A method for virtual screening of plant essential oil inhibitors of cellulose-degrading enzymes based on structure-activity relationship and molecular docking
By using structure-activity relationship and molecular docking technology, highly efficient essential oil antibacterial agents were screened, solving the problem of low screening efficiency in existing technologies. This enabled precise screening of cellulase inhibitors, which is suitable for the preservation of paper cultural relics and has universality.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- LIAONING UNIVERSITY
- Filing Date
- 2026-03-13
- Publication Date
- 2026-06-05
AI Technical Summary
Existing technologies for screening plant essential oils that inhibit cellulase degradation rely on in vitro antibacterial experiments, which are labor-intensive, time-consuming, and costly. Furthermore, they are difficult to reveal the specific interaction mechanism between essential oil components and target enzymes, resulting in low screening efficiency.
A virtual screening method based on structure-activity relationship and molecular docking technology was adopted. A three-dimensional structural model of cellulase was constructed by homology modeling. Molecular docking was performed with 20 representative essential oil molecules to screen out essential oil molecules with excellent binding ability. The structure-activity relationship was clarified by visualization analysis.
This method enables the efficient screening of natural and safe essential oil antibacterial agents, shortens the screening cycle, improves screening efficiency, and clarifies the binding mode between essential oil components and cellulase, providing a theoretical basis for subsequent structural optimization of active molecules.
Smart Images
Figure CN122157756A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of green preservation of paper cultural relics, and in particular to a method for inhibiting plant essential oil inhibitors of cellulase based on structure-activity relationship and molecular docking virtual screening. Background Technology
[0002] Paper artifacts, as important material carriers of Chinese civilization and human historical memory, face severe challenges in their long-term safe preservation. The main components of paper are cellulose, hemicellulose, and lignin. These organic materials, under suitable temperature and humidity conditions, easily become a nutrient substrate for the growth of microorganisms such as mold. Among the various microorganisms that cause the biological degradation of paper artifacts, fungi, represented by *Aspergillus niger* and *Aspergillus flavus*, are particularly harmful. These fungi secrete a series of extracellular hydrolases, especially cellulases (such as cellulase and cellobiase), which catalyze the breakage of β-1,4 glycosidic bonds in the cellulose chain. This directly leads to a decrease in the degree of polymerization and loss of mechanical strength in the paper, ultimately causing irreversible damage to the artifacts, such as paper embrittlement, adhesion, fading, and mold formation.
[0003] Traditionally, chemical fumigation or the development of inorganic nanomaterials have been used to curb the erosion of paper artifacts by microorganisms. However, chemical fumigants are often highly toxic and carcinogenic, posing a threat to the health of operators, and some inorganic antibacterial agents suffer from poor long-term stability. Therefore, there is an urgent need to develop efficient, safe, environmentally friendly, and non-damaging green preservation technologies for artifacts.
[0004] Plant essential oils, due to their broad-spectrum antibacterial activity, natural origin, biodegradability, and relatively low environmental and health risks, are widely used in food preservation and agricultural disease control, and are increasingly regarded as highly promising antibacterial agents in cultural relic preservation. Plant essential oils are complex mixtures of various volatile terpenes, aromatic compounds, and their derivatives (such as alcohols, aldehydes, and phenols). Studies have shown that their antibacterial mechanisms are diverse, potentially involving disruption of microbial cell membrane integrity, interference with energy metabolism, inhibition of key enzyme activity, and induction of oxidative stress. Although some studies have confirmed that single essential oil components such as cinnamaldehyde, thymol, and eugenol have certain inhibitory effects on common molds that cause damage to cultural relics (such as Aspergillus niger), the complex composition of plant essential oils results in highly specific antibacterial efficacy; components with different chemical structures exhibit significantly different inhibitory abilities against specific target enzymes. Currently, the screening of essential oils for preventing mold growth on paper artifacts mostly relies on in vitro antibacterial experiments. This method is labor-intensive, time-consuming, and costly. Furthermore, it is difficult to intuitively reveal the specific interaction mechanism between essential oil components and key target enzymes at the molecular level, resulting in low screening efficiency and high trial-and-error costs.
[0005] With the rapid development of computational biology and structural bioinformatics, molecular docking technology offers a new approach to solving the aforementioned challenges. Based on the principle of energy matching, molecular docking technology can simulate the binding modes and affinities of small molecule ligands (such as essential oil components) and biological macromolecular targets (such as cellulases) to achieve rapid screening of a large number of candidate molecules.
