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  1. Artikel: TransExION: a transformer based explainable similarity metric for comparing IONS in tandem mass spectrometry.

    Bui-Thi, Danh / Liu, Youzhong / Lippens, Jennifer L / Laukens, Kris / De Vijlder, Thomas

    Journal of cheminformatics

    2024  Band 16, Heft 1, Seite(n) 61

    Abstract: Small molecule identification is a crucial task in analytical chemistry and life sciences. One of the most commonly used technologies to elucidate small molecule structures is mass spectrometry. Spectral library search of product ion spectra (MS/MS) is a ...

    Abstract Small molecule identification is a crucial task in analytical chemistry and life sciences. One of the most commonly used technologies to elucidate small molecule structures is mass spectrometry. Spectral library search of product ion spectra (MS/MS) is a popular strategy to identify or find structural analogues. This approach relies on the assumption that spectral similarity and structural similarity are correlated. However, popular spectral similarity measures, usually calculated based on identical fragment matches between the MS/MS spectra, do not always accurately reflect the structural similarity. In this study, we propose TransExION, a Transformer based Explainable similarity metric for IONS. TransExION detects related fragments between MS/MS spectra through their mass difference and uses these to estimate spectral similarity. These related fragments can be nearly identical, but can also share a substructure. TransExION also provides a post-hoc explanation of its estimation, which can be used to support scientists in evaluating the spectral library search results and thus in structure elucidation of unknown molecules. Our model has a Transformer based architecture and it is trained on the data derived from GNPS MS/MS libraries. The experimental results show that it improves existing spectral similarity measures in searching and interpreting structural analogues as well as in molecular networking. SCIENTIFIC CONTRIBUTION: We propose a transformer-based spectral similarity metrics that improves the comparison of small molecule tandem mass spectra. We provide a post hoc explanation that can serve as a good starting point for unknown spectra annotation based on database spectra.
    Sprache Englisch
    Erscheinungsdatum 2024-05-28
    Erscheinungsland England
    Dokumenttyp Journal Article
    ZDB-ID 2486539-4
    ISSN 1758-2946
    ISSN 1758-2946
    DOI 10.1186/s13321-024-00858-5
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  2. Artikel ; Online: Predicting compound-protein interaction using hierarchical graph convolutional networks.

    Bui-Thi, Danh / Rivière, Emmanuel / Meysman, Pieter / Laukens, Kris

    PloS one

    2022  Band 17, Heft 7, Seite(n) e0258628

    Abstract: Motivation: Convolutional neural networks have enabled unprecedented breakthroughs in a variety of computer vision tasks. They have also drawn much attention from other domains, including drug discovery and drug development. In this study, we develop a ... ...

    Abstract Motivation: Convolutional neural networks have enabled unprecedented breakthroughs in a variety of computer vision tasks. They have also drawn much attention from other domains, including drug discovery and drug development. In this study, we develop a computational method based on convolutional neural networks to tackle a fundamental question in drug discovery and development, i.e. the prediction of compound-protein interactions based on compound structure and protein sequence. We propose a hierarchical graph convolutional network (HGCN) to encode small molecules. The HGCN aggregates a molecule embedding from substructure embeddings, which are synthesized from atom embeddings. As small molecules usually share substructures, computing a molecule embedding from those common substructures allows us to learn better generic models. We then combined the HGCN with a one-dimensional convolutional network to construct a complete model for predicting compound-protein interactions. Furthermore we apply an explanation technique, Grad-CAM, to visualize the contribution of each amino acid into the prediction.
    Results: Experiments using different datasets show the improvement of our model compared to other GCN-based methods and a sequence based method, DeepDTA, in predicting compound-protein interactions. Each prediction made by the model is also explainable and can be used to identify critical residues mediating the interaction.
    Mesh-Begriff(e) Amino Acid Sequence ; Drug Discovery ; Neural Networks, Computer
    Sprache Englisch
    Erscheinungsdatum 2022-07-21
    Erscheinungsland United States
    Dokumenttyp Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2267670-3
    ISSN 1932-6203 ; 1932-6203
    ISSN (online) 1932-6203
    ISSN 1932-6203
    DOI 10.1371/journal.pone.0258628
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  3. Artikel ; Online: MeRgeION: a Multifunctional R Pipeline for Small Molecule LC-MS/MS Data Processing, Searching, and Organizing.

