more favorable than in the R3C1 system. The MMPBSA method provides a detailed breakdown of the interaction energies, including van der Waals (vdW) and electrostatic contributions. The results show that the electrostatic interactions dominate the total energy, with the R2C2 system exhibiting the most favorable electrostatic interactions (−32,215 kJ/mol). This suggests that electrostatic interactions between RNA and chitosan play a crucial role in stabilizing the complex. The findings reveal that an equal proportion of chitosan and RNA molecules results in a more stable complex. This optimization can directly affect both the efficacy of the therapeutic agent and its interaction with biological barriers such as cell membranes. Furthermore, the results suggest that the stability of the complex is influenced by electrostatic interactions rather than the van der Waals forces. The R2C2 system, which demonstrated the most favorable electrostatic interactions, also had the lowest total energy and the highest number of contacts, indicating a more stable complex. This highlights the importance of considering the balance between these interactions when designing therapeutic nanocomplexes. Overall, the combined analysis of contact numbers and interaction energies offers a comprehensive view of the molecular interactions underpinning complex stability. Table 2 Interaction energies (kJ/mol) between Chitosan and RNA in complex formation stage System vdW Electrostatic Total R1C3 -368 -22969 -23338 R2C2 -601 -32215 -32816 R3C1 -411 -20992 -21404 3.2. Force probe simulations 3.2.1. Force Profiles and Membrane Penetration The effect of different ratios of RNA-chitosan in facilitating complex passage across a bilayer membrane was investigated by force probe simulation. In this method, the change in force is measured as the complex is pulled through the bilayer barrier under an external force. Based on the charge, hydrophilicity, and molecular weight of the complex, the required force may vary to maintain constant displacement of the pull group through the lipophilic barrier. The complexes were placed next to the bilayer membrane and pulled perpendicular to the bilayer toward the opposite side using a harmonic external force. The results of the analysis for the different complexes are reported in Fig. 4 , and as can be seen, the applied force increases after the complex contacts the bilayer (representing the potential barrier) and decreases once it has passed through the membrane. The results reveal that the system with a higher RNA content, which possesses a net negative charge, requires a higher force to traverse the membrane. This observation is consistent with the fact that negatively charged particles often face difficulties penetrating cell membranes. In contrast, the system with a higher chitosan content, which has a net positive charge, requires a lower force to cross the membrane. The R2C2 system shows the same maximum force as R1C3; however, in this case the complex crossed the membrane over a longer period. These findings are consistent with previous literature reporting that positively charged nanoparticles penetrate the cells more effectively than negatively charged particles [36]. Interestingly, the R2C2 system, which has an equal ratio of RNA and chitosan, exhibits a similar maximum force to the R1C3 system with higher chitosan content. However, the R2C2 system requires a longer time to traverse the membrane. This suggests that the R2C2 configuration may encounter a higher energy barrier during penetration despite having a more favorable complex formation. These results align with prior studies demonstrating that the charge of the complex plays a crucial role in determining its ability to cross cell membranes. Overall, the findings indicate that the RNA-CS ratio has a significant impact on the complex’s capacity to penetrate lipid bilayers. The interaction energy profiles in fig. 5 illustrate the strength and stability of binding between different complexes under varying RNA-to-chitosan ratios and the phospholipid membrane. As shown, the complex with the lowest Chitosan content and highest RNA ratio exhibits the most negative interaction energy throughout the simulation, indicating stronger electrostatic and van der Waals interactions with the membrane surface. Among the three systems, R1C3 complex shows the least negative interaction energy, suggesting a higher probability of membrane penetration or fusion. The overall changes in interaction energy versus time is in line with the force/time diagram reveal that chitosan content plays a critical role in modulating membrane association and may govern the efficiency of RNA delivery across lipid bilayers. 3.2.2. Structural Dynamics from Force Profiles and Snapshots Overall, the findings of this study highlight the importance of considering multiple factors when designing RNA-based therapeutics, including the stability of the complex, its charge and hydrophilicity, and its interactions with the lipid bilayer. The force profiles and corresponding snapshots of RNA-chitosan complexes provide insights into their structural behavior during translocation across the lipid bilayer (Fig. 6). The snapshots visually represent the transition phases, illustrating how the complexes move through the environment, traverse the hydrophobic core, and exit the bilayer, with each complex demonstrating distinct interaction dynamics during these stages. The snapshots reveal that the R1C3 complex, which requires the minimum force to pass through the bilayer, exhibits a relatively smooth and stable transition across the bilayer. In contrast, the R3C1 complex, requires the maximum force to traverse the bilayer, shows a more turbulent and unstable transition. The R2C2 complex, needs a moderate force to cross the bilayer, exhibits a transition characterized by a balance between stability and instability. This observation is consistent with the fact that the R2C2 complex has more favorable interaction energy and a higher number of contacts compared to the R3C1 complex. 