Exploring the Structural Dynamics in aqueous medium of a novel Pentafluorophenyl-Ureido Derivative: A Molecular Dynamics Study

Exploring the Structural Dynamics in aqueous medium of a novel Pentafluorophenyl-Ureido Derivative: A Molecular Dynamics Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Exploring the Structural Dynamics in aqueous medium of a novel Pentafluorophenyl-Ureido Derivative: A Molecular Dynamics Study Venkata Shivakumar Remella, Haridharan Neelamegan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6226678/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The synthesis and characterization of organic molecules with specialized structural and electronic features are crucial for applications in materials science, catalysis, and molecular electronics. This study introduces PDPF, a pentafluorophenyl-ureido derivative of 2,6-diaminopyridine, recognized for its unique crystal architecture and intermolecular interactions. Single-crystal X-ray diffraction reveals that PDPF crystallizes in a triclinic system (Pī space group), with a densely packed lattice stabilized by N—H···O and C—H···F hydrogen bonds. The pentafluorophenyl rings and urea moieties contribute electron-withdrawing effects, enhancing stability and unique steric properties. To investigate PDPF’s stability and conformational dynamics, molecular dynamics (MD) simulations were conducted under thermal fluctuations and solvent interactions. Principal component analysis (PCA) of MD trajectories identified localized flexibility in carbon and nitrogen atoms, with rapid stabilization along principal components. Radial distribution function (RDF) profiles indicate limited solubility and hydrophobic tendencies, with minimal hydrogen bonding in aqueous environments. Further analyses of temperature, pressure, density, and energy fluctuations confirm structural integrity, with stable kinetic and potential energy profiles across conditions. This comprehensive study provides insights into PDPF’s intermolecular interactions and stability while demonstrating the effectiveness of computational methods in characterizing complex organic molecules with intricate packing arrangements. Pentafluorophenyl-ureido derivative Crystal architecture Hydrogen bonding Molecular dynamics simulations Radial distribution function (RDF) Intermolecular interactions Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 1 Introduction The design and synthesis of organic molecules with tailored structural features and intermolecular interactions are essential for advancing applications in materials science, catalysis, and molecular electronics. Among these, compounds containing pentafluorophenyl (PFP) groups have attracted significant attention due to the unique electronic characteristics imparted by fluorination, which can enhance stability, modify reactivity, and promote distinctive intermolecular interactions[ 1 , 2 , 3 ]. This study focuses on a novel compound, PDPF, a pentafluorophenyl-ureido derivative of 2,6-diaminopyridine, which presents intriguing structural and electronic properties as evidenced by single-crystal X-ray diffraction and theoretical analyses[ 4 ]. The crystallographic analysis of PDPF reveals its adoption of a triclinic crystal system with a low-symmetry space group, Pī. The unit cell dimensions are given as a = 7.5939(5) A˚, b = 12.4169(9) A˚, c = 13.5494(9) A˚, and angles α = 100.927(2)°, β = 104.223(2)°, and γ = 96.779(2)°, producing a unit cell volume of 1197.67 ų. The compound exhibits a relatively high density of 1.665 g/cm³, with two molecules per unit cell (Z = 2), indicating efficient molecular packing. The dense packing is reinforced by N—H···O and C—H···F hydrogen bonding interactions, which contribute to the structural stability of the lattice. This intricate packing arrangement, associated with the triclinic symmetry and the low-symmetry space group Pī, underscores the complexity of PDPF’s crystalline architecture—a characteristic often observed in organic molecules with multiple functional groups and aromatic substituents. The structural configuration of PDPF combines two PFP rings and urea moieties, each contributing unique electronic and steric properties. The pentafluorophenyl rings introduce electron-withdrawing effects and potential sites for non-covalent interactions, particularly through C—H···F hydrogen bonds. Additionally, the urea functional groups engage in N—H···O hydrogen bonding, fostering intermolecular interactions that promote lattice stability. The complex interplay between these functional groups likely gives rise to the compound’s non-regular geometries, which are accommodated within the asymmetric triclinic lattice. Such structural features are essential to investigate further, as they may influence the electronic distribution, molecular reactivity, and potential applications of PDPF in molecular electronics or as a ligand in coordination chemistry. Given these unique structural characteristics, PDPF presents an interesting candidate for in-depth theoretical and computational exploration. To this end, we aim to employ a molecular dynamics (MD) simulation[ 5 , 6 ]. The MD simulations will provide insight into the dynamic stability and conformational behavior of PDPF under various conditions, such as thermal fluctuations and solvent environments. These simulations will allow us to identify potential conformational changes and assess how the crystalline structure’s stability is influenced by environmental factors. In summary, this study provides a detailed theoretical analysis of PDPF, leveraging its unique crystal structure and intermolecular interactions to explore its dynamic and reactive properties. The MD simulations will not only deepen our understanding of PDPF’s behavior in different conditions but also demonstrate the utility of such integrated computational approaches in predicting the properties of novel organic compounds with complex packing arrangements and multiple functional groups. Research Objectives The pentafluorophenyl-ureido derivative of 2,6-diaminopyridine (PDPF) presents a structurally unique and complex system for theoretical analysis. The presence of dual pentafluorophenyl-ureido moieties introduces distinctive intermolecular and intramolecular interactions, influencing molecular stability, reactivity, and self-assembly behavior. These properties are particularly relevant for applications in materials science, catalysis, and coordination chemistry[ 7 , 8 ]. This study employs Molecular Dynamics (MD) simulations to provide a comprehensive investigation into PDPF’s structural stability, conformational dynamics, and interaction patterns, offering insights into its behavior under diverse environmental conditions. The primary research objectives are: To Analyze the Structural Stability of PDPF via MD Simulations - The first objective is to assess the stability of PDPF over time, examining how its dual pentafluorophenyl-ureido groups influence molecular packing and response to thermal fluctuations and solvent effects; MD simulations will reveal conformational preferences, highlighting how pentafluorophenyl rings and urea moieties align or shift under dynamic conditions; By investigating intramolecular hydrogen bonding and steric effects, the study will determine whether PDPF maintains rigid structural integrity or undergoes significant conformational changes over time. To Characterize Conformational Dynamics and Interaction Patterns - This objective focuses on the dynamic interplay between the two pentafluorophenyl-ureido moieties and the central 2,6-diaminopyridine core. Since these moieties are attached at the N-atoms of the 2- and 6-positions, their rotational freedom and interaction tendencies may significantly impact PDPF’s overall geometry and stability; MD simulations will assess molecular motions, intramolecular interactions, and steric effects, determining whether PDPF’s functional groups exhibit rigidity or flexibility under simulated conditions; Understanding these dynamics is critical for predicting PDPF’s reactivity, solubility, and potential binding interactions, which influence its suitability for supramolecular assemblies, catalysis, and coordination chemistry applications. Impact and Significance : The insights gained from this study will deepen the theoretical understanding of PDPF, bridging the gap between computational modeling and experimental validation. By identifying key structural and dynamic properties, this research provides a foundation for future experimental studies and potential applications in functional materials, catalysis, and molecular engineering. 2 Computational methods Molecular Dynamics (MD) Simulation Setup with GROMACS The molecular dynamics (MD) simulations were conducted using the GROMACS[ 9 , 10 ] suite, encompassing energy minimization, equilibration in both NVT and NPT ensembles, and production MD. The simulation aimed to explore the structural and thermodynamic properties of the system over time. System Setup and Preprocessing: Preprocessing and Initial Settings : The simulation employed the leap-frog integrator (integrator = md) with a 2 fs timestep (dt = 0.002) across all phases. Center-of-mass motion was managed by applying linear center-of-mass removal every 100 steps (comm-mode = Linear). Constraints : Bonds involving hydrogen atoms were constrained using the LINCS algorithm (constraints = h-bonds, constraint-algorithm = Lincs), with the tolerance set to 0.0001 and a LINCS order of 4 to ensure stability. Energy Minimization : Initial energy minimization targeted a force tolerance of 10 kJ/mol/nm using the steepest descents algorithm (emtol = 10). NVT Equilibration : Conducted for 100 ps to equilibrate the system at 300 K without volume changes. Temperature Control : Temperature was maintained using the V-rescale thermostat (tcoupl = V-rescale) with a reference temperature of 300 K and a coupling constant of 0.1 ps. Output Settings : Coordinates, velocities, and energies were recorded every 500 steps. Electrostatics and Cutoffs : Particle Mesh Ewald (PME) was applied for long-range electrostatics with a Fourier spacing of 0.16 nm and cubic interpolation (pme_order = 4), and both Coulombic and van der Waals interactions were cut off at 1.0 nm. Periodic Boundary Conditions : Applied in all three dimensions (pbc = xyz). NPT Equilibration : Conducted for 500 ps to stabilize pressure at 1 bar. Pressure Control : The Parrinello-Rahman barostat (pcoupl = Parrinello-Rahman) managed isotropic pressure scaling, with a compressibility of 4.5×10 − 5 bar − 1 and a coupling constant of 5.0 ps. Settings for Neighbor Searching : Verlet cutoff scheme with an update frequency of 10 steps, and a cutoff distance of 1.0 nm for both short-range electrostatics and van der Waals interactions. Production MD : Duration : A 5 ns production run (2,500,000 steps) was carried out to enable detailed analysis. Thermostat and Barostat : Temperature and pressure were controlled as in the equilibration phases. Output Configuration : Data for trajectories, energies, and log files were saved at intervals of 10 ps for in-depth post-simulation analysis. Dispersion Corrections : Long-range dispersion corrections were applied to both energy and pressure for greater accuracy in thermodynamic properties. Electrostatics : PME parameters from the equilibration phases were retained, including the use of a grid spacing of 0.16 nm. Other Parameters : Simulated Annealing : Not applied in this study. Free Energy Calculations : Disabled, as the focus was on structural and dynamic properties. Pressure and Temperature Constraints : All bond lengths involving hydrogen atoms were constrained, with constraints applied uniformly across the molecules for stability in longer simulations. Analysis of MD Trajectories Conformational Stability : Key stability metrics, including root-mean-square deviation (RMSD) and radius of gyration (R g )[ 11 , 12 ], were calculated over time to monitor the structural integrity of PDPF during the simulation. These analyses helped assess the molecule’s stability under thermal fluctuations[ 13 , 14 ] and potential conformational changes[ 15 , 16 ]. Intramolecular Interactions : The hydrogen bonding pattern was analyzed to investigate interactions within the two pentafluorophenyl-ureido moieties and the 2,6-diaminopyridine core. Hydrogen bond count and lifetimes were monitored to observe any significant stabilizing or destabilizing interactions. Dynamic Conformational Changes : Torsional angles [ 17 , 18 ] within PDPF, especially around the pentafluorophenyl-ureido bonds, were tracked to evaluate the flexibility and possible intramolecular interactions of the functional groups. GROMACS tools, such as gmx angle, were used to extract and analyze these angles over time. 3 Results & Discussion 3.2 RMSD analysis This bimodal distribution suggests that the molecule might have undergone a conformational change or sampled different structural states during the simulation. Analysis and Interpretation of the Plot - First Peak (around 0.1 nm): This could correspond to the molecule remaining in a stable conformation with low deviation from the initial structure. Second Peak (around 0.3 nm): The secondary peak might indicate a second conformational state that the molecule adopted during the simulation, potentially showing larger structural deviation from the initial structure. The area between 0.1 and 0.3 nm might represent intermediate conformations or fluctuations between these two main states. 3.3 Radial Distribution Function[ 19 , 20 ] Initial Rise The RDF, g(r), starts close to zero and rises sharply as r increases. This rise indicates a high probability of finding atoms at a certain preferred distance, which corresponds to the first solvation shell. Plateau The RDF levels off around g(r) = 1 at larger distances. This plateau signifies that the spatial correlation between atoms has diminished, and they are now distributed randomly, as expected in bulk. The shape is typical for RDFs, showing structural ordering at short distances (the rise) and approaching random distribution at long distances (the plateau). The analysis of radial distribution functions (RDF) and thermodynamic properties in simulations with and without water reveals insights into the solute’s solubility and interactions with water. In both cases, the RDF stabilizes around 0.8–0.9 nm, suggesting minimal differences in solute-water structuring, indicating limited solubility. This lack of solute integration into the water network points to weak solute-water interactions, likely causing the solute to cluster rather than disperse within the solvent. While temperature stabilizes effectively in the simulation, the pressure fluctuates widely, which aligns with the weak solute-water attraction observed in the RDF. Stable pressure in molecular dynamics simulations often depends on consistent intermolecular forces, particularly in systems where water molecules interact uniformly with solute molecules. Here, the solute’s clustering disrupts the uniformity of forces, leading to significant pressure variations. Furthermore, without solute-water hydrogen bonding or consistent interactions, the water molecules maintain their hydrogen-bond network mostly intact but with localized disruptions. These disruptions result in density fluctuations and inconsistent pressure readings. Overall, the combination of a stable temperature profile and fluctuating pressure further underscores the solute's low solubility in water and the lack of significant solute-water interaction, which destabilizes pressure and prevents the system from achieving a homogeneous equilibrium. 