Molecular Dynamics Study on Anti-fouling Properties of Polybenzimidazole Membrane

doi:10.21203/rs.3.rs-4347876/v1

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Molecular Dynamics Study on Anti-fouling Properties of Polybenzimidazole Membrane

https://doi.org/10.21203/rs.3.rs-4347876/v1

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Polybenzimidazole (PBI) is a kind of aromatic polymer with high heat resistance and mechanical strength. In this study, polybenzimidazole (PBI) and its two derivatives PBIO, PBIO2) were constructed by N-substitution reaction grafted hydrophilic side chains (Original, 1-Bromo-3-methoxypropane and 1-Bromo-2-(2-methoxyethoxy) ethane), and the structure and protein resistance of the three polymer membranes were studied by molecular dynamics. The results of hydrophilic group grafted PBI were confirmed by calculation, can improve the anti-fouling ability of the membrane surface. Therefore, the development of PBI films with excellent hydrophilic properties and good anti-fouling performance is of great significance.

Polymer membrane

Polymeric composites

Protein resistance

Anti-fouling surface

Simulation and modelling.

Polybenzimidazole (PBI), a rigid polymeric membrane material, has good mechanical strength, thermal stability [1, 2, 3]. On the other hand, its inherently weak hydrophilic [4] leads to reduced water permeability as well as severe membrane fouling [5]. The buildup of dirt substances within the membrane surface or pore structure results in a reduction in flux, and other undesirable effects [5, 6]. Many studies have proven that severe membrane fouling is caused by proteins [7], which tend to adsorb on hydrophobic membrane surfaces because their organic parts are hydrophobic [8]. It has become a broad consensus to improve the membrane's anti-fouling ability by introducing hydrophilic components [9].

In this study, we report a simulation scheme for constructing hydrophilic side-linked clade BI, and test its resistance to protein contamination. Simulation studies offer detailed insights into the impact of hydrophilicity on the anti-fouling performance of polymer membranes [10]. Specifically, unbiased molecular dynamics (MD) simulations were employed to investigate the interactions between polymer membranes (PBI, PBIO, PBIO2) and the lysozyme molecules under the condition.

2.1 Model Construction

Three polymer membranes (PBI, PBIO, and PBIO2) with various water contents are modeled using the All-Atom MD Simulation (AA-MD). Figure 1(a) depicts the polymer's monomer configurations. Using Material Studio, all polymers made up of 150 monomer units are constructed [11]. We built a module to randomly insert molecules in a cube box by the amorphous cell for production of the initial configuration at a density of 0.5 g/cm3, and we produced each simulation system with 15 polymer chains. The three-dimensional (3D) structure of lysozyme fragment was obtained from the protein database (PDB ID 253L)233 with ligand deletion. This structure of lysozyme fragment was chosen because it derived from common E. coli in water treatment [12, 13]. The simulation employed the OPLSAA force field [14]. To further understand their surface hydration in the absence of lysozyme (Fig. 1c) and protein resistance in the presence of lysozyme (Fig. 1d), water molecules that were mimicked by TIP3P [15] were solvated.

presence of lysozyme.

2.2 MD Simulation

AA-MD simulations utilizing the LAMMPS large-scale atomic/molecular parallel simulator, Visual Molecular Dynamics (VMD), and OVTO software [14, 15], and visualization and post-processing. Density functional theory was used to determine the atomic partial charge of polymer molecules in each monomer configuration using the Merz-Kollman (MK) approach. Using the particle grid Ewald (PME) approach, long-range electrostatic interactions were calculated, and the Lennard-Jones pair interaction was assessed at a cutoff distance of 12 without a truncated displacement function. The Nosé-Hoover thermostat and the Constant voltage holder were used to regulate temperature and pressure, respectively. Each simulated system is heated in accordance with an annealing technique to eliminate the polymer system's insufficient structure from its initial configuration and produce a more stable equilibrium state. To achieve equilibrium density at 298 K and 1 bar, the polymers were equilibrated using a 21-step MD compression/decompression process. The aforementioned approach repeatedly applies greater external temperatures and pressures (T_max= 800 K, P_max = 5×10⁴ bar) before progressively lowering pressure to the required settings (T_final =298K, P_final=1bar) in order to compress and relax the low-density polymer. The 21-step compression/decompression scheme's ultimate density is quite similar to the genuine density. Following annealing, equilibrium MD simulations are run for 20 ns at T = 298K and P = 1 bar, which is long enough to bring the water structures to equilibrium and affect if lysozyme preferentially adsorbs onto the polymer membranes during this period. All runs have a constant time step of 1 fs, and snapshots are taken for analysis every 1 ps.