    Liu, Youzhong / Zhang, Yingjie / Vennekens, Tom / Lippens, Jennifer L / Duijsens, Luc / Bui-Thi, Danh / Laukens, Kris / de Vijlder, Thomas

    Analytical chemistry

    2023  Band 95, Heft 22, Seite(n) 8433–8442

    Abstract: Small molecule structure elucidation using tandem mass spectrometry (MS/MS) plays a crucial role in life science, bioanalytical, and pharmaceutical research. There is a pressing need for increased throughput of compound identification and transformation ... ...

    Abstract Small molecule structure elucidation using tandem mass spectrometry (MS/MS) plays a crucial role in life science, bioanalytical, and pharmaceutical research. There is a pressing need for increased throughput of compound identification and transformation of historical data into information-rich spectral databases. Meanwhile, molecular networking, a recent bioinformatic framework, provides global displays and system-level understanding of complex LC-MS/MS data sets. Herein we present meRgeION, a multifunctional, modular, and flexible R-based toolbox to streamline spectral database building, automated structural elucidation, and molecular networking. The toolbox offers diverse tuning parameters and the possibility to combine various algorithms in the same pipeline. As an open-source R package, meRgeION is ideally suited for building spectral databases and molecular networks from privacy-sensitive and preliminary data. Using meRgeION, we have created an integrated spectral database covering diverse pharmaceutical compounds that was successfully applied to annotate drug-related metabolites from a published nontargeted metabolomics data set as well as reveal the chemical space behind this complex data set through molecular networking. Moreover, the meRgeION-based processing workflow has demonstrated the usefulness of a spectral library search and molecular networking for pharmaceutical forced degradation studies. meRgeION is freely available at: https://github.com/daniellyz/meRgeION2.
    Mesh-Begriff(e) Tandem Mass Spectrometry ; Chromatography, Liquid/methods ; Algorithms ; Metabolomics/methods ; Pharmaceutical Preparations ; Software
    Chemische Substanzen Pharmaceutical Preparations
    Sprache Englisch
    Erscheinungsdatum 2023-05-23
    Erscheinungsland United States
    Dokumenttyp Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 1508-8
    ISSN 1520-6882 ; 0003-2700
    ISSN (online) 1520-6882
    ISSN 0003-2700
    DOI 10.1021/acs.analchem.2c04343
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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    Verfügbar in ZB MED Köln/Königswinter

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  4. Artikel ; Online: MeRgeION: a Multifunctional R Pipeline for Small Molecule LC-MS/MS Data Processing, Searching, and Organizing

    Liu, Youzhong / Zhang, Yingjie / Vennekens, Tom / Lippens, Jennifer L. / Duijsens, Luc / Bui-Thi, Danh / Laukens, Kris / de Vijlder, Thomas

    Analytical Chemistry. 2023 May 23, v. 95, no. 22 p.8433-8442

    2023  

    Abstract: Small molecule structure elucidation using tandem mass spectrometry (MS/MS) plays a crucial role in life science, bioanalytical, and pharmaceutical research. There is a pressing need for increased throughput of compound identification and transformation ... ...

    Abstract Small molecule structure elucidation using tandem mass spectrometry (MS/MS) plays a crucial role in life science, bioanalytical, and pharmaceutical research. There is a pressing need for increased throughput of compound identification and transformation of historical data into information-rich spectral databases. Meanwhile, molecular networking, a recent bioinformatic framework, provides global displays and system-level understanding of complex LC-MS/MS data sets. Herein we present meRgeION, a multifunctional, modular, and flexible R-based toolbox to streamline spectral database building, automated structural elucidation, and molecular networking. The toolbox offers diverse tuning parameters and the possibility to combine various algorithms in the same pipeline. As an open-source R package, meRgeION is ideally suited for building spectral databases and molecular networks from privacy-sensitive and preliminary data. Using meRgeION, we have created an integrated spectral database covering diverse pharmaceutical compounds that was successfully applied to annotate drug-related metabolites from a published nontargeted metabolomics data set as well as reveal the chemical space behind this complex data set through molecular networking. Moreover, the meRgeION-based processing workflow has demonstrated the usefulness of a spectral library search and molecular networking for pharmaceutical forced degradation studies. meRgeION is freely available at: https://github.com/daniellyz/meRgeION2.
    Schlagwörter analytical chemistry ; bioinformatics ; data collection ; databases ; metabolites ; metabolomics ; tandem mass spectrometry
    Sprache Englisch
    Erscheinungsverlauf 2023-0523
    Umfang p. 8433-8442.
    Erscheinungsort American Chemical Society
    Dokumenttyp Artikel ; Online
    ZDB-ID 1508-8
    ISSN 1520-6882 ; 0003-2700
    ISSN (online) 1520-6882
    ISSN 0003-2700
    DOI 10.1021/acs.analchem.2c04343
    Datenquelle NAL Katalog (AGRICOLA)