3.4 Radius Of Gyration[ 21 , 22 ] Table 1 Statistical Summary of Radius of Gyration Data Time(ps) Rg (total) Rg (X-axis) Rg (Y-axis) Rg (Z-axis) Mean 2500 0.395021 0.324636 0.321122 0.317093 S.D 1447.70 0.027886 0.040035 0.042102 0.034831 Minimum 0.00 0.366348 0.236188 0.229274 0.231649 25% 1250.00 0.377414 0.298759 0.295281 0.294871 50% 2500.00 0.384397 0.320590 0.315844 0.316088 75% 3750.00 0.402117 0.344729 0.340233 0.335489 Max (100%) 5000.00 0.515192 0.476247 0.498858 0.447164 R g (total) Stability - The blue line representing the total radius of gyration seems to stabilize after an initial fluctuation phase. This indicates that the molecule reached a relatively stable compactness over time, suggesting that the system may have equilibrated. Fluctuations in Axes - The individual Rg components along the X, Y, and Z axes (red, green, and orange lines) show greater fluctuations, especially in the early stages, but also display some stability over time. This variability along different axes might suggest some degree of anisotropy or flexibility in the molecule's structure, with potential alignment along certain axes as it stabilizes. Initial Contraction - The total Rg value decreases initially, indicating that the molecule might be compacting or folding slightly in the early phase, likely settling into a more stable conformation. The stabilization of Rg (Total) suggests that the molecular structure has reached equilibrium during the simulation. Observing higher variability in certain axes may reflect the molecule’s structural characteristics, such as flexibility along specific dimensions, which could be relevant depending on the molecule's function or binding properties. 3.5 Number of H-bonds [ 23 , 24 ] Fluctuating Hydrogen Bond Count The count varies between 0 and 2 hydrogen bonds, indicating that hydrogen bonding interactions are transient. This can suggest that the molecule is undergoing conformational changes or intermittent interactions, especially with surrounding solvent molecules. Frequency of Bond Formation Peaks in the graph suggest periods where the molecule forms more stable hydrogen bonds, while troughs or zero values indicate phases with little to no bonding. Such behavior could imply that specific conditions or conformations favor hydrogen bond formation. Average Bond Count The average hydrogen bond count over the simulation provides a quantitative measure of bonding stability. This average could be included in the discussion to contextualize the transient nature of interactions. Implications for Stability The presence of only a maximum of two hydrogen bonds, despite having seven hydrogens in the molecule, can be attributed to several possible factors related to the molecular structure, chemical environment, and simulation conditions. Hydrogen Bond Donors and Acceptors : Not all hydrogens in a molecule are capable of forming hydrogen bonds. Only those attached to electronegative atoms (like oxygen or nitrogen) can act as hydrogen bond donors. If PDPF has limited electronegative atoms, only a few of the hydrogens would be suitable for hydrogen bonding. The remaining hydrogens may be attached to carbon atoms, which do not readily participate in hydrogen bonding. Molecular Structure and Accessibility - The 3D structure of the molecule affects the accessibility of potential hydrogen bond donors and acceptors. If the molecule adopts a compact or hydrophobic conformation, it could shield some hydrogen-bonding sites, making them unavailable for interactions with surrounding molecules. Certain conformations may expose only one or two hydrogen bond sites at any given time, limiting the overall bonding capacity. Solvent Interactions and Competition [ 25 , 26 ] - In a solvated system, water molecules or other surrounding solvents compete for hydrogen-bonding interactions. If water molecules are more readily available and positioned closer to each other than to the molecule, they might outcompete the molecule’s hydrogens, reducing the overall hydrogen bonds with the target. The simulation conditions (e.g., temperature and solvent type) also influence hydrogen bonding. Higher temperatures or weaker solvents might reduce the hydrogen bonding potential. Dynamic Nature of the Simulation - Molecular dynamics (MD) simulations represent an average over time. Transient hydrogen bonds may form and break frequently. In this case, the observed maximum of two hydrogen bonds may represent the most stable or common bonding pattern over the course of the simulation. The molecule may also undergo conformational changes that intermittently expose and hide hydrogen bond donors and acceptors, resulting in a low average count. Hydrogen Bond Geometric Criteria - In MD simulations, hydrogen bonds are typically defined by specific geometric criteria (distance and angle between donor and acceptor). If the molecule's hydrogen atoms do not consistently meet these criteria, even if they are close to other molecules, they may not register as hydrogen bonds. This could further limit the number of recorded hydrogen bonds. Structural and Chemical Constraints The limited hydrogen bonding could suggest that only certain functional groups are capable of stable interactions, which may inform the molecule's reactivity, binding affinity, or solubility properties. Implications for Molecular Functionality If this molecule is intended to interact with biological targets, its limited hydrogen bonding potential might indicate specificity for interactions or a reliance on other non-covalent interactions like van der Waals forces or hydrophobic effects. 3.6 Eigen Values [ 27 , 28 ] The eigenvalue analysis shows that most significant molecular motions are captured by a few principal components, as indicated by the steep drop in eigenvalues after the first few. This suggests that only a limited number of dimensions represent the main conformational changes, while the remaining components contribute minimally, likely reflecting minor fluctuations or noise. The largest eigenvalues correspond to collective, biologically relevant motions, whereas the low eigenvalues represent localized vibrations. Thus, the system’s essential dynamics are effectively described by focusing on the top components. This approach can help identify key motions, such as conformational changes critical to the molecule's function. 3.7 Distance Between Centres-Of-Mass[ 29 , 30 ] Centers of Mass in a Symmetrical Structure - In symmetrical molecules like this, the two COMs (COM1 and COM2) likely correspond to each of the symmetrical halves, specifically each pentafluorophenyl urea group . This symmetry implies that both flanking groups (the pentafluorophenyl urea moieties) would have similar mass distributions relative to the central diaminopyridine core. Therefore, the COMs would be roughly equidistant from the center along the axis connecting the two urea groups through the central pyridine ring. Distance Fluctuations - The fluctuations in the distance between COM1 and COM2 over time likely reflect conformational flexibility in the molecule. Although symmetrical, the urea linkages allow some rotational freedom around the central diaminopyridine ring. This rotation, combined with any solvent interactions, may cause the molecule to "breathe" slightly, varying the distance between the two pentafluorophenyl urea groups. Implications of Hydrogen Bonding and Stability - Given the presence of urea groups, hydrogen bonding might play a role in stabilizing certain conformations, particularly if there are interactions with solvent molecules or other surrounding entities. However, since only a maximum of two hydrogen bonds were observed during simulations, it’s possible that intra-molecular hydrogen bonding is limited in this structure, perhaps because the urea groups are oriented outward rather than toward each other. This limited hydrogen bonding also supports the idea that the molecule experiences minor conformational freedom without strong intramolecular constraints. Planar Rigidity of the Diaminopyridine Core - The diaminopyridine core is likely relatively rigid and planar , as is typical for aromatic systems. This rigidity maintains the overall symmetry and helps distribute mass equally to each side. Consequently, the observed distance fluctuations are likely due to the slight movement or flexibility in the attached urea and pentafluorophenyl groups rather than substantial bending or folding of the core itself. Functional and Interaction Insights The symmetrical structure and limited hydrogen bonding indicate that this molecule might maintain a relatively consistent shape in solution , with the main structural variations happening at the peripheral pentafluorophenyl rings. This property could be relevant if the molecule is intended for interactions with other molecules or surfaces, as the relatively stable structure would present the pentafluorophenyl groups outward consistently, which may influence binding or self-assembly behavior. Temperature fluctuations [ 31 , 32 ] Temperature Stability : The system temperature oscillates around a central value close to the target temperature of 300 K. These fluctuations are expected in molecular dynamics simulations, where the thermostat adjusts the system to maintain an average temperature. The observed variations between approximately 290 K and 320 K indicate natural thermal movement and system equilibration rather than instability. Thermostat Behavior : The consistent oscillation pattern suggests that the thermostat is effectively maintaining the system’s temperature near the desired setpoint. Any deviations from 300 K are corrected by the thermostat, keeping the average temperature within an acceptable range. Equilibration Observation : Given the narrow fluctuation range, it appears that the system is relatively well-equilibrated. Large or prolonged deviations might have indicated that the system needs further equilibration, but the present data suggests that temperature control is stable. Pressure Fluctuations and Density fluctuations [ 33 , 34 ] The pressure fluctuation graph exhibits significant variations over time, with values oscillating between approximately − 1000 and + 1500 bar across the XX, YY, and ZZ components. Such fluctuations are typical in molecular dynamics simulations, particularly for small systems, where pressure is derived from atomic-level interactions. Given that the compound PDPF is hydrophobic and the temperature has stabilized, these pressure variations likely arise from intrinsic molecular interactions and system size effects rather than instability. While instantaneous pressure values fluctuate widely, the system may still be well-equilibrated, and further averaging could provide a clearer representation of bulk pressure properties. Unless pressure-sensitive properties are of primary interest, these variations are generally expected and do not indicate a concern for system stability. The plot of density distribution across coordinates reveals several key points: Density Fluctuations : There are noticeable fluctuations in density as we move along the coordinate axis, with density values initially increasing to a peak and then gradually decreasing. High-Density Region : Between approximately 0.8 and 1.5 nm, there is a region of relatively high density, reaching up to 35 kg/m³. This could correspond to areas where the molecular structure is more compact or regions with higher atomic mass concentration. Low-Density Region : After 1.5 nm, the density steadily decreases, reaching a low around 2.5 nm, suggesting a less compact structure or lower atomic presence in these regions. This trend could indicate less dense packing or a void in the structure. Periodic Density Pattern : The oscillations indicate a periodic pattern, which might be due to structural symmetry in the molecule, particularly if the molecule has repeating units or symmetrical features. Eigen Vectors Trajectory The analysis focused on the first three eigenvectors. The output lists the minimum and maximum values along each eigenvector with the corresponding frame numbers. Table 2 Min-max values of first three eigenvectors Minimum Value Maximum Value Eigenvector 1 -1.753750 at frame 57 0.657369 at frame 245 Eigenvector 2 -0.872152 at frame 162 1.121700 at frame 286 Eigenvector 3 -1.260221 at frame 31 0.700323 at frame 40 These frames represent the most significant conformational changes along each eigenvector, which highlight large-amplitude motions. Eigenvector Analysis : The range of values along each eigenvector gives insight into the variability of the molecule's movement. Large values (positive or negative) along an eigenvector indicate significant motions in those directions. Conformational Flexibility : This analysis reveals which directions of motion contribute most to the structural fluctuations. Eigenvectors 1 and 2, in particular, have larger maximum values, indicating directions of substantial movement. Principal Component Analysis [ 35 , 36 ] The two principal components (PC1 and PC2) in PCA analysis represent the primary directions of variance or motion within the molecular structure. Each principal component (PC) is essentially an "axis" or "direction" in the multidimensional space of atomic coordinates. PC1 represents the direction along which the molecular structure exhibits the largest variance in its atomic positions throughout the trajectory. PC2 represents the second-largest variance, orthogonal to PC1. In physical terms, these components describe collective motions that involve multiple atoms moving in a correlated way, rather than focusing on specific atoms. The do not directly represent specific parts of the molecule. PCs are derived from the covariance matrix of atomic positions, meaning they represent global or collective motions rather than movements of individual atoms or parts of the molecule. However, certain parts of the molecule might contribute more significantly to each PC, especially if those parts exhibit higher mobility. For example, if flexible side chains or terminal regions of a protein are more mobile, they may contribute more to the variance captured by the first few PCs. By analyzing the eigenvectors corresponding to each principal component, we can identify which atoms contribute most to the component’s motion. This can help localize regions of the molecule involved in particular collective movements. For instance, a high contribution of atoms from a flexible loop, a side chain, or a particular segment of the molecule to PC1 might indicate that this segment is moving more freely in that direction. We can calculate the contribution of each atom to PC1 and PC2 to create a clearer picture of which regions of the molecule are moving the most along these directions. This is often done by projecting each atom's displacement onto the principal component vectors. Regions with larger projections are more mobile in that component's direction. Dominant Motion Along PC1 - The steep eigenvalue for PC1 suggests that most of the system's dynamic behavior is captured by this single component. This typically implies a dominant mode of fluctuation or conformational change in the structure. In many molecular systems, a large eigenvalue in PC1 might indicate a breathing or twisting mode, where significant segments of the molecule undergo coordinated movement. Secondary Contributions (PC2 and beyond) - While PC1 captures the most significant movement, the second principal component (PC2) also contributes some variance, albeit far less. This mode may correspond to smaller, less coordinated adjustments in the molecular structure, such as localized flexibility in specific groups. Given the quick drop-off in eigenvalues beyond PC2, the additional