Research Article www.acsami.org
Polymer Coating Materials and Their Fouling Release Activity: A Cheminformatics Approach to Predict Properties Bakhtiyor Rasulev,*,†,§ Farukh Jabeen,† Shane Stafslien,‡ Bret J. Chisholm,§ James Bahr,‡ Martin Ossowski,† and Philip Boudjouk*,†,∥ †
Center for Computationally Assisted Science and Technology, North Dakota State University, Fargo, North Dakota, United States Research and Creative Activities, North Dakota State University, Fargo, North Dakota, United States § Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota, United States ∥ Department of Chemistry and Biochemistry, North Dakota State University, Fargo, North Dakota, United States
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‡
S Supporting Information *
ABSTRACT: A novel cheminformatics-based approach has been employed to investigate a set of polymer coating materials designed to mitigate the accumulation of marine biofouling on surfaces immersed in the sea. Specifically, a set of 27 nontoxic, amphiphilic polysiloxane-based polymer coatings was synthesized using a combinatorial, high-throughput approach and characterized for fouling-release (FR) activity toward a number of relevant marine fouling organisms, including bacteria, microalgae, and adult barnacles. In order to model these complex systems adequately, a new computational technique was used in which all investigated polymer-based coating materials were considered as mixture systems comprising several compositional variables at a range of concentrations. By applying a combination of methodologies for mixture systems and a quantitative structure−activity relationship approach (QSAR), seven unique QSAR models were developed that were able to successfully predict the desired FR properties. Furthermore, the developed models identified several significant descriptors responsible for FR activity of investigated polymer-based coating materials, with correlation coefficients ranging from rtest2 = 0.63 to 0.94. The computational models derived from this study may serve as a powerful set of tools to predict optimal combinations of source components to produce amphiphilic polysiloxane-based coating systems with effective, broad-spectrum FR properties. KEYWORDS: polymer coating materials, QSAR, mixture, polysiloxane, antifouling, fouling-release
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INTRODUCTION Coating materials are widely used in industry and medicine. In this regard, polymer-based coating materials have innumerable applications because they are versatile, cost-effective, and can be tailored to a variety of specifications.1−4 The science of polymer synthesis allows for excellent control over the properties of a bulk polymer system. However, control over surface properties is a major challenge in modern polymer science. It is particularly formidable to predict the surface property of complex polymer materials when the initial mixture of polymer components changes.5,6 Marine biofouling affects all submerged surfaces causing detrimental effects on shipping and leisure vessels, heat exchangers, oceanographic sensors, and aquaculture systems. © 2016 American Chemical Society
Biofouling of ship hulls is an ongoing issue that has major economic and environmental impact. Biofouling increases hydrodynamic drag that translates to dramatic reductions in fuel efficiency.7,8 In this regard, early antifouling (AF) strategies involved embedding metal and/or organometallic-based biocides into the polymer matrix of an AF paint. For example, in the 1960s, organotin-based AF coatings were developed and found to be highly effective.9 However, the broad-spectrum activity of the organotin group, primarily tributyltin (TBT), resulted in adverse effects on nontargeted organisms10 and was Received: October 7, 2016 Accepted: December 16, 2016 Published: December 16, 2016 1781
DOI: 10.1021/acsami.6b12766 ACS Appl. Mater. Interfaces 2017, 9, 1781−1792
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ACS Applied Materials & Interfaces
eration, (7) and validation and predictability evaluation of the developed models.28,29 To date, there are only a few publications related to QSARs of polymers, since polymers are inherently complicated macromolecular systems, possessing large molecular sizes, conformationally labile structures, nonsystematic cross-linking, etc.30−32 For example, attempts have been made to develop special descriptors that encode polymer structures.31 As the next step, several efforts were made to build QSAR models for various properties of polymers.30,32 For this purpose, researchers often use the information based on monomer structure.30,32 Yu et al.32 developed two artificial neural network (ANN) models to predict reactivity parameters ln Q and e of acrylate monomers, applying data from density functional theory (DFT) calculations at the B3LYP/6-31G(d,p) level. The authors found that the resonance and polar effects of acrylate monomers can be reflected by quantum-chemical descriptors such as Mulliken and atomic polar tensor (APT) charges, the total dipole moment (μT), the lowest unoccupied molecular orbital energy (ELUMO), and the total energy (ET). In other work, authors developed a QSPR model for refractive indices of 234 structurally diverse polymers.30 The reported model involved a single molecular descriptor and a conformationindependent approach, in which the most appropriate polymer structures were investigated by considering 1−5 monomeric repeating units. Another study, which is very close to the current study attempting to investigate and predict surface adhesion property of a library of 496 polymers, was published by Winkler and coauthors.33 The authors used a data set provided by Yang et al.34 and applied a QSAR approach to build neural network predictive models. The authors generated satisfactory models with rtest2 = 0.63; however, no detailed information on the data set and which polymer structures were used for descriptor generation (linear or cross-linked) were provided in this paper, which makes it difficult to reproduce the results and investigate further applications. In this regard, systematic and reproducible investigations of a number of data sets is still needed for these kinds of systems, to more adequately address new challenges in this field. For polymer coating systems, the situation is even more complicated, since these materials often comprise highly crosslinked polymers, combining several different monomers in the polymeric system. As a result of this complexity, the classical QSAR approaches are not readily applicable for polymer coating systems, and no publications on this topic are presently known. Molecular modeling approaches to deal with polymer coatings, computational investigations, and rational design have been unsuccessful because of the highly complicated structures of the systems which prevent direct modeling. As mentioned above, the prediction of surface properties for complex polymer coating systems when the initial mixture of polymer substrates changes is an exceedingly difficult and challenging task. However, we recently reported preliminary studies with application of a novel mixture-QSAR approach for polymer coatings.35,36 In this paper, an attempt to develop a first QSAR-based model(s) is reported which provides a possible avenue in which to computationally predict FR properties of the coatings investigated. Described herein is a computational methodology applied to amphiphilic polysiloxane coatings and structure−activity modeling results obtained for several marine organisms, including bacteria, microalgae, and adult barnacles.