3.2.3. Stability Assessment during Translocation In addition to facilitating passage through the biomembrane, a proper carrier system should maintain stability during translocation. To ensure the stability of the simulated systems, the number of close contacts between different components of the simulation’s systems was monitored. During complex transmission through the lipid bilayer, the number of contacts between siRNA and chitosan remained nearly constant across all systems, indicating excellent stability of the complex while crossing the bilayer. As shown in Fig. 7, despite an increase in contacts between the pull group and lipids, no significant decrease is observed in the number of contacts between RNA and chitosan compared to their values in the reference complexes. These results suggest that the complex is able to maintain its stability and integrity even while interacting with the lipid bilayer. This property is critical for a carrier system, as it ensures effective and efficient payload delivery to the targeting site and passes the cargo across different barriers in the body. The constant number of contacts between RNA and chitosan during translocation further supports the complex’s ability to preserve its stability and integrity. The hydrogen-bondings between RNA and chitosan are essential for the stability of the complex, as they provide a strong and specific binding force between the two molecules. The results in figure 6 show that the number of hydrogen bonds between RNA and chitosan remains stable during the passage of the complex through the biomembrane, again indicating that the stability of complex is not compromised by the translocation process. Maintaining these hydrogen bonds is critical, as it preserves the structural integrity of the complex, ensuring protection of the RNA cargo and enhancing delivery efficiency across biological barriers. This finding aligns with the contact analysis which demonstrated that the number of contacts between RNA and chitosan remains constant during translocation. The consistency between these analyses suggests that the complex maintains its stability throughout the transmission process and withstands environmental changes without undergoing significant structural alterations. 4. Conclusions This study demonstrates the role of molecular dynamics simulations in elucidating how a chitosan-RNA nanocomplex passes the lipid bilayers in a therapeutic nucleic acid delivery system. Our results reveal that the RNA-to-chitosan ratio profoundly influences the biophysical properties and behavior of the complexes, impacting both their stability and translocation dynamics through the lipid membrane. The 1:3 RNA-to-chitosan ratio (R1C3) was identified as optimal for efficient membrane translocation, despite the 2:2 ratio (R2C2) presented a balanced system with stable interactions. Overall, these findings suggest that increasing chitosan content enhances transmission efficiency, but an optimal balance between nucleic acid and chitosan is essential to form a stable complex that facilitates translocation across biological membranes. These insights have significant implications for the rational design of effective siRNA delivery systems and underscore the potential of molecular dynamics simulations as a predictive tool to optimize nanocarrier development for gene therapy applications. Declarations Conflict of interest The authors declare no competing interests. Funding This study was supported in part by grant number 4040428 from Kermanshah University of Medical Sciences, Kermanshah, Iran. Author Contribution P.J. and Sh.R. Conceptualization, Investigation, Methodology, Validation, Writing – Original Draft. M.Sh. Data curing, Methodology, Review & Editing. Z.A. and S.M. Conceptualization, Project Administration, Supervision, Validation, Funding Acquisition, Writing – Review & Editing Data Availability The authors declare that the all data supporting the findings of this study are available within the paper. Should any raw data files be needed in another format they are available from the corresponding author upon reasonable request. References Cotrim, A. P. & Baum, B. J. (2008). Gene therapy: some history, applications, problems, and prospects. Toxicologic pathology. 36 , 97-103. Friedmann, T. & Roblin, R. (1972). Gene therapy for human genetic disease? Science. 175 , 949-955. Chuan, D., Jin, T., Fan, R., Zhou, L. & Guo, G. (2019). Chitosan for gene delivery: Methods for improvement and applications. Advances in colloid and interface science. 268 , 25-38. Keshtkar, S., Asvar, Z., Najafi, H., Heidari, M., Kaviani, M., Sarvestani, F. S., Tamaddon, A. M., Sadati, M. S., Hamidizadeh, N. & Azarpira, N. (2025). 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Supplementary Files GraphicalabstractAtomisticsimulationsdemonstratechitosan.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 19 Jan, 2026 Reviews received at journal 18 Jan, 2026 Reviewers agreed at journal 07 Jan, 2026 Reviews received at journal 11 Dec, 2025 Reviewers agreed at journal 11 Dec, 2025 Reviews received at journal 28 Nov, 2025 Reviewers agreed at journal 24 Nov, 2025 Reviewers invited by journal 20 Nov, 2025 Editor assigned by journal 20 Nov, 2025 Editor invited by journal 20 Nov, 2025 Submission checks completed at journal 17 Nov, 2025 First submitted to journal 12 Nov, 2025 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. 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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-8062956","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":550852781,"identity":"83397971-3306-4fcf-b441-3e301ff787cf","order_by":0,"name":"Parvin Jalali","email":"","orcid":"","institution":"Kermanshah University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Parvin","middleName":"","lastName":"Jalali","suffix":""},{"id":550852782,"identity":"c718d7f3-a96e-469a-8ba1-d9c70c215bfd","order_by":1,"name":"Shokoufeh Rahmani","email":"","orcid":"","institution":"Kermanshah University of Medical 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12:40:06","extension":"xml","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":90148,"visible":true,"origin":"","legend":"","description":"","filename":"8df0c06427d8485d85f62208c48e47951structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8062956/v1/b23823502d94b6652acece10.xml"},{"id":96998053,"identity":"66b8afef-434b-4e3c-9819-ea78a6c3c034","added_by":"auto","created_at":"2025-11-28 12:40:06","extension":"html","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":101061,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8062956/v1/fbf1290e9acf77cab07442af.html"},{"id":96998030,"identity":"cdfe4b37-dfb3-4484-a62b-d0dd776eda0d","added_by":"auto","created_at":"2025-11-28 