principal components represent minor fluctuations around a stable structure, likely due to smaller conformational adjustments or thermal fluctuations. Structural Stability - The clustering seen in the projection along PC1 and PC2, with only a few larger deviations, indicates that the molecule remains in a stable conformation for most of the simulation. The presence of outliers could correspond to occasional, transient shifts, possibly due to interactions or the molecule adjusting to environmental changes. In the case of symmetrical molecules, the stability around the mean structure is often reinforced by the molecular symmetry itself, which imposes constraints on how the molecule can flex. Correlation with Physical Properties - Analyzing these fluctuations can provide insights into the flexibility and resilience of the molecule. For example, a high degree of movement in PC1 might indicate susceptibility to certain types of deformation. Conversely, stability along other PCs could mean that other structural modes are less likely to vary under similar conditions. If additional physical properties like binding or interaction energies were calculated, we could correlate those with movements along PC1 or PC2, identifying if certain fluctuations correspond to significant physical interactions or stability changes. The graph here shows the eigenvalues (variance explained by each mode) for the first two principal components. In PCA, eigenvalues represent the amount of variance each principal component (PC) captures from the original data. Variance Explained by PC1 and PC2 :The first principal component (PC1) has a significantly larger eigenvalue, meaning it explains a major portion of the variance in the data. In this case, PC1 has an eigenvalue around 12, while PC2 has a much lower eigenvalue (approximately 2). This steep drop in eigenvalues indicates that PC1 captures most of the structural or conformational variability in the trajectory, while PC2 contributes relatively little. Dimension Reduction Feasibility : Since PC1 captures the majority of the variance, focusing on this component alone might give a good approximation of the primary motion within the system. This finding justifies reducing the dimensionality of the dataset and focusing primarily on PC1, as further components are less informative. System Dynamics Insight : The dominance of PC1 suggests that the molecule or system has one primary axis of motion or a major dynamic mode. This could correspond to a significant conformational change or flexibility along a particular structural feature. The much smaller variance in PC2 might represent a secondary, less prominent mode of motion, potentially associated with minor or localized movements within the structure. Detailed Interpretation with Specific Atom Contributions PC1 Contributions (Top Subplot) In PC1, the primary contributors are specific carbon (C), nitrogen (N), fluorine (F), and hydrogen (H) atoms distributed across each molecule. Here’s a breakdown of the most influential atoms: Fluorine Atoms (F1, F2, F6, etc.) - These fluorine atoms show significant positive contributions in PC1 across the molecules, suggesting they play a crucial role in the stretching or bending modes of the molecular structure. Fluorine atoms are relatively rigid due to the C-F bond strength, and their substantial mass could contribute to strong movements. Carbon Atoms (C3, C7, C9, etc.) - Specific carbons, especially those in the aromatic rings or near functional groups (like C3 and C9), display both positive and negative contributions. This pattern implies opposing motions within these regions, indicating that certain carbons are likely involved in a flexing motion across the molecule. For instance, C3 and C7 contribute positively, whereas others, like C9, contribute negatively, suggesting dynamic interplay within the aromatic system. Nitrogen Atoms (N1, N2) - The nitrogen atoms in the amine and urea groups (such as N1 and N2) have significant negative contributions, which may indicate torsional or bending motions localized around these groups. These groups could act as pivot points, as nitrogen atoms tend to participate in hydrogen bonding and intermolecular interactions , adding flexibility to these regions. Hydrogen Atoms (H4, H37, H40) - Hydrogen atoms such as H4, H37, and H40 contribute prominently in PC1, which is noteworthy since hydrogen is typically lighter and thus more responsive to minor structural changes. These hydrogens could be situated in flexible regions or side chains that experience rapid oscillations or bending . PC2 Contributions (Bottom Subplot) For PC2, we observe a different set of prominent atomic contributors, with key roles for fluorine, carbon, and nitrogen atoms, but in a different motion profile: Fluorine Atoms (F1, F4, F6) - Fluorine atoms, such as F1 and F6, display significant contributions to PC2, with both positive and negative values across the molecules. This suggests that rotational or out-of-plane bending motions might be occurring around these atoms, likely due to their positions at the molecular periphery. These fluorine atoms, often at terminal positions, may be involved in torsional vibrations where the molecule twists around its core. Carbon Atoms (C10, C12, C18, etc.) - Carbons C10, C12, and C18 have distinct positive or negative contributions in PC2, indicating localized flexing or twisting motions. These atoms might be part of the aromatic ring or close to functional groups, which are critical in dictating molecular flexibility. The contrasting contributions (e.g., positive for C10, negative for C18) suggest a coordinated bending along the length of the molecule, which could resemble a "hinge-like" movement. Nitrogen Atoms (N1, N2) - Nitrogens, particularly in functional groups like amines and ureas, contribute notably to PC2 as well, supporting torsional flexibility around these groups. Their involvement in hydrogen bonds or other intermolecular interactions might restrict some motion, creating resistance that results in vibrational modes captured by PC2. Hydrogens (H37, H41) - S pecific hydrogen atoms, like H37 and H41, also play a role in PC2, albeit with generally smaller contributions compared to PC1. These atoms might be involved in rotational adjustments around the molecule’s core. Overall Molecular Dynamics Based on Atom Contributions Fluorine Atoms Fluorine atoms are consistently strong contributors across both PC1 and PC2, highlighting their role in the rigidity and vibrational patterns of the molecule. Their positions, often at the periphery, may lead to significant stretching or torsional motions . Carbon Atoms in Aromatic Rings Certain carbons in the aromatic rings (C3, C7, C10) are essential in both PCs, showing that aromaticity plays a role in controlling the flexibility of the molecule. The alternating positive and negative contributions indicate that aromatic rings might rotate or flex in sync with neighboring atoms, creating coordinated motions. Nitrogen and Hydrogen Contributions Nitrogen atoms in functional groups (e.g., amine, urea) serve as pivot points for motion, with hydrogen atoms responding to these shifts. This combination suggests that the molecule’s core is more flexible than its terminal groups , which aligns with its structural function and potential interactions. Projection Analysis of Molecular Dynamics Trajectory along Principal Components PC1 Projections - We see an initial rapid fluctuation in projection values in the first few frames, followed by stabilization around zero after frame 20. This suggests that the molecule undergoes significant movement along the PC1 direction at the beginning, potentially indicating an initial conformational change or relaxation, after which it reaches a relatively stable configuration. PC2 Projections - Similar to PC1, PC2 also shows initial fluctuations, although they are less intense than those observed along PC1. After frame 10, PC2 projection values stabilize around zero, implying that any major structural changes along this component are completed within the initial few frames. This pattern is typical of molecular dynamics simulations where the system undergoes initial relaxation, and then stabilizes as it reaches an equilibrium state. Conclusions Principal Component Analysis (PCA) on Molecular Trajectory : PCA of the molecular dynamics (MD) trajectory identified the primary modes of motion within the molecule, with significant contributions from specific atoms, particularly carbon (C) and nitrogen (N). These atoms exhibited prominent movements along PC1 and PC2, indicating flexibility in certain regions of the molecule's structure. The periodic nature of conformational fluctuations was evident across the three repeating units of the molecule, confirming structural consistency and highlighting potential functional regions within the molecule. Projection of Trajectory Along Principal Components : The projections of the trajectory along PC1 and PC2 displayed rapid stabilization, with values reaching a steady state within the initial simulation stages. This implies that major conformational shifts occur early, after which the molecule settles into a stable configuration. The consistency of these projections with precomputed eigenvector projections further validates the reliability of the observed motion patterns. Radial Distribution Function (RDF) Analysis : RDF profiles, both with and without water, stabilized around 0.8–0.9 nm, suggesting limited solubility in water. The similarity in RDF profiles indicates that water does not significantly interact with or alter the molecular structure, supporting the hypothesis that the molecule may have hydrophobic characteristics. Temperature and Pressure Behavior During NVT and NPT Phases : Temperature stabilized effectively during NVT and NPT simulations, demonstrating proper thermal equilibration. However, pressure exhibited significant anisotropic fluctuations across the XX, YY, and ZZ axes, failing to reach a steady mean value. These pressure fluctuations likely reflect directional stresses and internal interactions within the simulation box, possibly due to the molecule’s minimal interactions with water. This anisotropy may imply that the molecule's structural behavior is relatively independent of the surrounding solvent environment. Hydrogen Bond Analysis : Hydrogen bond analysis showed minimal interaction between the molecule and water, supporting the RDF findings and indicating limited solvation. This lack of hydrogen bonding further emphasizes the molecule’s probable hydrophobic nature and solvent-independent stability. Density Fluctuations : Density analysis revealed stable fluctuations around a mean density, consistent with RDF and pressure data. This further suggests a stable, solvent-independent structure, with the molecule’s primary interactions unaffected by water presence. Energy Analysis : The total energy and its components (potential and kinetic energy) were monitored throughout the simulation to assess system stability. Potential energy remained relatively stable, indicating that the molecular conformation did not undergo any drastic changes. The kinetic energy stabilized with the temperature, reflecting a thermally equilibrated system. The limited variations in potential energy corroborate the findings from PCA and RDF analyses, where the molecule achieved a stable conformation early in the simulation. This stability, despite fluctuating pressure values, implies that the molecular structure maintains its integrity and does not experience significant external or internal perturbations. In summary, the PDPF molecule displays stable conformational dynamics, limited interaction with water, and minimal hydrogen bonding, suggesting a hydrophobic character. The anisotropy in pressure fluctuations, stable density, and consistent energy profile all support the hypothesis that the molecule’s behavior and structure are largely independent of the solvent. This analysis provides comprehensive insights into the molecule’s stability, interaction profile, and dynamic properties, making it suitable for applications where hydrophobic or solvent-resistant molecules are advantageous. Declarations Conflict of Interest The authors declare that they have no conflict of interest. Author Contribution VSR and HN wrote the main manuscript; VSR prepared the figures and tables; All authors reviewed the manuscript References Kumar, R., Santa Chalarca, C.F., Bockman, M.R., Bruggen, C.V., Grimme, C.J., Dalal, R.J., Hanson, M.G., Hexum, J.K. and Reineke, T.M., 2021. Polymeric delivery of therapeutic nucleic acids. Chemical reviews , 121 (18), pp.11527-11652. Geng, Z., Shin, J.J., Xi, Y. and Hawker, C.J., 2021. Click chemistry strategies for the accelerated synthesis of functional macromolecules. Journal of Polymer Science , 59 (11), pp.963-1042. Zhao, W., Li, C., Chang, J., Zhou, H., Wang, D., Sun, J., Liu, T., Peng, H., Wang, Q., Li, Y. and Whittaker, A.K., 2023. Advances and prospects of RAFT polymerization-derived nanomaterials in MRI-assisted biomedical applications. Progress in Polymer Science , 146 , p.101739. Remella, V. S., Neelamegan, H. (2025). Topological Descriptor Analysis with Chemical Graph Theory Insights and Predicted ADME Data Analysis for Pentafluorophenylurea-Based Pyridine Derivative, Chemical Methodologies , 9(3), pp. 172-189. doi: 10.48309/chemm.2025.502403.1887 Hospital, A., Goñi, J.R., Orozco, M. and Gelpí, J.L., 2015. Molecular dynamics simulations: advances and applications. Advances and Applications in Bioinformatics and Chemistry , pp.37-47. Filipe, H.A. and Loura, L.M., 2022. Molecular dynamics simulations: advances and applications. Molecules , 27 (7), p.2105. Piers, W.E. and Chivers, T., 1997. Pentafluorophenylboranes: from obscurity to applications. Chemical Society Reviews , 26 (5), pp.345-354. Liu, L.H. and Yan, M., 2010. Perfluorophenyl azides: new applications in surface functionalization and nanomaterial synthesis. Accounts of chemical research , 43 (11), pp.1434-1443. Jo, S., Cheng, X., Lee, J., Kim, S., Park, S.J., Patel, D.S., Beaven, A.H., Lee, K.I., Rui, H., Park, S. and Lee, H.S., 2017. CHARMM‐GUI 10 years for biomolecular modeling and simulation. Journal of computational chemistry , 38 (15), pp.1114-1124. Vieira, I.H.P., Botelho, E.B., de Souza Gomes, T.J., Kist, R., Caceres, R.A. and Zanchi, F.B., 2023. Visual dynamics: a WEB application for molecular dynamics simulation using GROMACS. BMC bioinformatics , 24 (1), p.107. Filipe, H.A. and Loura, L.M., 2022. Molecular dynamics simulations: advances and applications. Molecules , 27 (7), p.2105. Haydukivska, K., Blavatska, V. and Paturej, J., 2020. Universal size ratios of Gaussian polymers with complex architecture: radius of gyration vs hydrodynamic radius. Scientific Reports , 10 (1), p.14127. Hickman, J. and Mishin, Y., 2016. Temperature fluctuations in canonical systems: Insights from molecular dynamics simulations. Physical Review B , 94 (18), p.184311. Hale, J.P., Marcelli, G., Parker, K.H., Winlove, C.P. and Petrov, P.G., 2009. Red blood cell thermal fluctuations: comparison between experiment and molecular dynamics simulations. Soft Matter , 5 (19), pp.3603-3606. Anandakrishnan, R., Drozdetski, A., Walker, R.C. and Onufriev, A.V., 2015. Speed of conformational change: comparing explicit and implicit solvent molecular dynamics simulations. Biophysical journal , 108 (5), pp.1153-1164. Melvin, R.L., Godwin, R.C., Xiao, J., Thompson, W.G., Berenhaut, K.S. and Salsbury Jr, F.R., 2016. Uncovering large-scale conformational change in molecular dynamics without prior knowledge. Journal of chemical theory and computation , 12 (12), pp.6130-6146. Stein, E.G., Rice, L.M. and