Method for predicting toxicity of PFAS on microalgae based on molecular docking and molecular dynamics simulation

Abstract

The invention discloses a method for predicting the toxicity of PFAS to microalgae cells based on molecular docking and molecular dynamics simulation. Collecting a toxicity data set of the PFASs on the microalgae to obtain a half inhibitory concentration toxicity index; collecting structural information of known compounds and structural information of microalgae key proteins and nucleic acid receptors; performing density functional theory calculation based on a B3LYP / 6-31 + G (d, p) base group by adopting Gaussian 09 software, optimizing the geometrical shape and the minimum energy conformation of the PFAS, and determining structural parameters; screening binding sites of the PFASs and a receptor by using an Autolock tool, and selecting the binding site with the minimum binding energy as a docking result; the method comprises the following steps: carrying out molecular dynamics simulation by adopting Gromacs on the basis of determining a complex docking position through molecular docking; using MM / PB (GB) SA to calculate the binding free energy of the PFASs and the receptor; a linear regression model is utilized to analyze the toxicity index of PFASs and combine free energy correlation, and modeling is carried out.

Description

Technical Field

[0001] The present invention relates to the fields of environmental science and biotechnology, and in particular to a method for predicting the toxic effects of perfluoroalkyl substances (PFAS) on microalgae cells by using molecular docking technology and molecular dynamics simulation. Background Art

[0002] PFAS is a widely used class of chemicals that are used in many industrial fields due to their unique thermal stability and surface activity, such as textile waterproofing and non-stick pan coatings. However, due to the high stability and difficulty in degradation of PFAS, it accumulates in the environment for a long time and can be transmitted to humans through the food chain, posing a potential threat to the ecological environment and human health. The widespread use of PFAS has led to its ubiquity around the world, and they can accumulate in water, soil, and organisms. Especially for aquatic ecosystems, PFAS pollution has become an important environmental issue. Microalgae, as the basic members of aquatic ecosystems, are very sensitive to pollutants in the environment, so it is crucial to study the impact of PFAS on microalgae.

[0003] Traditional ecotoxicity testing usually relies on biological experiments under laboratory conditions. Although this method is reliable, it is time-consuming, costly and requires a large number of biological samples. In addition, for emerging chemicals, traditional toxicity testing is often lagging and cannot timely assess potential risks. In order to overcome the limitations of traditional methods, computational biology techniques developed in recent years, such as molecular docking and molecular dynamics simulation, provide new ways to quickly and accurately predict the biological activity of compounds. These technologies can help scientists understand the interaction mechanism between compounds and biological macromolecules, thereby predicting their possible biological effects.

[0004] The present invention aims to develop a method based on molecular docking and molecular dynamics simulation to predict the toxic effects of PFAS on microalgae in an efficient and low-cost manner. This method can not only speed up the evaluation process, but also reduce the demand for experimental animals, which has significant economic benefits and social significance. Summary of the invention

[0005] In order to solve the technical problems in the background technology, the present invention proposes a technical solution: a method for predicting the toxicity of PFASs to microalgae based on quantitative structure-activity relationship and molecular dynamics simulation, comprising the following steps:

[0006] (1) Collect toxicity data sets of PFASs to microalgae and obtain the half inhibitory concentration (IC50) toxicity index;

[0007] (2) Collect structural information of known compounds and key proteins and nucleic acid receptors of microalgae;

[0008] (3) Density functional theory (DFT) calculations were performed using Gaussian 09 software based on the B3LYP / 6-31+G(d,p) basis set to optimize the geometry and minimum energy conformation of PFAS and determine the structural parameters;

[0009] (4) The Autodock tool was used to screen the binding sites of PFASs and receptors, and the one with the smallest binding energy was selected as the docking result. Each PFAS was scored according to the binding energy value (kcal / mol), where the lower the binding energy value, the higher the affinity for binding to D1.

[0010] (5) Based on the docking position of the complex determined by molecular docking, molecular dynamics simulation was performed using Gromacs.

