Towards predictive resistance models for agrochemicals by combining chemical and protein similarity via proteochemometric modelling
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Resistance to pesticides is an increasing problem in agriculture. Despite practices such as phased use and cycling of ‘orthogonally resistant’ agents, resistance remains a major risk to national and global food security. To combat this problem, there is a need for both new approaches for pesticide design, as well as for novel chemical entities themselves. As summarized in this opinion article, a technique termed ‘proteochemometric modelling’ (PCM), from the field of chemoinformatics, could aid in the quantification and prediction of resistance that acts via point mutations in the target proteins of an agent. The technique combines information from both the chemical and biological domain to generate bioactivity models across large numbers of ligands as well as protein targets. PCM has previously been validated in prospective, experimental work in the medicinal chemistry area, and it draws on the growing amount of bioactivity information available in the public domain. Here, two potential applications of proteochemometric modelling to agrochemical data are described, based on previously published examples from the medicinal chemistry literature.
KeywordsPolypharmacology Cheminformatics Machine learning Resistance
In agriculture, resistance to pesticides forms a complex and growing problem, which includes the development of resistance to insecticides , fungicides , as well as herbicides [7, 25, 6]. For each of these resistance types, a multitude of different resistance mechanisms are possible, all of which, however, lead to phenotypic resistance (i.e. the concentration of pesticide needed to kill the pests is higher for resistant variants compared to wild type). Commonly observed resistance mechanisms are similar to those observed in microbial and cancer mechanisms of resistance; examples include increased expression of efflux proteins, increased expression of metabolizing proteins, and point mutations in the protein targeted by the agrochemical agent [8, 10, 24]. Due to the spectrum of possible adaptations in the target organism, it is difficult to capture and model all potential forms of resistance for a certain compound in a model a priori, which is analogous to antibiotic and anti-cancer drug resistance. Out of these possibilities, the current opinion article will deal specifically with the impact of point mutations at the ligand-binding site and their effect on resistance. This is an area for which there is prior successful experience in the medicinal chemistry and drug design field, including prospective experimental validation of the models developed. Here, previous research of the authors as well as other related groups will be outlined, with the aim to transfer these methods also to the world of agrochemical research [22, 20].
Complementary ligand and target information
In the case of agrochemistry, the authors are of the opinion that the nature of the PCM technique would be equally suited to identify potential agrochemicals that have the most favourable resistance profile. Similarly, models could be used to deconvolute contributions of mutations to an increase or reduction of resistance displayed by individual mutants.
These approaches are also transferable to the pesticide field. Analogous to the aforementioned adenosine receptors, gamma-aminobutyric acid A (GABA-A) ligand-gated ion channels can be aligned to capture and represent the (dis)similarities between species. These ion channels form the target for phenylpyrazole insecticides, and resistance has been demonstrated through point mutations [2, 4, 3]. An MDS of the similarity between mammalian and insect isoforms of these complexes is shown in Fig. 3b, illustrating that mammalian channels are more similar to each other than they are to their insect counterparts. Furthermore, the selected arthropods display a larger variation between species than do the selected mammals. As it was previously demonstrated that it is possible to model the protein similarity space shown in Fig. 3a (for the adenosine receptors), it stands to reason that it is feasible to model the space shown in Fig. 3b (representing the GABA-A ion channels). PCM models are agnostic of the particular target and application area they are used in—their applicability depends on the amount of data available both from the chemical and biological side (however, this requirement should not be neglected). Hence, the data visualized in Fig. 3b should allow for the construction of a predictive model that can predict activity (and toxicity) of candidate compounds on GABA-A in the species included in the analysis, in a manner similar to the adenosine receptors described above.
Finally, it should be noted that the ribosome has gained significant attention as a druggable target (specifically for antibiotics) . It has been shown that bacteria can gain resistance via multiple diverse mutations in 23S ribosomal RNA [11, 13]. Yet a complementary mode of action leading to novel pesticides should provide a useful addition to established modes of action and increase the resistance threshold.
In summary, PCM is a versatile quantitative modelling technique for interactions between ligands and their biological targets. In the current opinion paper, we highlight application of the technique and precedence for success from related fields and sketch applications to the agrochemical context, comprising insecticides, fungicides, and herbicides. Based on the prospective experimental validation that has been performed for enzymes and receptors in previous studies, such work is likely to be successful. Firstly, there is the modelling and prediction of resistance towards pesticides by weeds, fungi, or insects. A second application is the prediction of activity of pesticides or other agricultural chemicals in organisms were this is undesired (‘off-targets’). Finally, the technique can be used in virtual screening in the identification of potential new agrochemicals, aimed at a higher resistance threshold (broader activity against multiple mutants), or potential agrochemicals anticipated to have less toxic effects on non-pest species. We anticipate a great future for studies in this area, as the cost of sequencing (which directly relates to the generation of protein-side descriptors) continues to drop and the amount of bioactivity data available increases, both of which increases our ability to generate predictive PCM models considerably.
GvW thanks EMBL (EIPOD) and Marie Curie Actions (COFUND) for funding. AB thanks Unilever and the European Research Commission (Starting Grant ERC-2013-StG 336159 MIXTURE) for funding. JPO thanks the Wellcome Trust for funding under the Strategic Award (WT086151/Z/08/Z).
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