Brünger, A.T., 1997. Torsion-angle molecular dynamics as a new efficient tool for NMR structure calculation. Journal of Magnetic Resonance , 124 (1), pp.154-164. Chen, J., Im, W. and Brooks III, C.L., 2005. Application of torsion angle molecular dynamics for efficient sampling of protein conformations. Journal of computational chemistry , 26 (15), pp.1565-1578. Levine, B.G., Stone, J.E. and Kohlmeyer, A., 2011. Fast analysis of molecular dynamics trajectories with graphics processing units—Radial distribution function histogramming. Journal of computational physics , 230 (9), pp.3556-3569. Dimitroulis, C., Raptis, T. and Raptis, V., 2015. POLYANA—A tool for the calculation of molecular radial distribution functions based on Molecular Dynamics trajectories. Computer Physics Communications , 197 , pp.220-226. Han, M., Chen, P. and Yang, X., 2005. Molecular dynamics simulation of PAMAM dendrimer in aqueous solution. Polymer , 46 (10), pp.3481-3488. Ahmed, M.C., Crehuet, R. and Lindorff-Larsen, K., 2020. Computing, analyzing, and comparing the radius of gyration and hydrodynamic radius in conformational ensembles of intrinsically disordered proteins. Intrinsically disordered proteins: methods and protocols , pp.429-445. Lopez, C.F., Nielsen, S.O., Klein, M.L. and Moore, P.B., 2004. Hydrogen bonding structure and dynamics of water at the dimyristoylphosphatidylcholine lipid bilayer surface from a molecular dynamics simulation. The Journal of Physical Chemistry B , 108 (21), pp.6603-6610. Auffinger, P., Louise-May, S. and Westhof, E., 1996. Molecular Dynamics Simulations of the Anticodon Hairpin of tRNAAsp: Structuring Effects of C− H⊙⊙⊙ O Hydrogen Bonds and of Long-Range Hydration Forces. Journal of the American Chemical Society , 118 (5), pp.1181-1189. Bizzarri, A.R. and Cannistraro, S., 2002. Molecular dynamics of water at the protein− solvent interface. The Journal of Physical Chemistry B , 106 (26), pp.6617-6633. Prabhu, N. and Sharp, K., 2006. Protein− solvent interactions. Chemical reviews , 106 (5), pp.1616-1623. Hinrichs, N.S. and Pande, V.S., 2007. Calculation of the distribution of eigenvalues and eigenvectors in Markovian state models for molecular dynamics. The Journal of chemical physics , 126 (24). Gust, E.D. and Reichl, L.E., 2009. Molecular dynamics simulation of collision operator eigenvalues. Physical Review E—Statistical, Nonlinear, and Soft Matter Physics , 79 (3), p.031202. Méndez-Maldonado, G.A., González-Melchor, M., Alejandre, J. and Chapela, G.A., 2012. On the centre of mass velocity in molecular dynamics simulations. Revista mexicana de física , 58 (1), pp.55-60. Giulini, D. and Großardt, A., 2014. Centre-of-mass motion in multi-particle Schrödinger–Newton dynamics. New Journal of Physics , 16 (7), p.075005. Hickman, J. and Mishin, Y., 2016. Temperature fluctuations in canonical systems: Insights from molecular dynamics simulations. Physical Review B , 94 (18), p.184311. Binder, K., Horbach, J., Kob, W., Paul, W. and Varnik, F., 2004. Molecular dynamics simulations. Journal of Physics: Condensed Matter , 16 (5), p.S429. Uline, M.J. and Corti, D.S., 2013. Molecular dynamics at constant pressure: allowing the system to control volume fluctuations via a “shell” particle. Entropy , 15 (9), pp.3941-3969. UEDA, K., KOMAI, T., YU, I. and NAKAYAMA, H., 2002. Molecular dynamics study on the density fluctuation of supercritical water. Journal of Computer Chemistry, Japan , 1 (3), pp.83-88. Stein, S.A.M., Loccisano, A.E., Firestine, S.M. and Evanseck, J.D., 2006. Principal components analysis: a review of its application on molecular dynamics data. Annual Reports in Computational Chemistry , 2 , pp.233-261. Sittel, F., Jain, A. and Stock, G., 2014. Principal component analysis of molecular dynamics: On the use of Cartesian vs. internal coordinates. The Journal of Chemical Physics , 141 (1). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6226678","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":431049466,"identity":"4b95d8bb-1e6b-4442-ba5e-69d711ca46ff","order_by":0,"name":"Venkata Shivakumar Remella","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYHACgwMJFTZyDAzMDUDOASBOACPcgI3B8MCHM2nGDAyMjQ3EajE+OLPtcGIDiha8rrrfvOEwb9vh9A3HG9sffKi5w8DPnmPA8HAHHi3H2AoO85xLz91w5mBj44xjzxgke94YMCSewa1Fso3H4DBPmXXuzBmJjc28DYcZDG4AbUlsI6SFjTldcv7Dxua/QC32hLTws/EYHJzR5pzAL8HY2MwIskWCoJa0AlAgG/bzJDbO7Dl2mEfizLOCA/i0sDEf3vwBGJXybOyHD3z4UXNYjr89eePDn3i0YAAeEHGABA2jYBSMglEwCrAAAAwsXBAWkFdIAAAAAElFTkSuQmCC","orcid":"","institution":"Vel Tech Rangarajan Dr.Sagunthala R \u0026 D Institute of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Venkata","middleName":"Shivakumar","lastName":"Remella","suffix":""},{"id":431049467,"identity":"9efaf75c-4458-4e18-b995-1936050a6387","order_by":1,"name":"Haridharan Neelamegan","email":"","orcid":"","institution":"Vel Tech Rangarajan Dr.Sagunthala R \u0026 D Institute of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Haridharan","middleName":"","lastName":"Neelamegan","suffix":""}],"badges":[],"createdAt":"2025-03-14 13:23:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6226678/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6226678/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78879790,"identity":"f295a131-2e5b-4e65-a178-d2026959787d","added_by":"auto","created_at":"2025-03-20 08:14:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":39913,"visible":true,"origin":"","legend":"\u003cp\u003ePDPF molecule\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6226678/v1/ae87d883cdb1244db2313443.png"},{"id":78880846,"identity":"63be5cb3-fabc-4451-a96d-845a49c9157b","added_by":"auto","created_at":"2025-03-20 08:30:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":140263,"visible":true,"origin":"","legend":"\u003cp\u003eEnergy Components vs Time\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6226678/v1/c02d85ded83ea09bbe8baaa8.png"},{"id":78879792,"identity":"09d0c9dc-1d14-48f1-86cb-1592578d3e4a","added_by":"auto","created_at":"2025-03-20 08:14:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":84923,"visible":true,"origin":"","legend":"\u003cp\u003eRMSD of PDPF\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6226678/v1/a72c4c5ec678ec0233f4f509.png"},{"id":78879936,"identity":"7ea1668f-7d9e-4b93-9f6d-a107992ff459","added_by":"auto","created_at":"2025-03-20 08:22:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":33882,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Radial Distribution without Water\u003c/p\u003e\n\u003cp\u003e(b) Radial Distribution with Water\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6226678/v1/7e4ba71f9aa11335d9c7b047.png"},{"id":78879937,"identity":"5b14a45c-a35c-436a-9011-614cbab9ff0a","added_by":"auto","created_at":"2025-03-20 08:22:19","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":196995,"visible":true,"origin":"","legend":"\u003cp\u003eRadius of Gyration over time\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6226678/v1/26dd1e935132d6fa25f8d664.png"},{"id":78879795,"identity":"c18084ab-e8d6-49fd-91be-f28bb13f1b35","added_by":"auto","created_at":"2025-03-20 08:14:19","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":131673,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of H-bonds over time\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6226678/v1/81793bbd9f9c23d3e783ff49.png"},{"id":78880848,"identity":"9bb3afe6-6b9b-4140-bcc9-fa48e54488c9","added_by":"auto","created_at":"2025-03-20 08:30:19","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":25767,"visible":true,"origin":"","legend":"\u003cp\u003eEigenvalues of the Covariance Matrix\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6226678/v1/a48712ae27904a5e2df301f6.png"},{"id":78879938,"identity":"b87b0a6d-fd27-4c6d-b17b-e31838c1a05c","added_by":"auto","created_at":"2025-03-20 08:22:19","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":128335,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Distance between Centers of Mass over time\u003c/p\u003e\n\u003cp\u003e(b) Distribution of Distance between Centers of Mass\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6226678/v1/1dc47bab2fd57eff1269d5e2.png"},{"id":78880847,"identity":"c5a57b35-79ad-4ac6-9c32-6c3d34bbf197","added_by":"auto","created_at":"2025-03-20 08:30:19","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":42963,"visible":true,"origin":"","legend":"\u003cp\u003eTemperature fluctuations with time\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-6226678/v1/50ed35a7812de23b349e8ee2.png"},{"id":78879801,"identity":"fdb14400-6f2f-4623-ae3d-f7548d0abac9","added_by":"auto","created_at":"2025-03-20 08:14:19","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":165607,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Pressure fluctuations with time\u003c/p\u003e\n\u003cp\u003e(b) Density distribution across coordinates\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-6226678/v1/5d357e87fdb6b0ee94441cbc.png"},{"id":78879799,"identity":"5c6ed9c3-1f31-4480-9647-40dd16b92a54","added_by":"auto","created_at":"2025-03-20 08:14:19","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":63157,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Eigenvalues Variance\u003c/p\u003e\n\u003cp\u003e(b) Projection along top 2 principal components\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-6226678/v1/dd2709ab04044a5605b33075.png"},{"id":78879942,"identity":"c4fb3f45-1d5b-4e5e-9d3b-4f98bbec67ed","added_by":"auto","created_at":"2025-03-20 08:22:19","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":29034,"visible":true,"origin":"","legend":"\u003cp\u003eEigenvalues – Variance explained by each mode\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-6226678/v1/4d0fc7e5f19440b6bcd1c7b4.png"},{"id":78879943,"identity":"963682f0-ff21-4c43-97a6-aef4b5c778d5","added_by":"auto","created_at":"2025-03-20 08:22:19","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":188679,"visible":true,"origin":"","legend":"\u003cp\u003eAtom Contributions to PC1 and PC2\u003c/p\u003e","description":"","filename":"13.png","url":"https://assets-eu.researchsquare.com/files/rs-6226678/v1/2461c52fcf9044ba7094a33e.png"},{"id":78879802,"identity":"2fe8c594-7b82-4f00-be49-78911c5e71a8","added_by":"auto","created_at":"2025-03-20 08:14:19","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":66407,"visible":true,"origin":"","legend":"\u003cp\u003eProjections along PC1 and PC2\u003c/p\u003e","description":"","filename":"14.png","url":"https://assets-eu.researchsquare.com/files/rs-6226678/v1/53e158dae8a4a22884031613.png"},{"id":78881138,"identity":"0c7f8d9a-2f50-4ea2-a540-57f8955d7678","added_by":"auto","created_at":"2025-03-20 08:38:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3513686,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6226678/v1/b434c503-57e3-4a3b-9a92-4fdec3c9d5f9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring the Structural Dynamics in aqueous medium of a novel Pentafluorophenyl-Ureido Derivative: A Molecular Dynamics Study","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe design and synthesis of organic molecules with tailored structural features and intermolecular interactions are essential for advancing applications in materials science, catalysis, and molecular electronics. Among these, compounds containing pentafluorophenyl (PFP) groups have attracted significant attention due to the unique electronic characteristics imparted by fluorination, which can enhance stability, modify reactivity, and promote distinctive intermolecular interactions[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This study focuses on a novel compound, PDPF, a pentafluorophenyl-ureido derivative of 2,6-diaminopyridine, which presents intriguing structural and electronic properties as evidenced by single-crystal X-ray diffraction and theoretical analyses[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe crystallographic analysis of PDPF reveals its adoption of a triclinic crystal system with a low-symmetry space group, Pī. The unit cell dimensions are given as a\u0026thinsp;=\u0026thinsp;7.5939(5) A˚, b\u0026thinsp;=\u0026thinsp;12.4169(9) A˚, c\u0026thinsp;=\u0026thinsp;13.5494(9) A˚, and angles α\u0026thinsp;=\u0026thinsp;100.927(2)\u0026deg;, β\u0026thinsp;=\u0026thinsp;104.223(2)\u0026deg;, and γ\u0026thinsp;=\u0026thinsp;96.779(2)\u0026deg;, producing a unit cell volume of 1197.67 \u0026Aring;\u0026sup3;. The compound exhibits a relatively high density of 1.665 g/cm\u0026sup3;, with two molecules per unit cell (Z\u0026thinsp;=\u0026thinsp;2), indicating efficient molecular packing. The dense packing is reinforced by N\u0026mdash;H\u0026middot;\u0026middot;\u0026middot;O and C\u0026mdash;H\u0026middot;\u0026middot;\u0026middot;F hydrogen bonding interactions, which contribute to the structural stability of the lattice. This intricate packing arrangement, associated with the triclinic symmetry and the low-symmetry space group Pī, underscores the complexity of PDPF\u0026rsquo;s crystalline architecture\u0026mdash;a characteristic often observed in organic molecules with multiple functional groups and aromatic substituents.\u003c/p\u003e \u003cp\u003eThe structural configuration of PDPF combines two PFP rings and urea moieties, each contributing unique electronic and steric properties. The pentafluorophenyl rings introduce electron-withdrawing effects and potential sites for non-covalent interactions, particularly through C\u0026mdash;H\u0026middot;\u0026middot;\u0026middot;F hydrogen bonds. Additionally, the urea functional groups engage in N\u0026mdash;H\u0026middot;\u0026middot;\u0026middot;O hydrogen bonding, fostering intermolecular interactions that promote lattice stability. The complex interplay between these functional groups likely gives rise to the compound\u0026rsquo;s non-regular geometries, which are accommodated within the asymmetric triclinic lattice. Such structural features are essential to investigate further, as they may influence the electronic distribution, molecular reactivity, and potential applications of PDPF in molecular electronics or as a ligand in coordination chemistry.\u003c/p\u003e \u003cp\u003eGiven these unique structural characteristics, PDPF presents an interesting candidate for in-depth theoretical and computational exploration. To this end, we aim to employ a molecular dynamics (MD) simulation[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The MD simulations will provide insight into the dynamic stability and conformational behavior of PDPF under various conditions, such as thermal fluctuations and solvent environments. These simulations will allow us to identify potential conformational changes and assess how the crystalline structure\u0026rsquo;s stability is influenced by environmental factors.