Insect Aggregation Pheromone Response Synergized by "Host-Type

Chapter 14

Insect Aggregation Pheromone Response Synergized by "Host-Type" Volatiles

Downloaded by STANFORD UNIV GREEN LIBR on October 14, 2012 | http://pubs.acs.org Publication Date: March 31, 1995 | doi: 10.1021/bk-1995-0589.ch014

Molecular Modeling Evidence for Close Proximity Binding of Pheromone and Coattractant in Carpophilushemipterus (L.) (Coleoptera: Nitidulidae) 1

Richard J. Petroski and Roy Vaz

2

l

Bioactive Constituents Research, National Center for Agricultural Utilization Research, Agricultural Research Service, U. S. Department of Agriculture, Peoria, IL 61604 Marion Merrell Dow, Cincinnati, OH 45242 2

The driedfruit beetle, Carpophilus hemipterus (L.) is a worldwide pest of avariety of fruits and grains, both before and after harvest. Attractiveness of the male-produced aggregation pheromone is enhanced by the presence of a "host-type" volatile coattractant. A set of 26 compounds was used to explore relationships between pheromone structure and activity by 3DQSAR/CoMFA methods. Significant differences in aggregation pheromone CoMFA-coefficient contour maps were observed in the presence and absence of the "host-type" volatile coattractant. The driedfruit beetle, Carpophilus hemipterus (L.) (Coleoptera: Nitidulidae), attacks a large number of agricultural commodities in the field, during storage after harvest or in transport (1). It is also able to vector microorganisms responsible for the souring of figs (1) and mycotoxin production in corn (2). Both sexes of C. hemipterus respond to a male-produced aggregation pheromone (3). A wind tunnel bioassay guided the isolation of eleven all-is tetraene hydrocarbons, two Z-isomer tetraene hydrocarbons and one all-is triene hydrocarbon (3,4). The pheromone components were tentatively identified by spectroscopic methods then the assigned structures were proven by synthesis (3-5). Structures of the synthesized compounds are shown in Figure 1. Compounds A to Ν have been identified in the C. hemipterus pheromone blend (3,4); the additional compounds were prepared to explore structure activity relationships (4). Previous studies have shown that aggregation pheromone activity may be enhanced when the pheromone is used in combination with attractive chemicals produced by the host plant or associated microorganisms, termed host-type volatiles or host-type coattractants (6-9). In order to investigate relationships between the structure of the pheromone molecule and biological activity, as well as explore possible additional relationships between the coattractant and pheromone structureactivity relationships, all compounds (A to Z) were tested for activity both with and 0097-6156/95/0589-0197$12.00A) © 1995 American Chemical Society In Computer-Aided Molecular Design; Reynolds, C., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 1995.

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Figure 1. Hydrocarbon structures used in the Carpophilus hemipterus data set.

In Computer-Aided Molecular Design; Reynolds, C., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 1995.


