A molecular simulation method for analyzing the interaction between antibiotics and organic matter in sewage sludge

Abstract

The present invention provides a molecular simulation method for analyzing the interaction between antibiotics and organic matter in sludge, and belongs to the technical field of molecular simulation method analysis of organic matter interaction research. The present invention adopts the sludge organic matter-antibiotic equilibrium adsorption experiment to preliminarily screen the main organic matter types that interact with antibiotics in the sludge, thereby eliminating the interference of other substances and complex conditions, and separately constructs molecular systems for the interaction between different organic matter and different antibiotics. Based on molecular dynamics simulation technology, the interaction types, strength differences and stability between different antibiotic molecules and main organic matter are compared, and the influencing factors and mechanisms are analyzed, so as to put forward reasonable suggestions for the optimization of existing sludge treatment processes, and provide important reference value and research means for the research on removing various antibiotics in sludge.

Description

Technical Field

[0001] The invention relates to the technical field of molecular simulation method analysis of organic matter interaction research, and in particular to a molecular simulation method for analyzing the interaction between antibiotics and organic matter in sludge. Background Art

[0002] Antibiotics, as a drug for treating and preventing the spread of diseases, have been widely used in human clinical treatment and animal husbandry disease prevention and control. Sewage treatment plants are important sinks for antibiotics in the environment. Antibiotics can be directly discharged into the sewage treatment system through human excrement, livestock and poultry feces, and medical wastewater. However, the existing sewage biological treatment system cannot effectively remove antibiotics, and about 70% of undegraded antibiotics will be adsorbed in the sludge. The concentration of antibiotics detected in the residual sludge of my country's sewage treatment plants generally reaches μg / kg to mg / kg. These antibiotics may be redistributed through the adsorption-release process on the sludge solid phase during the resource treatment of residual sludge such as hydrothermal and anaerobic digestion, thus having the opportunity to enter the soil and water environment, causing further spread of antibiotics. However, there is currently little research on the solid-liquid phase distribution of antibiotics in the sludge treatment process, especially the interaction mechanism between antibiotics and sludge organic matter in the liquid phase, which limits the resource utilization of sludge and the development and optimization of antibiotic pollution control technology.

[0003] Molecular dynamics (MD) simulation is a computer simulation method used to simulate and predict molecules or molecular systems. It can analyze the dynamic changes of thermodynamics, kinetics, structure, morphology and other dynamic changes of complex molecular systems. It is a powerful tool for studying molecular interactions. Since the organic matter in sludge is complex, it contains complex components such as organic matter (protein (PN), humic acid (HA), polysaccharides, etc.), inorganic particles, mycelium and extracellular polymers (EPS), which makes the interaction between antibiotics and sludge organic matter very complicated. It is difficult to deeply explore its interaction mechanism through ordinary experimental methods.

[0004] At present, there is no evaluation method for the interaction between organic matter and antibiotics in sludge. The microscopic mechanism of the interaction between sludge organic matter and antibiotics cannot be revealed through traditional experimental characterization methods. Some existing evaluation methods for organic matter interactions are not fully applicable to the evaluation of the interaction between organic matter and antibiotics in sludge systems.

[0005] Patent CN 116453587 B calculates the binding free energy between receptor protein molecules and ligand compounds (such as drug molecules) in a simulated environment through molecular simulation and molecular docking to evaluate the protein-ligand affinity strength, which is helpful for drug screening and development.

[0006] Patent CN 102930152 B obtains the action mode and reaction (binding / dissociation) mode of target receptor-ligand at the molecular level through computer simulation, and can qualitatively and quantitatively analyze the interaction mode and strength between ligand molecules (drug molecules) and their target receptors.

[0007] Patent CN 111672457 A calculates and compares the self-diffusion coefficient, density distribution function and radial distribution function of modified montmorillonite-pollutant systems with different structures through molecular simulation to find the modified montmorillonite with the best performance.

[0008] The above methods all involve the field of evaluating the receptor-ligand interaction mode and strength. The first two are mainly for the qualitative and quantitative evaluation of the interaction model after the one-to-one binding of ligand-receptor molecules is stabilized, and the latter is the interaction evaluation of the one-to-many system of receptor-ligand molecules. However, for the interaction system between organic matter and antibiotics in sludge, on the one hand, the simulation conditions and modeling methods of the above methods are still far from the sludge organic matter-antibiotic system, and on the other hand, they ignore the hard evaluation indicators such as time and distance, which are not enough to fully and conveniently measure the interaction strength and stability of sludge organic matter-antibiotics. In addition, there is a lack of sludge organic matter screening steps, and it is impossible to realize the systematic screening-simulation-evaluation process of sludge organic matter-antibiotic interaction system. Summary of the invention

[0009] The purpose of the present invention is to address the above-mentioned deficiencies in the prior art and provide a molecular simulation method for analyzing the interaction between antibiotics and organic matter in sludge. Based on MD simulation technology, the interaction mode, intensity and stability of different antibiotics and organic matter in sludge are explored, and the influencing factors and mechanisms are analyzed, so as to put forward reasonable suggestions for the optimization of existing sludge treatment processes, and provide important reference value and research means for the research on removing various antibiotics in sludge.