found to be unacceptably persistent in the marine environment.11 As a result, the International Maritime Organization Convention on the Control of Harmful AF Systems banned the use of TBT in marine coatings after 2008. Due to this ban, new AF coatings have been primarily based on copper-containing biocides.12 Although the toxic effects of copper to nontarget organisms are not as severe as those of tin, the use of copper still represents a serious environmental concern. As a nontoxic alternative to AF coatings, fouling-release (FR) coatings were developed and commercialized. Unlike AF coatings, FR coatings do not contain biocides and combat ship hull fouling by minimizing the adhesion strength between the attached organism and the coating surface so that the fouling can be removed through hydrodynamic shearing. Some relationships between the type of surface (hydrophobic or hydrophilic) and fouling adhesion was discussed by Krishnan et al.13−16, where the authors indicated that different marine organisms have different adhesion properties to hydrophobic and hydrophilic surfaces. For example, the authors showed that while Navicula cells released more easily from hydrophilic surfaces, Ulva sporelings showed higher removal from hydrophobic surfaces. Ideally, the adhesion strength between the attached fouling organisms and coating surface is low enough to enable release of the majority of the attached fouling when the vessel is underway. Thus, commercially available FR coatings are generally based on polysiloxanes17 It is generally accepted that the relatively good FR performance of polysiloxane-based coatings is a result of both the low surface energy of polysiloxanes and their low moduli.18,19 As a result, it has been of interest to investigate the modification of polysiloxanes with hydrophilic moieties to reduce biofouling. For example, polydimethylsiloxane (PDMS) elastomers were surface modified with PEOs using platinum-catalyzed hydrosilylation, and modified surfaces provided a 90% reduction of fibrinogen adsorption compared to the unmodified PDMS control.20 Polysiloxane-based amphiphilic polymer coating materials have been recently investigated for the fouling release (FR) activity by other researchers.21,22 In previous work22 these materials were synthesized using combinatorial analysis and investigated for the best components’ composition to obtain a polymer coating with desired properties. However, combinatorial analysis enables synthesis of a limited number of polymer coating materials and does not directly specify the main structural factors that are responsible for imparting optimal release of biofouling from coating surfaces. In this case, computational methods, such as quantitative structure− activity/property relationships (QSAR/QSPR), can be helpful in elucidating these key attributes and generate ideas for further improvement of these coating systems. QSAR analysis is based on the premise that the structure of a molecule is the principal determinant of its physicochemical, toxicological, and biomedicinal properties.23−26 Presently, QSAR is widely applied to the development of rationales to enhance desirable properties by tuning the structure within the congeneric series of compounds, including polymers. This popular approach utilizes statistical methods to determine a correlation among structural features of compounds and the studied property(s). Most QSAR/QSPR models tend to follow a similar strategy.27 This includes the following: (1) data set selection, (2) molecular structure generation, (3) geometry optimization of molecular structures using appropriate procedures, (4) various descriptors generation, (5) variable selection and/or data reduction methods, (6) model gen1782
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Table 1. List of Polymer Coatings, Concentration of Components, and Observed FR Activities (End Point Values) samplesc 0PEG/ 11.0CF3 0PEG/ 13.75CF3 0PEG/ 16.5CF3 0PEG/ 19.25CF3 0PEG/ 22.0CF3 0PEG/ 24.75CF3 0PEG/ 27.5CF3 0PEG/ 30.25CF3 0PEG/ 0CF3 6PEG/ 11.0CF3 6PEG/ 13.75CF3 6PEG/ 16.5CF3 6PEG/ 19.25CF3 6PEG/ 22.0CF3 6PEG/ 24.75CF3 6PEG/ 27.5CF3 6PEG/ 30.25CF3 6PEG/ 0CF3 8PEG/ 11.0CF3 8PEG/ 13.75CF3 8PEG/ 16.5CF3 8PEG/ 19.25CF3 8PEG/ 22.0CF3 8PEG/ 24.75CF3 8PEG/ 27.5CF3 8PEG/ 30.25CF3 8PEG/ 0CF3
