In-silico methods in antifouling paint technology

Abstract

The invention relates to the method of determining the antifouling performance of biocides by determining the binding affinity score obtained as a result of in-silico molecular docking analysis of the candidate biocide-target protein complex.

Description

[0001] IN-SILICO METHODS IN ANTIFOULING PAINT TECHNOLOGY

[0002] Technical Field

[0003] The invention relates to an in-silico method used in the development and production of novel environmentally friendly antifouling paints developed for toxic paints (antifouling coatings) developed specifically for the marine area. The invention also relates to an in-silico method used in the development and production of novel and environmentally friendly herbicides, pesticides, and biocides that will not harm non-target organisms and developed for the elimination of harmful pests.

[0004] State of the Art (Background)

[0005] The accumulation of organisms on artificial surfaces is defined as biofouling. Biofouling is known as the undesirable accumulation of algae, bacteria, diatoms, various organisms, and plants on any surface immersed in seawater. Biofouling occurs rapidly when an artificial surface such as ship hull, oil pipeline, aquaculture networks is immersed in seawater.

[0006] The accumulation of organisms on natural surfaces under the sea (cliffs, reefs) is beneficial for the ecosystem and marine life. However, bioaccumulation by organisms on human-made surfaces causes undesirable effects. Industrial aquatic processes such as maritime transportation industry, wastewater treatment, aquaculture are in danger due to biofouling.

[0007] Researchers have been developing technologies to eliminate the biofouling issue for many years since it poses a danger to all surfaces used in the maritime industry. The methods used to eliminate the biofouling problem are known as "antifouling technologies". Antifouling technologies include all different treatments such as biomimetic approaches, surface cleaning, non-toxic and toxic coatings.

[0008] The first stage in the historical development of antifouling technologies involves primitive methods such as covering surfaces immersed in the sea with wax, tar, and pitch-like materials that have little protection against organisms that cause biofouling. This first stage in the development of toxic paint technologies does not include a scientific research and development process and is considered as a whole of studies carried out by trial and error.

[0009] The process of developing truly toxic paint technologies appears in the second stage as a historical sequence. Copper-based coatings, which are still in commercial use today, have been a turning point in the history of toxic paint technologies that started to develop in the 1600s. Copper-based coatings have high antifouling performance compared to those used in the first stage.

[0010] Historically, the final stage of toxic paint development begins with high-temperature plastic coatings. One of the most important inventions in this period is the discovery of the antifouling performance of the tributyltin (TBT) molecule. TBT has been the strongest antifouling biocide used to date. Therefore, it has been used in high amounts in every coating developed for the marine area.

[0011] The TBT molecule has a high antifouling effect, but in the 1970s, the harmful effects of this molecule on non-target organisms (e.g. Crassostrea gig t.s) (shell development abnormality) were first detected in the port of Arcachon, France. A high amount of TBT was detected in the analyzed seawater samples. Pacific oyster production has been significantly damaged due to this highly toxic molecule in seawater. TBT has also caused reproductive disorders in nontarget organisms in addition to shell development disorders. The production of TBT since 2003 and its use in antifouling paints since 2008 have been prohibited by the World Maritime Organization due to its harmful effects.

[0012] The antifouling paint industry has started to work for the discovery of new toxic molecules (biocides) after the prohibition of the use of the TBT molecule in the marine area.

[0013] The need for effective biocides to prevent marine organisms from settling and developing their target proteins and enzymes in underwater components such as ship hulls, oil pipelines, fish cages, gears and aquaculture networks has come to the forefront after the prohibition of TBT-based paints.

[0014] Supportive biocides have been presented as alternative options to organotin-derived molecules in toxic paint production. Copper oxides and supporting biocides were often used as substitutes. Alternative coating formulas containing supporting biocides have become more widely available due to the prohibition of the use of TBT.

[0015] The most commonly used biocides in antifouling paints include econea, zinc pyrite, diuron, irgarol 1051, dichlofluanid, zineb, diuron, ziram, sea-nine 211, copper pyrite, chlorothalonil and TCMTB [2-(thiocyanomethylthio) benzothiazole]. Paint manufacturers use these toxic substances at increased amounts to enhance the performance of antifouling paints.