[0010] In order to achieve the above object, the present invention adopts the following technical solutions:

[0011] The purpose of the present invention is to provide a molecular simulation method for analyzing the interaction between antibiotics and organic matter in sludge, comprising the following specific steps:

[0012] S1. Screening of research subjects

[0013] Extracting organic matter from sludge, analyzing its components, and screening the organic matter through an organic matter-antibiotic adsorption experiment; the types of the organic matter include protein, humic acid, polysaccharide or polypeptide;

[0014] S2. Build a molecular system model for molecular dynamics simulation

[0015] S21. Constructing molecular configuration

[0016] The configurations of the various molecules of the organic matter and antibiotics screened in step S1 are constructed through databases and literature libraries;

[0017] S22. Determine force field parameters

[0018] GROMACS was used as the simulation software, the TIP3P water model was used to construct the solvation environment, and the CHARMM36 force field was used as the basis to construct the force field environment; the topological structure files of each molecule of organic matter and antibiotics under the CHARMM36 force field were generated using CHARMM-GUI, and the topological parameters of each molecule were integrated into the parameter file of the force field, the basic force field parameters were corrected and supplemented, and the force field parameters of the optimized molecular system model were determined;

[0019] S23. Build a system model for simulating molecular dynamics

[0020] The molecular structure models of organic matter and antibiotics constructed in step S21 are used to construct an antibiotic-organic complex model, and the antibiotic-organic complex model is solvated and charged balanced using GROMACS software to obtain an electrically neutral cubic water model; the antibiotic-organic complex model includes any one or more of an antibiotic-protein model, an antibiotic-humic acid model, an antibiotic-polysaccharide model, and an antibiotic-peptide model;

[0021] S24. System model optimization

[0022] Using the force field determined in step S22, the steepest descent method and the conjugate gradient method are used to minimize the energy of the entire system model, and then NVT position restriction simulation and NPT position restriction simulation are performed to obtain a system model that can be used to perform molecular dynamics simulations;

[0023] S25. Run molecular dynamics simulation

[0024] Remove the position constraints of all molecules in the system, change some algorithms, and run molecular dynamics simulations under the same temperature and pressure conditions;

[0025] S3. Analysis of molecular dynamics simulation results

[0026] S31. Trajectory processing

[0027] After extracting, repairing and integrating the generated system molecular trajectory files, the radius of gyration and density were calculated using the GROMACS program to determine whether the dynamics simulation reached stability and equilibrium;

[0028] S32. Interaction evaluation

[0029] When the gyration radius and density indicators change steadily, the dynamic simulation of the molecular system is considered to have reached stability and equilibrium, and the interaction between the molecules in the system is evaluated. The evaluation indicators include binding energy, contact distance and time, and diffusion coefficient.

[0030] S33. Visual analysis of interaction patterns

[0031] Molecular docking is performed on the antibiotic-organic matter to obtain a binding mode between the antibiotic and the organic matter, and the molecular docking result and the results of steps S31 and S32 are visualized; the binding mode includes the mode, quantity and characteristics of the interaction between the two, wherein the interaction mode includes hydrogen bonding, weak hydrogen bonding, and cation-π interaction dominated by electrostatic force; π-π stacking and van der Waals interaction dominated by dispersion force, and hydrophobic interaction;

[0032] S4. Evaluation of interactions

[0033] The intensity and stability of the interaction between different sludge organic matter and different antibiotics were generally evaluated based on three indicators: the binding energy between antibiotics and organic matter, the contact distance and time, and the diffusion coefficient of antibiotics. Then, the reasons for the above results were explained based on the visual analysis of the interaction pattern.

[0034] Furthermore, in step S1, the sludge is produced by an activated sludge method of an anaerobic-aerobic process.

[0035] Furthermore, in step S21, the configuration of each molecule is obtained through rcsb, PubChem database and literature channels, and constructed through ChemDraw.

[0036] Furthermore, in step S23, the side length calculation formula of the cubic water model is: side length = maximum diameter of protein + 2×non-bonded interaction cutoff radius.

[0037] Furthermore, in step S24, the steepest descent method and the conjugate gradient method are used in turn in the energy minimization process to make the maximum force in the system less than 200 kJ / mol and 100 kJ / mol respectively.

[0038] Furthermore, in step S24, the NVT position constraint simulation and the NPT position constraint simulation use a V-rescale thermostat algorithm and a Berendsen barostat algorithm, so that the temperature and pressure of the kinetic equilibrium parameters are maintained at 25°C and 1 bar, respectively.

[0039] Furthermore, in step S25, while other algorithm settings remain unchanged, the V-rescale thermostat and Parrinello-Rahman barostat are used to maintain the same temperature and pressure conditions, the position constraints of all molecules are released, and a molecular dynamics simulation with a duration of 100 ns is performed.

[0040] Furthermore, in step S32, the binding energy is calculated by using the gmx_mmpbsa script, setting the corresponding temperature and relative dielectric constant, and calculating the binding energy of the interacting molecular pairs, wherein the binding energy includes electrostatic interaction energy and van der Waals interaction energy.

[0041] Furthermore, in step S32, the diffusion coefficient is calculated by using the msd function in the GROMACS program and the following formula to calculate the diffusion coefficient of the antibiotic molecules in the system:

[0042]

[0043] Where N represents the number of molecules, n traj represents the number of molecular dynamics simulation trajectory frames, r i (t0+t) and r i (t0) represents the position of the center of mass of the molecule at a certain time and the initial time, and D is the diffusion coefficient.

[0044] Furthermore, in step S32, the contact distance and contact time are calculated by using the mindist function in the GROMACS program to systematically calculate how the minimum distance between the antibiotic and the organic matter changes over time. The minimum distance in the program is defined as the distance between the two nearest non-hydrogen atoms between the ligand and the receptor. When the distance is less than 0.4 nm, it is considered that the antibiotic and the organic matter are in an adsorption contact state, and the contact time is obtained by counting the number of frames in the contact state in the trajectory.