\u003c/p\u003e \u003cp\u003eIn summary, this study provides a detailed theoretical analysis of PDPF, leveraging its unique crystal structure and intermolecular interactions to explore its dynamic and reactive properties. The MD simulations will not only deepen our understanding of PDPF\u0026rsquo;s behavior in different conditions but also demonstrate the utility of such integrated computational approaches in predicting the properties of novel organic compounds with complex packing arrangements and multiple functional groups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eResearch Objectives\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe pentafluorophenyl-ureido derivative of 2,6-diaminopyridine (PDPF) presents a structurally unique and complex system for theoretical analysis. The presence of dual pentafluorophenyl-ureido moieties introduces distinctive intermolecular and intramolecular interactions, influencing molecular stability, reactivity, and self-assembly behavior. These properties are particularly relevant for applications in materials science, catalysis, and coordination chemistry[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study employs Molecular Dynamics (MD) simulations to provide a comprehensive investigation into PDPF\u0026rsquo;s structural stability, conformational dynamics, and interaction patterns, offering insights into its behavior under diverse environmental conditions. The primary research objectives are:\u003c/p\u003e \u003cp\u003e \u003cb\u003eTo Analyze the Structural Stability of PDPF via MD Simulations\u003c/b\u003e - The first objective is to assess the stability of PDPF over time, examining how its dual pentafluorophenyl-ureido groups influence molecular packing and response to thermal fluctuations and solvent effects; MD simulations will reveal conformational preferences, highlighting how pentafluorophenyl rings and urea moieties align or shift under dynamic conditions; By investigating intramolecular hydrogen bonding and steric effects, the study will determine whether PDPF maintains rigid structural integrity or undergoes significant conformational changes over time.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTo Characterize Conformational Dynamics and Interaction Patterns\u003c/b\u003e - This objective focuses on the dynamic interplay between the two pentafluorophenyl-ureido moieties and the central 2,6-diaminopyridine core. Since these moieties are attached at the N-atoms of the 2- and 6-positions, their rotational freedom and interaction tendencies may significantly impact PDPF\u0026rsquo;s overall geometry and stability; MD simulations will assess molecular motions, intramolecular interactions, and steric effects, determining whether PDPF\u0026rsquo;s functional groups exhibit rigidity or flexibility under simulated conditions; Understanding these dynamics is critical for predicting PDPF\u0026rsquo;s reactivity, solubility, and potential binding interactions, which influence its suitability for supramolecular assemblies, catalysis, and coordination chemistry applications.\u003c/p\u003e \u003cp\u003e \u003cb\u003eImpact and Significance\u003c/b\u003e: The insights gained from this study will deepen the theoretical understanding of PDPF, bridging the gap between computational modeling and experimental validation. By identifying key structural and dynamic properties, this research provides a foundation for future experimental studies and potential applications in functional materials, catalysis, and molecular engineering.\u003c/p\u003e"},{"header":"2 Computational methods","content":"\u003cp\u003e \u003cb\u003eMolecular Dynamics (MD) Simulation Setup with GROMACS\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe molecular dynamics (MD) simulations were conducted using the GROMACS[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] suite, encompassing energy minimization, equilibration in both NVT and NPT ensembles, and production MD. The simulation aimed to explore the structural and thermodynamic properties of the system over time. \u003cb\u003eSystem Setup and Preprocessing: Preprocessing and Initial Settings\u003c/b\u003e: The simulation employed the leap-frog integrator (integrator\u0026thinsp;=\u0026thinsp;md) with a 2 fs timestep (dt\u0026thinsp;=\u0026thinsp;0.002) across all phases. Center-of-mass motion was managed by applying linear center-of-mass removal every 100 steps (comm-mode\u0026thinsp;=\u0026thinsp;Linear). \u003cb\u003eConstraints\u003c/b\u003e: Bonds involving hydrogen atoms were constrained using the LINCS algorithm (constraints\u0026thinsp;=\u0026thinsp;h-bonds, constraint-algorithm\u0026thinsp;=\u0026thinsp;Lincs), with the tolerance set to 0.0001 and a LINCS order of 4 to ensure stability. \u003cb\u003eEnergy Minimization\u003c/b\u003e: Initial energy minimization targeted a force tolerance of 10 kJ/mol/nm using the steepest descents algorithm (emtol\u0026thinsp;=\u0026thinsp;10). \u003cb\u003eNVT Equilibration\u003c/b\u003e: Conducted for 100 ps to equilibrate the system at 300 K without volume changes. \u003cb\u003eTemperature Control\u003c/b\u003e: Temperature was maintained using the V-rescale thermostat (tcoupl\u0026thinsp;=\u0026thinsp;V-rescale) with a reference temperature of 300 K and a coupling constant of 0.1 ps. \u003cb\u003eOutput Settings\u003c/b\u003e: Coordinates, velocities, and energies were recorded every 500 steps. \u003cb\u003eElectrostatics and Cutoffs\u003c/b\u003e: Particle Mesh Ewald (PME) was applied for long-range electrostatics with a Fourier spacing of 0.16 nm and cubic interpolation (pme_order\u0026thinsp;=\u0026thinsp;4), and both Coulombic and van der Waals interactions were cut off at 1.0 nm. \u003cb\u003ePeriodic Boundary Conditions\u003c/b\u003e: Applied in all three dimensions (pbc\u0026thinsp;=\u0026thinsp;xyz). \u003cb\u003eNPT Equilibration\u003c/b\u003e: Conducted for 500 ps to stabilize pressure at 1 bar. \u003cb\u003ePressure Control\u003c/b\u003e: The Parrinello-Rahman barostat (pcoupl\u0026thinsp;=\u0026thinsp;Parrinello-Rahman) managed isotropic pressure scaling, with a compressibility of 4.5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003ebar\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and a coupling constant of 5.0 ps. \u003cb\u003eSettings for Neighbor Searching\u003c/b\u003e: Verlet cutoff scheme with an update frequency of 10 steps, and a cutoff distance of 1.0 nm for both short-range electrostatics and van der Waals interactions. \u003cb\u003eProduction MD\u003c/b\u003e: \u003cb\u003eDuration\u003c/b\u003e: A 5 ns production run (2,500,000 steps) was carried out to enable detailed analysis. \u003cb\u003eThermostat and Barostat\u003c/b\u003e: Temperature and pressure were controlled as in the equilibration phases. \u003cb\u003eOutput Configuration\u003c/b\u003e: Data for trajectories, energies, and log files were saved at intervals of 10 ps for in-depth post-simulation analysis. \u003cb\u003eDispersion Corrections\u003c/b\u003e: Long-range dispersion corrections were applied to both energy and pressure for greater accuracy in thermodynamic properties. \u003cb\u003eElectrostatics\u003c/b\u003e: PME parameters from the equilibration phases were retained, including the use of a grid spacing of 0.16 nm. \u003cb\u003eOther Parameters\u003c/b\u003e: \u003cb\u003eSimulated Annealing\u003c/b\u003e: Not applied in this study. \u003cb\u003eFree Energy Calculations\u003c/b\u003e: Disabled, as the focus was on structural and dynamic properties. \u003cb\u003ePressure and Temperature Constraints\u003c/b\u003e: All bond lengths involving hydrogen atoms were constrained, with constraints applied uniformly across the molecules for stability in longer simulations.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAnalysis of MD Trajectories\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eConformational Stability\u003c/b\u003e: Key stability metrics, including root-mean-square deviation (RMSD) and radius of gyration (R\u003csub\u003eg\u003c/sub\u003e)[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], were calculated over time to monitor the structural integrity of PDPF during the simulation. These analyses helped assess the molecule\u0026rsquo;s stability under thermal fluctuations[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] and potential conformational changes[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. \u003cb\u003eIntramolecular Interactions\u003c/b\u003e: The hydrogen bonding pattern was analyzed to investigate interactions within the two pentafluorophenyl-ureido moieties and the 2,6-diaminopyridine core. Hydrogen bond count and lifetimes were monitored to observe any significant stabilizing or destabilizing interactions. \u003cb\u003eDynamic Conformational Changes\u003c/b\u003e: Torsional angles [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] within PDPF, especially around the pentafluorophenyl-ureido bonds, were tracked to evaluate the flexibility and possible intramolecular interactions of the functional groups. GROMACS tools, such as gmx angle, were used to extract and analyze these angles over time.\u003c/p\u003e"},{"header":"3 Results \u0026 Discussion","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 RMSD analysis\u003c/h2\u003e\n \u003cp\u003eThis bimodal distribution suggests that the molecule might have undergone a conformational change or sampled different structural states during the simulation.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAnalysis and Interpretation of the Plot -\u003c/strong\u003e First Peak (around 0.1 nm): This could correspond to the molecule remaining in a stable conformation with low deviation from the initial structure. Second Peak (around 0.3 nm): The secondary peak might indicate a second conformational state that the molecule adopted during the simulation, potentially showing larger structural deviation from the initial structure. The area between 0.1 and 0.3 nm might represent intermediate conformations or fluctuations between these two main states.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Radial Distribution Function[\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/h2\u003e\n \u003cp\u003e\u003cstrong\u003eInitial Rise\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe RDF, g(r), starts close to zero and rises sharply as r increases. This rise indicates a high probability of finding atoms at a certain preferred distance, which corresponds to the first solvation shell.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ePlateau\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe RDF levels off around g(r)\u0026thinsp;=\u0026thinsp;1 at larger distances. This plateau signifies that the spatial correlation between atoms has diminished, and they are now distributed randomly, as expected in bulk.\u003c/p\u003e\n \u003cp\u003eThe shape is typical for RDFs, showing structural ordering at short distances (the rise) and approaching random distribution at long distances (the plateau).\u003c/p\u003e\n \u003cp\u003eThe analysis of radial distribution functions (RDF) and thermodynamic properties in simulations with and without water reveals insights into the solute\u0026rsquo;s solubility and interactions with water. In both cases, the RDF stabilizes around 0.8\u0026ndash;0.9 nm, suggesting minimal differences in solute-water structuring, indicating limited solubility. This lack of solute integration into the water network points to weak solute-water interactions, likely causing the solute to cluster rather than disperse within the solvent. While temperature stabilizes effectively in the simulation, the pressure fluctuates widely, which aligns with the weak solute-water attraction observed in the RDF. Stable pressure in molecular dynamics simulations often depends on consistent intermolecular forces, particularly in systems where water molecules interact uniformly with solute molecules. Here, the solute\u0026rsquo;s clustering disrupts the uniformity of forces, leading to significant pressure variations. Furthermore, without solute-water hydrogen bonding or consistent interactions, the water molecules maintain their hydrogen-bond network mostly intact but with localized disruptions. These disruptions result in density fluctuations and inconsistent pressure readings. Overall, the combination of a stable temperature profile and fluctuating pressure further underscores the solute\u0026apos;s low solubility in water and the lack of significant solute-water interaction, which destabilizes pressure and prevents the system from achieving a homogeneous equilibrium.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Radius Of Gyration[\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e,\u0026nbsp;\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/h2\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eStatistical Summary of Radius of Gyration Data\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTime(ps)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRg (total)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRg (X-axis)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRg (Y-axis)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRg (Z-axis)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.395021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.324636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.321122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.317093\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS.D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1447.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.027886\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.040035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.042102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.034831\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMinimum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.366348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.236188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.229274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.231649\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1250.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.377414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.298759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.295281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.294871\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2500.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.384397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.320590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.315844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.316088\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3750.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.402117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.344729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.340233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.335489\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMax (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5000.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.515192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.476247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.498858\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.447164\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\u003eR\u003c/strong\u003e \u003csub\u003e\u0026nbsp;\u003cstrong\u003eg\u003c/strong\u003e\u0026nbsp;\u003c/sub\u003e \u003cstrong\u003e(total) Stability -\u003c/strong\u003e The blue line representing the total radius of gyration seems to stabilize after an initial fluctuation phase. This indicates that the molecule reached a relatively stable compactness over time, suggesting that the system may have equilibrated.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFluctuations in Axes -\u003c/strong\u003e The individual Rg components along the X, Y, and Z axes (red, green, and orange lines) show greater fluctuations, especially in the early stages, but also display some stability over time. This variability along different axes might suggest some degree of anisotropy or flexibility in the molecule\u0026apos;s structure, with potential alignment along certain axes as it stabilizes.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eInitial Contraction -\u003c/strong\u003e The total Rg value decreases initially, indicating that the molecule might be compacting or folding slightly in the early phase, likely settling into a more stable conformation. The stabilization of Rg (Total) suggests that the molecular structure has reached equilibrium during the simulation. Observing higher variability in certain axes may reflect the molecule\u0026rsquo;s structural characteristics, such as flexibility along specific dimensions, which could be relevant depending on the molecule\u0026apos;s function or binding properties.