[0016] Performance analyses of biocides traditionally used in antifouling paints are inspected with field tests carried out in the marine ecosystem. Antifouling paint manufacturers cover various surfaces (metal, polymer plates, fishing nets, etc.) with the paints they produce. Biofouling species and their accumulation on the surfaces are inspected by leaving the covered surfaces in the marine ecosystem for long periods (30-365 days). The less the accumulation of biofouling species on the surface as a result of the test, the more effective the biocide used is deemed to be. This allows the performance of biocides used in newly prepared antifouling paints to be measured. Therefore, the performance tests of the toxic paints must be carried out within the marine area where the paint will be used before the toxic paint is put up for sale. This is quite costly and time-consuming.

[0017] The disadvantages encountered in traditional methods and the performance measurement methods of biocides used in antifouling paints are listed below:

[0018] • Toxic biocides released to the environment during performance tests from antifouling paints whose performances are tested with the traditional method cause great damage to organisms in the marine ecosystem. Micro and macro particles released from antifouling paints during the tests accumulate on the seafloor. Paint particles accumulated on the seafloor cause the release of toxic biocides to continue for long periods of time. Traditional performance tests cause serious damage to the environment.

[0019] • Traditional performance tests are carried out over very long periods of time (30-365 days) and lead to a loss of time in the introduction of new products to the market.

[0020] • Biocides alone cannot be tested in the marine ecosystem. Therefore, new coatings must be prepared for the performance analysis of each new biocide. The production of new coatings results in additional costs for the product to be developed. • The biodiversity of the seas / ports in different regions is different. Therefore, a highly effective biocide in one region may show a loss of performance in another. The performance of the biocides to be used in different regions should be specifically tested in that region.

[0021] In the patent document numbered WO2021262127A1, the invention relates to an environmentally friendly antifouling paint composition created using in-silico methods for fishing nets used in fish farming in aquaculture. The specified methodology is also suitable for the discovery of new supporting biocides or their chemical equivalents through virtual screening for antifouling paints to be used in antifouling paint compositions for artificial surfaces.

[0022] Said patent document discloses that the binding energy between the ligand and the receptor is determined by the molecular docking method and that the binding energy also shows the antifouling performance of the molecule studied. This patent document intends to propose two algae-based secondary metabolites as antifouling biocides.

[0023] Definitions of Figures Describing the Invention

[0024] Figure 1: The methodological flow chart of the study of the invention

[0025] Figure 2: The graphical summary of the method of the invention

[0026] Figure 3: 3D modeled GPCR Ramachandran plot of Balanus amphitrite.

[0027] Figure 4: The molecular surface of the GPCR extracellular residues was visualized (A). The rest of the residues has been blacked out to eliminate image clutter (B)

[0028] Figure 5: The best binding pose and related interactions of the Diuron-GPCR complex Figure 6: The best binding pose and related interactions of the Econea-GPCR complex Figure 7: The best binding pose and related interactions of the Irgarol-GPCR complex Figure 8: Field test results of uncoated control group and biocidal rosin paint formulation (1- 4; uncoated nets, 5-8; 3% econea, 9-12; 3% diuron, 13-16; 3% irgarol 1051)

[0029] Brief Description and Objects of the Invention

[0030] The main object of the invention is to carry out antifouling performance tests of biocides using in-silico methods and computer-based simulations without the need for field tests, and in a fast, cost-effective, and environmentally friendly manner. Thus, the development of environmentally friendly antifouling paints has been ensured.

[0031] The object of the invention is to develop a method of determining the biocide performance, which is environmentally friendly and can be obtained quickly (within two days) without the need for field tests, i.e. the antifouling performance test of biocides. Decreases in the amount of CO2 to be released into the atmosphere are expected thanks to the invention.

[0032] Another object of the invention is to develop a method of determining the performance of a biocide, where there is no need to repeat performance tests for different marine ecosystems.