[0045] Furthermore, in step S33, the molecular docking results and the results of steps S31 and S32 are visualized using the pymol tool.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] The present invention provides a molecular simulation method for analyzing the interaction between antibiotics and organic matter in sludge. The EPS-antibiotic equilibrium adsorption experiment in sludge is used to preliminarily screen the main organic matter types that interact with antibiotics in sludge, thereby eliminating the interference of other substances and complex conditions, and separately constructing an interaction system between antibiotic molecules and main organic matter molecules in sludge. Based on molecular dynamics simulation technology, the interaction types, strength differences and stability between different antibiotic molecules and main organic matter are compared, and the influencing factors and mechanisms are analyzed, thereby making reasonable suggestions for optimizing the existing sludge treatment process and providing important reference value and research means for the research on removing various antibiotics in sludge. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 A flow chart of a molecular simulation method for analyzing the interaction between antibiotics and organic matter in sludge provided by the present invention;

[0049] Figure 2a A schematic diagram of the protein-azithromycin molecular simulation system provided in Example 1;

[0050] Figure 2b A schematic diagram of the molecular simulation system of humic acid-azithromycin provided in Example 1;

[0051] Figure 3 This is a graph showing the binding energy analysis results of the protein / humic acid-antibiotic system provided in Example 1;

[0052] Figure 4 This is a graph showing the diffusion coefficient analysis results of the protein / humic acid-antibiotic system provided in Example 1;

[0053] Figure 5 The contact time distribution diagram of the protein / humic acid-antibiotic system provided in Example 1;

[0054] Figure 6a A schematic diagram of the oxytetracycline-protein interaction provided in Example 1;

[0055] Figure 6b A schematic diagram of the interaction between ofloxacin and protein provided in Example 1;

[0056] Figure 6c A schematic diagram of the azithromycin-protein interaction provided in Example 1;

[0057] Figure 6d A schematic diagram of the interaction between oxytetracycline and humic acid provided in Example 1;

[0058] Figure 6e A schematic diagram of the interaction between ofloxacin and humic acid provided in Example 1;

[0059] Figure 6f A schematic diagram of the interaction between azithromycin and humic acid provided in Example 1. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solution and advantages of the present invention clearer, embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0061] The molecular simulation method for analyzing the interaction between antibiotics and organic matter in sludge of the present invention is described by using the activated sludge produced by the traditional anaerobic-aerobic activated sludge method. The activated sludge produced by the traditional anaerobic-aerobic activated sludge method has a high organic matter content. Its protein molecules can refer to the methylmalonyl-CoA epimerase in the case of the present invention, the polypeptide can be selected from the polypeptide molecules in the aquatic environment DOM molecular system model constructed by Devarajan et al., the humic acid can be selected from the Temple-Northeastern-Birmingham (TNB) model, and the polysaccharide can be selected; for the sludge produced by the aerobic process, the organic matter content is relatively low, and representative protein submolecules can be selected according to the characterization of its organic matter in the existing data, such as the most commonly detected formate C-acetyltransferase or acetate kinase in the aerobic granular sludge characterized by Agnieszka Cydzik-Kwiatkowska et al., and the humic acid can also be selected from the above-mentioned TNB model.

[0062] Example 1

[0063] The source of activated sludge is the residual sludge produced by the anaerobic-aerobic process of a municipal sewage treatment plant in Nanjing. The basic characteristics of the original sludge sample are as follows.

[0064] Table 1. Basic characteristics of raw sludge

[0065]

[0066] This embodiment provides a molecular simulation method for analyzing the interaction between antibiotics and organic matter in sludge, referring to Figure 1 , the specific process is as follows:

[0067] (1) Screening of research objects: Extract organic matter from sludge, mainly extracellular polymeric substances (EPS). Dilute the EPS solution to 50 mg TOC / L. Then add the stock solution of the antibiotic to be studied to the EPS solution, add 0.1 mmol / L HCl / NaOH solution, adjust the initial pH of the solution to 7.00±0.02, and obtain an EPS-antibiotic mixture. Place the EPS-antibiotic mixture in the dark at 25±0.5℃ for 6 hours to reach adsorption equilibrium. Use a three-dimensional fluorescence spectrophotometer (F-4500, Hitachi, Japan) to obtain 3D-EEM spectra before and after the equilibrium adsorption experiment of EPS and different antibiotics. The excitation scan is 200~450nm, with an interval of 5nm; the emission scan is 250~550nm, with an interval of 2nm. Under the same conditions, the EEM spectrum of Milli-Q ultrapure water was measured and recorded as a blank, and the blank spectrum was subtracted from the EEM spectrum of each sample to eliminate Raman scattering and other background noise of water; different components in EPS were separated from the EEM spectrum by parallel factor analysis (PARAFAC). Since antibiotics bind to components in EPS, fluorescence quenching will occur, and the fluorescence intensity of the components will decrease, which can preliminarily determine which type of organic matter in EPS adsorbs antibiotics.

[0068] In a specific implementation, by extracting sludge organic matter and conducting sludge organic matter-antibiotic adsorption experiments, the samples before and after the experiment were characterized by three-dimensional fluorescence. It was found that the fluorescence intensity of protein and humic acid in the system changed significantly before and after the adsorption experiment. It is inferred that the substances in the sludge organic matter that interact with antibiotics are mainly protein (PN) and humic acid (HA).