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without adding a host-type coattractant (propyl acetate) to the bioassay treatments (4). The results of this previous work are summarized in Table 1. Individual compounds are capable of eliciting the pheromonal response, as opposed to an obligate requirement for a blend of compounds (4). This observation is consistent with a hypothesis that all the structures interact with a single recognition site or a family of component recognition sites having conservation of the required bioactive conformation of the ligands at the recognition sites. A n obligate requirement for a blend of compounds would indicate the action of distinctly different recognition sites, each with its own structural and conformational requirements. Recent advances in computational chemistry enabled us to probe quantitative structure-activity relationships (QSAR) in three-dimensional space. We report 3D QSAR studies with the aid of Comparative Molecular Field Analysis (CoMFA) methodology (10-13). With CoMFA, a suitable sampling of the steric and electrostatic fields surrounding a set of ligand molecules might provide all the information necessary for understanding their observed biological properties (13). Materials and Methods Data. Chemical structures (Figure 1) and the corresponding bioassay data with and without coattractant (Table 1) were taken from Bartelt et al. (4). The data corresponded to a counting of the number of beetles alighting on pieces of filter paper in a wind tunnel bioassay; two treatment preparations to be compared (pheromone versus control or pheromone plus coattractant versus control) were applied to pieces of filter paper, and those were hung side by side in the upwind end of the wind tunnel. The coattractant (propyl acetate) alone vs a blank filter paper control was only minimally attractive to C. hemipterus; relative bioassay activity was less than 5 percent (4). Two CoMFA analyses were done, one for the data without a coattractant (propyl acetate) and one for the data with the coattractant. Establishing the Conformation of Each Molecule. A computation using the MOP A C (14) program and the A M I Hamiltonian was done on the sequence of model structures shown in Figure 2 which shows the optimal geometries as well as the bond orders. The doubly substituted structure is twisted more than the singly substituted structure. The amount of derealization decreases as substituent methyl groups are introduced in the progression. Some conformational searching is required to find the low energy conformations. Hence, the single bonds in structure A were assumed to be rotatable with a reasonable energy barrier in terms of all states being populated at room temperature. The 3D structures represented in Figure 1 were constructed using structure A from the figure as a template. Structure A was subjected to conformational searching about the rotatable bonds using the Tripos 5.2 Molecular Mechanics Force Field (10). The minima encountered in the conformational search of compound A were optimized with the A M I Hamiltonian and the minimum conformation was used as the template. If there were any extensions made, in terms of adding rotatable bonds to structure A such as in structure B , a molecular mechanics force field conforma-



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ORTHOGONAL VIEW

J00

1

ORTHOGONAL VIEW

Figure 2. A sequence of model structures (2E 4E,6E, 8£,-tetradecenes having 0,1, or 2 methyl substituents on carbons 5 and 7) showing lower derealization and thus lowering the rotational barrier for the single bond between carbons 5 and 6 with lesser substitution. 9


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tional search was again done on the additional rotatable bond and the minima obtained, optimized using the A M I Hamiltonian and the energies compared, choosing the minimum energy conformation again. Conformations described here are vapor phase. Since all the compounds examined in this study are only unsaturated hydrocarbons, "solvent" effect on conformations at the putative pheromone recognition site located on an insect antenna should be minimal. Superimposing the Molecules Within a Region. Once an optimal conformation was obtained for all the structures, the structures were then overlapped via an RMS fit using the atoms labeled with an asterisk in Figure 3. The overlapped structures are depicted in Figure 4. A region, as shown in Figure 4, was then constructed such that all structures fell at least 2 A away from the region extents. The region only had carbon atoms used as probes and the lowest and highest points had the coordinates of 9.6170, 7.2766, -5.0496) and (10.4965, 4.2695, 6.5532) respectively. The points were separated at intervals of 2 A along each axis. 0

0

Comparative Molecular Field Analysis. This region containing the superimposed structures was utilized in a Comparative Molecular Field Analysis (CoMFA) experiment (Figure 5). Normally in a CoMFA, two probes are used. One probe is a carbon atom with no charge and the other probe is a positive charge (with no mass or van der Waals radius). The energy of die probe at each point of the region is calculated using the Tripos 5.2 force field (10). The two terms of interest in the force field are the 6-12 van der Waals terms which account for the London dispersion forces, and the coulombic terms representing coulombic forces arising from point charges. The positive charge probe was not used in this study because the π electron clouds of the unsaturated hydrocarbons in the analysis are not reflected by the charge on the carbon atoms. Points in the region where the energy of the carbon probe exceeded 30 kcal/mol were dropped from the analysis. The biological activities used were those listed in Table 1. CoMFA columns whose standard deviation was less than 2.0 kcal/mol were ignored in the calculation. This reduced the number of columns involved in the Partial Least Squares (PLS) statistical analysis (75) substantially. Also, changing the dropped columns to those having a standard deviation of less than 1.0 kcal/mol did not have any significant impact on the statistics. The predictive ability of both models (3D QSAR with and without coattractant) were evaluated using cross-validation in which the cross-validation was done using as many groups as there were rows except as noted. Cross-validation involves pretending that one of the rows does not have experimental data. The resulting equation is used to predict the experimental measurement for the omitted compound. The cross-validation cycle is repeated, leaving out one different compound until each compound has been excluded and predicted exactly once. The resulting individual squared errors of prediction are accumulated. The result of the cross-validation is the sum of squared prediction errors, sometimes termed the PRESS (Predictive Residual Sum of Squares). In PLS, the iterations are continued until the PRESS no longer decreases significantly. Substituting PLS, which operates on all independent variables simultaneously, for regression, which operates on one independent variable at a time, reduces the probability of accepting a chance correlation (75).