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 Number of H-bonds [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/h2\u003e\n \u003cp\u003e\u003cstrong\u003eFluctuating Hydrogen Bond Count\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe count varies between 0 and 2 hydrogen bonds, indicating that hydrogen bonding interactions are transient. This can suggest that the molecule is undergoing conformational changes or intermittent interactions, especially with surrounding solvent molecules.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency of Bond Formation\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ePeaks in the graph suggest periods where the molecule forms more stable hydrogen bonds, while troughs or zero values indicate phases with little to no bonding. Such behavior could imply that specific conditions or conformations favor hydrogen bond formation.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAverage Bond Count\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe average hydrogen bond count over the simulation provides a quantitative measure of bonding stability. This average could be included in the discussion to contextualize the transient nature of interactions.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eImplications for Stability\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe presence of only a maximum of two hydrogen bonds, despite having seven hydrogens in the molecule, can be attributed to several possible factors related to the molecular structure, chemical environment, and simulation conditions.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eHydrogen Bond Donors and Acceptors\u003c/strong\u003e:\u003c/p\u003e\n \u003cp\u003eNot all hydrogens in a molecule are capable of forming hydrogen bonds. Only those attached to electronegative atoms (like oxygen or nitrogen) can act as hydrogen bond donors.\u003c/p\u003e\n \u003cp\u003eIf PDPF has limited electronegative atoms, only a few of the hydrogens would be suitable for hydrogen bonding. The remaining hydrogens may be attached to carbon atoms, which do not readily participate in hydrogen bonding.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMolecular Structure and Accessibility -\u003c/strong\u003e The 3D structure of the molecule affects the accessibility of potential hydrogen bond donors and acceptors. If the molecule adopts a compact or hydrophobic conformation, it could shield some hydrogen-bonding sites, making them unavailable for interactions with surrounding molecules. Certain conformations may expose only one or two hydrogen bond sites at any given time, limiting the overall bonding capacity.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSolvent Interactions and Competition\u003c/strong\u003e[\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e] \u003cstrong\u003e-\u003c/strong\u003e In a solvated system, water molecules or other surrounding solvents compete for hydrogen-bonding interactions. If water molecules are more readily available and positioned closer to each other than to the molecule, they might outcompete the molecule\u0026rsquo;s hydrogens, reducing the overall hydrogen bonds with the target. The simulation conditions (e.g., temperature and solvent type) also influence hydrogen bonding. Higher temperatures or weaker solvents might reduce the hydrogen bonding potential.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eDynamic Nature of the Simulation -\u003c/strong\u003e Molecular dynamics (MD) simulations represent an average over time. Transient hydrogen bonds may form and break frequently. In this case, the observed maximum of two hydrogen bonds may represent the most stable or common bonding pattern over the course of the simulation. The molecule may also undergo conformational changes that intermittently expose and hide hydrogen bond donors and acceptors, resulting in a low average count.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eHydrogen Bond Geometric Criteria -\u003c/strong\u003e In MD simulations, hydrogen bonds are typically defined by specific geometric criteria (distance and angle between donor and acceptor). If the molecule\u0026apos;s hydrogen atoms do not consistently meet these criteria, even if they are close to other molecules, they may not register as hydrogen bonds. This could further limit the number of recorded hydrogen bonds.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eStructural and Chemical Constraints\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe limited hydrogen bonding could suggest that only certain functional groups are capable of stable interactions, which may inform the molecule\u0026apos;s reactivity, binding affinity, or solubility properties.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eImplications for Molecular Functionality\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIf this molecule is intended to interact with biological targets, its limited hydrogen bonding potential might indicate specificity for interactions or a reliance on other non-covalent interactions like van der Waals forces or hydrophobic effects.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.6 Eigen Values [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/h2\u003e\n \u003cp\u003eThe eigenvalue analysis shows that most significant molecular motions are captured by a few principal components, as indicated by the steep drop in eigenvalues after the first few. This suggests that only a limited number of dimensions represent the main conformational changes, while the remaining components contribute minimally, likely reflecting minor fluctuations or noise. The largest eigenvalues correspond to collective, biologically relevant motions, whereas the low eigenvalues represent localized vibrations. Thus, the system\u0026rsquo;s essential dynamics are effectively described by focusing on the top components. This approach can help identify key motions, such as conformational changes critical to the molecule\u0026apos;s function.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.7 Distance Between Centres-Of-Mass[\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/h2\u003e\n \u003cp\u003e\u003cstrong\u003eCenters of Mass in a Symmetrical Structure -\u003c/strong\u003e In symmetrical molecules like this, the \u003cstrong\u003etwo COMs (COM1 and COM2)\u003c/strong\u003e likely correspond to each of the symmetrical halves, specifically each \u003cstrong\u003epentafluorophenyl urea group\u003c/strong\u003e. This symmetry implies that both flanking groups (the pentafluorophenyl urea moieties) would have similar mass distributions relative to the central diaminopyridine core. Therefore, the COMs would be roughly equidistant from the center along the axis connecting the two urea groups through the central pyridine ring.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eDistance Fluctuations -\u003c/strong\u003e The fluctuations in the distance between COM1 and COM2 over time likely reflect \u003cstrong\u003econformational flexibility\u003c/strong\u003e in the molecule. Although symmetrical, the urea linkages allow some rotational freedom around the central diaminopyridine ring. This rotation, combined with any solvent interactions, may cause the molecule to \u0026quot;breathe\u0026quot; slightly, varying the distance between the two pentafluorophenyl urea groups.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eImplications of Hydrogen Bonding and Stability -\u003c/strong\u003e Given the presence of urea groups, \u003cstrong\u003ehydrogen bonding\u003c/strong\u003e might play a role in stabilizing certain conformations, particularly if there are interactions with solvent molecules or other surrounding entities. However, since only a maximum of two hydrogen bonds were observed during simulations, it\u0026rsquo;s possible that \u003cstrong\u003eintra-molecular hydrogen bonding is limited\u003c/strong\u003e in this structure, perhaps because the urea groups are oriented outward rather than toward each other. This limited hydrogen bonding also supports the idea that the molecule experiences \u003cstrong\u003eminor conformational freedom\u003c/strong\u003e without strong intramolecular constraints.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ePlanar Rigidity of the Diaminopyridine Core -\u003c/strong\u003e The diaminopyridine core is likely \u003cstrong\u003erelatively rigid and planar\u003c/strong\u003e, as is typical for aromatic systems. This rigidity maintains the overall symmetry and helps distribute mass equally to each side. Consequently, the observed distance fluctuations are likely due to the slight movement or flexibility in the attached urea and pentafluorophenyl groups rather than substantial bending or folding of the core itself.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFunctional and Interaction Insights\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe symmetrical structure and limited hydrogen bonding indicate that this molecule might maintain a relatively \u003cstrong\u003econsistent shape in solution\u003c/strong\u003e, with the main structural variations happening at the peripheral pentafluorophenyl rings. This property could be relevant if the molecule is intended for interactions with other molecules or surfaces, as the relatively stable structure would present the pentafluorophenyl groups outward consistently, which may influence binding or self-assembly behavior.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTemperature fluctuations\u003c/strong\u003e [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTemperature Stability\u003c/strong\u003e: The system temperature oscillates around a central value close to the target temperature of 300 K. These fluctuations are expected in molecular dynamics simulations, where the thermostat adjusts the system to maintain an average temperature. The observed variations between approximately 290 K and 320 K indicate natural thermal movement and system equilibration rather than instability. \u003cstrong\u003eThermostat Behavior\u003c/strong\u003e: The consistent oscillation pattern suggests that the thermostat is effectively maintaining the system\u0026rsquo;s temperature near the desired setpoint. Any deviations from 300 K are corrected by the thermostat, keeping the average temperature within an acceptable range. \u003cstrong\u003eEquilibration Observation\u003c/strong\u003e: Given the narrow fluctuation range, it appears that the system is relatively well-equilibrated. Large or prolonged deviations might have indicated that the system needs further equilibration, but the present data suggests that temperature control is stable.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ePressure Fluctuations and Density fluctuations\u003c/strong\u003e[\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/p\u003e\n \u003cp\u003eThe pressure fluctuation graph exhibits significant variations over time, with values oscillating between approximately \u0026minus;\u0026thinsp;1000 and +\u0026thinsp;1500 bar across the XX, YY, and ZZ components. Such fluctuations are typical in molecular dynamics simulations, particularly for small systems, where pressure is derived from atomic-level interactions. Given that the compound PDPF is hydrophobic and the temperature has stabilized, these pressure variations likely arise from intrinsic molecular interactions and system size effects rather than instability. While instantaneous pressure values fluctuate widely, the system may still be well-equilibrated, and further averaging could provide a clearer representation of bulk pressure properties. Unless pressure-sensitive properties are of primary interest, these variations are generally expected and do not indicate a concern for system stability. The plot of density distribution across coordinates reveals several key points: \u003cstrong\u003eDensity Fluctuations\u003c/strong\u003e: There are noticeable fluctuations in density as we move along the coordinate axis, with density values initially increasing to a peak and then gradually decreasing. \u003cstrong\u003eHigh-Density Region\u003c/strong\u003e: Between approximately 0.8 and 1.5 nm, there is a region of relatively high density, reaching up to 35 kg/m\u0026sup3;. This could correspond to areas where the molecular structure is more compact or regions with higher atomic mass concentration. \u003cstrong\u003eLow-Density Region\u003c/strong\u003e: After 1.5 nm, the density steadily decreases, reaching a low around 2.5 nm, suggesting a less compact structure or lower atomic presence in these regions. This trend could indicate less dense packing or a void in the structure. \u003cstrong\u003ePeriodic Density Pattern\u003c/strong\u003e: The oscillations indicate a periodic pattern, which might be due to structural symmetry in the molecule, particularly if the molecule has repeating units or symmetrical features.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eEigen Vectors Trajectory\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe analysis focused on the first three eigenvectors. The output lists the \u003cstrong\u003eminimum\u003c/strong\u003e and \u003cstrong\u003emaximum\u003c/strong\u003e values along each eigenvector with the corresponding \u003cstrong\u003eframe numbers.\u003c/strong\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMin-max values of first three eigenvectors\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMinimum Value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMaximum Value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEigenvector 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.753750 at frame 57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.657369 at frame 245\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEigenvector 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.872152 at frame 162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.121700 at frame 286\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEigenvector 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.260221 at frame 31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.700323 at frame 40\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\u003eThese frames represent the \u003cstrong\u003emost significant conformational changes\u003c/strong\u003e along each eigenvector, which highlight large-amplitude motions.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eEigenvector Analysis\u003c/strong\u003e: The range of values along each eigenvector gives insight into the variability of the molecule\u0026apos;s movement. Large values (positive or negative) along an eigenvector indicate significant motions in those directions. \u003cstrong\u003eConformational Flexibility\u003c/strong\u003e: This analysis reveals which directions of motion contribute most to the structural fluctuations. Eigenvectors 1 and 2, in particular, have larger maximum values, indicating directions of substantial movement.