12:40:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":210928,"visible":true,"origin":"","legend":"\u003cp\u003eThe final complexes formed after molecular dynamics simulations of RNA–chitosan at different ratios: (A) RNA1:chitosan3, (B) RNA2:chitosan2, (C) RNA3:chitosan1. RNA is shown in green, chitosan in purple\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8062956/v1/a94fa475c95a04131eae7df8.png"},{"id":96998031,"identity":"c5eec8d4-025d-45f1-b294-c23b26f51a51","added_by":"auto","created_at":"2025-11-28 12:40:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":168576,"visible":true,"origin":"","legend":"\u003cp\u003eRoot-mean-square deviation (RMSD) of the RNA–chitosan complexes during equilibration at different RNA-to-chitosan ratios: (A) RNA1:chitosan3, (B) RNA2:chitosan2, (C) RNA3:chitosan1\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8062956/v1/e6af559261e37b8ac72f6084.png"},{"id":96998036,"identity":"5bf508d7-cbb3-4aea-90d5-f78b056e431d","added_by":"auto","created_at":"2025-11-28 12:40:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":215067,"visible":true,"origin":"","legend":"\u003cp\u003eComparative time-dependent profiles of the number of contacts between RNA and chitosan during MD simulations at different RNA-to-chitosan ratios: (A) RNA1:chitosan3, (B) RNA2:chitosan2, (C) RNA3:chitosan1\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8062956/v1/15f73d355449dad5e6f81d37.png"},{"id":97138489,"identity":"505530fa-b6de-4625-ab6a-206d7fcbebde","added_by":"auto","created_at":"2025-12-01 09:58:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":174399,"visible":true,"origin":"","legend":"\u003cp\u003eComparative force-probe MD simulations of RNA-chitosan complexes at different RNA-to-chitosan ratios: (A) RNA1:chitosan3, (B) RNA2:chitosan2, (C) RNA3:chitosan1\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8062956/v1/8a09882415ef8f43337762e1.png"},{"id":96998034,"identity":"f7f98fb1-7c28-4b77-b51a-d0430905f237","added_by":"auto","created_at":"2025-11-28 12:40:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":196517,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction energy profiles of RNA-chitosan complexes with phospholipid membranes during steered molecular dynamics simulations at different RNA-to-chitosan ratios: (A) RNA3:chitosan1, (B) RNA1:chitosan3, and (C) RNA2:chitosan2\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8062956/v1/6c43e9562165652f529a9efc.png"},{"id":96998032,"identity":"00d2de01-5619-4b3c-bb8a-b61ed30aed99","added_by":"auto","created_at":"2025-11-28 12:40:06","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":589972,"visible":true,"origin":"","legend":"\u003cp\u003eTime-dependent force profiles of RNA–chitosan complexes during translocation across the lipid bilayer, with accompanying snapshots of the process: (A) RNA1:chitosan3, (B) RNA2:chitosan2, (C) RNA3:chitosan1\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8062956/v1/794019f854f44fcd4c5bc8bb.png"},{"id":97138621,"identity":"ae6b0f0b-cef4-4cb8-8f3d-d4c77b3643df","added_by":"auto","created_at":"2025-12-01 09:59:08","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":458689,"visible":true,"origin":"","legend":"\u003cp\u003eTime evolution of the number of hydrogen bonds and number of contacts between RNA and chitosan during MD simulations at different RNA-to-chitosan ratios: (A) RNA1:chitosan3, (B) RNA2:chitosan2, (C) RNA3:chitosan1. The curves labeled (a), (b), and (c) represent distinct molecular types:\u003cbr\u003e\n (a) Number of RNA–chitosan H-bonds during complex formation,\u003c/p\u003e\n\u003cp\u003e(b) Number of RNA–chitosan H-bonds during the pulling stage,\u003c/p\u003e\n\u003cp\u003e(c) Number of H-bonds between the RNA–chitosan complex (pull group) and membrane lipids during the pulling stage\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8062956/v1/cde6b113bafa1028d5b8b78b.png"},{"id":97144948,"identity":"21bc3e9f-4547-47d2-9984-aaa6907161be","added_by":"auto","created_at":"2025-12-01 10:12:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2567539,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8062956/v1/d6baab1c-340a-4ebf-975d-a5271e6a0e4d.pdf"},{"id":97139180,"identity":"5d1b323f-7b53-449f-a10a-990bcc49afc9","added_by":"auto","created_at":"2025-12-01 09:59:43","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":893806,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalabstractAtomisticsimulationsdemonstratechitosan.docx","url":"https://assets-eu.researchsquare.com/files/rs-8062956/v1/300967d854ced28e4b75e75a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Steered Molecular Dynamics Investigation of Chitosan Nano-Carrier Mediated siRNA Translocation Across Biological Membranes","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eNucleic acid therapy has gained considerable attention as a next-generation therapeutic strategy aimed at correcting or modulating gene function at its molecular origin. Unlike conventional treatments that primarily alleviate symptoms, gene therapies offer the potential to address the underlying genetic and regulatory defects responsible for a wide spectrum of diseases [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The concept of gene therapy first emerged in the 1970s, coinciding with the rapid expansion of knowledge about the human genome [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Despite its considerable promise, the clinical translation of gene therapy faces persistent challenges, many of which stem from unfavorable pharmacokinetic properties. Naked nucleic acids are highly susceptible to hydrolysis and enzymatic degradation by endo and exonucleases in circulation, and they also exhibit limited capacity to traverse biological barriers such as the cell membrane. This limitation arises partly from their hydrophilic nature, which hinders passage through the hydrophobic lipid bilayer, as well as their substantial molecular weight and the negative charge of their phosphate backbone, which collectively impede cellular uptake [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Consequently, the design of effective delivery carriers is a critical determinant of gene transfer success. Recent multidisciplinary advances have highlighted the pivotal role of biocompatible