CF3− PDMS
PDMS
silica disp.a
BA
2.06
21.76
12.57
19.8
2.58
21.1
12.58
3.09
20.42
3.59
MeTAcSi
cat. soln.b
c. lyt retention
c. lyt retraction
c. lyt removal
h. pac retention
a. amph attachmnt
a. amph adhesion
0
3.06
3.75
1.99
99.8
46.1
0.52
5.2
0
0.18
19.81
0
3.06
3.76
2.30
99.9
55.0
0.65
20.9
0
0.16
12.57
19.81
0
3.06
3.75
2.10
100.0
40.6
0.46
0.0
0
0.16
19.65
12.51
19.7
0
3.04
3.73
2.33
99.8
40.3
0.59
23.3
0
0.18
4.11
19.04
12.55
19.77
0
3.05
3.75
2.18
99.0
37.9
0.64
30.8
0
0.15
4.63
18.4
12.57
19.8
0
3.06
3.75
1.85
88.9
41.6
0.56
15.3
0
0.13
6.31
21.74
15.42
24.29
0
3.75
4.6
1.95
97.9
45.7
0.51
9.2
1
0.15
6.99
21.06
15.52
24.45
0
3.78
4.63
1.99
93.0
50.9
0.62
52.4
3
0.15
0
24.43
12.56
19.78
0
3.06
3.75
1.87
100.0
35.1
0.63
6.6
1
0.23
1.93
20.38
11.78
20.56
1.83
2.87
3.76
0.83
5.4
85.6
0.43
16.1
2
0.13
2.41
19.75
11.77
20.55
1.82
2.86
3.76
0.72
1.9
93.1
0.30
0.0
1
0.10
2.9
19.14
11.79
20.58
1.83
2.87
3.76
0.72
4.2
95.8
0.34
33.1
2
0.11
3.31
18.14
11.55
20.17
1.79
2.81
3.69
0.67
13.5
96.3
0.26
15.8
3
0.13
3.88
17.95
11.83
20.66
1.83
2.88
3.78
0.33
0.1
97.1
0.23
41.5
3
0.08
4.34
17.25
11.78
20.57
1.83
2.87
3.76
0.35
0.8
93.6
0.20
29.4
2
0.07
5.92
20.38
14.45
25.23
2.24
3.52
4.62
0.28
0.5
93.9
0.17
84.8
5
0.10
6.55
19.74
14.55
25.4
2.26
3.54
4.65
0.46
2.4
95.8
0.22
49.6
6
0.08
0
22.95
11.8
20.6
1.83
2.87
3.77
2.14
100.0
36.6
0.52
26.1
0
0.13
1.89
19.93
11.51
20.75
2.38
2.8
3.76
0.90
9.4
94.8
0.32
0.0
0
0.11
2.36
19.3
11.51
20.75
2.38
2.8
3.76
0.41
1.7
96.0
0.33
27.3
3
0.08
2.83
18.72
11.53
20.78
2.38
2.81
3.76
0.39
0.6
96.7
0.24
16.3
3
0.09
3.3
18.1
11.52
20.78
2.38
2.8
3.76
0.60
3.2
95.9
0.39
52.5
3
0.08
3.77
17.47
11.52
20.77
2.38
2.8
3.76
0.73
20.0
94.7
0.05
59.9
7
0.09
4.25
16.86
11.52
20.77
2.38
2.8
3.76
1.04
9.6
92.3
0.21
63.9
8
0.17
5.8
19.96
14.15
25.52
2.93
3.44
4.62
0.85
8.2
97.3
0.10
98.4
7
0.08
6.42
19.34
14.26
25.7
2.95
3.47
4.65
0.65
13.1
99.6
0.03
100.0
5
0.05
0
22.39
11.51
20.75
2.38
2.8
3.76
1.99
92.7
30.9
0.32
0.0
1
0.15
TMSPEG
h. pac removal
Silica was dispersed in BA (butyl acetate) at a 20:80 silica/BA wt./wt. ratio (silica disp.)22 bTBAF (tetrabutylammonium fluoride) was diluted with MIBK (4-methyl-2-pentanone) to produce a 50 mM solution (cat. soln.). cCoating compositions were encoded as follows: xPEG/yCF3 where x is the TMS-PEG content expressed as a weight percentage relative to the total weight of PDMS, CF3-PDMS, silica, TMS-PEG, and MeTAcSi, and y is the content of CF3-PDMS expressed as a weight percentage relative to total weight of CF3-PDMS and PDMS. a
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and Halomonas pacif ica, and a microalgae diatom, Navicula incerta, to coatings prepared in multiwell plates using methods that have been previously described in detail.12 In addition, the coatings were evaluated for their ability to prevent or minimize the adhesion strength of barnacles (Amphibalanus Amphitrite) using a rapid laboratory reattachment assay that has been previously described in detail.22 All components of the polymer coatings were identified structurally and characterized computationally.21,22
MATERIALS AND METHODS
Polymer Coating Materials. A set of 27 polymer coatings was produced using a combinatorial approach and characterized for activity toward a number of relevant marine fouling organisms, including bacteria, microalgae, and adult barnacles22 (Table 1). The details on coating preparation and fouling-release experiments were described previously.22 An automated water-jet method was used to evaluate the adhesion of two marine bacteria in a rapid manner, Cellulophaga lytica 1783
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Figure 1. A representation of a highly cross-linked polymer coating (represented as a mustard colored material within a blue box) derived from multiple reactive components.