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Figure 4. A l l optimized structures aligned using the atoms marked in Figure 3.



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I Partial Least Squares (PLS) Activity = const + C1*VDW1 + C2*VDW2 +

Figure 5. Schematic of the CoMFA steric field for structure A .


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Conventional Λ-squared values (regression range from 0 to 1; however, the crossvalidated ^-squared values (PLS) range from negative infinity to 1.0.


Results and Discussion Some synthetic analogs, which were never detected from the beetles (compounds Ο to Ζ in Table 1), showed activity in the bioassay (e.g. compounds Ο, Ρ, V and W). This observation led us to the conclusion, shared by others (16), that insect pheromone communication systems are not as rigid as once thought. Some generalizations can be made about structure-activity relationships (4): (1) The left-hand terminal alkyl group (as drawn in structure A) should be methyl; substitution of ethyl for methyl renders the compound inactive or nearly so. Compounds E, H , J, K , and X have low or no activity. (2) The left-hand alkyl branch should be methyl, but the one example with an ethyl group at that position (compound R) did have slight activity. (3) A n ethyl group as the middle alkyl branch (e.g., the 5-ethyl group in compound D) also renders the compound inactive. (4) The right-hand alkyl branch (e.g., the 7-position of compound B) can be methyl, ethyl (as in compound F), or propyl (as in compound V) and still have activity; however, only a hydrogen in that position (compounds S and T) renders the compound inactive. (5) The right-hand terminal alkyl group can be methyl (compound A), ethyl (compound B) or propyl (compound W) and still have activity, but the ethyl group seems most consistent with high activity. (6) Alkyl groups in the 9-position (compound Y) or in the 2-position (compound Z) greatly reduce activity. (7) The presence of cis double bonds at any position reduces activity (compounds Μ, Ν, Ο, Ρ and Q). Another important general feature was evident from the results shown in Table 1. Relative activity was often enhanced when each unsaturated hydrocarbon was separately tested in the presence of the coattractant but the proportion of enhancement varied from hydrocarbon to hydrocarbon tested. In some cases (e.g. compounds A , N , and O), activity decreased in the presence of the coattractant. These observations revealed a relationship between the structure of the hydrocarbon tested and the role of the coattractant; maximal activity in the presence of the coattractant was observed when the right-hand terminal alkyl group was ethyl. Compounds A , N , and Ο all have methyl as the terminal alkyl group. Beyond this observation, it is hard to imagine a more precise role for the coattractant without use of modern computational tools. Although insights can be acquired by looking at two-dimensional representations of structures as are shown in Figure 1, the compounds are actually threedimensional. A more refined examination is gained by using modern 3D QSAR methods. The predicted versus actual plots for the 3D-QSAR analyses with and without the coattractant show that both CoMFA models are workable predictors of biological activity (Figure 6). The ^-squared values and other relevant statistics for both analyses are reasonable (Table 2). Thus, the CoMFA results also support the hypothesis of either a single pheromone recognition site or (less likely) a family of



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Predicted vs actual for structures without coattractant

/ r Predicted vs actual for structures with coattractant

Figure 6. Plots of predicted versus actual biological activity values for structures with and without coattractant.


