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System-wide detection of protein-small molecule complexes suggests extensive metabolite regulation in plants.

ABSTRACT: Protein small molecule interactions are at the core of cell regulation controlling metabolism and development. We reasoned that due to the lack of system wide approaches only a minority of those regulatory molecules are known. In order to see whether or not this assumption is true we developed an effective approach for the identification of small molecules having potential regulatory role that obviates the need of protein or small molecule baits. At the core of this approach is a simple biochemical co-fractionation taking advantage of size differences between proteins and small molecules. Metabolomics based analysis of small molecules co-fractionating with proteins identified a multitude of small molecules in Arabidopsis suggesting the existence of numerous, small molecules/metabolites bound to proteins representing potential regulatory molecules. The approach presented here uses Arabidopsis cell cultures, but is generic and hence applicable to all biological systems.

SUBMITTER: Veyel D

PROVIDER: S-EPMC5304321 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature

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System-wide detection of protein-small molecule complexes suggests extensive metabolite regulation in plants.

Veyel Daniel D Kierszniowska Sylwia S Kosmacz Monika M Sokolowska Ewelina Maria EM Michaelis Aenne A Luzarowski Marcin M Szlachetko Jagoda J Willmitzer Lothar L Skirycz Aleksandra A

Scientific reports 20170213

Protein small molecule interactions are at the core of cell regulation controlling metabolism and development. We reasoned that due to the lack of system wide approaches only a minority of those regulatory molecules are known. In order to see whether or not this assumption is true we developed an effective approach for the identification of small molecules having potential regulatory role that obviates the need of protein or small molecule baits. At the core of this approach is a simple biochemi ...[more]

PMID: 28205532

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Project description:BACKGROUND:A number of computational tools for metabolism prediction have been developed over the last 20 years to predict the structures of small molecules undergoing biological transformation or environmental degradation. These tools were largely developed to facilitate absorption, distribution, metabolism, excretion, and toxicity (ADMET) studies, although there is now a growing interest in using such tools to facilitate metabolomics and exposomics studies. However, their use and widespread adoption is still hampered by several factors, including their limited scope, breath of coverage, availability, and performance. RESULTS:To address these limitations, we have developed BioTransformer, a freely available software package for accurate, rapid, and comprehensive in silico metabolism prediction and compound identification. BioTransformer combines a machine learning approach with a knowledge-based approach to predict small molecule metabolism in human tissues (e.g. liver tissue), the human gut as well as the environment (soil and water microbiota), via its metabolism prediction tool. A comprehensive evaluation of BioTransformer showed that it was able to outperform two state-of-the-art commercially available tools (Meteor Nexus and ADMET Predictor), with precision and recall values up to 7 times better than those obtained for Meteor Nexus or ADMET Predictor on the same sets of pharmaceuticals, pesticides, phytochemicals or endobiotics under similar or identical constraints. Furthermore BioTransformer was able to reproduce 100% of the transformations and metabolites predicted by the EAWAG pathway prediction system. Using mass spectrometry data obtained from a rat experimental study with epicatechin supplementation, BioTransformer was also able to correctly identify 39 previously reported epicatechin metabolites via its metabolism identification tool, and suggest 28 potential metabolites, 17 of which matched nine monoisotopic masses for which no evidence of a previous report could be found. CONCLUSION:BioTransformer can be used as an open access command-line tool, or a software library. It is freely available at https://bitbucket.org/djoumbou/biotransformerjar/ . Moreover, it is also freely available as an open access RESTful application at www.biotransformer.ca , which allows users to manually or programmatically submit queries, and retrieve metabolism predictions or compound identification data.

Dataset Information

System-wide detection of protein-small molecule complexes suggests extensive metabolite regulation in plants.

Publications

System-wide detection of protein-small molecule complexes suggests extensive metabolite regulation in plants.

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