Table 2. A List of the Components Used to Produce the Polysiloxane-Based Fouling-Release Coatings Investigated
In order to model these complex systems, all coatings investigated were considered as mixture systems, having components in various initial concentrations (Figure 1). The main components involved in the study are listed in Table 2. A set of parameters/descriptors was generated that encoded each component of the system. Descriptors Generation. To relate components’ structure changes to FR properties for each initial component of the polymer coating, a set of properties/descriptors was computationally generated that encoded the chemical structure. The structures were built and prepared for further use by Chemaxon software.37 Dragon 6 software38 was used to generate a set of descriptors. This software provides more than 4500 various descriptors corresponding to 0D, 1D, 2D, and 3D indexes. The descriptors are comprised of 20 different classes constitutional, topological, walk and path counts, connectivity indices, information indices, 2D autocorrelations, edge adjacency indices, Burden eigenvalues, topological charge indices, eigenvalue based
indices, randic molecular profiles, geometrical descriptors, RDF descriptors, 3D-MoRSE descriptors, WHIM descriptors, GETAWAY descriptors, functional groups, atom-centered fragments, charge descriptors, and molecular property descriptors.38,39 Constant and near to constant descriptors were eliminated. After filtering out constant and close to constant descriptors, about 1200 descriptors in total were generated per each main component of the polymer coating. Mixture Descriptors. Due to the complexity of polymer coatings, a new approach to describe the polymer coating system was applied. For this work, the following methodology was utilized. Each polymer coating was considered as a mixture system, where components were in various concentrations, and descriptors for each polymer coating were calculated based on structures and concentrations of each component (so-called mixture descriptors) in the coating. Figure 1 represents the overall idea and complexity of the polymer coating. To implement this approach, the following equations were applied: 1784
DOI: 10.1021/acsami.6b12766 ACS Appl. Mater. Interfaces 2017, 9, 1781−1792
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ACS Applied Materials & Interfaces Dmix = f (C1 × Dn , C2 × Dn , C3 × Dn , C4 × Dn...Cn × Dn)
applied in recent studies as a powerful tool to address many problems in QSAR studies.40−43 The method is based on the mechanism of natural evolution, where the higher descriptor weights are more preserved in the mathematic evolution process and finally in the model, while the lower weight descriptor is eliminated. In this study, the GA variable selection technique was used to reduce the number of descriptors that is applied for the final model and GA variable selection started with a population of 500 random models and 2000 iterations to evolution with the mutation probability specified at 40%. The MLRA technique was used to develop final QSAR models, since it is transparent, easy to interpret, and ideal to obtain reproducible results. Several QSAR models were developed (one model per each end point), followed by statistical analysis with evaluation by squared correlation coefficient r2, root-mean-square error RMSE, Fisher coefficient F, and noncollinearity of descriptors in the model. A final set of QSARs was generated by using the GA-MLR approach and tested by applying the “leave-one-out” technique (the process of removing a molecule from the set, then creating and validating the model against the individual molecules, which was performed for the entire training set), q2. Thus, we utilized the following equations to calculate correlation coefficient, r2 (eq 4), and the root-mean-square error of calibration (training) RMSEC, as the measures of goodness-of-fit for each developed model (eq 5):
(1)
where Dmix is a mixture descriptor, Cn is the concentration of an individual component in the mixture, and Dn is a descriptor of individual component A mix = f (P1, P2 , P3 , P4 , ... Pn)
(2)
where Amix is the activity of the mixture system, P is the component property, P = (Cn × Dmix_n)
A mix = (C1 × Dmix1) + (C 2 × Dmix2)... + (Cn × Dmix3)
(3)
where C is the coefficient, Dmix_n is the mixture descriptor, and Amix is the activity of the mixture system The first equation, eq 1, shows how the mixture descriptors were obtained, by including the same type of descriptor (Dn) from each component to describe the mixture system. Equation 2 represents the activity of the mixture system that depends on properties of the components in this system. The next equation, eq 3, represents the summary of eq 1 and eq 2, which can be considered as an overall MLR-based QSAR equation, covering the presence of mixture descriptors and coefficients in the model. The overall scheme of the applied approach is represented in Figure 2.
n
R2 = 1 −
∑i = 1 (yiobs − yipred )2 n
∑i = 1 (yiobs − y obs ̃ )2
(4)
n
RMSEC =
∑i = 1 (yiobs − yipred )2 (5)
n
To verify stability of the models (sensitivity to the composition of the training set), in each case, we calculated the cross-validated coefficient qLOO2 (leave-one-out method) and root-mean-square error of cross-validation RMSECV. Both statistics were calculated according to eq 6 and eq 7: n
2 Q LOO
=1−
∑i = 1 (yiobs − yipredcv )2 n
∑ j = 1 (yjobs − y obs ̃ )2
(6)
n
RMSECV = Figure 2. Scheme of applied mixture−QSAR approach to polymer coatings, where Comp 1···Comp 5 are components of the polymer coating, C1...C5 are concentrations of components, and Dn are descriptors’ values for the components.
∑i = 1 (yiobs − yipredcv )2 (7)
n
Following the recommendations by Gramatica and Chirico and by Lin,46,47 we calculated the Concordance Correlation Coefficient (CCC) as a more restrictive parameter for expressing external predictivity of each model in comparing to the commonly used external (test set) validation coefficient qExt2 (eq 8) and root-meansquare error of prediction (RMSEP; eq 9). In this work, we applied all three of these statistics.
