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Predicting mammalian metabolism and toxicity of pesticides in silico.

ABSTRACT: Pesticides must be effective to be commercially viable but they must also be reasonably safe for those who manufacture them, apply them, or consume the food they are used to produce. Animal testing is key to ensuring safety, but it comes late in the agrochemical development process, is expensive, and requires relatively large amounts of material. Surrogate assays used as in vitro models require less material and shift identification of potential mammalian toxicity back to earlier stages in development. Modern in silico methods are cost-effective complements to such in vitro models that make it possible to predict mammalian metabolism, toxicity and exposure for a pesticide, crop residue or other metabolite before it has been synthesized. Their broader use could substantially reduce the amount of time and effort wasted in pesticide development. This contribution reviews the kind of in silico models that are currently available for vetting ideas about what to synthesize and how to focus development efforts; the limitations of those models; and the practical considerations that have slowed development in the area. Detailed discussions are provided of how bacterial mutagenicity, human cytochrome P450 (CYP) metabolism, and bioavailability in humans and rats can be predicted. © 2018 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

SUBMITTER: Clark RD

PROVIDER: S-EPMC6099302 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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Predicting mammalian metabolism and toxicity of pesticides in silico.

Clark Robert D RD

Pest management science 20180515

Pesticides must be effective to be commercially viable but they must also be reasonably safe for those who manufacture them, apply them, or consume the food they are used to produce. Animal testing is key to ensuring safety, but it comes late in the agrochemical development process, is expensive, and requires relatively large amounts of material. Surrogate assays used as in vitro models require less material and shift identification of potential mammalian toxicity back to earlier stages in devel ...[more]

PMID: 29762898

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Project description:BackgroundOver the past few decades, scientific research has been focused on developing peptide/protein-based therapies to treat various diseases. With the several advantages over small molecules, including high specificity, high penetration, ease of manufacturing, peptides have emerged as promising therapeutic molecules against many diseases. However, one of the bottlenecks in peptide/protein-based therapy is their toxicity. Therefore, in the present study, we developed in silico models for predicting toxicity of peptides and proteins.DescriptionWe obtained toxic peptides having 35 or fewer residues from various databases for developing prediction models. Non-toxic or random peptides were obtained from SwissProt and TrEMBL. It was observed that certain residues like Cys, His, Asn, and Pro are abundant as well as preferred at various positions in toxic peptides. We developed models based on machine learning technique and quantitative matrix using various properties of peptides for predicting toxicity of peptides. The performance of dipeptide-based model in terms of accuracy was 94.50% with MCC 0.88. In addition, various motifs were extracted from the toxic peptides and this information was combined with dipeptide-based model for developing a hybrid model. In order to evaluate the over-optimization of the best model based on dipeptide composition, we evaluated its performance on independent datasets and achieved accuracy around 90%. Based on above study, a web server, ToxinPred has been developed, which would be helpful in predicting (i) toxicity or non-toxicity of peptides, (ii) minimum mutations in peptides for increasing or decreasing their toxicity, and (iii) toxic regions in proteins.ConclusionToxinPred is a unique in silico method of its kind, which will be useful in predicting toxicity of peptides/proteins. In addition, it will be useful in designing least toxic peptides and discovering toxic regions in proteins. We hope that the development of ToxinPred will provide momentum to peptide/protein-based drug discovery (http://crdd.osdd.net/raghava/toxinpred/).