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ePrincipal Component Analysis\u003c/strong\u003e[\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/p\u003e\n \u003cp\u003eThe two principal components (PC1 and PC2) in PCA analysis represent the primary directions of variance or motion within the molecular structure. Each principal component (PC) is essentially an \u0026quot;axis\u0026quot; or \u0026quot;direction\u0026quot; in the multidimensional space of atomic coordinates. PC1 represents the direction along which the molecular structure exhibits the largest variance in its atomic positions throughout the trajectory. PC2 represents the second-largest variance, orthogonal to PC1. In physical terms, these components describe collective motions that involve multiple atoms moving in a correlated way, rather than focusing on specific atoms. The do not directly represent specific parts of the molecule. PCs are derived from the covariance matrix of atomic positions, meaning they represent global or collective motions rather than movements of individual atoms or parts of the molecule. However, certain parts of the molecule might contribute more significantly to each PC, especially if those parts exhibit higher mobility. For example, if flexible side chains or terminal regions of a protein are more mobile, they may contribute more to the variance captured by the first few PCs. By analyzing the \u003cstrong\u003eeigenvectors\u003c/strong\u003e corresponding to each principal component, we can identify which atoms contribute most to the component\u0026rsquo;s motion. This can help localize regions of the molecule involved in particular collective movements. For instance, a high contribution of atoms from a flexible loop, a side chain, or a particular segment of the molecule to PC1 might indicate that this segment is moving more freely in that direction. We can calculate the contribution of each atom to PC1 and PC2 to create a clearer picture of which regions of the molecule are moving the most along these directions. This is often done by projecting each atom\u0026apos;s displacement onto the principal component vectors. Regions with larger projections are more mobile in that component\u0026apos;s direction.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eDominant Motion Along PC1 -\u003c/strong\u003e The steep eigenvalue for PC1 suggests that most of the system\u0026apos;s dynamic behavior is captured by this single component. This typically implies a dominant mode of fluctuation or conformational change in the structure. In many molecular systems, a large eigenvalue in PC1 might indicate a breathing or twisting mode, where significant segments of the molecule undergo coordinated movement.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSecondary Contributions (PC2 and beyond) -\u003c/strong\u003e While PC1 captures the most significant movement, the second principal component (PC2) also contributes some variance, albeit far less. This mode may correspond to smaller, less coordinated adjustments in the molecular structure, such as localized flexibility in specific groups. Given the quick drop-off in eigenvalues beyond PC2, the additional principal components represent minor fluctuations around a stable structure, likely due to smaller conformational adjustments or thermal fluctuations.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eStructural Stability -\u003c/strong\u003e The clustering seen in the projection along PC1 and PC2, with only a few larger deviations, indicates that the molecule remains in a stable conformation for most of the simulation. The presence of outliers could correspond to occasional, transient shifts, possibly due to interactions or the molecule adjusting to environmental changes. In the case of symmetrical molecules, the stability around the mean structure is often reinforced by the molecular symmetry itself, which imposes constraints on how the molecule can flex.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCorrelation with Physical Properties -\u003c/strong\u003e Analyzing these fluctuations can provide insights into the flexibility and resilience of the molecule. For example, a high degree of movement in PC1 might indicate susceptibility to certain types of deformation. Conversely, stability along other PCs could mean that other structural modes are less likely to vary under similar conditions. If additional physical properties like binding or interaction energies were calculated, we could correlate those with movements along PC1 or PC2, identifying if certain fluctuations correspond to significant physical interactions or stability changes.\u003c/p\u003e\n \u003cp\u003eThe graph here shows the eigenvalues (variance explained by each mode) for the first two principal components. In PCA, eigenvalues represent the amount of variance each principal component (PC) captures from the original data.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eVariance Explained by PC1 and PC2\u003c/strong\u003e:The first principal component (PC1) has a significantly larger eigenvalue, meaning it explains a major portion of the variance in the data. In this case, PC1 has an eigenvalue around 12, while PC2 has a much lower eigenvalue (approximately 2). This steep drop in eigenvalues indicates that PC1 captures most of the structural or conformational variability in the trajectory, while PC2 contributes relatively little. \u003cstrong\u003eDimension Reduction Feasibility\u003c/strong\u003e: Since PC1 captures the majority of the variance, focusing on this component alone might give a good approximation of the primary motion within the system. This finding justifies reducing the dimensionality of the dataset and focusing primarily on PC1, as further components are less informative. \u003cstrong\u003eSystem Dynamics Insight\u003c/strong\u003e: The dominance of PC1 suggests that the molecule or system has one primary axis of motion or a major dynamic mode. This could correspond to a significant conformational change or flexibility along a particular structural feature. The much smaller variance in PC2 might represent a secondary, less prominent mode of motion, potentially associated with minor or localized movements within the structure.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eDetailed Interpretation with Specific Atom Contributions\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ePC1 Contributions (Top Subplot)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIn PC1, the primary contributors are specific carbon (C), nitrogen (N), fluorine (F), and hydrogen (H) atoms distributed across each molecule. Here\u0026rsquo;s a breakdown of the most influential atoms:\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFluorine Atoms (F1, F2, F6, etc.) -\u003c/strong\u003e These fluorine atoms show significant positive contributions in PC1 across the molecules, suggesting they play a crucial role in the \u003cstrong\u003estretching or bending modes\u003c/strong\u003e of the molecular structure. Fluorine atoms are relatively rigid due to the C-F bond strength, and their substantial mass could contribute to strong movements.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCarbon Atoms (C3, C7, C9, etc.) -\u003c/strong\u003e Specific carbons, especially those in the aromatic rings or near functional groups (like C3 and C9), display both positive and negative contributions. This pattern implies \u003cstrong\u003eopposing motions\u003c/strong\u003e within these regions, indicating that certain carbons are likely involved in a \u003cstrong\u003eflexing motion\u003c/strong\u003e across the molecule. For instance, C3 and C7 contribute positively, whereas others, like C9, contribute negatively, suggesting dynamic interplay within the aromatic system.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNitrogen Atoms (N1, N2) -\u003c/strong\u003e The nitrogen atoms in the amine and urea groups (such as N1 and N2) have significant negative contributions, which may indicate \u003cstrong\u003etorsional or bending motions\u003c/strong\u003e localized around these groups. These groups could act as pivot points, as nitrogen atoms tend to participate in \u003cstrong\u003ehydrogen bonding\u003c/strong\u003e and \u003cstrong\u003eintermolecular interactions\u003c/strong\u003e, adding flexibility to these regions.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eHydrogen Atoms (H4, H37, H40) -\u003c/strong\u003e Hydrogen atoms such as H4, H37, and H40 contribute prominently in PC1, which is noteworthy since hydrogen is typically lighter and thus more responsive to minor structural changes. These hydrogens could be situated in flexible regions or side chains that experience \u003cstrong\u003erapid oscillations or bending\u003c/strong\u003e.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ePC2 Contributions (Bottom Subplot)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eFor PC2, we observe a different set of prominent atomic contributors, with key roles for fluorine, carbon, and nitrogen atoms, but in a different motion profile:\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFluorine Atoms (F1, F4, F6) -\u003c/strong\u003e Fluorine atoms, such as F1 and F6, display significant contributions to PC2, with both positive and negative values across the molecules. This suggests that \u003cstrong\u003erotational or out-of-plane bending motions\u003c/strong\u003e might be occurring around these atoms, likely due to their positions at the molecular periphery. These fluorine atoms, often at terminal positions, may be involved in \u003cstrong\u003etorsional vibrations\u003c/strong\u003e where the molecule twists around its core.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCarbon Atoms (C10, C12, C18, etc.) -\u003c/strong\u003e Carbons C10, C12, and C18 have distinct positive or negative contributions in PC2, indicating \u003cstrong\u003elocalized flexing or twisting\u003c/strong\u003e motions. These atoms might be part of the aromatic ring or close to functional groups, which are critical in dictating molecular flexibility. The contrasting contributions (e.g., positive for C10, negative for C18) suggest a \u003cstrong\u003ecoordinated bending\u003c/strong\u003e along the length of the molecule, which could resemble a \u0026quot;hinge-like\u0026quot; movement.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNitrogen Atoms (N1, N2) -\u003c/strong\u003e Nitrogens, particularly in functional groups like amines and ureas, contribute notably to PC2 as well, supporting \u003cstrong\u003etorsional flexibility\u003c/strong\u003e around these groups. Their involvement in hydrogen bonds or other intermolecular interactions might restrict some motion, creating resistance that results in \u003cstrong\u003evibrational modes\u003c/strong\u003e captured by PC2.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eHydrogens (H37, H41) - S\u003c/strong\u003epecific hydrogen atoms, like H37 and H41, also play a role in PC2, albeit with generally smaller contributions compared to PC1. These atoms might be involved in \u003cstrong\u003erotational adjustments\u003c/strong\u003e around the molecule\u0026rsquo;s core.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eOverall Molecular Dynamics Based on Atom Contributions\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFluorine Atoms\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eFluorine atoms are consistently strong contributors across both PC1 and PC2, highlighting their role in the \u003cstrong\u003erigidity and vibrational patterns\u003c/strong\u003e of the molecule. Their positions, often at the periphery, may lead to significant \u003cstrong\u003estretching or torsional motions\u003c/strong\u003e.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCarbon Atoms in Aromatic Rings\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eCertain carbons in the aromatic rings (C3, C7, C10) are essential in both PCs, showing that \u003cstrong\u003earomaticity\u003c/strong\u003e plays a role in controlling the \u003cstrong\u003eflexibility\u003c/strong\u003e of the molecule. The alternating positive and negative contributions indicate that aromatic rings might \u003cstrong\u003erotate or flex in sync\u003c/strong\u003e with neighboring atoms, creating coordinated motions.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNitrogen and Hydrogen Contributions\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eNitrogen atoms in functional groups (e.g., amine, urea) serve as \u003cstrong\u003epivot points\u003c/strong\u003e for motion, with hydrogen atoms responding to these shifts. This combination suggests that the molecule\u0026rsquo;s core is \u003cstrong\u003emore flexible than its terminal groups\u003c/strong\u003e, which aligns with its structural function and potential interactions.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eProjection Analysis of Molecular Dynamics Trajectory along Principal Components\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ePC1 Projections -\u003c/strong\u003e We see an initial rapid fluctuation in projection values in the first few frames, followed by stabilization around zero after frame 20. This suggests that the molecule undergoes significant movement along the PC1 direction at the beginning, potentially indicating an initial conformational change or relaxation, after which it reaches a relatively stable configuration.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ePC2 Projections -\u003c/strong\u003e Similar to PC1, PC2 also shows initial fluctuations, although they are less intense than those observed along PC1. After frame 10, PC2 projection values stabilize around zero, implying that any major structural changes along this component are completed within the initial few frames. This pattern is typical of molecular dynamics simulations where the system undergoes initial relaxation, and then stabilizes as it reaches an equilibrium state.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003e\u003cstrong\u003ePrincipal Component Analysis (PCA) on Molecular Trajectory\u003c/strong\u003e: PCA of the molecular dynamics (MD) trajectory identified the primary modes of motion within the molecule, with significant contributions from specific atoms, particularly carbon (C) and nitrogen (N). These atoms exhibited prominent movements along PC1 and PC2, indicating flexibility in certain regions of the molecule's structure. The periodic nature of conformational fluctuations was evident across the three repeating units of the molecule, confirming structural consistency and highlighting potential functional regions within the molecule. \u003cstrong\u003eProjection of Trajectory Along Principal Components\u003c/strong\u003e: The projections of the trajectory along PC1 and PC2 displayed rapid stabilization, with values reaching a steady state within the initial simulation stages. This implies that major conformational shifts occur early, after which the molecule settles into a stable configuration. The consistency of these projections with precomputed eigenvector projections further validates the reliability of the observed motion patterns. \u003cstrong\u003eRadial Distribution Function (RDF) Analysis\u003c/strong\u003e: RDF profiles, both with and without water, stabilized around 0.8–0.9 nm, suggesting limited solubility in water. The similarity in RDF profiles indicates that water does not significantly interact with or alter the molecular structure, supporting the hypothesis that the molecule may have hydrophobic characteristics. \u003cstrong\u003eTemperature and Pressure Behavior During NVT and NPT Phases\u003c/strong\u003e: Temperature stabilized effectively during NVT and NPT simulations, demonstrating proper thermal equilibration. However, pressure exhibited significant anisotropic fluctuations across the XX, YY, and ZZ axes, failing to reach a steady mean value. These pressure fluctuations likely reflect directional stresses and internal interactions within the simulation box, possibly due to the molecule’s minimal interactions with water. This anisotropy may imply that the molecule's structural behavior is relatively independent of the surrounding solvent environment. \u003cstrong\u003eHydrogen Bond Analysis\u003c/strong\u003e: Hydrogen bond analysis showed minimal interaction between the molecule and water, supporting the RDF findings and indicating limited solvation. This lack of hydrogen bonding further emphasizes the molecule’s probable hydrophobic nature and solvent-independent stability. \u003cstrong\u003eDensity Fluctuations\u003c/strong\u003e: Density analysis revealed stable fluctuations around a mean density, consistent with RDF and pressure data. This further suggests a stable, solvent-independent structure, with the molecule’s primary interactions unaffected by water presence. \u003cstrong\u003eEnergy Analysis\u003c/strong\u003e: The total energy and its components (potential and kinetic energy) were monitored throughout the simulation to assess system stability. Potential energy remained relatively stable, indicating that the molecular conformation did not undergo any drastic changes. The kinetic energy stabilized with the temperature, reflecting a thermally equilibrated system. The limited variations in potential energy corroborate the findings from PCA and RDF analyses, where the molecule achieved a stable conformation early in the simulation. This stability, despite fluctuating pressure values, implies that the molecular structure maintains its integrity and does not experience significant external or internal perturbations.