QSAR Modeling. After the calculation of all mixture descriptors, the next step was QSAR modeling. For that, the set of 27 coatings was divided into training (21 coatings) and test sets (6 coatings), 75% training set and 25% test set. Overall, QSAR is a mathematical relationship between a biological activity of a molecular system and its geometric, chemical, or physical characteristics. QSAR attempts to find a consistent relationship between biological activity and molecular properties, so that these “rules” can be used to evaluate the activity of new chemical systems. Once a valid QSAR has been determined, it should be possible to predict the physical property or biological activity of related compounds or drug candidates before they are put through expensive and time-consuming biological testing. In some cases, only computed values need to be known to make an assessment. In the current study, the correlation between activity and structural properties was developed by using the variable selection Genetic Algorithm (GA) and Multiple Linear Regression Analysis (MLRA) methods. Thus, preliminary model selection was performed by means of the GA-MLRA40−43 technique as implemented in the QSARINS 2.244,45 program. It is worth noting that genetic algorithms have been
k 2 Q EXT =1−
k
̂ )2 ∑ (yjobs − yjpred )2 / ∑ (yjobs − y obs j=1
j=1
(8)
k
RMSE P =
∑i = 1 (yjobs − yjpred )2 k
(9)
yjobs
is experimental (observed) value of the property for the where ith/jth compound; yjpred is the predicted value for the ith/jth compound; ỹ and ŷ are the mean experimental value of the property in the training and validation set, respectively; n and k are the number of compounds in the training and validation set, respectively. Additionally, the chemical applicability domain (AD) for the models obtained was calculated by the leverage approach to verify predictive reliability.29,48 To visualize the applicability domain of the QSPR models, the Williams plot was used. Thus, the Williams plot of standardized cross-validated residuals (RES) versus leverage (Hat diagonal) values (HAT) clearly depicts both the response outliers (Y 1785
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Figure 3. Scheme of the overall steps for polymer coating modeling and design.
Table 3. List of Developed Models for Seven Investigated End Points (Regression and Classification Models) regression models model/end point Model Model Model Model Model Model Model
1 2 3 4 5 6 7
descriptors
C.lyt Retention C.lyt. Retraction C.lyt Removal H.pac Retention H.pac Removal A.amph Attachment A.amph Adhesion
Mor18v Mor09m Mor09m SPAM, DLS_04 VR2_RG AVS_B(p) Mor09p accuracy (training)
classification models Model 1 C.lyt Retention Model 2 C.lyt. Retraction Model 3 C.lyt Removal
R2 (training)
RMSE (training)
Q2 (training)
0.72 0.81 0.85 0.86 0.76 0.63 0.84 error rate (training)
0.38 19.31 10.07 0.07 14.84 1.54 0.02 sensitivity (training)
0.66 0.77 0.82 0.80 0.71 0.54 0.78 specificity (training)
F-test
R2 (test)
RMSE (test)
R2 (y-scrambling)
48.98 0.63 0.46 82.01 0.75 24.08 110.67 0.94 9.16 53.21 0.79 0.08 61.78 0.67 24.35 31.85 0.91 0.76 91.69 0.85 0.01 accuracy error sensitivity (test) rate (test)
0.11 ± 0.40 0.14 ± 0.42 0.13 ± 0.44 0.01 ± 0.52 0.06 ± 0.54 0.04 ± 0.46 0.07 ± 0.55 specificity (test)
Mor18v
1.00
0.00
1.00
1.00
0.83
0.16
0.75
1.00
Mor09m
1.00
0.00
1.00
1.00
1.00
0.00
1.00
1.00
Mor09m
1.00
0.00
1.00
1.00
1.00
0.00
1.00
1.00
Visualization. All visualization plots and figures were obtained using QSARINS 2.2,43,44 Chemaxon Suite,37 and MS Office PowerPoint applications.
outliers) and structurally influential compounds (X outliers) in a model. All initial descriptors were normalized before calculation of mixture descriptors. The normalization of descriptors was carried out using Matlab.49 Decision Trees Method. The classification analysis was established by the Decision Trees method50 using Matlab.49 The quality of all developed models was determined using eqs 10, 13, and 14:
Classification accuracy =
TP + TN × 100 TP + TN + FP + FN
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RESULTS AND DISCUSSION The overall scheme of the study can be represented by Figure 3, where experimental data set and QSAR modeling are shown as Task 1 and Task 2 (only Cheminformatics), in order to find the desired properties, shown in Task 3. Table 2 lists the components and their structures used to produce the polymer coatings studied in this research, while in Table 1 can be found the list of components and concentrations of the components. After all preliminary steps (structure preparation, descriptors generation, and mixture descriptors calculation), the QSAR modeling was applied and a set of predictive models for various end points developed based on 27 polymer coatings with fouling-release properties. The list of models and associated descriptors with each model is provided in Table 3 (the additional statistical data are given in Table S3). Thus, Model 1 (eq 15) that predicts the marine bacteria Cellulophaga lytica biofilm retention index for the set of polymer coating materials is represented below:
(10)
where TP is the number of true positive classifications (toxic substance), FN is the number of false positive classifications, TN is the number of true negative classifications (nontoxic substance), and FP is the number of false negative classifications.
Error rate =
FP + FN × 100 TP + TN + FP + FN
(11)
Sensitivity =
TP × 100 TP + FN
(12)
Specificity =
TN × 100 TN + FP
(13)
Balanced accuracy =
sensitivity + specificity × 100 2
(14) 1786
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ACS Applied Materials & Interfaces Table 4. List of Models and Corresponding Descriptors Selected for Each Model model/end point
descriptor
definition and scope
descriptor type
Model1 C.lyt Retention Model2 C.lyt. Retraction Model 3 C.lyt Removal Model 4 H.pac Retention
Mor18v Mor09m Mor09m SPAM DLS_04
signal 18/weighted by van der Waals volume signal 09/weighted by mass signal 09/weighted by mass average span R modified drug-like score from Chen et al. (7 rules)
Model 5 H.pac Removal
VR2_RG
Model 6 A.amph Attachment Model 7 A.amph Adhesion
AVS_B(p)
normalized Randic-like eigenvector-based index from reciprocal squared geometrical matrix average vertex sum from Burden matrix weighted by polarizability
Mor09p
signal 09/weighted by polarizability
3D-MoRSE descriptors38 3D-MoRSE descriptors38 3D-MoRSE descriptors38 shape indices,38 basic indices38 3D-matrix based38 2D-matrix based38 3D-MoRSE descriptors38
Figure 4. Plots for C.lytica biofilm retention model 1: (a) obs vs pred correlation, (b) Williams plot, where yellow dots = training set, blue dots = test set; (c) y-scrambling results, where blue and dark blue dots are r2 and q2 of original model, other dots, yellow and red, are simulated r2 and q2 values.