[0006] Therefore, developing an integrated method based on structure-activity relationship analysis and molecular docking virtual screening is of great practical significance for achieving efficient screening of essential oil molecules targeting cellulose-degrading enzymes, and thus promoting the precise application of plant essential oils in the green preservation of paper cultural relics. Summary of the Invention
[0007] This invention proposes a method for screening antibacterial molecules from plant essential oils based on homology modeling, structure-activity relationship (SAR), and molecular docking techniques. Specifically, targeting key cellulolytic enzymes of typical putrefactive fungi, the invention first obtains their amino acid sequences from the UniProt database and uses Swiss-model for homology modeling and model quality assessment. Then, based on the structural differences of common antibacterial functional groups, 20 structurally diverse and representative essential oil molecules are selected, and their binding to the target enzyme is simulated using molecular docking technology. Finally, essential oil molecules with excellent binding ability are screened based on docking scores, and their structure-activity relationship is elucidated through visualization analysis.
[0008] The technical solution adopted in this invention is as follows: A method for detecting plant essential oil inhibitors that inhibit cellulase based on structure-activity relationship and molecular docking virtual screening, comprising the following steps:
[0009] Step 1) Identify key cellulose degradation enzymes by obtaining relevant databases from archives or by performing whole-genome sequencing on target biomass deteriorating fungi in paper artifacts, and determine them as target proteins; the cellulose degradation enzymes are derived from fungi, preferably Aspergillus niger.
[0010] Step 2) Obtain the protein ID and amino acid sequence from the Uniprot protein database based on the target protein obtained in Step 1), and submit them to the Swiss-model online platform for homology modeling;
[0011] Step 3) Compare all homologous templates obtained in Step 2) and select high-quality templates with sequence identity higher than 80% and the highest GMQE value to construct a three-dimensional structural model of the target protein; wherein, the GMQE is used to characterize the reliability of the model, and its value ranges from 0 to 1, and the higher the value, the stronger the reliability of the model.
[0012] Step 4) Evaluate the quality of the protein model obtained in Step 3). The evaluation indicators include GMQE and Ramachandran plots in the Swiss-Model scoring system, and VERIFY 3D, PROCHECK, WHATCHECK, and ERRAT criteria in the SAVES scoring system. If the evaluation results show that the quality does not meet the standards, return to Step 2) to re-screen homologous templates and adjust the multiple sequence alignment parameters for remodeling. If the quality is still not improved, use molecular mechanics or molecular dynamics simulation methods to minimize the energy of the model to eliminate unreasonable interatomic contacts until all evaluation indicators reach the preset thresholds before proceeding with further processing.
[0013] Step 5) Based on the common components of plant essential oils, and combined with the addition or subtraction of carbon chains, changes in the position of double bonds and modifications of functional groups, 20 representative essential oil small molecules were selected as candidate ligands. The small molecule structures were obtained from the PubChem small molecule database and converted into .pdb format using Open Babel software.
[0014] Step 6) The three-dimensional model of the target protein obtained in Step 4) and the essential oil small molecules obtained in Step 5) are preprocessed in molecular docking software, and then semi-flexible docking is performed in a periodic box.
[0015] Step 7) Perform cluster analysis on the docking results of the protein obtained in Step 6) with different essential oil molecules, and screen the protein-essential oil molecule complex model with the best docking score, i.e. the lowest binding free energy.
[0016] Step 8) Visualize the protein-essential oil molecule complex with the lowest binding free energy selected in Step 7) and analyze its intermolecular interaction mode.
[0017] In step 2), the protein ID and sequence selected from the protein database are derived from typical cellulase-degrading enzymes in Aspergillus niger.
[0018] In step 5), the 20 representative essential oil molecules are divided into four categories according to their chemical structure: saturated aliphatic aldehydes, enaldehydes, phenols, and alcohols. The selection criteria for the 20 representative essential oil molecules are: based on the common chemical skeletons in plant essential oils, covering saturated aliphatic, monoterpenoid, and aromatic compounds, and containing key antibacterial functional groups such as aldehyde groups, phenol / alcohol hydroxyl groups, and methoxy groups in their molecular structure. By selecting essential oil molecules with carbon chain lengths in the C6-C12 range and different degrees of unsaturation and different functional group positions, essential oil ligands with structural differences are constructed to analyze their structure-activity relationships.
[0019] In step 6), the docking is performed 20-30 times; the molecular docking simulation is run independently multiple times for each ligand, and the results are clustered to screen out the protein-essential oil molecule complex conformation with the lowest binding free energy.