nanomaterials and smart delivery systems in overcoming the barriers related to variety of delivery systems and enhancing their therapeutic efficacy. Therefore, it is essential to employ delivery platforms that can not only protect nucleic acids from degradation but also enhance cellular translocation by reducing effective particle size and modulating surface charge [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGene delivery into cells can be accomplished using either viral or non-viral vectors. Viral vectors have been widely utilized due to their high transduction efficiency; however, their clinical application is often limited by concerns regarding toxicity, immunogenicity, and challenges associated with large-scale production and repeated administration [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In contrast, non-viral vectors provide a safer and more versatile alternative, offering benefits such as improved biocompatibility, reduced immunogenicity, and simpler, more cost-effective manufacturing [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Ongoing research continues to focus on the design and optimization of these systems to enhance the efficiency, specificity, and safety of gene transfer for therapeutic applications.\u003c/p\u003e\u003cp\u003eAmong non-viral delivery systems, cationic polymers have received particular attention. The use of cationic polymers for gene transfer was first demonstrated by Wu et al. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Chitosan, a natural cationic polysaccharide, is one of the most extensively studied biopolymers in this context due to its biocompatibility, biodegradability, low toxicity, and minimal immunogenicity. Structurally, chitosan consists of randomly distributed β(1\u0026rarr;4)-linked N-acetyl-D-glucosamine and D-glucosamine units [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. At slightly acidic pH, the abundance of deacetylated amino groups imparts a high positive charge density, enabling strong electrostatic interactions with negatively charged nucleic acids and efficient gene condensation. This positive surface charge facilitates binding to the negatively charged cell membrane and promotes cellular uptake of nucleic acid cargo. In addition to this electrostatic mechanism, evidence suggests that chitosan-based nanoparticles may also engage specific endocytic receptors, further enhancing endocytosis and intracellular delivery [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eCell membranes serve as robust barriers with selective permeability, maintaining homeostasis by regulating the exchange of substances between the intracellular and extracellular environments. This structural feature has a profound impact on drug pharmacokinetics and biodistribution [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Hydrophilic, high-molecular-weight therapeutics, such as nucleic acid-based drugs employed in gene therapy, encounter significant obstacles in traversing this barrier and therefore typically require modification or incorporation into specialized nanocarriers. These nanoscale delivery systems, often formulated with cationic materials, condense genetic payloads and neutralize their inherent negative charge, thereby enhancing their affinity for the negatively charged cell membrane and facilitating more efficient cellular uptake and intracellular diffusion [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eComputational methods offer valuable insights into the structural and dynamic features of drug\u0026ndash;carrier interactions that are often inaccessible through experimental techniques alone. These approaches have been extensively applied to investigate the physicochemical and biological properties of drug delivery systems and their interactions with biological components such as lipid membranes and proteins [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Beyond elucidating molecular-level mechanisms, computational strategies substantially reduce the time and cost associated with experimental research. Among these techniques, molecular dynamics (MD) simulations are particularly advantageous, as they enable the acquisition of detailed, atomistic information about conformational behavior, binding energetics, and transport phenomena, thereby providing a level of resolution and complementarity that is frequently challenging or impractical to achieve experimentally [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBuilding upon these considerations and recognizing the widespread use of chitosan as a gene delivery vehicle, this study first investigates the formation of chitosan\u0026ndash;siRNA nanocomplex. Subsequently, steered molecular dynamics (SMD) simulations are employed to evaluate the role of chitosan in facilitating siRNA translocation across a lipid bilayer. Complexes with varying chitosan-to-RNA molar ratios (3:1, 2:2, and 1:3) are systematically pulled through the membrane while recording the corresponding force profiles. The objective is to elucidate how compositional ratios and intermolecular interactions govern the structural stability of the complexes and their efficiency in crossing the membrane barrier.\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cp\u003e\u003cstrong\u003e2.1. Preparation of Molecular Structures and Simulation Systems\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe initial three-dimensional structures and topologies of the lipid bilayer, chitosan, and RNA molecules were obtained from the CHARMM-GUI server (www.charmm-gui.org). The chitosan polymer was modeled as a linear oligomer of 11 repeating N-Acetylglucosamine units with a degree of deacetylation of approximately 95%. RNA molecules were also constructed as single-stranded oligonucleotides of approximately 11 nucleotides in length. All systems were solvated with TIP3P water molecules applying the Counter-ions of Na⁺\u0026nbsp;or Cl⁻\u0026nbsp;to neutralize the total system charge.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe model membrane was designed to reflect the compositional complexity of a typical human plasma membrane and included cholesterol, dipalmitoylphosphatidylcholine (DPPC), 1-palmitoyl-2-oleoylphosphatidylcholine (POPC), 1-stearoyl-2-oleoylphosphatidylcholine (SOPC), dipalmitoylphosphatidylethanolamine (DPPE), 1-palmitoyl-2-oleoylphosphatidylethanolamine (POPE), 1-stearoyl-2-oleoylphosphatidylethanolamine (SOPE), stearoyloleoylphosphatidylserine (SOPS), and 1-stearoyl-2-oleoylphosphatidylglycerol (SOPG). The molar ratios and quantities of each lipid species were selected based on existing biophysical literature (Table 1) [23].