[0069] (2) Constructing a molecular model: Based on the types of organic matter screened in the previous step and experimental testing, select representative molecules from various types of sludge organic matter for model construction. Molecular structure information can be obtained through databases such as rcsb and PubChem and literature channels, and can be constructed through ChemDraw. The types of organic matter such as protein, humic acid, and polysaccharides produced by different sewage treatment processes are different. It is necessary to select representative specific organic matter molecules to construct a molecular model based on the differences in the types of organic matter produced by the sludge from different treatment processes. According to the actual pH of the sludge sample, the molecular structure of the antibiotics to be studied and the sludge organic matter is modified. The prediction results of the Chemxaon website (https: / / playground.calculators.cxn.io) can be referred to.

[0070] In a specific implementation scheme, the sludge organic matter and antibiotic molecular models used in this example are constructed. For protein substances in sludge organic matter, the main chain of the macromolecular protein selects methylmalonyl-CoA epimerase, code-named Protein main chain, which is an essential enzyme for the degradation of odd-numbered fatty acids in the wastewater treatment process, and the molecular structure is derived from the PN database (http: / / www.rcsb.org, ID: 1JC4). According to the research of Devarajan et al., the polypeptide molecules in the aquatic environment DOM molecular system model constructed by them are selected as representative polypeptide molecules, code-named Polypeptide-1 and Polypeptide-2, which are pentapeptides and heptapeptides, respectively. The amino acid sequence of the pentapeptide is: Gln-Pro-Gly-Asp-Thr; the amino acid sequence of the heptapeptide is: Met-Gly-Cys-Thr-Tyr-Thr-Il, and the molecular structure is constructed using ChemDraw 20.0 software. The Temple-Northeastern-Birmingham (TNB) model is one of the most classic HA molecular models widely used in MD simulations, and the molecular structure was built using ChemDraw 20.0 software. The molecular structures of three typical antibiotics (oxytetracycline OTC, ofloxacin OFL, and azithromycin AZI) were obtained from the PubChem database (https: / / pubchem.ncbi.nlm.nih.gov). According to the actual pH of the sludge sample, specific groups of the three antibiotic molecules were modified. The carboxyl groups in each molecule were deprotonated and carried a unit negative charge; two hydroxyl groups in OTC were deprotonated, and the hydroxyl groups of the OTC molecule and the remaining molecules remained electrically neutral; except for OFL and AZI, the amino groups of the remaining molecules remained electrically neutral.

[0071] (3) Determine the simulation parameters: GROMACS (v2020.6) was used as the simulation software, the classical three-point (TIP3P) water model was used to construct the solvation environment, and the force field environment was constructed based on the CHARMM36 force field. CHARMM-GUI (https: / / www.charmm-gui.org) was used to generate the topological structure files of the above molecules under the CHARMM36 force field, and the topological parameters of each molecule were integrated into the parameter file of the force field to correct and supplement the basic force field parameters.

[0072] (4) Constructing simulation system model: The simulation system model is a one-to-one correspondence system between antibiotics and sludge organic matter, such as antibiotic A-organic matter A, antibiotic A-organic matter B, antibiotic B-organic matter A... and so on. The number of antibiotic molecules in each simulation system model is 20, and the number of receptor substances (such as PN, HA, polypeptide) molecules refers to the content of each component in the actual sludge sample, among which the number of large molecule PN is set to 1, and the amount of polypeptide and HA added refers to the relative value of the PN content. Use Packmol (v18.196) software to create a cubic box with a side length of 8nm (for protein molecules with a size of less than 1.8w, a cubic box with a side length of 8nm is used, and the box side length calculation formula (unit: nm): side length = maximum protein diameter + 2×non-bonded interaction cutoff radius). If it is a protein-antibiotic system, the protein is placed in the center of the box, and other molecules are placed randomly and evenly, and the distance between the closest atoms in any two molecules is kept greater than the sum of the van der Waals radii of the two atoms plus If it is a small molecule organic matter (humic acid, polysaccharide), the molecules in the group are randomly and evenly placed in the box, and the distance between the closest atoms in any two molecules is greater than the sum of the van der Waals radii of the two atoms plus At the same time, a topological file of the system is created, which contains information about the atoms, geometry, charge, number, force field parameters, etc. of each molecule. The solvation function in the GROMACS program is called, and the TIP3P water model is used to fill the box. The density defaults to 1000 kg / m 3 Call the insert ion function in the GROMACS program to automatically add a certain number of Na + or Cl - ions to achieve charge balance in the system.

[0073] In a specific embodiment, a total of 6 different molecular systems were constructed for MD simulation, as shown in Table 1, mainly including three different antibiotics and two main components of EPS (PN and HA). The solvent dielectric constant can be calculated by the GROMACS program. The specific information of each system is shown in Table 1.

[0074] Table 1.

