Dataset Information

High throughput mass spectrometry-based characterisation of Arabidopsis thaliana group H glycosyltransferases.

ABSTRACT: In this report, we cloned and characterised four members of group H glycosyltransferases (GTs) by studying their substrate specificities and kinetics. The formation of products and possible glycosylation position was confirmed using MS/MS. The results revealed that 76E1 and 76E5 have broader donor specificity, including UDP-glucose (UDPGlc), UDP-galactose (UDPGal) and UDP-N-acetylglucosamine (UDPGlcNAc) with various flavonoids as acceptor substrates. Pseudo-single substrate kinetics data showed a relatively low K _M, indicating a high affinity for substrate UDPGlc and also supported that 76E5 is more of a galactosyl and N-acetylglucosamine transferase. Sequence alignment and site-directed mutagenesis studies indeed suggested that serine is a crucial residue in the UDPGlcNAc and UDPGal activity.

SUBMITTER: Akere A

PROVIDER: S-EPMC9085408 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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High throughput mass spectrometry-based characterisation of <i>Arabidopsis thaliana</i> group H glycosyltransferases.

Akere Aishat A Liu Qian Q Wu Shibo S Hou Bingkai B Yang Min M

RSC advances 20180824 53

In this report, we cloned and characterised four members of group H glycosyltransferases (GTs) by studying their substrate specificities and kinetics. The formation of products and possible glycosylation position was confirmed using MS/MS. The results revealed that 76E1 and 76E5 have broader donor specificity, including UDP-glucose (UDPGlc), UDP-galactose (UDPGal) and UDP-<i>N</i>-acetylglucosamine (UDPGlcNAc) with various flavonoids as acceptor substrates. Pseudo-single substrate kinetics data ...[more]

PMID: 35546822

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Project description:MLinvitroTox is an automated Python pipeline developed for high-throughput hazard-driven prioritization of toxicologically relevant signals detected in complex environmental samples through high-resolution tandem mass spectrometry (HRMS/MS). MLinvitroTox is a machine learning (ML) framework comprising 490 independent XGBoost classifiers trained on molecular fingerprints from chemical structures and target-specific endpoints from the ToxCast/Tox21 invitroDBv4.1 database. For each analyzed HRMS feature, MLinvitroTox generates a 490-bit bioactivity fingerprint used as a basis for prioritization, focusing the time-consuming molecular identification efforts on features most likely to cause adverse effects. The practical advantages of MLinvitroTox are demonstrated for groundwater HRMS data. Among the 874 features for which molecular fingerprints were derived from spectra, including 630 nontargets, 185 spectral matches, and 59 targets, around 4% of the feature/endpoint relationship pairs were predicted to be active. Cross-checking the predictions for targets and spectral matches with invitroDB data confirmed the bioactivity of 120 active and 6791 nonactive pairs while mislabeling 88 active and 56 non-active relationships. By filtering according to bioactivity probability, endpoint scores, and similarity to the training data, the number of potentially toxic features was reduced by at least one order of magnitude. This refinement makes the analytical confirmation of the toxicologically most relevant features feasible, offering significant benefits for cost-efficient chemical risk assessment.Scientific Contribution:In contrast to the classical ML-based approaches for toxicity prediction, MLinvitroTox predicts bioactivity for HRMS features (i.e., distinct m/z signals) based on MS2 fragmentation spectra rather than the chemical structures from the identified features. While the original proof of concept study was accompanied by the release of a MLinvitroTox v1 KNIME workflow, in this study, we release a Python MLinvitroTox v2 package, which, in addition to automation, expands functionality to include predicting toxicity from structures, cleaning up and generating chemical fingerprints, customizing models, and retraining on custom data. Furthermore, as a result of improvements in bioactivity data processing, realized in the concurrently released pytcpl Python package for the custom processing of invitroDBv4.1 input data used for training MLinvitroTox, the current release introduces enhancements in model accuracy, coverage of biological mechanistic targets, and overall interpretability.

Dataset Information

High throughput mass spectrometry-based characterisation of Arabidopsis thaliana group H glycosyltransferases.

Publications

High throughput mass spectrometry-based characterisation of <i>Arabidopsis thaliana</i> group H glycosyltransferases.

Similar Datasets

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