[0033] Another object of the invention is to enable the production of highly effective new molecules with low toxicity properties by proposing molecular modifications to manufacturers on biocides with low effectiveness.

[0034] The interaction of the biocides in the paint content with the specific proteins in the target organisms has been revealed using the in-silico methods presented in the invention and computer-based simulations. The development of special paints depending on customer demand or geographical region is thus ensured.

[0035] Detailed Description of the Invention

[0036] The invention is based on the comparison of molecular binding energies obtained with in- silico molecular docking programs and performance parameters obtained after in-vitro field experiments. Protein-ligand interactions, binding affinity of the ligand to the protein, interaction types, and interacting molecule groups can be examined as a result of in-silico analyses. New and effective biocides (especially those with low toxicity for non-target organisms) can be produced by proposing molecular modifications on biocides with low antifouling effectiveness in this way.

[0037] A vital step in the search for active substances is the molecular docking method. The molecular docking method is based on the use of computational tools to estimate the complex structure of the ligand and the receptor. The molecular docking approach is divided into two stages. The first of these procedures is to determine the ligand conformation in the active site of the protein. The second stage is to use scoring functions to evaluate such structures. The main idea behind this method is that the binding modes of the concordance with the highest score between the created ligand-target protein complex can be determined empirically.

[0038] Our knowledge of the design and synthesis of active chemicals and biological processes draws on molecular docking. It is the virtual modeling of the binding site of the selected chemical on the target protein. This method is widely applied to the production of drugs with the help of a computer. These approaches are used to explain how chemicals interact with active compounds, drug candidates, and drugs. It is commonly used to estimate the affinity of a small molecule for a protein target.

[0039] The region in which signal transmission is triggered by molecular docking of the molecule (ligand) on the protein (receptor) to form a stable complex structure is called the binding site. The total energy the receptor has when the ligand and the receptor are separate from each other must be higher than the total energy of the complex obtained by ligand-receptor pairing in order for the receptor to form a signal response. This is because the resulting complex is less energetic and stable when the ligand and the receptor are bound together. Many factors affect the binding state, such as dissolution energy (AGdesolv), energies due to conformational changes (AGkonf), energies due to unbound interactions (AGetk), internal rotations (AGrot), and ligand-receptor coupling energy in receptor ligand interactions. (AGt / t). Scoring functions in molecular docking studies are determined by considering such factors. Force field parameters used in molecular mechanical calculations are used in molecular docking. In addition, information-based parameters or experimental data in the electronic environment are also used. These are information-based parameters obtained from protein databases or detailed experimental scoring methods such as Poisson-Boltzmann.

[0040] Several software is used in the molecular docking method (SWISS-Dock, Blind-Dock, Auto Dock, etc.). The structure of the proteins is not considered flexible and calculations containing all the conformations of the ligand on the protein are included in the placement studies whereas the ligand structure is considered flexible in most of the software used. As a result of the study, the most appropriate conformation of the ligand on the protein can be selected and the binding position of the ligand on the receptor can be simulated with a three-dimensional structure. Parameters such as Gibbs free energy, complete compatibility score, hydrogen bond length between the receptor and the ligand and between which atoms the bond is established are obtained with molecular docking studies. Predictions can be made about the binding between the receptor and the ligand by interpreting the obtained parameters. Successful binding of active molecules with the corresponding atoms of the receptor depends on many factors.

[0041] It is essential to understand the ligand and receptor interactions at the molecular level in order to determine the specificity of the receptors. Intermolecular interactions occur due to hydrogen bonds and Van der Waals forces. Therefore, understanding the interaction process depends on knowing the hydrogen bond and van der Waals parameters. The main purpose of molecular docking studies is to evaluate the parameters collected in a short time before wet chemistry laboratory applications and to predict the binding status.

[0042] If the molecular docking calculations are divided into phases, the first step is to obtain the receptor structure to be used in the ligand-receptor interaction from databases such as the Protein Data Bank (PDB) of the target proteins elucidated by X-ray crystallography. Target proteins whose molecular structure is not elucidated can be modeled with the SWISS-Model algorithm.