[0075]

[0076] Use Packmol (v18.196) software to create a cubic box with a side length of 8 nm, and randomly and evenly place the molecules in the group in the box. The effect is as follows Figure 2a and 2b Shown

[0077] (5) Model optimization: The GROMACS program was called to perform static structural optimization on the unreasonable configurations and contacts in the system. The steepest descent (steep) method and the conjugate gradient (cg) method were used to make the maximum force in the system less than 200 kJ / mol and 100 kJ / mol respectively. Subsequently, position restriction commands were imposed on other molecules except the solvent molecules in the topology file of the system so that they could only move slightly within a limited range. The execution file (.mdp file) was set as follows: hydrogen bonds were constrained by the LINCS algorithm with an integration step of 2 fs. The electrostatic interaction was calculated by the particle mesh Ewald method with a cutoff value of 1.0 nm. The non-bonded interaction cutoff value was 1.0 nm. The temperature was coupled to the target temperature of 25 °C using the V-rescale thermostat algorithm, and the first position-restricted simulation of the system was performed under the NVT ensemble for 100 ps. Then continue to use the V-rescale thermostat algorithm to couple the target temperature, use the Berendsen barostat algorithm to couple the system pressure to 1 bar, and perform a 100ps position-restricted simulation under the NPT ensemble. The significance of the restricted simulation is to relax the system to the target temperature and pressure conditions, so that the system is better immersed in the solvent and eliminate unreasonable contacts.

[0078] (6) Run the molecular dynamics simulation: while keeping other settings unchanged, maintain the same temperature and pressure conditions using the V-rescale thermostat and Parrinello-Rahman barostat, release the position restraints of all molecules, and perform a simulation for 100 ns.

[0079] (7) Trajectory processing: After extracting, repairing, and integrating the generated system molecular trajectory files, the following indicators are calculated using the GROMACS program and other scripts:

[0080] Indicator 1: The radius of gyration (Rg) describes the extension of the polymer chain in space, and its calculation formula is as follows:

[0081]

[0082] Where N represents the number of atoms, r i -r c represents the distance between atom i and the center of mass of the system, m i represents the mass of atom i.

[0083] The Rg of the largest molecular cluster (when the distance between any two atoms of a molecule is no more than 0.35 nm, the molecules are considered to belong to the same cluster) in the finished product simulation stage of each PN-antibiotic system was calculated to show the stability of the system and the dynamics of the PN-antibiotic complex.

[0084] Indicator 2: Density. The average density of each HA-antibiotic system in the NPT pre-equilibrium phase was calculated to demonstrate the stability of the system.

[0085] The above indicators are used to evaluate whether the molecular dynamics simulation has reached equilibrium. When the dynamic changes of the above two indicators are stable, it is considered that the dynamics simulation of the molecular system has reached stability and equilibrium, and the evaluation of the interaction between the molecules in the system can be started.

[0086] (8) Interaction evaluation system: After the dynamic changes of the above two indicators are stable, it is considered that the dynamic simulation of the molecular system has reached stability and equilibrium, and the interaction evaluation between the system molecules can be started. The trajectory of each system in the stable stage is intercepted. The interaction evaluation system between antibiotics and organic matter in sludge is mainly divided into three evaluation indicators: energy, contact distance and time, and diffusion coefficient, and two parts of visual analysis of the interaction mode. The indicator calculation method is as follows:

[0087] Indicator 3: Binding energy refers to the binding energy (adsorption energy) between molecules, which is the manifestation of the attraction between molecules. The larger the binding energy, the stronger the mutual attraction between molecules and the tighter the binding.

[0088] Calculation method: Use the gmx_mmpbsa script (https: / / github.com / Jerkwin / gmxtool / blob / master / gmx_mmpbsa), set the corresponding temperature and relative dielectric constant (calculated by GROMACS program), and calculate the binding energy of the interacting molecular pairs, including electrostatic interaction energy and van der Waals interaction energy. For all systems, select 5-6 pairs of relatively stable interacting molecular pairs in the simulated equilibrium stage. For each molecular pair, extract the time period trajectory of its stable interaction, evenly extract 100 frames from it for binding energy calculation, and obtain 100 binding energy data. Process the above data, remove the outliers with a deviation of more than two standard deviations from the average value, and then calculate the average value to obtain the average binding energy of each molecular pair group. Average the above results, and then remove all data of the abnormal molecular pair groups that exceed 20% of the average value. Clean the remaining molecular pair group binding energy data again, remove the outliers with a deviation of more than three standard deviations from the average value, and average the remaining data to obtain the average binding energy under the system, and perform T test on the data of each system.

[0089] refer to Figure 3, is the interaction binding energy between antibiotics and sludge organic matter in each system, including electrostatic interaction energy and van der Waals interaction energy, among which electrostatic interaction plays a leading role in the binding process. The electrostatic interaction energy accounts for the largest proportion in the PN-OTC system (-183.81 kJ / mol), followed by the PN-OFL system (-127.62 kJ / mol) and the PN-AZI system (-118.20 kJ / mol), indicating that the binding of OTC to proteins in sludge may be tighter than the other two, while the proportion of van der Waals interaction energy of AZI is higher than the other two, and the contact with proteins is better. The overall interaction binding energy between HA and antibiotics is lower than that of PN, indicating that the binding ability of antibiotics to HA in sludge is low. The binding of the two is greatly affected by the electrical properties of the two. The binding energy of AZI molecules with different electrical properties from HA is stronger, while the OTC with the same electrical properties is mainly electrostatic repulsion; the electrostatic force between the uncharged OFL and HA molecules is almost 0, which is mainly van der Waals interaction. In addition, the van der Waals force in the HA-antibiotic system is relatively weaker than that in the PN-antibiotic system, which also indicates that the contact and fit between HA and antibiotics is not as good as that between PN.

[0090] Indicator 4: Diffusion coefficient (D), which is defined as the mass or mole of a substance diffused vertically through a unit area under the condition of unit concentration gradient per unit time, is used to measure the diffusion rate of antibiotic molecules in the system.