\u003c/p\u003e\n\u003cp\u003eIn summary, the PDPF molecule displays stable conformational dynamics, limited interaction with water, and minimal hydrogen bonding, suggesting a hydrophobic character. The anisotropy in pressure fluctuations, stable density, and consistent energy profile all support the hypothesis that the molecule’s behavior and structure are largely independent of the solvent. This analysis provides comprehensive insights into the molecule’s stability, interaction profile, and dynamic properties, making it suitable for applications where hydrophobic or solvent-resistant molecules are advantageous.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of Interest\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eVSR and HN wrote the main manuscript; VSR prepared the figures and tables; All authors reviewed the manuscript\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKumar, R., Santa Chalarca, C.F., Bockman, M.R., Bruggen, C.V., Grimme, C.J., Dalal, R.J., Hanson, M.G., Hexum, J.K. and Reineke, T.M., 2021. Polymeric delivery of therapeutic nucleic acids. \u003cem\u003eChemical reviews\u003c/em\u003e, \u003cem\u003e121\u003c/em\u003e(18), pp.11527-11652. \u003c/li\u003e\n\u003cli\u003eGeng, Z., Shin, J.J., Xi, Y. and Hawker, C.J., 2021. Click chemistry strategies for the accelerated synthesis of functional macromolecules. \u003cem\u003eJournal of Polymer Science\u003c/em\u003e, \u003cem\u003e59\u003c/em\u003e(11), pp.963-1042. \u003c/li\u003e\n\u003cli\u003eZhao, W., Li, C., Chang, J., Zhou, H., Wang, D., Sun, J., Liu, T., Peng, H., Wang, Q., Li, Y. and Whittaker, A.K., 2023. Advances and prospects of RAFT polymerization-derived nanomaterials in MRI-assisted biomedical applications. \u003cem\u003eProgress in Polymer Science\u003c/em\u003e, \u003cem\u003e146\u003c/em\u003e, p.101739. \u003c/li\u003e\n\u003cli\u003eRemella, V. S., Neelamegan, H. (2025). Topological Descriptor Analysis with Chemical Graph Theory Insights and Predicted ADME Data Analysis for Pentafluorophenylurea-Based Pyridine Derivative, \u003cem\u003eChemical Methodologies\u003c/em\u003e, 9(3), pp. 172-189. doi: 10.48309/chemm.2025.502403.1887 \u003c/li\u003e\n\u003cli\u003eHospital, A., Go\u0026ntilde;i, J.R., Orozco, M. and Gelp\u0026iacute;, J.L., 2015. Molecular dynamics simulations: advances and applications. \u003cem\u003eAdvances and Applications in Bioinformatics and Chemistry\u003c/em\u003e, pp.37-47. \u003c/li\u003e\n\u003cli\u003eFilipe, H.A. and Loura, L.M., 2022. Molecular dynamics simulations: advances and applications. \u003cem\u003eMolecules\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e(7), p.2105. \u003c/li\u003e\n\u003cli\u003ePiers, W.E. and Chivers, T., 1997. Pentafluorophenylboranes: from obscurity to applications. \u003cem\u003eChemical Society Reviews\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e(5), pp.345-354. \u003c/li\u003e\n\u003cli\u003eLiu, L.H. and Yan, M., 2010. Perfluorophenyl azides: new applications in surface functionalization and nanomaterial synthesis. \u003cem\u003eAccounts of chemical research\u003c/em\u003e, \u003cem\u003e43\u003c/em\u003e(11), pp.1434-1443. \u003c/li\u003e\n\u003cli\u003eJo, S., Cheng, X., Lee, J., Kim, S., Park, S.J., Patel, D.S., Beaven, A.H., Lee, K.I., Rui, H., Park, S. and Lee, H.S., 2017. CHARMM‐GUI 10 years for biomolecular modeling and simulation. \u003cem\u003eJournal of computational chemistry\u003c/em\u003e, \u003cem\u003e38\u003c/em\u003e(15), pp.1114-1124. \u003c/li\u003e\n\u003cli\u003eVieira, I.H.P., Botelho, E.B., de Souza Gomes, T.J., Kist, R., Caceres, R.A. and Zanchi, F.B., 2023. Visual dynamics: a WEB application for molecular dynamics simulation using GROMACS. \u003cem\u003eBMC bioinformatics\u003c/em\u003e, \u003cem\u003e24\u003c/em\u003e(1), p.107. \u003c/li\u003e\n\u003cli\u003eFilipe, H.A. and Loura, L.M., 2022. Molecular dynamics simulations: advances and applications. \u003cem\u003eMolecules\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e(7), p.2105. \u003c/li\u003e\n\u003cli\u003eHaydukivska, K., Blavatska, V. and Paturej, J., 2020. Universal size ratios of Gaussian polymers with complex architecture: radius of gyration vs hydrodynamic radius. \u003cem\u003eScientific Reports\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(1), p.14127. \u003c/li\u003e\n\u003cli\u003eHickman, J. and Mishin, Y., 2016. Temperature fluctuations in canonical systems: Insights from molecular dynamics simulations. \u003cem\u003ePhysical Review B\u003c/em\u003e, \u003cem\u003e94\u003c/em\u003e(18), p.184311. \u003c/li\u003e\n\u003cli\u003eHale, J.P., Marcelli, G., Parker, K.H., Winlove, C.P. and Petrov, P.G., 2009. Red blood cell thermal fluctuations: comparison between experiment and molecular dynamics simulations. \u003cem\u003eSoft Matter\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e(19), pp.3603-3606. \u003c/li\u003e\n\u003cli\u003eAnandakrishnan, R., Drozdetski, A., Walker, R.C. and Onufriev, A.V., 2015. Speed of conformational change: comparing explicit and implicit solvent molecular dynamics simulations. \u003cem\u003eBiophysical journal\u003c/em\u003e, \u003cem\u003e108\u003c/em\u003e(5), pp.1153-1164. \u003c/li\u003e\n\u003cli\u003eMelvin, R.L., Godwin, R.C., Xiao, J., Thompson, W.G., Berenhaut, K.S. and Salsbury Jr, F.R., 2016. Uncovering large-scale conformational change in molecular dynamics without prior knowledge. \u003cem\u003eJournal of chemical theory and computation\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(12), pp.6130-6146. \u003c/li\u003e\n\u003cli\u003eStein, E.G., Rice, L.M. and Br\u0026uuml;nger, A.T., 1997. Torsion-angle molecular dynamics as a new efficient tool for NMR structure calculation. \u003cem\u003eJournal of Magnetic Resonance\u003c/em\u003e, \u003cem\u003e124\u003c/em\u003e(1), pp.154-164. \u003c/li\u003e\n\u003cli\u003eChen, J., Im, W. and Brooks III, C.L., 2005. Application of torsion angle molecular dynamics for efficient sampling of protein conformations. \u003cem\u003eJournal of computational chemistry\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e(15), pp.1565-1578. \u003c/li\u003e\n\u003cli\u003eLevine, B.G., Stone, J.E. and Kohlmeyer, A., 2011. Fast analysis of molecular dynamics trajectories with graphics processing units\u0026mdash;Radial distribution function histogramming. \u003cem\u003eJournal of computational physics\u003c/em\u003e, \u003cem\u003e230\u003c/em\u003e(9), pp.3556-3569. \u003c/li\u003e\n\u003cli\u003eDimitroulis, C., Raptis, T. and Raptis, V., 2015. POLYANA\u0026mdash;A tool for the calculation of molecular radial distribution functions based on Molecular Dynamics trajectories. \u003cem\u003eComputer Physics Communications\u003c/em\u003e, \u003cem\u003e197\u003c/em\u003e, pp.220-226. \u003c/li\u003e\n\u003cli\u003eHan, M., Chen, P. and Yang, X., 2005. Molecular dynamics simulation of PAMAM dendrimer in aqueous solution. \u003cem\u003ePolymer\u003c/em\u003e, \u003cem\u003e46\u003c/em\u003e(10), pp.3481-3488. \u003c/li\u003e\n\u003cli\u003eAhmed, M.C., Crehuet, R. and Lindorff-Larsen, K., 2020. Computing, analyzing, and comparing the radius of gyration and hydrodynamic radius in conformational ensembles of intrinsically disordered proteins. \u003cem\u003eIntrinsically disordered proteins: methods and protocols\u003c/em\u003e, pp.429-445. \u003c/li\u003e\n\u003cli\u003eLopez, C.F., Nielsen, S.O., Klein, M.L. and Moore, P.B., 2004. Hydrogen bonding structure and dynamics of water at the dimyristoylphosphatidylcholine lipid bilayer surface from a molecular dynamics simulation. \u003cem\u003eThe Journal of Physical Chemistry B\u003c/em\u003e, \u003cem\u003e108\u003c/em\u003e(21), pp.6603-6610. \u003c/li\u003e\n\u003cli\u003eAuffinger, P., Louise-May, S. and Westhof, E., 1996. Molecular Dynamics Simulations of the Anticodon Hairpin of tRNAAsp: Structuring Effects of C\u0026minus; H⊙⊙⊙ O Hydrogen Bonds and of Long-Range Hydration Forces. \u003cem\u003eJournal of the American Chemical Society\u003c/em\u003e, \u003cem\u003e118\u003c/em\u003e(5), pp.1181-1189. \u003c/li\u003e\n\u003cli\u003eBizzarri, A.R. and Cannistraro, S., 2002. Molecular dynamics of water at the protein\u0026minus; solvent interface. \u003cem\u003eThe Journal of Physical Chemistry B\u003c/em\u003e, \u003cem\u003e106\u003c/em\u003e(26), pp.6617-6633. \u003c/li\u003e\n\u003cli\u003ePrabhu, N. and Sharp, K., 2006. Protein\u0026minus; solvent interactions. \u003cem\u003eChemical reviews\u003c/em\u003e, \u003cem\u003e106\u003c/em\u003e(5), pp.1616-1623. \u003c/li\u003e\n\u003cli\u003eHinrichs, N.S. and Pande, V.S., 2007. Calculation of the distribution of eigenvalues and eigenvectors in Markovian state models for molecular dynamics. \u003cem\u003eThe Journal of chemical physics\u003c/em\u003e, \u003cem\u003e126\u003c/em\u003e(24). \u003c/li\u003e\n\u003cli\u003eGust, E.D. and Reichl, L.E., 2009. Molecular dynamics simulation of collision operator eigenvalues. \u003cem\u003ePhysical Review E\u0026mdash;Statistical, Nonlinear, and Soft Matter Physics\u003c/em\u003e, \u003cem\u003e79\u003c/em\u003e(3), p.031202. \u003c/li\u003e\n\u003cli\u003eM\u0026eacute;ndez-Maldonado, G.A., Gonz\u0026aacute;lez-Melchor, M., Alejandre, J. and Chapela, G.A., 2012. On the centre of mass velocity in molecular dynamics simulations. \u003cem\u003eRevista mexicana de f\u0026iacute;sica\u003c/em\u003e, \u003cem\u003e58\u003c/em\u003e(1), pp.55-60. \u003c/li\u003e\n\u003cli\u003eGiulini, D. and Gro\u0026szlig;ardt, A., 2014. Centre-of-mass motion in multi-particle Schr\u0026ouml;dinger\u0026ndash;Newton dynamics. \u003cem\u003eNew Journal of Physics\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(7), p.075005. \u003c/li\u003e\n\u003cli\u003eHickman, J. and Mishin, Y., 2016. Temperature fluctuations in canonical systems: Insights from molecular dynamics simulations. \u003cem\u003ePhysical Review B\u003c/em\u003e, \u003cem\u003e94\u003c/em\u003e(18), p.184311. \u003c/li\u003e\n\u003cli\u003eBinder, K., Horbach, J., Kob, W., Paul, W. and Varnik, F., 2004. Molecular dynamics simulations. \u003cem\u003eJournal of Physics: Condensed Matter\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(5), p.S429. \u003c/li\u003e\n\u003cli\u003eUline, M.J. and Corti, D.S., 2013. Molecular dynamics at constant pressure: allowing the system to control volume fluctuations via a \u0026ldquo;shell\u0026rdquo; particle. \u003cem\u003eEntropy\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(9), pp.3941-3969. \u003c/li\u003e\n\u003cli\u003eUEDA, K., KOMAI, T., YU, I. and NAKAYAMA, H., 2002. Molecular dynamics study on the density fluctuation of supercritical water. \u003cem\u003eJournal of Computer Chemistry, Japan\u003c/em\u003e, \u003cem\u003e1\u003c/em\u003e(3), pp.83-88. \u003c/li\u003e\n\u003cli\u003eStein, S.A.M., Loccisano, A.E., Firestine, S.M. and Evanseck, J.D., 2006. Principal components analysis: a review of its application on molecular dynamics data. \u003cem\u003eAnnual Reports in Computational Chemistry\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e, pp.233-261. \u003c/li\u003e\n\u003cli\u003eSittel, F., Jain, A. and Stock, G., 2014. Principal component analysis of molecular dynamics: On the use of Cartesian vs. internal coordinates. \u003cem\u003eThe Journal of Chemical Physics\u003c/em\u003e, \u003cem\u003e141\u003c/em\u003e(1). \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Pentafluorophenyl-ureido derivative, Crystal architecture, Hydrogen bonding, Molecular dynamics simulations, Radial distribution function (RDF), Intermolecular interactions ","lastPublishedDoi":"10.21203/rs.3.rs-6226678/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6226678/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe synthesis and characterization of organic molecules with specialized structural and electronic features are crucial for applications in materials science, catalysis, and molecular electronics. This study introduces PDPF, a pentafluorophenyl-ureido derivative of 2,6-diaminopyridine, recognized for its unique crystal architecture and intermolecular interactions. Single-crystal X-ray diffraction reveals that PDPF crystallizes in a triclinic system (Pī space group), with a densely packed lattice stabilized by \u003cb\u003eN\u0026mdash;H\u0026middot;\u0026middot;\u0026middot;O\u003c/b\u003e and \u003cb\u003eC\u0026mdash;H\u0026middot;\u0026middot;\u0026middot;F\u003c/b\u003e hydrogen bonds. The pentafluorophenyl rings and urea moieties contribute electron-withdrawing effects, enhancing stability and unique steric properties.\u003c/p\u003e \u003cp\u003eTo investigate PDPF\u0026rsquo;s stability and conformational dynamics, molecular dynamics (MD) simulations were conducted under thermal fluctuations and solvent interactions. Principal component analysis (PCA) of MD trajectories identified localized flexibility in carbon and nitrogen atoms, with rapid stabilization along principal components. Radial distribution function (RDF) profiles indicate limited solubility and hydrophobic tendencies, with minimal hydrogen bonding in aqueous environments.\u003c/p\u003e \u003cp\u003eFurther analyses of temperature, pressure, density, and energy fluctuations confirm structural integrity, with stable kinetic and potential energy profiles across conditions. This comprehensive study provides insights into PDPF\u0026rsquo;s intermolecular interactions and stability while demonstrating the effectiveness of computational methods in characterizing complex organic molecules with intricate packing arrangements.\u003c/p\u003e","manuscriptTitle":"Exploring the Structural Dynamics in aqueous medium of a novel Pentafluorophenyl-Ureido Derivative: A Molecular Dynamics Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-20 08:14:14","doi":"10.21203/rs.3.rs-6226678/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7aebb8e5-c2bb-4aac-9380-26e0988350f0","owner":[],"postedDate":"March 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-20T08:14:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-20 08:14:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6226678","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6226678","identity":"rs-6226678","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}