[0011] (6) Use MM / PB(GB)SA to calculate the binding free energy of PFASs to receptors to determine the binding affinity of PFASs to receptors.

[0012] (7) The linear regression model was used to analyze the correlation between the toxicity indicators and binding free energy of PFASs and to establish a model.

[0013] Furthermore, in step (2), the structural information of known compounds is collected, and the chemical structures of PFASs are collected from the PubChem website (https: / / pubchem.ncbi.nlm.nih.gov / ) to ensure the accuracy of the calculation basis of the descriptors. The structural information of proteins and nucleic acids is collected from the Uniprot website (https: / / www.uniprot.org / ).

[0014] Furthermore, the step (4) uses the Autodock tool to screen the binding sites of PFASs and receptors. During the molecular docking process, the genetic algorithm is run 3 times and the one with the smallest binding energy is selected as the docking result. In the docking calculation, the D1 structure is set to a rigid structure, and the structure of the tested chemical is set to a flexible structure.

[0015] Furthermore, in step (4), the Autodock tool is used to screen the binding sites of PFASs and receptors. The result evaluation is to compare the crystallographic pose of the ligand with the pose of the receptor docked by Autodock, and calculate the root mean square deviation (RMSD) value to evaluate the difference between the molecular docking result and the experimental observation value. Generally speaking, the RMSD is The following is considered a good docking result.

[0016] Furthermore, in step (5), Gromacs was used for molecular dynamics simulation, CHARMM36 force field was used to describe the macromolecular system, water and sodium ions / chloride ions were added for ion balance (TIP3P was selected as the water model), and the initial energy of each system was minimized by the steepest gradient method; the simulation temperature was controlled by the V-rescale (improved Berendsen) method, the temperature was set to 300K, and the pressure was kept constant throughout the simulation process;

[0017] A multi-time-step algorithm was used, in which the simulation time step was chosen to be 2 fs. Finally, a 200 ns MD simulation was performed, and snapshots were saved into the MD trajectory every 10 ps for further analysis and calculation of the root mean square deviation (RMSD).

[0018] The present invention has the following advantages:

[0019] 1. This method is based on biomolecular docking and molecular dynamics simulation, and predicts toxicity by binding sites and binding strength, which has good explanatory power.

[0020] 2. Compared with the traditional experimental method of observing biological toxicity, this method uses computational simulation method, which has the advantages of low cost and high efficiency.

[0021] 3. The present invention can reduce the need for direct experiments on living organisms, and the present invention follows the "3R" principle (replacement, reduction, and refinement). BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 Docking results of 8 PFASs with protein D1: (a) 5:3FTCA, (b) 6:2FTCA, (c) 7:3FTCA, (d) 8:2FTCA, (e) PFPeA, (f) PFDA, (g) 6:2FTUCA, and (h) 8:2FTUCA.

[0023] Figure 2 The correlation between binding free energy and IC50.

[0024] Figure 3 For the technology roadmap. DETAILED DESCRIPTION

[0025] The present invention is further described below in conjunction with specific embodiments and drawings. It should be emphasized that the following description is only for illustrative purposes and is not intended to limit the scope of use and application of the present invention.

[0026] like Figure 3 As shown, the method for predicting the toxicity of PFASs to microalgae based on quantitative structure-activity relationship and molecular dynamics simulation in the present invention comprises the following steps:

[0027] (1) Collect toxicity data sets of PFASs to microalgae and obtain the half inhibitory concentration (IC50) toxicity index;

[0028] (2) Collect structural information of known compounds and key proteins and nucleic acid receptors of microalgae;

[0029] (3) Density functional theory (DFT) calculations were performed using Gaussian 09 software based on the B3LYP / 6-31+G(d,p) basis set to optimize the geometry and minimum energy conformation of PFAS and determine the structural parameters;

[0030] (4) The Autodock tool was used to screen the binding sites of PFASs and receptors, and the one with the smallest binding energy was selected as the docking result. Each PFAS was scored according to the binding energy value (kcal / mol), where the lower the binding energy value, the higher the affinity for binding to D1.