investigated end point. The plot of observed vs predicted values shows the predictive ability of the model by representing the location of the points on the correlation line. The Williams plot reflects the applicability domain of the model and ability of the model to successfully predict the values for a similar set of coatings. Y-scrambling is a validation technique, which shows the unique character of the model developed, i.e., a good model has higher R2 (Q2) values and better separation from all other simulated (unrealistic) ones. Figure 4 displays plots produced using model 1 for the prediction of the C. lytica biofilm retention index. As can be seen, the correlation is quite good, where r2 = 0.82, Figure 4a. In Figure 4b, the AD plot is shown, which confirms that all points are located within 3σ of the error limit and, therefore, reaffirms that all coating compositions are within an applicability domain. The third plot in Figure 4c is from the y-scrambling experiment, which validates the model developed. Thus, all other generated y-scrambling models have lower r2 values and higher errors, which confirms the model developed is robust and is not a chance of correlation. However, it can be seen that experimental data for C. lytica biofilm retention are clustered into two main groups (Figure 4a). In this case, the use of regression methods for model development is not recommended. Therefore, Model 1 was recalculated using classification methods, where biofilm retention data were converted to binary format −0 and 1, where 0 indicates no significant effect and 1 indicates a significant effect. The converted data for C. lytica biofilm retention, retraction, and removal FR properties are shown in SI Table S1. The classification model for C. lytica biofilm retention shows excellent correlation for the training set with an accuracy of 100% and slightly lower accuracy for the test set,
Amix(C.lyt_ret) = 2.01 ( ±0.85) Mor18v − 0.39 (± 0.29) (15) 2 2 2 (n = 27, rtrain = 0.72, qloo = 0.66, rtest = 0.63, RMS
Etr = 0.384, F = 48.985)
where Mor18v is 3D-MoRSE descriptor signal 18/weighted by van der Waals volume.38 As can be seen from model 1, the C. lytica biofilm retention index depends here on signal 18/weighted by van der Waals volume, the 3D-MoRSE descriptor of main components.38 It is important to note that coatings possessing good antifouling (AF)/FR properties should ideally exhibit a low amount of biofilm retention. Therefore, the identification of all features (descriptors) of the coating components that are positively associated with a reduction in C. lytica biofilm retention is desirable. In model 1 (eq 15), Mor18v, is weighted by van der Waals volume, which means the specific van der Waals volume size of the polymer coating’s structure plays a significant role in the retention of C. lytica biofilm. Thus, the positive contribution of this descriptor suggests that the amount of C. lytica biofilm retention increases with increasing van der Waals volume of the polymeric system, and consequently, to adequately mitigate biofilm retention, the Mor18v descriptor needs to have low values. Table 4 provides a list of models utilized and the corresponding descriptors selected for each model (1−7). Figures 4−9 provide plots generated using models 1−7, respectively. Each figure contains a plot of (a) the correlation between observed and predicted values, (b) Williams plot (AD plot), and (c) Y-scrambling validation results for the 1787
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Figure 5. Decision trees for classification DT models 1−3 based on one-rule criteria. Classification DT models represented for (a) C. lytica biofilm retention, (b) C. lytica biofilm retraction, (c) C. lytica biofilm removal. For each DT model, the mixture descriptor name and the rule value for the normalized value of the descriptor are shown.
Figure 6. Plots for H. pacif ica biofilm retention model 4: (a) obs vs pred correlation, (b) Williams plot, where yellow dots = training set, blue dots = test set; (c) y-scrambling results, where blue and dark blue dots are the r2 and q2 of the original model, other dots, yellow and red, are simulated r2 and q2 values.