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Percentage of Lipids Forming the Membrane\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eLipids\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eOuter Layer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eInner Layer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eCholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003ePOPE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eDPPE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eSOPE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003ePOPC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eDPPC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eSOPC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eSOPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eSOPG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e1\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\u003cstrong\u003e2.2. Molecular Dynamics and Steered Molecular Dynamics Simulations\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMD simulations were performed using the GROMACS 2020 software package. Three RNA-to-chitosan molar ratios of 1:3 (R1C3), 2:2 (R2C2), and 3:1 (R3C1) were evaluated so that the complexes possess a positive, neutral and a negative charge respectively. In each case, RNA and chitosan molecules were initially placed in a 10 \u0026times; 10 \u0026times; 10 nm\u0026sup3; simulation box under periodic boundary conditions to enable unbiased complex formation. Energy minimization was conducted using the steepest descent algorithm until the maximum force dropped below 100 kJ\u0026middot;mol⁻\u0026sup1;\u0026middot;nm⁻\u0026sup1;. Following minimization, each system underwent equilibration under NVT and NPT ensembles for 100 ps at 310 K and 1 bar using the Nose\u0026ndash;Hoover thermostat and Parrinello\u0026ndash;Rahman barostat, respectively [24, 25]. Production simulations were then conducted for 100 ns using the leap-frog integrator and a time step of 2 fs [26, 27]. All covalent bonds involving hydrogen atoms were constrained using the LINCS algorithm [28]. Long-range electrostatics were computed using the Particle Mesh Ewald (PME) method, while short-range van der Waals and Coulombic interactions were handled using a cutoff distance of 1.2 nm [29]. The CHARMM36 all-atom additive force field\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ewas used for all simulations, providing parameters for lipids, nucleic acids, carbohydrates, and ions.\u003c/p\u003e\n\u003cp\u003eIn the second stage, steered molecular dynamics (SMD) simulations were carried out to examine the translocation of RNA-chitosan complexes across the lipid bilayer. The pulling direction was oriented along the membrane normal (z-axis), and the height of the simulation box was adjusted (25 nm) to ensure complete bilayer traversal without interference from periodic boundaries. A harmonic pulling force of 1000 kJ\u0026middot;mol⁻\u0026sup1;\u0026middot;nm⁻\u0026sup2; was applied to the center of mass of each complex at a constant velocity of 0.0005 nm\u0026middot;ps⁻\u0026sup1;. A single water molecule located opposite the pulling direction was restrained and served as a reference point for force application [30-32].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3. Analysis of Simulation Trajectories\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo quantify intermolecular interactions, the binding energies between RNA and chitosan were estimated using the Molecular Mechanics Poisson\u0026ndash;Boltzmann Surface Area (MMPBSA) approach. Visual molecular dynamics (VMD) were used to monitor conformational behavior throughout the simulations [33, 34].\u003c/p\u003e"},{"header":"3. Results and discussion","content":"\u003cp\u003e\u003cstrong\u003e3.1. Complex Formation and Stability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.1.1. Molecular Dynamics and Structural Stability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe lipid membrane is widely recognized as the most significant structural barrier preventing drug molecules and pharmaceutical formulations from reaching their intracellular sites of action. This barrier is particularly restrictive for highly charged, hydrophilic, high-molecular-weight therapeutics such as nucleic acids. Chitosan-based carriers have demonstrated the capability to transport nucleic acids across biological barriers. \u003cem\u003eFig. 1\u003c/em\u003e illustrates the results of molecular dynamics (MD) simulations of siRNA-chitosan complexes at the end of the production phase for the three examined ratios. All systems formed stable complexes, with the R2C2 system appearing the most compact and symmetric.\u003c/p\u003e\n\u003cp\u003eAnalysis of the root-mean-square deviation (RMSD) provided valuable insights into the stability of the complexes throughout the simulations. In molecular modeling, RMSD quantifies the average distance between corresponding atoms in a structure relative to a reference configuration, typically their initial positions [35]. Higher RMSD values indicate greater deviations from the starting structure. The time-dependent RMSD plots of RNA-chitosan complexes in the different systems are presented in Fig. 2 and highlight the dynamic stability of each configuration. The results demonstrate that the RNA-chitosan ratio significantly influences complex stability. At the onset of the simulations, a rapid increase in RMSD was observed, reflecting initial structural relaxation. Subsequently, RMSD values stabilized around a mean value, albeit with notable fluctuations. This stabilization indicates that the complexes achieved relatively stable configurations. In systems with excess chitosan (R1C3), the high density of positive charges surrounding the complexes induced electrostatic repulsion among chitosan molecules, driving them away from the nanoparticle surface and resulting in increased RMSD fluctuations. A comparable phenomenon was observed in systems with elevated RNA content (R3C1), although in this case, the mean value of the RMSD is higher than the other systems.