[0031] (5) Based on the docking position of the complex determined by molecular docking, molecular dynamics simulation was performed using Gromacs.

[0032] (6) Use MM / PB(GB)SA to calculate the binding free energy of PFASs to receptors to determine the binding affinity of PFASs to receptors.

[0033] (7) The linear regression model was used to analyze the correlation between the toxicity indicators and binding free energy of PFASs and to establish a model.

[0034] In this example, eight PFASs were selected as ligands, and the D1 protein, one of the most important proteins in microalgae photosynthesis, was selected as the receptor.

[0035] Molecular docking results showed that when the eight PFASs bound to the D1 protein, they bound to the same hydrophobic cavity, with only differences in binding strength, hydrogen bond length and position ( Figure 1 ), which provides favorable support for establishing toxicity prediction models based on binding strength.

[0036] Analysis of the correlation between binding free energy and toxicity index IC50 shows that the two are significantly correlated, R 2 =0.78(p<0.01)( Figure 2 ).

Claims

1. A method for predicting the toxicity of PFAS to microalgae based on molecular docking and molecular dynamics simulation, characterized in that: The following steps are involved: (1) Collect the toxicity data set of PFASs to microalgae and obtain the half inhibition concentration IC50 toxicity index; (2) Collect structural information of known compounds and receptor structure information of key receptors of microalgae; (3) Density functional theory calculations were performed using Gaussian 09 software based on the B3LYP / 6-31+G(d,p) basis set to optimize the geometry and minimum energy conformation of PFAS and determine the structural parameters; (4) The Autodock tool was used to screen the binding sites of PFASs and receptors, and the one with the smallest binding energy was selected as the docking result. Each PFAS was scored according to the binding energy value kcal / mol, where the lower the binding energy value, the higher the affinity for binding to D1; (5) Based on the docking position of the complex determined by molecular docking, molecular dynamics simulation was performed using Gromacs; (6) Use MM / PB(GB)SA to calculate the binding free energy of PFASs to receptors to determine the binding affinity of PFASs to receptors; (7) The linear regression model was used to analyze the correlation between the toxicity indicators and binding free energy of PFASs and to establish a model.

2. The prediction method according to claim 1, characterized in that: The step (2) collects structural information of known compounds. The chemical structures of PFASs are collected from the PubChem website (https: / / pubchem.ncbi.nlm.nih.gov / ) to ensure the accuracy of the calculation basis of descriptors. The structural information of proteins and nucleic acids is collected from the Uniprot website (https: / / www.uniprot.org / ).

3. The prediction method according to claim 1, characterized in that: The step (4) uses the Autodock tool to screen the binding sites of PFASs and receptors. During the molecular docking process, the genetic algorithm is run 3 times and the one with the smallest binding energy is selected as the docking result. In the docking calculation, the D1 structure is set to a rigid structure, and the structure of the chemical being tested is set to a flexible structure.

4. The prediction method according to claim 1, characterized in that: The step (4) uses the Autodock tool to screen the binding sites of PFASs and receptors. The result evaluation is to compare the crystallographic posture of the ligand with the posture of the receptor docked by Autodock, and calculate the root mean square deviation value to evaluate the difference between the molecular docking result and the experimental observation value. Generally speaking, the RMSD is The following is considered a good docking result.

5. The prediction method according to claim 1, characterized in that: In step (5), Gromacs is used for molecular dynamics simulation, CHARMM36 force field is used to describe the macromolecular system, water and sodium ions / chloride ions are added for ion balance (TIP3P is selected as the water model), and the initial energy of each system is minimized by using the steepest gradient method; The simulation temperature was controlled by the V-rescale (modified Berendsen) method, the temperature was set to 300 K, and the pressure was kept constant throughout the simulation; A multi-time-step algorithm was used, in which the simulation time step was chosen to be 2 fs. Finally, a 200 ns MD simulation was performed, and snapshots were saved into the MD trajectory every 10 ps for further analysis and calculation of the RMS deviation.

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