[0043] Three-dimensional digital structures of the receptors and ligands selected from the database are prepared using pretreatment software (Auto Dock Tool 1.5.6 / PyMOL etc.) before the molecular docking analysis. In the second step, mechanical and geometric optimization of the ligand and receptor is performed with AutoDock Tool 1.5.6, Shrbdinger Maestro, Avogadro, DS visualizer package software. Thus, the ligand and receptor structure is prepared for molecular docking and given to the electronic environment.

[0044] In the last step, molecular docking calculations between the ligand and the receptor are performed with various software and programs. Some of the software used for the molecular docking process are FleXX, Auto Dock Vina, CB-Dock, and SwissDock.

[0045] A flowchart of the method used in the study is visualized in Figure 1.

[0046] The method of determining the antifouling performance of biocides comprises the following process steps: Coating the test surface with paint containing biocides,

[0047] Conducting a field test for the determination of biofouling living flora in the relevant marine ecosystem, provided that it is a one-time only,

[0048] Scoring the accumulation of biofouling on the test surfaces after the field test, Detecting biofouling organisms on test surfaces,

[0049] Metabolism studies of organisms detected for the detection of target protein, Finding the elucidated structures of the determined target proteins,

[0050] Homology modeling of target proteins without three-dimensional structure, Structural validation of the modeled target proteins,

[0051] Obtaining three-dimensional structures of biocides used in paint production, Preparing target proteins and biocides for molecular docking analysis, Determining molecular docking and binding scores,

[0052] Calculating the correlation coefficient between field test scores and molecular docking scores.

[0053] Figure 2 summarizes the processes to take place after the detection of biofouling organisms. Figure 2 graphically summarizes the processes to be carried out after the detection of biofouling organisms on antifouling paint-coated surfaces. The FASTA sequences of the organisms detected first were found on Internet databases, homology modeling of the three- dimensional structures of the target proteins was carried out using the SWISS-model, structure validation was carried out with SAVES-server, and the grid region of the target protein was visualized. Three-dimensional digital structures of biocides were obtained from Internet data banks. Molecular docking analysis was performed with the AutoDock Vina software and ligand-protein binding affinities were obtained.

[0054] The method of determining the antifouling performance of biocides generally comprises the following process steps:

[0055] • Obtaining the biocide-target protein complex,

[0056] • Determining the binding affinity score obtained as a result of in-silico molecular docking analysis.

[0057] The method steps are listed below:

[0058] 1. Biocidal antifouling paint is prepared (Paint formulation: binder: rosin + solvent: xylene + biocide: econea / diuron / irgarol).

[0059] 2. The desired surface is coated with the prepared paint.

[0060] 3. Field test (antifouling paints prepared in Step 1 and coated on the test surfaces in Step 2 are immersed in the seawater during the biofouling season when seawater temperatures begin to increase) is carried out.

[0061] 4. The surfaces are examined; biofouling organisms are detected, and the antifouling performance of the coatings is scored after the test.

[0062] The field test was performed once to calculate the correlation coefficient in the study presented as a patent. No field test is required for each different study. The main purpose of the study is to create a new method that will test the antifouling performance of the new toxic antifouling paints produced or to be produced without the need for field testing.

[0063] For example, if high density Balanus amphitrite is found on the surfaces after the field test, the metabolism, vital enzymes, and proteins of Balanus amphitrite detected on the surfaces are investigated in the literature. The target enzyme and protein / proteins are determined for the elimination of Balanus amphitrite, based on the information obtained from the literature research.