\u003c/p\u003e\n\u003cp\u003eIn contrast, the medium value of RMSD and fluctuations observed in the system containing 1:1 ratio of RNA and chitosan (R2C2). This may be because of better intermolecular association between RNA and chitosan, ultimately leading to the formation of a more stable complex.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.1.2. Intermolecular Contacts\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe stability of the complexes was investigated by analyzing the number of contacts and interaction energies between RNA and chitosan (Fig. 3, Table 2). This analysis provides a more detailed understanding of the molecular interactions that governing the stability of the complexes. The R2C2 system exhibited the highest number of contacts, with a value nearly twice that observed in the other systems. This suggests that a greater number of intermolecular interactions occur in this configuration. Notably, the time required to reach the maximum number of contacts in the R2C2 system was longer compared to the other two systems. This delay may reflect the necessity for molecules to rearrange and optimize their interactions prior to forming a stable complex.\u003c/p\u003e\n\u003cp\u003eIn contrast, the R1C3 and R3C1 systems showed fewer interactions, with the R3C1 system exhibiting slightly lower contact numbers than the R1C3 system. The reduced number of contacts in the R1C3 system aligns with its lower interaction energy, suggesting the formation of a less stable molecular complex. This observation is consistent with the RMSD results, which showed higher fluctuations in the R1C3 configuration.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003e3.1.3. Implications for Complex Stability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe interaction energies of the complexes during the MD simulation were calculated using the MMPBSA method, and the results are reported in Table 1. The analysis reveals that the R2C2 system has the lowest total energy, with a value of -32,816 kJ/mol. This suggests that this system is the most energetically favorable complex, which is consistent with the higher number of contacts observed in the same configuration. The lower total energy of the R2C2 system indicates that the interactions between RNA and chitosan are more favorable, leading to a more stable complex. In contrast, the R1C3 and R3C1 systems exhibited higher energy values, with the R3C1 system being the least favorable in terms of energy. This is consistent with the lower number of contacts observed in this system, suggesting that the interactions between RNA and chitosan are less favorable. The R1C3 system demonstrated an intermediate energy value, indicating that the interactions between RNA and chitosan are less favorable than in the R2C2 system but more favorable than in the R3C1 system.\u003c/p\u003e\n\u003cp\u003eThe MMPBSA method provides a detailed breakdown of the interaction energies, including van der Waals (vdW) and electrostatic contributions. The results show that the electrostatic interactions dominate the total energy, with the R2C2 system exhibiting the most favorable electrostatic interactions (\u0026minus;32,215 kJ/mol). This suggests that electrostatic interactions between RNA and chitosan play a crucial role in stabilizing the complex. The findings reveal that an equal proportion of chitosan and RNA molecules results in a more stable complex. This optimization can directly affect both the efficacy of the therapeutic agent and its interaction with biological barriers such as cell membranes. Furthermore, the results suggest that the stability of the complex is influenced by electrostatic interactions rather than the van der Waals forces. The R2C2 system, which demonstrated the most favorable electrostatic interactions, also had the lowest total energy and the highest number of contacts, indicating a more stable complex. This highlights the importance of considering the balance between these interactions when designing therapeutic nanocomplexes. Overall, the combined analysis of contact numbers and interaction energies offers a comprehensive view of the molecular interactions underpinning complex stability.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Interaction energies (kJ/mol) between Chitosan and RNA in complex formation stage\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"475\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSystem\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003evdW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eElectrostatic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eR1C3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e-368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e-22969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e-23338\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eR2C2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e-601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e-32215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e-32816\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eR3C1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e-411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e-20992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e-21404\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3.2. Force probe simulations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.1. Force Profiles and Membrane Penetration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe effect of different \u003cstrong\u003eratios\u003c/strong\u003e of RNA-chitosan in facilitating complex passage across a bilayer membrane was investigated by force probe simulation. In this method, the change in force is measured as the complex is pulled through the bilayer barrier under an external force. Based on the charge, hydrophilicity, and molecular weight of the complex, the required force may \u003cstrong\u003evary to maintain constant displacement of\u003c/strong\u003e the pull group through the lipophilic barrier. The complexes were placed next to the bilayer membrane and pulled perpendicular to the bilayer toward the opposite side using a harmonic external force. The results of the analysis for the different complexes are reported in \u003cstrong\u003eFig. 4\u003c/strong\u003e, and as can be seen, the applied force \u003cstrong\u003eincreases\u003c/strong\u003e after the complex contacts the bilayer \u003cstrong\u003e(representing the potential barrier)\u003c/strong\u003e and \u003cstrong\u003edecreases\u003c/strong\u003e once it has passed through the membrane.