A similar case was observed for Model 3, which represents the model for the C. lytica biofilm removal index, where both regression and classification models were developed. The regression model has almost the same predictive power with only one descriptor, as for Model 2 (Table 3, Figure S2, Figure 5). For this end point, a high removal index is associated with good FR properties. Model 3 has only one descriptor, similarly to the previous model, Mor09m (Table 4), which is the 3DMoRSE descriptor weighted by mass,38 where a higher value for this descriptor corresponds with an increase in the C. lytica biofilm removal index. This very well corresponds to required values of the same descriptor for model 2. Interestingly, the classification model for C. lytica biofilm removal shows excellent accuracy for both the training set and test set, with an accuracy of 100% (Table 3, classification models, Model 2), and a decision tree model based on one descriptor rule (Figure 5c). In the classification model, the value of mixture descriptor Mor09m perfectly predicts a C. lytica biofilm removal index, where if Mor09m is larger than 0.63, then the C. lytica biofilm removal index is large and vice versa (Figure 5c, Table 3, classification models). At the same time, regression-based Model 3 shows a less accurate prediction, 85% (r2 = 0.85) and has one outlier, coating #27 (Figure S2a). The AD plot is good (Figure S2b) and y-scrambling (Figure S2c) has a good split between original model and simulated ones. Model 4 shows the biofilm retention index prediction for a different marine bacterium, Halomonas pacif ica (Table 3, Figure
83% (Table 3, classification models, Model 1). In addition, Figure 5a shows the classification decision tree model based on the one-descriptor rule, where the value of mixture descriptor Mor18v determines a low and high C. lytica biofilm retention level, providing a great accuracy in prediction. For the C. lytica biofilm retraction model also was developed a regression model, Model 2, and is represented in Figure S1. However, taking into account clustered data, for this property a classification model is developed as well, which gives much better accuracy in comparing to a regression one, Figure S1a and Table 3 (classification models, Model 2). Thus, a low value of biofilm retraction index indicates good FR properties of the coating. The C. lytica biofilm retraction model (2) consists of one descriptor, Mor09m38 (Table 4). The Mor09m descriptor is weighted by the mass of polymer fragment (component) and has a negative sign. In this case, a larger value for this descriptor is associated with a lower C. lytica biofilm retraction index, thus an improved FR property for this bacterium. The classification model for C. lytica biofilm retraction shows excellent correlation for both the training set and for the test set with an accuracy of 100% (Table 3, classification models, Model 2). In addition, Figure 5b shows the classification decision tree model based on a one-descriptor rule, where the value of mixture descriptor Mor09m determines a low and high C. lytica biofilm retraction level, providing a high degree of predictive accuracy. 1788
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Figure 7. Plots for H. pacif ica biofilm removal model 5: (a) obs vs pred correlation, (b) Williams plot, where yellow dots = training set, blue dots = test set; (c) y-scrambling results, where blue and dark blue dots are the r2 and q2 of the original model; other dots, yellow and red, are simulated r2 and q2 values.
Figure 8. Plots for A. amphitrite reattachment model 6: (a) obs vs pred correlation, (b) Williams plot, where yellow dots = training set, blue dots = test set; (c) y-scrambling results, where blue and dark blue dots are the r2 and q2 of the original model; other dots, yellow and red, are simulated r2 and q2 values.
Model 5 represents the prediction for the H. pacif ica biofilm removal index (Table 3 and Figure 7). Model 5 has only one descriptor, VR2_RG (Table 4), which is based on the geometric topology of the components’ structure.38 To achieve good FR properties, the biofilm removal index should be maintained as high as possible, while the value of the VR2_RG descriptor needs to be high as well. As can be seen Figure 7a, model 5 has no outliers, and the correlation coefficient is satisfactory, r2 = 0.76. The prediction points related to coatings in the AD plot are within 3σ of the error limit (Figure 7b). Moreover, the y-scrambling plot in Figure 7c shows wellseparated values of r2 and q2 for the original model from all other simulated y-scrambling models, which confirms the robustness of the developed model. The last two models, models 6 and 7, predict end points for the barnacle species, Amphibalanus amphitrite. Thus, model 6 represents the reattachment index (i.e., the percentage of barnacles that could not attach) prediction for A. Amphitrite, while model 7 represents the adhesion index (i.e., shear force adhesion strength of attached barnacles (MPa)). Model 6 has only one descriptor, AVS_B(p) (Table 4), which is a geometrical descriptor weighted by polarizability.21 For good FR properties, the reattachment index needs to be higher, close to 1. On the basis of model 6, the higher the value of the
6). Thus, model 4 has only two descriptors, SPAM and DLS_04 (Table 4). The SPAM descriptor is responsible for the shape38 of structural fragments in the polymer composition. The DLS_04 descriptor is related to drug-like score indices, similar to drug-like filters implemented by Chen et al.51 This index takes into account a set of properties, including the number of H-bond donors, H-bond acceptors, molecular weight, lipophilicity, number of C(cp3) atoms, ratio of hydrogen atoms to nonhalogen heavy atoms, and unsaturation index. Lower values of the DLS_04 index assumes that the chemical is not good for drug-like purposes (i.e., has properties not suitable for interaction with biological systems) and is in accordance with the aim of this study, since a good (low) biofilm retention index suggests a poor interaction with biomass (i.e., mitigates adhesive bonding). Thus, an increase in the value of SPAM descriptor corresponds with a decrease in the H. pacif ica biofilm retention index, while the value of the DLS_04 descriptor should be kept low to maintain a good low biofilm retention index level. As can be seen in Figure 6a, this model has no outliers and possesses a good correlation coefficient, r2 = 0.86. The AD plot shows all points within 3σ of the error limit (Figure 6b). The y-scrambling plot on Figure 6c confirms that all other simulated y-scrambling models have much lower r2 values than the original model. 1789
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Figure 9. Plots for A. amphitrite adhesion model 7: (a) obs vs pred correlation, (b) Williams plot, where yellow dots = training set, blue dots = test set; (c) y-scrambling results, where blue and dark blue dots are the r2 and q2 of the original model; other dots, yellow and red, are simulated r2 and q2 values.
