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Protein-Ligand Fishing in planta for Biologically Active Natural Products Using Glutathione Transferases.

ABSTRACT: Screening for natural products which bind to proteins in planta has been used to identify ligands of the plant-specific glutathione transferase (GST) tau (U) and phi (F) classes, that are present in large gene families in crops and weeds, but have largely undefined functions. When expressed as recombinant proteins in Escherichia coli these proteins have been found to tightly bind a diverse range of natural product ligands, with fatty acid-and porphyrinogen-derivatives associated with GSTUs and a range of heterocyclic compounds with GSTFs. With an interest in detecting the natural binding partners of these proteins in planta, we have expressed the two best characterized GSTs from Arabidopsis thaliana (At), AtGSTF2 and AtGSTU19, as Strep-tagged fusion proteins in planta. Following transient and stable expression in Nicotiana and Arabidopsis, respectively, the GSTs were recovered using Strep-Tactin affinity chromatography and the bound ligands desorbed and characterized by LC-MS. AtGSTF2 predominantly bound phenolic derivatives including S-glutathionylated lignanamides and methylated variants of the flavonols kaempferol and quercetin. AtGSTU19 captured glutathionylated conjugates of oxylipins, indoles, and lignanamides. Whereas the flavonols and oxylipins appeared to be authentic in vivo ligands, the glutathione conjugates of the lignanamides and indoles were artifacts formed during extraction. When tested for their binding characteristics, the previously undescribed indole conjugates were found to be particularly potent inhibitors of AtGSTU19. Such ligand fishing has the potential to both give new insight into protein function in planta as well as identifying novel classes of natural product inhibitors of enzymes of biotechnological interest such as GSTs.

SUBMITTER: Dixon DP

PROVIDER: S-EPMC6253249 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Protein-Ligand Fishing in planta for Biologically Active Natural Products Using Glutathione Transferases.

Dixon David P DP Edwards Robert R

Frontiers in plant science 20181115

Screening for natural products which bind to proteins in planta has been used to identify ligands of the plant-specific glutathione transferase (GST) tau (U) and phi (F) classes, that are present in large gene families in crops and weeds, but have largely undefined functions. When expressed as recombinant proteins in Escherichia coli these proteins have been found to tightly bind a diverse range of natural product ligands, with fatty acid-and porphyrinogen-derivatives associated wi ...[more]

PMID: 30510558

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Project description:Target fishing is the process of identifying the protein target of a bioactive small molecule. To do so experimentally requires a significant investment of time and resources, which can be expedited with a reliable computational target fishing model. The development of computational target fishing models using machine learning has become very popular over the last several years because of the increased availability of large amounts of public bioactivity data. Unfortunately, the applicability and performance of such models for natural products has not yet been comprehensively assessed. This is, in part, due to the relative lack of bioactivity data available for natural products compared to synthetic compounds. Moreover, the databases commonly used to train such models do not annotate which compounds are natural products, which makes the collection of a benchmarking set difficult. To address this knowledge gap, a data set composed of natural product structures and their associated protein targets was generated by cross-referencing 20 publicly available natural product databases with the bioactivity database ChEMBL. This data set contains 5589 compound-target pairs for 1943 unique compounds and 1023 unique targets. A synthetic data set comprising 107 190 compound-target pairs for 88 728 unique compounds and 1907 unique targets was used to train k-nearest neighbors, random forest, and multilayer perceptron models. The predictive performance of each model was assessed by stratified 10-fold cross-validation and benchmarking on the newly collected natural product data set. Strong performance was observed for each model during cross-validation with area under the receiver operating characteristic (AUROC) scores ranging from 0.94 to 0.99 and Boltzmann-enhanced discrimination of receiver operating characteristic (BEDROC) scores from 0.89 to 0.94. When tested on the natural product data set, performance dramatically decreased with AUROC scores ranging from 0.70 to 0.85 and BEDROC scores from 0.43 to 0.59. However, the implementation of a model stacking approach, which uses logistic regression as a meta-classifier to combine model predictions, dramatically improved the ability to correctly predict the protein targets of natural products and increased the AUROC score to 0.94 and BEDROC score to 0.73. This stacked model was deployed as a web application, called STarFish, and has been made available for use to aid in target identification for natural products.

Dataset Information

Protein-Ligand Fishing in planta for Biologically Active Natural Products Using Glutathione Transferases.

Publications

Protein-Ligand Fishing <i>in planta</i> for Biologically Active Natural Products Using Glutathione Transferases.

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