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Table 1 Bioassay Activity for Individual Hydrocarbons a Relative Activity (%)


Hydrocarbon

A Β c D Ε F G H I J κ L M N 0 P

Q R S T u V w X Y z 8

Without Coattractant

With Coattractant

24 29 21 0 2 16 2 0 0 0 4 5 5 11 11 13 7 6 0 0 8 35 14 0 0 3

18 60 41 2 3 49 8 17 1 0 5 5 3 3 8 17 4 6 0 1 1 41 12 11 8 4

Data from Bartelt et al., J. Chem. Ecol. 18(3) 379-402 (1992). The coattractant was propyl acetate (20 μΙ, 10% in mineral oil).

Table 2 Partial Least Squares (PLS) Analysis of CoMFA Data (only steric field included) Without Coattractant

With Coattractant

Number of components Standard error of estimate R-squared R-squared (crossvalidated ) Standard error of prediction

4 3.727 0.851 0.490 6.891

3 2.957 0.964 0.811 6.730

Compounds dropped from analysis

V, K, W

U, J, X, I

Statistics

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The R-squared is related to the "PRESS" via the equation: (S.D. - PRESSys.D. where S.O. is the sum over all moleciles of squared deviations of each biological parameter from the mean and PRESS (Predictive Sum of Squares) is the sum over all molecules of the squared differences between the actual and predicted biological parameters (range is neg infinity to 1).



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pheromone component recognition sites having bioactive conformation conservation in C. hemipterus. The main analysis tools, in terms of computer aided molecular design, are the coefficient plots as shown in figures 7 and 8. These plots are actually contours of the standard deviation times PLS coefficient [(std dev)*(coefficient)] at each point in the region that fall in a particular range. The field is created as the point by point product of the PLS coefficient and the standard deviation of energies at the point among all compounds in the study. The view of this field is preferred to the view of only the PLS coefficients field because it reduces the visual cluster of moderately large coefficients that arise by chance association with larger scale trends. The contours are centered at -0.7 (light gray, both Figures) and 0.14 (black, Figure 7) or 0.19 (black, Figure 8) with structure A embedded in the contour plots. These contours have the same meaning as the plots in reference 3 viz. if the contour is for a region corresponding to a negative value, in that case, that region in space would need lower van der Waals interaction energies i f a carbon probe atom were placed in that region or at the very most, no change would be made in that region for increased activity. Similarly, a positive region would prefer increased van dar Waals interaction energies for a carbon probe for increased activity. This can be derived from the equation in Figure 5. The positive and negative coefficient regions show that extending the structure A by a methylene in the direction as in structure Β puts the methylene in a positive coefficient region and similarly extending structure A by a methylene in the other direction, as in structure E, puts this latter methylene in a negative coefficient region. Also, extending molecule A by a methylene such as in structure Β or differently such as in structure C, even though both regions have positive coefficients, their relative values are different and thus the structural extensions have different consequences on the activity. The contour plot from the analysis of the structures with a coattractant is quite interesting. A new, sharp, and very well-defined most-negative region seems to have been created which could possibly be attributed to the coattractant occupying this region on the putative receptor. The region corresponding to the most negative coefficient region for the analysis without the coattractant is still present in the analysis with the coattractant. The field times coefficient field where the field value represents the product of the molecule's field energy and the PLS coefficient from the appropriate analysis from which they were dropped between the two analyses did not lead to any activity in the new negative region for the appropriate dropped molecules, thereby eliminating this new region as arising from the outliers not used in the analysis. The (field)*(coefficient) plot represents the contribution of this field for this molecule to its predicted activity. The significant change in the CoMFA contour plot with coattractant versus the corresponding CoMFA contour plot without coattractant suggests a close proximity in binding sites for the pheromone and the coattractant near the 10-position of the pheromone (e.g., compound A in Figure 8) If compound Β were pictured in the figure, the new most negative area would still reside at the 10-position, which would be over the methylene portion of the right-hand terminal ethyl group. A n


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Figure 8. The std. dev. * coefficient CoMFA contour plot for the structures with coattractant showing only structure A .