[0064] It was found as a result of the literature review that GPCR (G Protein Coupled Receptor) protein was of high importance for B. amphitrite. GPCR receives extracellular signals and activates the production of intracellular cement protein. It allows the cement protein cyprid (larva form of B. amphitrite) to adhere to the surfaces and undergo metamorphosis. B. amphitrite cannot adhere to surfaces without cement protein. The three-dimensional elucidated structures of this protein are investigated in internet data banks after the target protein (GPCR) is determined. The most trusted data banks are known as: RCSB-PDB, NCBI and UniProtKB, respectively. The elucidated protein structure to be obtained should be in three-dimensional geometry, with the lowest conformational energy, without ligands and other inorganic structures in its structure, and in the file format with the extension ".pdb" determined by XRD or NMR. No elucidated structure could be found for GPCR when the Internet data banks were examined. For this reason, the FASTA sequence of the determined target was obtained from the UniProtKB data bank (uniprot ID: Q93127) and homology modeling was performed using the SWISS-Model interface (https: / / swissmodel.expasy.org / ). The three-dimensional protein structure, which was homology modeled, was brought to its lowest energy conformation with the energy minimization step before molecular docking. The YASARA-server was used to perform the energy minimization process (http: / / www.yasara.org / minimizationserver.htm). PROCHECK and ERRAT analyzes were performed on the SAVES-server for the determination of conformational accuracy after energy minimization (https: / / saves.mbi.ucla.edu / ). three-dimensional structures of biocides used in paint formulation were found on Internet data banks in the first step (PubChem data bank was used in this study https: / / pubchem.ncbi.nlm.nih.gov / . Diuron (CID:3120), Irgarol 1051 (CID:91590), Econea (CID: 18355)). Preliminary preparation was made before molecular docking after the structures of the target protein and biocides were obtained. Polar hydrogens and collagen charges are added to the target protein structure and water molecules are removed in the preliminary process. The active center of the target protein was included in the grid, and the x, y, z coordinates of the active center were recorded. Only polar hydrogens were added to the three-dimensional structures of the biocides. The file extensions of the biocide and target protein were recorded with the ".pdb" extension before molecular docking. All files to have been analyzed (grid.txt file containing three-dimensional structures and grid coordinates of the target protein and ligands) were collected into a single file. AutoDockVina was run on the command prompt and analysis was performed upon the completion of the preliminary step. Post-analysis molecular interactions were visualized using the Maestro vl2.7 software.

[0065] Molecular Docking Results

[0066] The GPCR protein of B. amphitrite was selected as the molecular insertion target due to its significant effects on biofouling. The primary sequence of the target protein was obtained from UniProt (uniprot ID: Q93127). The SWISS-model was used to create the three- dimensional structure, HHblits and BLAST algorithms were used to find a suitable template for the initiation of homology modeling, and BLAST was used to compare the target sequence with the main sequence in the SMTL. The protein skeleton required for homology modeling was selected from 394 templates proposed by the SWISS-Model algorithm. Models were created according to the intended alignment using ProMod3. The model was edited using the coordinates recorded from the selected template. The SWISS-model fragment library was used to perform insertion, deletion, and remodeling after the cloning of the template coordinates. Then, the geometry of the target model was regularized by rebuilding the side chains. The force field of the SWISS-Model algorithm was used to minimize the error rate in the arrangement of the side chains. Alpha 2A adrenergic receptor was selected as a template for three-dimensional modeling (template ID: 6kuy.l.A). The sequence identity and resolution of the template were found to be 37.10 and 3.20A, respectively. The total scope of the template was calculated as 0.91 by the SWISS-model. The structure created after the modeling was verified by PROCHECK and ERRAT analyses on the Structural Analysis and Validation Server (SAVES v6.0). The amino acid side chain was found to be 93.5% in the most preferred regions of the structure and 6.1% in the allowed regions according to the scores obtained. However, only 0.3% side chain was obtained in the disallowed region. (Cool position: 271). The error rate of the homology modeling performed with the SWISS-Model was found to be 0.3% after the saves v6.0 analysis. The threshold value of PROCHECK analysis for a quality model is 90%. The 3-dimensional structure was obtained with good quality with a score of 93.5% according to the score obtained (Figure 3 and Table 1).