Project description:5'-Deoxy-5'-methylthioadenosine, a by-product of polyamine synthesis, can support the growth of Raji cells in a methionine-free medium, but not the growth of CCL39 cells, although these cells are also able to incorporate radiolabelled 5'-deoxy-5'-methylthioadenosine (MeSAdo) into methionine, S-adenosyl-L-methionine (AdoMet) and proteins [Christa, Kersual, Augé & Pérignon (1986) Biochem. Biophys. Res. Commun. 135, 131-138]. We first tested the hypothesis of a toxic effect of MeSAdo in the conditions of growth experiments: we could not demonstrate any toxic effect of MeSAdo on the synthesis of macromolecules, nor any toxicity mediated by polyamines or pyrimidine starvation, and we found that the growth of CCL39 cells was strictly dependent on the supply of exogenous methionine. We then tried to determine whether the ability of CCL39 cells to metabolize MeSAdo to methionine and AdoMet was modulated by the proliferation state of CCL39 cells, which is dependent on the supply of exogenous methionine. Studies of the incorporation of radiolabelled MeSAdo show that: (i) the total synthesis of methionine from MeSAdo is twice as high in subconfluent cells (grown in 100 microM-methionine) as in resting cells (cultured in 0 microM-methionine); (ii) the incorporation into proteins does not parallel the total protein synthesis, and the methionine derived from MeSAdo mostly flows out of the cell; (iii) addition of methionine to resting cells immediately leads to a transient and marked increase in metabolism of MeSAdo to AdoMet, presumably reflecting the rapid replenishment of the AdoMet pool of the cells. Taken together, these results suggest that the methionine derived from MeSAdo is preferentially used to synthesize AdoMet rather than proteins, and that this synthesis of AdoMet depends on the ability of the CCL39 cells to grow, and hence on the supply of exogenous methionine. It is proposed that, in CCL39 cells, the metabolic pathway leading from MeSAdo (a by-product of polyamine synthesis) to methionine and to AdoMet (a precursor of polyamine synthesis) is part of a metabolic cycle the activity of which depends, like polyamine synthesis itself, on cell proliferation.

Project description:BACKGROUND: Laccases (E.C. 1.10.3.2) are multi-copper oxidases that have gained importance in many industries such as biofuels, pulp production, textile dye bleaching, bioremediation, and food production. Their usefulness stems from the ability to act on a diverse range of phenolic compounds such as o-/p-quinols, aminophenols, polyphenols, polyamines, aryl diamines, and aromatic thiols. Despite acting on a wide range of compounds as a family, individual Laccases often exhibit distinctive and varied substrate ranges. This is likely due to Laccases involvement in many metabolic roles across diverse taxa. Classification systems for multi-copper oxidases have been developed using multiple sequence alignments, however, these systems seem to largely follow species taxonomy rather than substrate ranges, enzyme properties, or specific function. It has been suggested that the roles and substrates of various Laccases are related to their optimal pH. This is consistent with the observation that fungal Laccases usually prefer acidic conditions, whereas plant and bacterial Laccases prefer basic conditions. Based on these observations, we hypothesize that a descriptor-based unsupervised learning system could generate homology independent classification system for better describing the functional properties of Laccases. RESULTS: In this study, we first utilized unsupervised learning approach to develop a novel homology independent Laccase classification system. From the descriptors considered, physicochemical properties showed the best performance. Physicochemical properties divided the Laccases into twelve subtypes. Analysis of the clusters using a t-test revealed that the majority of the physicochemical descriptors had statistically significant differences between the classes. Feature selection identified the most important features as negatively charges residues, the peptide isoelectric point, and acidic or amidic residues. Secondly, to allow for classification of new Laccases, a supervised learning system was developed from the clusters. The models showed high performance with an overall accuracy of 99.03%, error of 0.49%, MCC of 0.9367, precision of 94.20%, sensitivity of 94.20%, and specificity of 99.47% in a 5-fold cross-validation test. In an independent test, our models still provide a high accuracy of 97.98%, error rate of 1.02%, MCC of 0.8678, precision of 87.88%, sensitivity of 87.88% and specificity of 98.90%. CONCLUSION: This study provides a useful classification system for better understanding of Laccases from their physicochemical properties perspective. We also developed a publically available web tool for the characterization of Laccase protein sequences (http://lacsubpred.bioinfo.ucr.edu/). Finally, the programs used in the study are made available for researchers interested in applying the system to other enzyme classes (https://github.com/tweirick/SubClPred).

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Predicting mammalian metabolism and toxicity of pesticides in silico.

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Predicting mammalian metabolism and toxicity of pesticides in silico.

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