[0091] Calculation method: The msd function in the GROMACS program is used to calculate the diffusion coefficient of the antibiotic molecules in the system using the following formula:

[0092]

[0093] Where N represents the number of molecules, n traj represents the number of molecular dynamics simulation trajectory frames, r i (t0+t) and r i (t0) represents the position of the center of mass of the molecule at a certain time and the initial time, and D is the diffusion coefficient.

[0094] refer to Figure 4 The figure shows the diffusion coefficient of antibiotics in each system, which reflects the diffusion speed of antibiotics, which is mainly determined by the Brownian motion. It can be seen that the diffusion speed of OFL and AZI in PN and HA systems is not much different, while the diffusion speed of OTC in HA system is significantly higher than that in PN system.

[0095] Indicator 5: contact distance and contact time. The contact distance refers to the distance between the antibiotic molecules and the sludge organic matter molecules. It is a hard indicator of whether the two have a stable interaction, and can also reflect the degree of interaction between the two. The contact time refers to the time for the two to have a stable interaction.

[0096] Calculation method: The mindist function in the GROMACS program is used to calculate the change of the minimum distance between the ligand (antibiotic) and the receptor molecule (protein backbone / humic acid) over time. The minimum distance in the program is defined as the distance between the two nearest non-hydrogen atoms of the ligand and the receptor. When the distance is less than 0.4nm, the ligand and the receptor are considered to be in an adsorption contact state (i.e., a stable interaction occurs), and the contact time is obtained by counting the number of frames in the contact state in the trajectory.

[0097] refer to Figure 5 , the figure shows the distribution of the contact time between antibiotics and their receptors. For the PN-antibiotic system, the binding stability of the three antibiotics with proteins at room temperature is similar; for the HA-antibiotic system, AZI has the longest contact time with HA and is more stable than the other two, followed by OFL, and OTC has the worst binding stability with HA. Compared with PN, the contact time between antibiotics and HA is very short, which indicates that the interaction between antibiotics and HA is very unstable.

[0098] Based on the above three indicators, the binding strength and stability of the three antibiotics with proteins at room temperature are similar, mainly electrostatic interaction, among which OTC has slightly higher binding strength and stability with proteins than the other two. The binding strength and stability of AZI with HA are higher than the other two; OFL with HA is mainly van der Waals interaction, with average stability; while OTC and HA mainly show electrostatic repulsion. The binding strength and stability of antibiotics with proteins in sludge organic matter are significantly higher than HA.

[0099] (9) The interaction pattern visualization analysis method is as follows: First, the pymol tool is used to visualize the molecular simulation trajectory, observe the dynamic changes of molecules during the simulation process, and the contact between organic matter and antibiotic molecules to preliminarily determine the interaction between organic matter and antibiotics.

[0100] Secondly, the interaction patterns between different organic matter and antibiotics were deeply analyzed. For the protein-antibiotic system, CB-Dock 2 (https: / / cadd.labshare.cn / cb-dock2 / php / index.php) was used to perform molecular docking between antibiotics and proteins to obtain the typical binding mode between antibiotics and proteins. Pymol (2.6.0) (http: / / www.pymol.org / pymol) was used to visualize the molecular docking results and molecular simulation trajectories. The binding of antibiotics and proteins in the molecular simulation trajectory was compared with the binding mode of antibiotics and proteins obtained by molecular docking. Typical frames with similar binding modes of antibiotics and proteins in the molecular simulation trajectory and molecular docking results were selected as typical representative modes of interaction between antibiotics and proteins in the molecular simulation process. Then, a deeper interaction analysis was performed, namely the interaction mode, characteristics, principle, and quantity, to obtain the interaction mode between a certain antibiotic and protein. For relatively small molecules such as humic acid, polypeptides, and polysaccharides, it is difficult to perform molecular docking. We can directly select multiple frames in the molecular simulation trajectory where they interact with antibiotic molecules, analyze their binding mode and characteristics, and compare the results of multiple frames to obtain the interaction mode between small molecule organic matter and antibiotics. When conducting the analysis, we should first start from the differences in molecular structure and charge distribution of different antibiotics, and explain their interaction mode with a certain sludge organic matter and the strength and stability of this action mode. Then, we can compare the differences in molecular structure and charge distribution of different sludge organic matter, and explain the characteristics and differences when they interact with antibiotics.

[0101] refer to Figure 6a-6f , combined with the molecular docking results generated by CB-Dock, typical frames of antibiotic-sludge organic matter interactions were selected from the simulation trajectory and visualized using Pymol software, and the typical interaction patterns of antibiotics with PN and HA were obtained. The strength and stability of the interaction between sludge organic matter and antibiotics are determined by the interaction mode between the two, and the interaction mode between the two is determined by the differences in their molecular structures such as different functional groups and special atoms and the resulting differences in charge distribution.

[0102] like Figure 6a As shown, for OTC, the hydrogen atoms on the hydroxyl and amino groups and the hydrogen atoms on the benzene ring obtain higher positive charges due to the inductive and conjugated effects, which makes it easier for them to form hydrogen bonds with PN; at the same time, its polycyclic structure and the presence of branched hydrocarbon groups are also conducive to its hydrophobic interaction with the hydrophobic groups in PN.

[0103] like Figure 6bAs shown in the figure, the typical interaction modes between OFL and PN include hydrogen bonding, cation-π interaction, π-π stacking and hydrophobic interaction. OFL has protonated amino groups, which are easier to form cation-π interactions; its ring structure and relatively compact molecular shape are also easier to form π-π stacking and hydrophobic interactions with PN. The fluorine atoms in OFL have high electronegativity, which contributes to its stable electrostatic interaction with PN.