[0067] Table 1 Ramachandran plot statistics for three-dimensional modeled GPCR

[0068] The average overall quality factor score of the ERRAT analysis was set at 91% for lower resolution templates (2.5 to 3 A). The template used in this study has a resolution of 3.20 A and the ERRAT score was evaluated as 84.384. The resolution value of the template was also lower than specified even though the score of this analysis was obtained below the threshold value. Therefore, it can be said that the modeled structure is of good quality by considering the score obtained as a result of the analysis. In addition, the PROCHECK score supports this information. The conformational energy of the modeled protein is minimized by the YASARA energy minimization server. The initial energy and score of the modeled protein were evaluated as -113612.0 kJ / mol and -2.75, respectively. The final energy and score were obtained as -179155.2 kJ / mol and -0.87 after the minimization steps. 8249 water molecules were used in the energy minimization procedure. AutoDockVina was used to perform the molecular insertion. AutoDockVina is known as the grid placement algorithm. The three- dimensional area (grid) where the analysis is performed is selected before molecular insertion with AutoDockVina. In this study, grid dimensions were selected as center x = 29.7244283458, center_y = 1.78939427671, center_z = 58.7687041596, size_x = 45.8700312227, size_y = 40.9236442313, size_z = 27.1115826546. The grid was placed on extracellular residues of the GPCR protein. The extracellular residues (target region) of the GPCR protein are visualized through Maestro. The molecular surface of the target region is shown in Figure 4.

[0069] Ligands were selected as diuron, econea and irgarol 1051 as the most used commercial biocides. The highest binding affinities of diuron, econea and irgarol 1051 to the B. amphitrite GPCR protein were found to be -6.6 kcal / mol, -7.0 kcal / mol and -5.6 kcal / mol, respectively, according to our molecular docking analysis. Interaction poses of target-ligand complexes created through Maestro according to the best binding scores are presented in Figure 5-7. The molecular interactions of the target-ligand complexes are found in the second poses of Figures 5 to 7. The antifouling performances of the biocides according to the molecular docking scores were determined to be the first, diuron second and irgarol last, respectively.

[0070] Field Tests Results

[0071] Three different toxic paints were produced using econea, diuron and irgarol biocides at a rate of 3% (w / w) in the rosin binder and coated on fishnet samples (all types of surfaces where the produced paint can be used) in the field test conducted in the study.

[0072] The field test was carried out for 1 month, and at the end of the test, the samples were taken from the sea and photographed. Figure 8 shows the results of the field test.

[0073] Figure 8 shows the development of biofouling over time on uncoated control group and biocidal rosin paint-coated net samples (1-4; uncoated nets, 5-8; 3% econea, 9-12; 3% diuron, 13-16; 3% irgarol 1051)

[0074] The number of biofouling organisms on the nets was scored after the field test. The correlation coefficient was calculated with the help of Microsoft Excel using the molecular docking results after biofouling scoring. The closer the calculated coefficient is to 1, the higher the consistency between the molecular docking results and the field test (Table 2). Table 2: Correlation coefficient calculation parameters

[0075] In conclusion, the results of this study demonstrated that in-silico methodologies can be used to evaluate the toxic paint performance of novel and candidate biocides without the need for field tests. Molecular docking scores represent the antifouling activity of newly produced molecules.

Claims

CLAIMS1. A method of determining the antifouling performance of biocides, characterized in that it comprises the following process steps:• Obtaining the biocide-target protein complex,• Determining the binding affinity score obtained as a result of in-silico molecular docking analysis.

2. A method according to Claim 1, characterized in that it comprises the following process steps:• Coating the test surface with paint containing biocides,• Conducting a field test for the determination of biofouling living flora in the relevant marine ecosystem, provided that it is a one-time only,• Scoring the accumulation of biofouling on the test surfaces after the field test,• Detecting biofouling organisms on test surfaces,• Metabolism studies of organisms identified for the detection of target protein,• Finding the elucidated structures of the determined target proteins,• Homology modeling of target proteins without three-dimensional structure,• Structural validation of the modeled target proteins,• Obtaining three-dimensional structures of biocides used in paint production,• Preparing target proteins and biocides for molecular docking analysis,• Determining molecular docking and binding scores,• Calculating the correlation coefficient between field test scores and molecular docking scores.

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