AVS_B(p) descriptor, the better the A. amphitrite reattachment index. According to this model, the polarizability index of the coating’s components should be as large as possible. As can be seen in Figure 8a, this model has no outliers, and the correlation coefficient is satisfactory for the training set, r2 = 0.63, while for the validation set, this model shows a very good prediction, r2 = 0.91. The AD plot shows all points within 3σ of the error limit (Figure 8b). Furthermore, the y-scrambling plot in Figure 8c shows much higher r2 (q2) values for the original model in comparison to all other simulated y-scrambling models, which confirms the robustness of the model. Model 7 represents the adhesion index for the same barnacle species, A. amphitrite (Table 3, Figure 9). Thus, model 7 consists of one descriptor, Mor09p (Table 4), and it represents signal 09, weighted by polarizability, a 3D-MoRSE descriptor.21 For good FR properties, the values of the adhesion index should be low. Then, according to model 7, since the regression coefficient of the Mor09p descriptor has a negative sign, then this descriptor has to possess a larger value in order to have an adhesion index at a low level. This model has only one outlier (#24) and possesses a good correlation coefficient after its removal, r2 = 0.84. The validation set’s prediction coefficient is high, r2 = 0.85. The AD plot shows that all the coatings’ prediction points are within 3σ of the error limit (outlier #24 is not shown; Figure 9b). In addition, the y-scrambling plot confirms that the original model is robust and all other generated y-scrambling models have significantly lower r2 values (Figure 9c). Each of the above model eqs 1−7 underwent a randomization process, where up to 300 simulations per model were carried out, but none of the identified simulated models showed any chance correlation (y-randomization). In addition, all models were validated by external test sets and have shown statistical robustness with rtest2 within the range 0.63−0.94 (Table 3). All descriptors that appeared in developed models had no cross-correlation higher than 0.6. This, again, confirms that the developed QSAR models based on the new approach are able to provide a good prediction performance for further rational design of polymer coatings with improved FR properties. Importantly, since each applied descriptor is mixture based, it takes into account not only the change in concentration of a particular component but the overall pattern of components
mixture, i.e., relative fraction to each other. Even if only one component’s concentration changes, the information on other components’ relative concentration to each other at this step is still taken into account. In this way, the mixture-based descriptor keeps the overall concentration pattern-related information to make predictions. As an example, a significant influence on FR properties, including on C. lytica retention, retraction, and removal indexes plays a concentration of CF3− PDMS, where the absence of this component significantly changes the FR property (Table S2, highlighted items). The influence of each component’s concentration was also discussed in our original experimental study,22 while the current study is mainly focused on the overall methodology-related idea test for polymer coating properties prediction, taking into account a combination of factors, structural and concentration-based. On the basis of the findings discussed above, the following structural and physicochemical criteria of the components must be considered when designing coatings with optimal FR properties. In this regard, two polarizability indices showed significant contribution to the C. lytica biofilm retention index and A. Amphitrite attachment index, where in both cases components need to have a high polarizability. Two other activities, C.lyt. biofilm retention and C.lyt. biofilm retraction, showed a strong correlation with the descriptor that weighted by mass of the fragments, which suggests that structural fragments of the components need to have higher mass, while keeping a lower value of van der Waals volume, according to model 1 (C.lyt. retention). Thus, in this study, several descriptors responsible for the size, mass, and volume of the components are shown to be important for obtaining the desired FR properties. Specifically, high values of SPAM (shape and size of polymer fragments), Mor09m (mass of the polymer fragments), and VR2_RG (geometric topology size) resulted in improved FR properties, while values of Mor18v (van der Waals volume) and drug-like score (DLS_04) need to be low. Furthermore, the A. amphitrite adhesion index possesses very specific properties, and it requires certain values of few peculiar physicochemical properties, different from other end points, such as high polarizability of the components’ structures. One more specific index that should be taken into account is DLS_04, which is responsible for drug-like properties. In our case, the value of this index should be lower to diminish the interaction with biomass (i.e., adhesive bonding). 1790
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CONCLUSIONS In this work, a comprehensive structure−activity analysis for a series of polymer coating materials was performed with the application of a novel cheminformatics-based mixture-QSAR approach. A set of 27 polymer coating materials with fouling release activity was investigated. To describe the properties of the investigated polymer coatings, a set of 1200 structural mixture-based descriptors was generated. The experimental and computational data were combined to find the best predictive models, and for this purpose a GA-MLR-based analysis was applied. As a result, seven mixture-QSAR models were developed for various organisms, including bacteria, algae, and barnacles, applying multiple-linear regression methods as well as classification methods. For models 1−3, the classification models showed a better accuracy in prediction. Several structural and physicochemical criteria of the coating components were found to be important for obtaining good, broad-spectrum FR properties. The selected structural and physicochemical criteria include polarizability of the components, several descriptors that are responsible for the size, mass and volume of the components, shape index, and specific druglike index to describe interaction with biomass, based on the components’ structures. The correlation coefficients for the predicted external sets of the models range from r2 = 0.63 to 0.94. All predictions were tested on an external validation set to confirm the models’ performances. The contributions of certain structural properties to the investigated activities were analyzed and discussed. On the basis of developed models, it is now possible to predict an optimal combination of source components to develop a polymer coating material with the desired FR properties. In summary, our results clearly indicate that the developed QSAR models based on a mixture approach are able to provide a good prediction performance for polymer coating materials. This will allow rational design of materials with improved FR properties. The methodology can be applied to predict other properties of polymer coating materials, by developing a mixture-based cheminformatics model for properties of interest.
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Assisted Science and Technology and the Department of Energy through Grant No. DE-SC0001717 and Office of Naval Research awards N00014-11-1-0032 and N00014-12-1-0641 are gratefully acknowledged.
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ASSOCIATED CONTENT
S Supporting Information *
The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acsami.6b12766. Tables S1 and S2 and S3 and Figures S1 and S2 (PDF)
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REFERENCES
AUTHOR INFORMATION
Corresponding Authors
*E-mail:
[email protected]. *E-mail:
[email protected]. ORCID
Bakhtiyor Rasulev: 0000-0002-7845-4884 Author Contributions
The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS Computer access and financial and administrative support from the North Dakota State University Center for Computationally 1791
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