[0104] like Figure 6c As shown in the figure, the interaction mode between AZI and PN is similar to that of OTC, which is mainly based on hydrogen bonds and hydrophobic interactions. However, compared with the other two antibiotics, AZI is different in that it has a macrocyclic structure and a larger molecular weight, its molecular deformability is better, its contact with protein molecules is better, and its hydrophobic interaction and van der Waals force are relatively stronger.

[0105] like Figure 6d , 6e As shown in Figure 6f, the contact between the three antibiotics and HA is looser than that between PN, and their mode of action is relatively simple. Since HA molecules mainly carry negatively charged groups and are relatively small, their interaction with antibiotic molecules is also dominated by electrostatic interactions. OTC molecules mainly have negatively charged groups, while AZI molecules have more positively charged groups, so the interaction between HA and OTC is mainly electrostatic repulsion, which also accelerates the diffusion rate of OTC in the HA system, and its attraction is mainly provided by hydrophobic effect and van der Waals interaction; while the interaction between HA and AZI is mainly electrostatic attraction, and the interaction is more diverse, with hydrogen bonds, π-π stacking and hydrophobic effects; OFL and HA have almost no electrostatic interaction, and are mainly based on van der Waals interaction and hydrophobic interaction. The extreme instability of the interaction between antibiotics and HA is due to the fact that the charged groups contained in HA molecules are all negatively charged, and the repulsion between them and the same negatively charged antibiotic molecules is more obvious, and the degree of domination by electrical properties is highly determined; on the other hand, because both HA and antibiotic molecules are small, their interaction is more seriously affected by Brownian motion, hydrophobic effect and other intermolecular interactions, and the molecules will migrate more obviously, and the interaction stability is very poor.

[0106] Compared with HA, PN has multiple activation sites (such as hydroxyl, carboxyl, and amine), which makes PN more diverse and flexible in electrostatic and other types of interactions, and also makes its interaction with antibiotics stronger and more stable than that of HA.

[0107] (9) Systematic analysis and evaluation logic of interaction indicators: The systematic evaluation of the interaction between the two is divided into two parts: results and causes. The binding energy, contact distance and time between antibiotics and organic matter, and the diffusion coefficient of antibiotics presented by indicators 3, 4, and 5 generally reflect the intensity and stability of the interaction between the two, and are the evaluation results of the interaction between the two; indicator 6 - the interaction pattern between antibiotics and sludge organic matter presented by the visual analysis is a deeper explanation of the intensity and stability of their interaction. The two are interrelated and corresponding causal relationships. When analyzing, the intensity and stability of the interaction between different sludge organic matter and different antibiotics should be evaluated overall based on the first three indicators, and then the reasons for the above results should be explained based on the visual analysis of the interaction pattern.

[0108] The evaluation results of the interaction between organic matter and antibiotics in the sludge of this example are as follows:

[0109] The binding strength and stability of the three antibiotics with proteins at room temperature are similar, mainly electrostatic interactions, among which OTC has slightly higher binding strength and stability with proteins than the other two. The interaction mode between AZI and proteins is similar to that of OTC, both of which are mainly hydrogen bonds and hydrophobic interactions, but AZI has a large ring structure, a larger molecular weight, and stronger deformability, and its hydrophobic interaction is stronger than the other two; OFL has a variety of interaction modes with proteins, including hydrogen bonds, cation-π interactions, π-π stacking, and hydrophobic interactions.

[0110] The interaction between antibiotics and HA is generally unstable and is highly dominated by the charge of the molecule. The binding strength and stability of AZI and HA are relatively higher than the other two; OFL and HA are mainly van der Waals interactions with average stability; while OTC and HA mainly show electrostatic repulsion, and the attraction is provided by hydrophobic interaction and van der Waals interaction.

[0111] The intensity and stability of the interaction between antibiotics and proteins in sludge organic matter are significantly higher than HA. PN has multiple activation sites (such as hydroxyl, carboxyl, and amine), which makes PN more diverse and flexible in electrostatic and other types of interactions, and also leads to higher intensity and stability of its interaction with antibiotics compared to HA.

[0112] For matters not mentioned above, the prior art applies.

[0113] Although some specific embodiments of the present invention have been described in detail through examples, those skilled in the art should understand that the above examples are for illustration only and are not intended to limit the scope of the present invention. Those skilled in the art to which the present invention belongs may make various modifications or supplements to the specific embodiments described or replace them in a similar manner, but they will not deviate from the direction of the present invention or exceed the scope defined by the attached claims. Those skilled in the art should understand that any modifications, equivalent substitutions, improvements, etc. made to the above embodiments based on the technical essence of the present invention should be included in the protection scope of the present invention.