[0020] The beneficial effects of this invention are as follows:
[0021] 1) The essential oil components screened in this invention are derived from natural plants and have the characteristics of biodegradability, low toxicity and environmental friendliness. While effectively protecting paper cultural relics, it avoids secondary damage to the cultural relics and the operators, which meets the technical requirements of green cultural relic protection.
[0022] (2) By integrating structure-activity relationship analysis and molecular docking technology, this invention elucidates the antibacterial mechanism of plant essential oil components at the molecular structure level, and clarifies the binding mode and interaction characteristics between them and cellulase, thereby providing a theoretical basis for the subsequent structural optimization and rational design of active molecules.
[0023] (3) The present invention uses a computational simulation method to efficiently predict the binding affinity of essential oil components to target proteins at the molecular level, significantly shortening the screening cycle and improving screening efficiency, overcoming the limitations of traditional in vitro antibacterial experiments which involve large workload, long cycle and high cost.
[0024] (4) This method is not only applicable to the screening of cellulase inhibitors in the field of paper cultural relic protection, but can also be further extended to the virtual screening of inhibitors of other enzymes or biological targets, and has good versatility and promotion and application value. Attached Figure Description
[0025] Figure 1 This is a model diagram of the 1,4-β-D-glucosidase cellobiase B protein.
[0026] Figure 2 This is a Ramachandran diagram of cellobiose hydrolase B;
[0027] Figure 3 This is a hydrophilicity / hydrophobicity analysis diagram of cellobiase B;
[0028] Figure 4 This is a three-dimensional visualization of the docking between carvacrol and cellobiose hydrolase B molecules;
[0029] Figure 5 This is a diagram showing the interaction forces between carvacrol and cellobiose hydrolase B molecules. Detailed Implementation
[0030] To better understand the present invention, specific examples will be used to further illustrate the solution of the present invention below.
[0031] Example 1
[0032] A method for virtually screening plant essential oil inhibitors that inhibit cellulase based on structure-activity relationship and molecular docking includes the following steps:
[0033] 1. By performing whole-genome sequencing on target fungi in the archives and combining it with literature review, this invention selects a key cellulose degradation enzyme (1,4-β-D-glucosidase cellobiase B, commonly referred to as cellobiase B) as the target protein.
[0034] 2. Obtain the protein ID and amino acid sequence of cellobiase B from the Uniprot protein database and submit them to the Swiss-model online platform for homology modeling;
[0035] 3. Compare all obtained homologous templates and select templates with sequence identity higher than 80% and GMQE value close to 1 for constructing the three-dimensional structural model of the target protein.
[0036] 4. Evaluate the quality of the protein model obtained above using six criteria: GMQE, Ramachandran plot score, Verify 3D, ProCheck, WhatCheck, and Terrat. If the evaluation quality does not meet the standards, further optimization can be performed until the requirements are met before proceeding with subsequent processing.
[0037] 5. Based on the common components of plant essential oils, and combined with the addition or subtraction of carbon chains, changes in the position of double bonds, and modifications of functional groups, essential oil molecules classified into saturated aliphatic aldehydes, enaldehydes, phenols, and alcohols were selected, specifically including hexanal, octanal, decanal, nonanal, lauraldehyde, trans-2-hexenal, cinnamaldehyde, citral, perillaldehyde, trans-2-decenal, carvacrol, thymol, eugenol, isoeugenol, sesamol, linalool, geraniol, sennarol, octanol, and citronellol.
[0038] The small molecule structures were then obtained from the PubChem small molecule database and converted to .pdb format using Open Babel software.
[0039] 6. In Yasara software, hydrogen atoms were added to the target protein and essential oil molecules through a clean operation. The protonation state was adjusted to simulate physiological pH conditions. Then, a periodic cubic box with a minimum distance of 5 Å was set up to contain the protein and essential oil molecules within the periodic box, and the energy of the structure was minimized to ensure stability.
[0040] 7. The pretreated protein and essential oil molecules were sequentially imported into the Yasara software. Clean hydrogenation was performed on both, and the pH was set. The minimum distance between the periodic cube boxes was set to 5 Å. The protein and metal were then placed within the periodic boxes, and the steepest descent method was used to minimize energy. The result was then saved as a scene. Finally, 25 semi-flexible docking operations were performed using the corresponding script.
[0041] 8. Analyze the molecular docking results separately, and then screen the protein-essential oil molecular complex model with the best docking score (lowest binding free energy);
[0042] 9. The best complex models selected above are visualized in three dimensions and their intermolecular interaction patterns are analyzed to further elucidate the structure-activity relationship of essential oil molecules with different structures.
