MINE 2.0: enhanced biochemical coverage for peak identification in untargeted metabolomics

Strutz, Jonathan; Shebek, Kevin M.; Broadbelt, Linda J.; Tyo, Keith E.J.

doi:10.1093/bioinformatics/btac331

Cited by 7 publications

(4 citation statements)

References 27 publications

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“…Nevertheless, the number of experimental data did not cover the expected entire metabolome. Consequently, various tools were developed that utilized different heuristic methods to create possible structures from known metabolites, such as MINE [46] and BioTransformer [47].…”

Section: Network Analysis and Metabolite Annotationmentioning

confidence: 99%

Molecular networking as a natural products discovery strategy

Zhang

Otsuki

2023

Acta Materia Medica

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The rapid development of bioinformatics tools has recently broken through the bottleneck in natural products research. These advances have enabled natural products researchers to rapidly separate and efficiently target and discover previously undescribed molecules. Among these advances, tandem mass spectrometry molecular networking is a promising method for rapidly de-replicating complex natural mixtures, thus leading to an accelerated revolution in the “art of natural products isolation” field. In this review we describe the current molecular networking-based metabolite analysis methods that are widely applied or implementable in natural products discovery research, metabolomics, and related fields. The main objective of this review was to summarize strategies that can be rapidly implemented as alternative de-replication approaches for efficient natural products discovery and to list examples of successful applications that combine networking with other techniques.

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Section: Network Analysis and Metabolite Annotationmentioning

confidence: 99%

Molecular networking as a natural products discovery strategy

Zhang

Otsuki

2023

Acta Materia Medica

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“…Most, if not all, enzymes are promiscuous, acting on substrates other than the ones they evolved to catalyze ( Nobeli et al , 2009 ; Tawfik, 2020 ). Three applications, constructing of de novo synthesis pathways ( Otero-Muras and Carbonell, 2021 ), creating extended metabolic models (EMMs) that account for enzyme promiscuity ( Porokhin et al , 2021 ), and identifying metabolic products measured through metabolomics ( Strutz et al , 2022 ), have driven the development of tools to analyze broad promiscuity. The prevailing approach is to first identify a set of reaction rules (e.g.…”

Section: Introductionmentioning

confidence: 99%

Using graph neural networks for site-of-metabolism prediction and its applications to ranking promiscuous enzymatic products

2023

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Motivation While traditionally utilized for identifying site-specific metabolic activity within a compound to alter its interaction with a metabolizing enzyme, predicting the Site-of-Metabolism (SOM) is essential in analyzing the promiscuity of enzymes on substrates. The successful prediction of SOMs and the relevant promiscuous products has a wide range of applications that include creating extended metabolic models that account for enzyme promiscuity and the construction of novel heterologous synthesis pathways. There is therefore a need to develop generalized methods that can predict molecular SOMs for a wide range of metabolizing enzymes. Results This paper develops a Graph Neural Network (GNN) model for the classification of an atom (or a bond) being an SOM. Our model, GNN-SOM, is trained on enzymatic interactions, available in the KEGG database, that span all enzyme commission numbers. We demonstrate that GNN-SOM consistently outperforms baseline Machine Learning (ML) models, when trained on all enzymes, on Cytochrome P450 (CYP) enzymes, or on non-CYP enzymes. We showcase the utility of GNN-SOM in prioritizing predicted enzymatic products due to enzyme promiscuity for two biological applications: the construction of Extended Metabolic Models (EMMs) and the construction of synthesis pathways. Availability A python implementation of the trained SOM predictor model can be found at https://github.com/HassounLab/GNN-SOM Supplementary information Not applicable

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“…Generated networks can be used directly in a python workflow, written to a comma separated value (CSV) file, or stored in a MongoDB database for further network analysis. Pickaxe runs standalone, but is included within the MINE Database software, which utilizes Pickaxe to generate a database in order to identify potential structures for unannotated metabolomics peaks [ 29 ]. This software is open-source and can be found at https://github.com/tyo-nu/MINE-Database or can be installed as a python package ( https://pypi.org/project/minedatabase/2.0.0/ ).…”

Section: Introductionmentioning

confidence: 99%

Pickaxe: a Python library for the prediction of novel metabolic reactions

et al. 2023

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Background Biochemical reaction prediction tools leverage enzymatic promiscuity rules to generate reaction networks containing novel compounds and reactions. The resulting reaction networks can be used for multiple applications such as designing novel biosynthetic pathways and annotating untargeted metabolomics data. It is vital for these tools to provide a robust, user-friendly method to generate networks for a given application. However, existing tools lack the flexibility to easily generate networks that are tailor-fit for a user’s application due to lack of exhaustive reaction rules, restriction to pre-computed networks, and difficulty in using the software due to lack of documentation. Results Here we present Pickaxe, an open-source, flexible software that provides a user-friendly method to generate novel reaction networks. This software iteratively applies reaction rules to a set of metabolites to generate novel reactions. Users can select rules from the prepackaged JN1224min ruleset, derived from MetaCyc, or define their own custom rules. Additionally, filters are provided which allow for the pruning of a network on-the-fly based on compound and reaction properties. The filters include chemical similarity to target molecules, metabolomics, thermodynamics, and reaction feasibility filters. Example applications are given to highlight the capabilities of Pickaxe: the expansion of common biological databases with novel reactions, the generation of industrially useful chemicals from a yeast metabolome database, and the annotation of untargeted metabolomics peaks from an E. coli dataset. Conclusion Pickaxe predicts novel metabolic reactions and compounds, which can be used for a variety of applications. This software is open-source and available as part of the MINE Database python package (https://pypi.org/project/minedatabase/) or on GitHub (https://github.com/tyo-nu/MINE-Database). Documentation and examples can be found on Read the Docs (https://mine-database.readthedocs.io/en/latest/). Through its documentation, pre-packaged features, and customizable nature, Pickaxe allows users to generate novel reaction networks tailored to their application.

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MINE 2.0: enhanced biochemical coverage for peak identification in untargeted metabolomics

Cited by 7 publications

References 27 publications

Molecular networking as a natural products discovery strategy

Molecular networking as a natural products discovery strategy

Using graph neural networks for site-of-metabolism prediction and its applications to ranking promiscuous enzymatic products

Pickaxe: a Python library for the prediction of novel metabolic reactions

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