Claims

1. A molecular simulation method for analyzing the interaction between antibiotics and organic matter in sludge, characterized in that: The specific steps include: S1. Screening of research subjects Extracting organic matter from sludge, analyzing its components, and screening the organic matter through an organic matter-antibiotic adsorption experiment; the types of the organic matter include protein, humic acid, polysaccharide or polypeptide; S2. Build a molecular system model for molecular dynamics simulation S21. Constructing molecular configuration The configurations of the various molecules of the organic matter and antibiotics screened in step S1 are constructed through databases and literature libraries; S22. Determine force field parameters GROMACS was used as the simulation software, the TIP3P water model was used to construct the solvation environment, and the CHARMM36 force field was used as the basis to construct the force field environment; the topological structure files of each molecule of organic matter and antibiotics under the CHARMM36 force field were generated using CHARMM-GUI, and the topological parameters of each molecule were integrated into the parameter file of the force field, the basic force field parameters were corrected and supplemented, and the force field parameters of the optimized molecular system model were determined; S23. Build a system model for simulating molecular dynamics The molecular structure models of organic matter and antibiotics constructed in step S21 are used to form an antibiotic-organic complex model, and the antibiotic-organic complex model is solvated using GROMACS software to balance the system to obtain an electrically neutral cubic water model; the antibiotic-organic complex model includes any one or more of an antibiotic-protein model, an antibiotic-humic acid model, an antibiotic-polysaccharide model, and an antibiotic-peptide model; S24. System model optimization Using the force field determined in step S22, the steepest descent method and the conjugate gradient method are used to minimize the energy of the entire system model, and then NVT position restriction simulation and NPT position restriction simulation are performed to obtain a system model that can be used to perform molecular dynamics simulations; S25. Run molecular dynamics simulation Remove the position constraints of all molecules in the system, change some algorithms, and run molecular dynamics simulations under the same temperature and pressure conditions; S3. Analysis of molecular dynamics simulation results S31. Trajectory processing After extracting, repairing and integrating the generated system molecular trajectory files, the radius of gyration and density were calculated using the GROMACS program to determine whether the dynamics simulation reached stability and equilibrium; S32. Interaction evaluation When the gyration radius and density indicators change steadily, the dynamic simulation of the molecular system is considered to have reached stability and equilibrium, and the interaction between the molecules in the system is evaluated. The evaluation indicators include binding energy, contact distance and time, and diffusion coefficient. S33. Visual analysis of interaction patterns Molecular docking is performed on the antibiotic-organic matter to obtain a binding mode between the antibiotic and the organic matter, and the molecular docking result and the results of steps S31 and S32 are visualized; the binding mode includes the mode, quantity and characteristics of the interaction between the two, wherein the interaction mode includes hydrogen bonding, weak hydrogen bonding, and cation-π interaction dominated by electrostatic force; π-π stacking and van der Waals interaction dominated by dispersion force, and hydrophobic interaction; S4. Evaluation of interactions The intensity and stability of the interaction between different sludge organic matter and different antibiotics were generally evaluated based on three indicators: the binding energy between antibiotics and organic matter, the contact distance and time, and the diffusion coefficient of antibiotics. Then, the reasons for the above results were explained based on the visual analysis of the interaction pattern.

2. The method according to claim 1, characterized in that In step S1, the sludge is produced by an activated sludge method of an anaerobic-aerobic process.

3. The method according to claim 1, characterized in that In step S21, the configuration of each molecule is obtained through rcsb, PubChem database and literature channels, and constructed through ChemDraw.

4. The method according to claim 3, characterized in that In step S23, the side length calculation formula of the cubic water model is: side length = maximum diameter of protein + 2×non-bonded interaction cutoff radius.

5. The method according to claim 4, characterized in that In step S24, the steepest descent method and the conjugate gradient method are used in turn in the energy minimization process to make the maximum force in the system less than 200 kJ / mol and 100 kJ / mol respectively.

6. The method according to claim 5, characterized in that In step S24, the NVT position restriction simulation and the NPT position restriction simulation use the V-rescale thermostat algorithm and the Berendsen barostat algorithm so that the temperature and pressure of the kinetic equilibrium parameters are maintained at 25°C and 1 bar, respectively; In step S25, while other algorithm settings remain unchanged, the V-rescale thermostat and Parrinello–Rahman barostat are used to maintain the same temperature and pressure conditions, the position constraints of all molecules are released, and a molecular dynamics simulation with a duration of 100 ns is performed.

7. The method according to claim 6, characterized in that In step S32, the binding energy is calculated by using the gmx_mmpbsa script, setting the corresponding temperature and relative dielectric constant, and calculating the binding energy of the interacting molecular pairs, wherein the binding energy includes electrostatic interaction energy and van der Waals interaction energy.

8. The method according to claim 6, characterized in that In step S32, the diffusion coefficient is calculated by using the msd function in the GROMACS program and the following formula to calculate the diffusion coefficient of the antibiotic molecules in the system: Where N represents the number of molecules, n traj represents the number of molecular dynamics simulation trajectory frames, r i (t0+t) and r i (t0) represents the position of the center of mass of the molecule at a certain time and the initial time, and D is the diffusion coefficient.

9. The method according to claim 6, characterized in that In step S32, the contact distance and contact time are calculated by using the mindist function in the GROMACS program to calculate the change of the minimum distance between the antibiotic and the organic matter over time. The minimum distance in the program is defined as the distance between the two nearest non-hydrogen atoms of the ligand and the receptor. When the distance is less than 0.4 nm, it is considered that the antibiotic and the organic matter are in an adsorption contact state, and the contact time is obtained by counting the number of frames in the contact state in the trajectory; In step S33, for the protein-antibiotic system, the docking results of CB-DOCK 2 can be referred to; for other organic matter-antibiotic systems, when molecular docking cannot be performed, multiple frames where interactions occur can be selected from the trajectory and compared with each other to obtain a typical interaction pattern.

10. The method according to claim 6, characterized in that In step S33, the molecular docking results and the results of steps S31 and S32 are visualized using the pymol tool.

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