[0043] Table 1 Quality Evaluation Table for Homology Modeling of Cellobiose Hydrolase B
[0044]
[0045] Table 1 is the scoring table for the preliminary modeling of cellobiase B, where the Swiss-model's Laplace plot score is 93.26% (>90%). Figure 2 The image shows a Ramachandran plot of the cellobiose hydrolase B protein model. The plot reveals that only a very small number of amino acid residues are located in the colorless, seemingly inappropriate region, indicating that this is a reliable protein model. Furthermore, all four SAVES scores (ERRAT score > 85%, Procheck score > 90%) passed, meeting the docking requirements.
[0046] Figure 1 This is a secondary structure diagram of a protein homology modeling cartoon, with a Rainbow gradient chromatogram (blue→green→yellow→red) strictly corresponding to the amino acid sequence direction, i.e., from the N-terminus to the C-terminus. The hydrophilicity / hydrophobicity characteristics of cellobiase B were analyzed using the Expasy-ProtScale database. Figure 3 In the curves shown in the figure, peaks (positive values) correspond to hydrophobic regions, and troughs (negative values) correspond to hydrophilic regions. The protein's overall average hydrophilicity (GRAVY) index is -0.330, and its lipid index is 54.44 (at a moderately low level), indicating that it is a hydrophilic protein overall. Furthermore, the protein's instability index is 29.89, which is below the stability threshold (generally considered <40 for stable proteins), indicating good structural stability.
[0047] Table 2. Results of cellobiose hydrolase B docking with different essential oil molecules.
[0048]
[0049] According to the scoring function used in the molecular docking software Yasara, a larger negative value of the binding free energy (ΔG) (i.e., the optimal docking score) generally indicates a more stable binding between the ligand and the target protein, and a higher affinity, thus predicting the effectiveness in inhibiting the cellulose degradation activity of cellobiose hydrolase B. Table 2 shows that the binding effect with the target protein is phenols > alcohols > aldehydes, with enaldehydes showing greater binding efficiency than saturated aliphatic aldehydes. Further comparison of binding energies reveals that carvacrol has the strongest binding energy to the target (-6.4000 Kcal / mol), thus predicting that carvacrol has the best inhibitory effect on the cellulose degradation of paper artifacts.
[0050] By molecular docking complex of ligand molecule carvacrol with target protein active pocket ( Figure 4 Two-dimensional interaction analysis () Figure 5 The study found that the target protein binding pocket contains both hydrophobic and hydrophilic amino acid residues. The carvacrol ligand is primarily stable within the active pocket of the target protein through hydrophobic interactions via hydrogen bonds and van der Waals forces. The oxygen atom in the phenol-OH group of the carvacrol ligand forms a hydrogen bond (3.20 Å) with the nitrogen atom of the -NH2 group in the key residue Arg128(A) within the active pocket, inhibiting enzyme activity. This hydrogen bond, of moderate strength, significantly contributes to the stability of the complex. The hydrophobic domains of the carvacrol's aromatic ring and isopropyl side chain form dense hydrophobic interactions and van der Waals contacts (distance < 4.0 Å) with the hydrophobic side chains of multiple residues within the pocket. The main residues involved include Trp59(A), Asp200(A), Asn58(A), Asn221(A), and Thr222(A). These residues tightly encapsulate the ligands through steric hindrance and van der Waals forces, restricting their conformational freedom and enhancing binding affinity.
[0051] Combining the preceding analysis of the proteins as hydrophilic proteins with the structural analysis of different essential oil molecules, this indicates that, in terms of functional group effects, the binding energy follows the order of phenols > alcohols > aldehydes. This may be attributed to the stronger hydrogen bond donor capacity of phenols (-OH groups), which is the most crucial factor in forming a hydrogen bond network with the active pocket of the target protein to contribute high binding affinity. Furthermore, the hydrophobic interaction with the benzene ring enhances conformational stability. Alcohols exhibit relatively weaker binding efficiency, possibly due to the relatively weak hydrogen bond binding efficiency of alcohols (-OH groups) and the lack of conjugation effect from the benzene ring.
[0052] In summary, this invention, through structure-activity relationship analysis and molecular docking technology, systematically elucidates the differences in the inhibition of cellulase activity by essential oil molecules with different structures at the molecular level. Phenolic substances containing benzene ring structures and -OH functional groups exhibit superior efficacy in inhibiting cellulose degradation. This is expected to provide reasonable guidance for screening novel, efficient, and safe cellulase inhibitors for paper artifacts, enabling better application in the field of cultural relic preservation.