Figure 2 shows the adsorption or desorption of lysozyme on three polymer membranes superficies during a 20ns MD simulation. The MD trajectory clearly shows that lysozyme is desorbed from PBIO (Fig. 2b), PBIO2 (Fig. 2c), but adsorbed on PBI (Fig. 2a). Different adsorption/desorption situations at the atomic level are shown by time-dependent separation distance distributions. Lysozyme rapidly flies away from the PBO and PBIO2 within the first 8 ns, where the desorption of lysozyme from the PBIO2 is more pronounced. Therefore, lysozyme is unlikely to adsorb on the surface of the PBO and PBIO2. Unlike the two polymer membranes described above, lysozyme binds tightly to PBI throughout the 20ns simulation. This implies that lysozyme adsorption on PBI is probably mediated by a mixture of hydrophobic interactions, hydrogen bonds, and electrostatic interactions at the interface, potentially because the higher hydrophobicity of PBIs lowers the surface hydration strength.

Using the separation distance distribution, we conducted a statistical analysis of the likelihood (%) of lysozyme binding to three distinct polymer membranes are shown in Fig. 3. The strong, weak, and no binding events of lysozyme and polymer membranes were defined by lysozyme-membrane separation distances of less than 4.5 Å, 4.5 ~ 9 Å, and > 9 Å, respectively. During the 20ns MD simulation, these binding events are counted every 1ps and transformed into binding probabilities. In the figure, the strong/weak/non-binding probability (%) of lysozyme fragments to PBI, PBIO, and PBIO2 was 98.2%/1.8%/0.0%, 3.8%/10.3%/85.9%, and 0.85%/3.0%/96.15%, respectively. In contrast, PBI had 0.0% of no binding, but nearly 100.0% of strong binding probability to lysozyme, indicating its non-inert property.

In this paper, a series of MD simulations were performed to study the protein anti-fouling performance of both PBI and PBI derivatives, and determine the dynamics among proteins and PBI membranes. The role of hydrophilic groups in the surface resistance of protein adsorption was demonstrated through molecular dynamics simulation of the adsorption process of lysozyme fragments on three polybenzimidazole (PBI) membranes. This study improves our understanding of anti-fouling behavior and contributes to the advancement of more effective anti-fouling materials and surfaces.

Acknowledgment

In my research work in the master stage,the teacher uses her solid theoretical knowledge and rich practical experience to help me analyze the existing problems and find solutions, so that I can move forward more smoothly on the road of scientific research.

Funding Declaration

This work was supported by the National Natural Science Foundation of China (Nos. 52070065).

Author Contribution

Zhiwei Lv authors reviewed the manuscript,Shujuan Jia authors reviewed the manuscript, Xuan Wang authors reviewed the manuscript,Simin Li authors reviewed the manuscript

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No competing interests reported.

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Molecular Dynamics Study on Anti-fouling Properties of Polybenzimidazole Membrane

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Abstract

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1. Introduction

2. Materials and Methods

2.1 Model Construction

2.2 MD Simulation

3. Results and discussion

4. Conclusions

Declarations

Acknowledgment

Funding Declaration

Author Contribution

References

Additional Declarations

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