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alternative, but less likely, interpretation of our CoMFA data would be binding of the coattractant at a separate binding site but affecting the binding of the pheromone (allosterism). This placement of the coattractant would not have been possible without a 3D analysis. If C hemipterus pheromone recognition sites have enough fluidity then it is theoretically possible to develop pheromone analogs that serve as species-specific insect control agents. Based on our CoMFA results, it might be possible that an oxygen atom from the coattractant (propyl acetate) resides at this new most negative site. It would be interesting to place an oxygen atom in a pheromone analog at this position in 3D space, but this has yet to be tested experimentally. Carefully designed pheromone analogs, or blends thereof, might surpass the natural pheromone in biological activity, ease of preparation, or stability under field conditions. Such analogs would improve our ability to monitor pest populations, lower pest populations by mass trapping, or lower pest populations by use of combinations of pheromone and either insecticides or biological control agents. It is also theoretically possible that pheromone perception inhibitors (antagonists) could be developed against C. hemipterus. Pheromone perception inhibitors could be used for the protection of commodities during storage or transport. Literature Cited 1. Hinton, H. E. A Monograph of the Beetles Associated with Stored Products; Jarrold and Sons: Norwich, U.K., 1945, 443 pp. 2. Wicklow, D. T. In Phytochemical Ecology: Allelochemicals, Mycotoxins and Insect Pheromones and Allomones; Chou, C. H.; Waller, G. R., Eds.; Institute of Botany, Academia Sinica Monograph Series No. 9: Taipei, ROC., 1989, p263. 3. Bartelt, R. J.; Dowd, P. F.; Plattner, R. D.; Weisleder, D. J. Chem. Ecol. 1990, 16, 1015. 4. Bartelt, R. J.; Weisleder, D.; Dowd, P. F.; Plattner, R. D. J. Chem. Ecol. 1992, 18, 379. 5. Bartelt, R. J.; Weisleder, D.; Plattner, R. D. J. Agric. Food Chem. 1990, 18, 2192. 6. Walgenbach, C. Α.; Burkholder, W. E.; Curtis, M. J.; Khan, Z. A. J Econ. Entomol. 1987, 80, 763. 7. Oehlschlager, A. C.; Pierce, A. M.; Pierce H. D. Jr.; Bprden, J. H. J. Chem. Ecol. 1988, 14, 2071. 8. Birch, M. C. In Chemical Ecology of Insects; Bell, W. J.; Carde, R. T., Eds.; Sinauer Assoc.: Sunderland, Massachusetts, 1984; Chapter 12. 9. Bartelt, R. J.; Schaner, A. M.; Jackson, L. L. Physiol. Entomol. 1986, 11, 367. 10. Clark, M.; Cramer III, R. D.; Van Opdenbosh, N. J. Comp. Chem. 1989, 10, 982. 11. Cramer III, R. D.; Patterson, D. E.; Bunce, J. D. J. Am. Chem. Soc., 1988, 110, 5959. 12. Cramer III, R. D., DePriest, S. Α., Patterson, D. E., Hecht, P. in "3D QSAR in Drug Design: Theory and Applications"; Kabinyi, H, Ed; ESCOM, The Netherlands, 1993, p 443.


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13. Cramer III, R. D., Simeroth, P., Patterson, D. E. in "QSAR: Rational Approaches to the Design of Bioactive Compounds"; Silipo, C. and Vittoria, Α., Eds; Elsevier Science Publishers Β. V., Amsterdam 14. MOPAC 5.0 is available from QCPE, Indiana University, Bloomington, IN. 15. Cramer III, R. D., Bunce, J. D., Patterson, D. E., Frank, I. E. Quant. Struct.Act. Relat. Pharmacol., Chem. Biol. 1988, 7, 18. 16. Carlson, D. Α.; McLaughlin, J. R. Experientia 1982, 38, 309.


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Insect Aggregation Pheromone Response Synergized by "Host-Type

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