In Silico Tool for the Development of Environmentally Friendly Antifouling Paints: SwissTargetPrediction

Summary

The submerged portions of marine vessels are colonized by fouling organisms. This condition causes significant problems. To prevent these problems, the submerged sections of ships are coated with special paints.

These paints are called toxic or antifouling paints. Their composition contains chemical agents that are highly harmful to non-target organisms in the marine ecosystem. This study will introduce an in silico tool that can be used in developing environmentally friendly antifouling paint formulations.

1. What is Antifouling Paint?

Biofouling refers to the adsorption and colonization of fouling organisms on artificial surfaces submerged in seawater. Although biofouling is a natural phenomenon, it causes significant problems in marine vessels including reduced maneuverability, excessive fuel consumption and increased CO2 emissions. Antifouling coating technologies have been developed to prevent biofouling events [1]. From the past to the present, many toxic compounds (arsenic, lead, mercury compounds) have been used in these paints, and protection from fouling organisms has been provided through these harmful agents. Because these compounds created numerous environmental risks, they were banned and the search for new alternatives began [2]. In this context, TBT (tributyltin) was proposed as the most effective biocide and was commercialized due to its superior protective properties. However, studies demonstrated that TBT had toxic effects on non-target organisms. For example, TBT at nanomolar levels has been reported to cause problems in shell development in the marine organism Crassostrea gigas [3]. It was also shown that reproductive problems emerged in Nucella lapillus species exposed to TBT [4]. Due to these side effects on non-target marine organisms, the production of TBT-based paints was banned by IMO (International Maritime Organization) after 2003, and their use was banned after 2008 [5,6]. Following the ban on TBT, biocide-containing paints continued to be used, and numerous compounds began to be used under the name booster biocide. Additionally, development of environmentally friendly alternative solutions began [7]. Antifouling paints are divided into two groups according to the chemical properties of the binder, as shown in Figure 1.

Figure 1. Antifouling paint types according to binder type [8]

2. Toxic Compounds Used in Antifouling Paints

After the ban on TBT-based paints, marine paint manufacturers began searching for formulations that could replace TBT [9]. As a result of the research conducted, some examples of booster biocides used to date are as follows: Irgarol 1051, Diuron, Sea-nine 211 (DCOIT), Chlorothalonil, Dichlofluanid, Thiram, Ziram, Zinc pyrithione, Maneb, Zineb, Preventol A5-S, Copper Omadine, PK (triphenylboron pyridine (TPBP or PK)), Econea (Tralopyril), Medetomidine (SelektopeTM) [10,11].

3. SwissTargetPrediction and Its Application

To increase the efficacy of antifouling paints, a considerable variety and number of booster biocides are used. The use of these toxic compounds causes many toxic effects on non-target organisms as well. To evaluate the toxicity of a newly developed booster biocide requires a significant number of experimental studies. However, software developed in the bioinformatics field can provide important information about which targets (proteins or receptors) newly developed molecules can bind to in living organisms. SwissTargetPrediction, developed by the Swiss Institute of Bioinformatics (SIB), Molecular Modelling Group (MMG), provides in silico information about which targets toxic molecules can bind to in living organisms [12, 13].

Figure 2. SwissTargetPrediction search interface

Figure 2 shows the homepage of SwissTargetPrediction. The steps for using this program are as follows: As shown in Figure 2, the SMILES version of the bioactive molecule of interest must be entered or the shape of the molecule must be drawn on the right side. The SMILES version of molecules is a format that allows computer software to evaluate molecules much faster and more efficiently. Figure 3 shows the structure and SMILES version of bisphenol-A.

Figure 3. a) Structure and b) SMILES version of Bisphenol-A

SMILES versions of molecules can be easily accessed from many databases. SwissTargetPrediction searches in the species Homo sapiens, Mus musculus, Rattus norvegicus, Bos taurus and Equus caballus. However, the proteins found in these species show fundamental similarities in marine organisms as well. For example, Figure 4 compares the amino acid sequences of cytochrome C oxidase subunit 1 in humans and the Atlantic dogwhelk Nucella lapinus. This comparison was performed on Uniprot.org.  

Figure 4. Comparison of amino acid sequence of Cytochrome C oxidase subunit 1 in Homo sapiens and Nucella lapinus.

After species selection in SwissTargetPrediction, the "submit" button is pressed and SwissTargetPrediction returns the results shown in Figure 5. Figure 5 presents the results for diuron.

Figure 3. SwissTargetPrediction search results for Diuron biocide

In the SwissTargetPrediction search results, we see that this biocide binds to proteases and membrane receptors at 7%, to enzymes at 60%, and to unclassified other groups at 27% (Figure 5) [13].

Figure 4. SwissTargetPrediction search results for Diuron biocide

Figure 6 shows in detail the targets that diuron is likely to bind to in metabolism. The protein with the highest binding rate was identified as lipid-phosphate phosphatase. This protein is followed by P2Y purinoreceptor 1, muscleblind-like protein 1 and other proteins shown in Figure 6. As shown in Figure 6, by evaluating the proteins listed, the toxicity of the relevant molecule can be assessed and environmentally friendly formulations can be prepared.

Discussion

Marine transportation is of great importance in world commerce. Speed and fuel consumption are the most important parameters in marine transportation. Both parameters are directly affected by antifouling paints. Low-cost paints generally contain copper(I) oxide as the main biocide, and their efficacy is enhanced with booster biocides. To protect marine ecosystems, environmentally friendly paints must be developed. According to a study conducted by Yebra and Catala (2011), the development of biocide-free paints would protect marine ecosystems in one year from approximately 70 million tons of copper-based biocides, 6 million booster biocides and 20 million solvents released from ships [14]. Today, biocide-free paints are not yet widely used at the desired level due to their cost. For this reason, in developing biocide-containing paints, special attention must be paid to non-target organisms other than fouling organisms in marine ecosystems. The development of environmentally friendly antifouling paints is also important for sports and recreational activities carried out in marine ecosystems [15]. Within the scope of this article, possible contributions of SwissTargetPrediction, a bioinformatics tool, to the development of environmentally friendly antifouling paints have been discussed. The example study presented within the scope of the article can be applied to molecules of interest, and target proteins and receptors can be identified to assess possible toxicities to organisms. We believe that Swiss-TargetPrediction is an important in silico tool in evaluating the effects of small molecules whose bioactivity is to be determined. In the light of recent advances in artificial intelligence technology, we believe that the SwissTargetPrediction tool will provide results with near-accurate precision in the very near future.

Acknowledgments

We thank Prof. Dr. Vincent Zoete for granting permission to use screenshots of SwissTargetPrediction in this article. SwissTargetPrediction was developed by the Swiss Institute of Bioinformatics (SIB), Molecular Modelling Group (MMG). Seher Biler Chemist Department of Chemistry Faculty of Science Dokuz Eylül Üniversitesi     Prof. Dr. Levent Çavaş Department of Chemistry Faculty of Science Dokuz Eylül Üniversitesi  

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