Biological Filtering and Substrate Promiscuity Prediction for Annotating Untargeted Metabolomics
Mass spectrometry coupled with chromatography separation techniques provides a powerful platform for untargeted metabolomics. Determining the chemical identities of detected compounds however remains a major challenge. Here, we present a novel computational workflow, termed extended metabolic model...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-04-01
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| Series: | Metabolites |
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| Online Access: | https://www.mdpi.com/2218-1989/10/4/160 |
| _version_ | 1797570128164945920 |
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| author | Neda Hassanpour Nicholas Alden Rani Menon Arul Jayaraman Kyongbum Lee Soha Hassoun |
| author_facet | Neda Hassanpour Nicholas Alden Rani Menon Arul Jayaraman Kyongbum Lee Soha Hassoun |
| author_sort | Neda Hassanpour |
| collection | DOAJ |
| description | Mass spectrometry coupled with chromatography separation techniques provides a powerful platform for untargeted metabolomics. Determining the chemical identities of detected compounds however remains a major challenge. Here, we present a novel computational workflow, termed extended metabolic model filtering (EMMF), that aims to engineer a candidate set, a listing of putative chemical identities to be used during annotation, through an extended metabolic model (EMM). An EMM includes not only canonical substrates and products of enzymes already cataloged in a database through a reference metabolic model, but also metabolites that can form due to substrate promiscuity. EMMF aims to strike a balance between discovering previously uncharacterized metabolites and the computational burden of annotation. EMMF was applied to untargeted LC–MS data collected from cultures of Chinese hamster ovary (CHO) cells and murine cecal microbiota. EMM metabolites matched, on average, to 23.92% of measured masses, providing a > 7-fold increase in the candidate set size when compared to a reference metabolic model. Many metabolites suggested by EMMF are not catalogued in PubChem. For the CHO cell, we experimentally confirmed the presence of 4-hydroxyphenyllactate, a metabolite predicted by EMMF that has not been previously documented as part of the CHO cell metabolic model. |
| first_indexed | 2024-03-10T20:20:31Z |
| format | Article |
| id | doaj.art-a5fa7edde2da45fdacf23d5ceb73ec3f |
| institution | Directory Open Access Journal |
| issn | 2218-1989 |
| language | English |
| last_indexed | 2024-03-10T20:20:31Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metabolites |
| spelling | doaj.art-a5fa7edde2da45fdacf23d5ceb73ec3f2023-11-19T22:14:39ZengMDPI AGMetabolites2218-19892020-04-0110416010.3390/metabo10040160Biological Filtering and Substrate Promiscuity Prediction for Annotating Untargeted MetabolomicsNeda Hassanpour0Nicholas Alden1Rani Menon2Arul Jayaraman3Kyongbum Lee4Soha Hassoun5Department of Computer Science, Tufts University, Medford, MA 02421, USADepartment of Chemical and Biological Engineering, Tufts University, Medford, MA 02421, USADepartment of Chemical Engineering, Texas A&M, College Station, TX 77843, USADepartment of Chemical Engineering, Texas A&M, College Station, TX 77843, USADepartment of Chemical and Biological Engineering, Tufts University, Medford, MA 02421, USADepartment of Computer Science, Tufts University, Medford, MA 02421, USAMass spectrometry coupled with chromatography separation techniques provides a powerful platform for untargeted metabolomics. Determining the chemical identities of detected compounds however remains a major challenge. Here, we present a novel computational workflow, termed extended metabolic model filtering (EMMF), that aims to engineer a candidate set, a listing of putative chemical identities to be used during annotation, through an extended metabolic model (EMM). An EMM includes not only canonical substrates and products of enzymes already cataloged in a database through a reference metabolic model, but also metabolites that can form due to substrate promiscuity. EMMF aims to strike a balance between discovering previously uncharacterized metabolites and the computational burden of annotation. EMMF was applied to untargeted LC–MS data collected from cultures of Chinese hamster ovary (CHO) cells and murine cecal microbiota. EMM metabolites matched, on average, to 23.92% of measured masses, providing a > 7-fold increase in the candidate set size when compared to a reference metabolic model. Many metabolites suggested by EMMF are not catalogued in PubChem. For the CHO cell, we experimentally confirmed the presence of 4-hydroxyphenyllactate, a metabolite predicted by EMMF that has not been previously documented as part of the CHO cell metabolic model.https://www.mdpi.com/2218-1989/10/4/160metabolomicsmetabolite annotationenzyme promiscuityextended metabolic models |
| spellingShingle | Neda Hassanpour Nicholas Alden Rani Menon Arul Jayaraman Kyongbum Lee Soha Hassoun Biological Filtering and Substrate Promiscuity Prediction for Annotating Untargeted Metabolomics Metabolites metabolomics metabolite annotation enzyme promiscuity extended metabolic models |
| title | Biological Filtering and Substrate Promiscuity Prediction for Annotating Untargeted Metabolomics |
| title_full | Biological Filtering and Substrate Promiscuity Prediction for Annotating Untargeted Metabolomics |
| title_fullStr | Biological Filtering and Substrate Promiscuity Prediction for Annotating Untargeted Metabolomics |
| title_full_unstemmed | Biological Filtering and Substrate Promiscuity Prediction for Annotating Untargeted Metabolomics |
| title_short | Biological Filtering and Substrate Promiscuity Prediction for Annotating Untargeted Metabolomics |
| title_sort | biological filtering and substrate promiscuity prediction for annotating untargeted metabolomics |
| topic | metabolomics metabolite annotation enzyme promiscuity extended metabolic models |
| url | https://www.mdpi.com/2218-1989/10/4/160 |
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