\u003c/p\u003e\n\u003cp\u003eThe results reveal that the system with a higher RNA content, which possesses a net negative charge, requires a higher force to traverse the membrane. This observation is consistent with the fact that negatively charged particles often face difficulties penetrating cell membranes. In contrast, the system with a higher chitosan content, which has a net positive charge, requires a lower force to cross the membrane. The R2C2 system shows the same maximum force as R1C3; however, in this case the complex crossed the membrane over a longer period. These findings are consistent with previous literature reporting that positively charged nanoparticles penetrate the cells more effectively than negatively charged particles [36].\u003c/p\u003e\n\u003cp\u003eInterestingly, the R2C2 system, which has an equal ratio of RNA and chitosan, exhibits a similar maximum force to the R1C3 system with higher chitosan content. However, the R2C2 system requires a longer time to traverse the membrane. This suggests that the R2C2 configuration may encounter a higher energy barrier during penetration despite having a more favorable complex formation.\u003c/p\u003e\n\u003cp\u003eThese results align with prior studies demonstrating that the charge of the complex plays a crucial role in determining its ability to cross cell membranes. Overall, the findings indicate that the RNA-CS ratio has a significant impact on the complex\u0026rsquo;s capacity to penetrate lipid bilayers. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe interaction energy profiles in fig. 5 illustrate the strength and stability of binding between different complexes under varying RNA-to-chitosan ratios and the phospholipid membrane. As shown, the complex with the lowest Chitosan content and highest RNA ratio exhibits the most negative interaction energy throughout the simulation, indicating stronger electrostatic and van der Waals interactions with the membrane surface.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAmong the three systems, R1C3 complex shows the least negative interaction energy, suggesting a higher probability of membrane penetration or fusion. The overall changes in interaction energy versus time is in line with the force/time diagram reveal that chitosan content plays a critical role in modulating membrane association and may govern the efficiency of RNA delivery across lipid bilayers. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.2. Structural Dynamics from Force Profiles and Snapshots\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOverall, the findings of this study highlight the importance of considering multiple factors when designing RNA-based therapeutics, including the stability of the complex, its charge and hydrophilicity, and its interactions with the lipid bilayer. The force profiles and corresponding snapshots of RNA-chitosan complexes provide insights into their structural behavior during translocation across the lipid bilayer (Fig. 6). The snapshots visually represent the transition phases, illustrating how the complexes move through the environment, traverse the hydrophobic core, and exit the bilayer, with each complex demonstrating distinct interaction dynamics during these stages.\u003c/p\u003e\n\u003cp\u003eThe snapshots reveal that the R1C3 complex, which requires the minimum force to pass through the bilayer, exhibits a relatively smooth and stable transition across the bilayer. In contrast, the R3C1 complex, requires the maximum force to traverse the bilayer, shows a more turbulent and unstable transition. The R2C2 complex, needs a moderate force to cross the bilayer, exhibits a transition characterized by a balance between stability and instability. This observation is consistent with the fact that the R2C2 complex has more favorable interaction energy and a higher number of contacts compared to the R3C1 complex.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.3. Stability Assessment during Translocation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn addition to facilitating passage through the biomembrane, a proper carrier system should maintain stability during translocation. To ensure the stability of the simulated systems, the number of close contacts between different components of the simulation\u0026rsquo;s systems was monitored. During complex transmission through the lipid bilayer, the number of contacts between siRNA and chitosan remained nearly constant across all systems, indicating excellent stability of the complex while crossing the bilayer. As shown in Fig. 7, despite an increase in contacts between the pull group and lipids, no significant decrease is observed in the number of contacts between RNA and chitosan compared to their values in the reference complexes.\u003c/p\u003e\n\u003cp\u003eThese results suggest that the complex is able to maintain its stability and integrity even while interacting with the lipid bilayer. This property is critical for a carrier system, as it ensures effective and efficient payload delivery to the targeting site and passes the cargo across different barriers in the body. The constant number of contacts between RNA and chitosan during translocation further supports the complex\u0026rsquo;s ability to preserve its stability and integrity.\u003c/p\u003e\n\u003cp\u003eThe hydrogen-bondings between RNA and chitosan are essential for the stability of the complex, as they provide a strong and specific binding force between the two molecules. The results in figure 6 show that the number of hydrogen bonds between RNA and chitosan remains stable during the passage of the complex through the biomembrane, again indicating that the stability of complex is not compromised by the translocation process. Maintaining these hydrogen bonds is critical, as it preserves the structural integrity of the complex, ensuring protection of the RNA cargo and enhancing delivery efficiency across biological barriers. This finding aligns with the contact analysis which demonstrated that the number of contacts between RNA and chitosan remains constant during translocation. The consistency between these analyses suggests that the complex maintains its stability throughout the transmission process and withstands environmental changes without undergoing significant structural alterations.