Claims
1. A method for virtual screening of plant essential oil inhibitors that inhibit cellulase based on structure-activity relationship and molecular docking, characterized in that, Includes the following steps: Step 1) Identify key enzymes of cellulose degradation by obtaining relevant databases from archives or by performing whole-genome sequencing on target biomass deteriorating fungi in paper artifacts, and determine them as target proteins; Step 2) Obtain the protein ID and amino acid sequence from the Uniprot protein database based on the target protein obtained in Step 1), and submit them to the Swiss-model online platform for homology modeling; Step 3) Compare all homologous templates obtained in Step 2) and select high-quality templates with sequence identity higher than 80% and the highest GMQE value to construct a three-dimensional structural model of the target protein; wherein, the GMQE is used to characterize the reliability of the model, and its value ranges from 0 to 1, and the higher the value, the stronger the reliability of the model. Step 4) Evaluate the quality of the protein model obtained in Step 3). The evaluation indicators include GMQE and Ramachandran plots in the Swiss-Model scoring system, and VERIFY 3D, PROCHECK, WHATCHECK, and ERRAT criteria in the SAVES scoring system. If the evaluation results show that the quality does not meet the standards, return to Step 2) to re-screen homologous templates and adjust the multiple sequence alignment parameters for remodeling. If the quality is still not improved, use molecular mechanics or molecular dynamics simulation methods to minimize the energy of the model to eliminate unreasonable interatomic contacts until all evaluation indicators reach the preset thresholds before proceeding with further processing. Step 5) Based on the common components of plant essential oils, and combined with the addition or subtraction of carbon chains, changes in the position of double bonds and modifications of functional groups, 20 representative essential oil small molecules were selected as candidate ligands. The small molecule structures were obtained from the PubChem small molecule database and converted into .pdb format using Open Babel software. Step 6) The three-dimensional model of the target protein obtained in Step 4) and the essential oil small molecules obtained in Step 5) are preprocessed in molecular docking software, and then semi-flexible docking is performed in a periodic box. Step 7) Perform cluster analysis on the docking results of the protein obtained in Step 6) with different essential oil molecules, and screen the protein-essential oil molecule complex model with the best docking score, i.e. the lowest binding free energy. Step 8) Visualize the protein-essential oil molecule complex with the lowest binding free energy selected in Step 7) and analyze its intermolecular interaction mode.
2. The method for detecting plant essential oil inhibitors based on structure-activity relationship and molecular docking virtual screening to inhibit cellulase degradation enzymes according to claim 1, characterized in that, In step 1), the cellulose-degrading enzyme is derived from fungi.
3. The method for detecting plant essential oil inhibitors based on structure-activity relationship and molecular docking virtual screening to inhibit cellulase degradation enzymes according to claim 2, characterized in that, The cellulase mentioned is Aspergillus niger.
4. The method for detecting plant essential oil inhibitors based on structure-activity relationship and molecular docking virtual screening to inhibit cellulase degradation enzymes according to claim 1, characterized in that, In step 2), the protein ID and sequence selected from the protein database are derived from typical cellulase-degrading enzymes in Aspergillus niger.
5. The method for virtual screening of plant essential oil inhibitors against cellulase based on structure-activity relationship and molecular docking according to claim 1, characterized in that, In step 5), the selection criteria for 20 representative essential oil small molecules are as follows: based on the common chemical skeletons in plant essential oils, covering saturated aliphatic, monoterpenoid, and aromatic compounds, and containing key antibacterial functional groups such as aldehyde, phenol / alcohol hydroxyl, and methoxy groups in the molecular structure; by selecting essential oil small molecules with carbon chain lengths in the C6-C12 range and different degrees of unsaturation and different functional group positions, essential oil small molecule ligands with structural differences are constructed to analyze their structure-activity relationship.
6. The method for detecting plant essential oil inhibitors based on structure-activity relationship and molecular docking virtual screening to inhibit cellulase degradation enzymes according to claim 1, characterized in that, In step 5), the 20 representative essential oil molecules selected are divided into four categories according to their chemical structure: saturated aliphatic aldehydes, enaldehydes, phenols, and alcohols.
7. The method for detecting plant essential oil inhibitors based on structure-activity relationship and molecular docking virtual screening to inhibit cellulase degradation enzymes according to claim 1, characterized in that, In step 6), the number of docking operations is 20-30.
8. The method for detecting plant essential oil inhibitors based on structure-activity relationship and molecular docking virtual screening to inhibit cellulase degradation enzymes according to claim 1, characterized in that, In step 6), the molecular docking simulation is run independently multiple times for each ligand, and the results are clustered to screen out the protein-essential oil molecule complex conformation with the lowest binding free energy.