\u003c/p\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eThis study demonstrates the role of molecular dynamics simulations in elucidating how a chitosan-RNA nanocomplex passes the lipid bilayers in a therapeutic nucleic acid delivery system. Our results reveal that the RNA-to-chitosan ratio profoundly influences the biophysical properties and behavior of the complexes, impacting both their stability and translocation dynamics through the lipid membrane. The 1:3 RNA-to-chitosan ratio (R1C3) was identified as optimal for efficient membrane translocation, despite the 2:2 ratio (R2C2) presented a balanced system with stable interactions. Overall, these findings suggest that increasing chitosan content enhances transmission efficiency, but an optimal balance between nucleic acid and chitosan is essential to form a stable complex that facilitates translocation across biological membranes. These insights have significant implications for the rational design of effective siRNA delivery systems and underscore the potential of molecular dynamics simulations as a predictive tool to optimize nanocarrier development for gene therapy applications.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eConflict of interest\u003c/h2\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis study was supported in part by grant number 4040428 from Kermanshah University of Medical Sciences, Kermanshah, Iran.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eP.J. and Sh.R. Conceptualization, Investigation, Methodology, Validation, Writing \u0026ndash; Original Draft. M.Sh. Data curing, Methodology, Review \u0026amp; Editing. Z.A. and S.M. Conceptualization, Project Administration, Supervision, Validation, Funding Acquisition, Writing \u0026ndash; Review \u0026amp; Editing\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe authors declare that the all data supporting the findings of this study are available within the paper. Should any raw data files be needed in another format they are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCotrim, A. P. \u0026amp; Baum, B. J. (2008). Gene therapy: some history, applications, problems, and prospects. Toxicologic pathology. \u003cstrong\u003e36\u003c/strong\u003e, 97-103. \u003c/li\u003e\n\u003cli\u003eFriedmann, T. \u0026amp; Roblin, R. (1972). Gene therapy for human genetic disease? Science. \u003cstrong\u003e175\u003c/strong\u003e, 949-955. \u003c/li\u003e\n\u003cli\u003eChuan, D., Jin, T., Fan, R., Zhou, L. \u0026amp; Guo, G. (2019). 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Journal of Molecular Liquids. \u003cstrong\u003e259\u003c/strong\u003e, 284-290. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Molecular dynamic simulation, drug delivery, molecular modeling, gene delivery, nanomedicine","lastPublishedDoi":"10.21203/rs.3.rs-8062956/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8062956/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe delivery of nucleic acids based therapeutics such as siRNA into cells remains a major challenge in gene therapy, primarily due to their hydrophilicity and intrinsic negative charge, which hinder membrane translocation. Chitosan, a biocompatible and biodegradable cationic polysaccharide, has emerged as a promising carrier for gene delivery applications. Nevertheless, the optimal stoichiometric ratio between chitosan and siRNA required to achieve efficient membrane penetration has not been fully elucidated. In this study, molecular dynamics simulations were employed to examine the formation of chitosan-RNA complexes and their interactions with a model lipid bilayer. The results indicate that the chitosan-to-RNA ratio has a pronounced impact on the biophysical properties of the complexes, affecting both their structural stability and dynamic interactions with the membrane. Specifically, a 1:3 RNA-to-chitosan ratio (R1C3) was found to facilitate more efficient membrane translocation, whereas a 2:2 ratio (R2C2) produced more stable complexes overall. The findings in this study offer valuable insights for the rational design of chitosan-based siRNA delivery systems and underscore the importance of balancing nucleic acid and carrier content to achieve both stability and effective transmembrane delivery.\u003c/p\u003e","manuscriptTitle":"Steered Molecular Dynamics Investigation of Chitosan Nano-Carrier Mediated siRNA Translocation Across Biological Membranes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-28 12:40:01","doi":"10.21203/rs.3.rs-8062956/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-20T04:50:03+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-18T11:42:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"127013512346387176635010147131882116595","date":"2026-01-08T03:10:42+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-11T10:35:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"110627645778476299847339497225592931898","date":"2025-12-11T07:07:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-28T19:22:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"134548813756419416127389361965190439659","date":"2025-11-24T13:34:50+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-20T18:29:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-20T18:14:57+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-20T13:19:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-17T18:52:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-11-12T12:25:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7d973d13-116e-4884-bad0-a91e0deb6e49","owner":[],"postedDate":"November 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":58613779,"name":"Biological sciences/Biochemistry"},{"id":58613780,"name":"Biological sciences/Biological techniques"},{"id":58613781,"name":"Biological sciences/Biotechnology"},{"id":58613782,"name":"Physical sciences/Nanoscience and technology"}],"tags":[],"updatedAt":"2026-01-20T04:58:44+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-28 12:40:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8062956","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8062956","identity":"rs-8062956","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}