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    Verfügbar in ZB MED Bonn

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  5. Artikel ; Online: On the viability of unsupervised T-cell receptor sequence clustering for epitope preference

    Meysman, Pieter / De Neuter, Nicolas / Gielis, Sofie / Bui Thi, Danh / Ogunjimi, Benson / Laukens, Kris

    Bioinformatics. 2019 May 01, v. 35, no. 9, p. 1461-1468

    2019  , Seite(n) 1461–1468

    Abstract: The T-cell receptor (TCR) is responsible for recognizing epitopes presented on cell surfaces. Linking TCR sequences to their ability to target specific epitopes is currently an unsolved problem, yet one of great interest. Indeed, it is currently unknown ... ...

    Abstract The T-cell receptor (TCR) is responsible for recognizing epitopes presented on cell surfaces. Linking TCR sequences to their ability to target specific epitopes is currently an unsolved problem, yet one of great interest. Indeed, it is currently unknown how dissimilar TCR sequences can be before they no longer bind the same epitope. This question is confounded by the fact that there are many ways to define the similarity between two TCR sequences. Here we investigate both issues in the context of TCR sequence unsupervised clustering. We provide an overview of the performance of various distance metrics on two large independent datasets with 412 and 2835 TCR sequences respectively. Our results confirm the presence of structural distinct TCR groups that target identical epitopes. In addition, we put forward several recommendations to perform unsupervised T-cell receptor sequence clustering. Source code implemented in Python 3 available at https://github.com/pmeysman/TCRclusteringPaper. Supplementary data are available at Bioinformatics online.
    Schlagwörter T-lymphocytes ; bioinformatics ; data collection ; epitopes ; viability
    Sprache Englisch
    Erscheinungsverlauf 2019-0501
    Umfang p. 1461-1468
    Erscheinungsort Oxford University Press
    Dokumenttyp Artikel ; Online
    ZDB-ID 1468345-3
    ISSN 1367-4811 ; 1460-2059
    ISSN 1367-4811 ; 1460-2059
    DOI 10.1093/bioinformatics/bty821
    Datenquelle NAL Katalog (AGRICOLA)

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  6. Artikel ; Online: On the viability of unsupervised T-cell receptor sequence clustering for epitope preference.

    Meysman, Pieter / De Neuter, Nicolas / Gielis, Sofie / Bui Thi, Danh / Ogunjimi, Benson / Laukens, Kris

    Bioinformatics (Oxford, England)

    2018  Band 35, Heft 9, Seite(n) 1461–1468

    Abstract: Motivation: The T-cell receptor (TCR) is responsible for recognizing epitopes presented on cell surfaces. Linking TCR sequences to their ability to target specific epitopes is currently an unsolved problem, yet one of great interest. Indeed, it is ... ...

    Abstract Motivation: The T-cell receptor (TCR) is responsible for recognizing epitopes presented on cell surfaces. Linking TCR sequences to their ability to target specific epitopes is currently an unsolved problem, yet one of great interest. Indeed, it is currently unknown how dissimilar TCR sequences can be before they no longer bind the same epitope. This question is confounded by the fact that there are many ways to define the similarity between two TCR sequences. Here we investigate both issues in the context of TCR sequence unsupervised clustering.
    Results: We provide an overview of the performance of various distance metrics on two large independent datasets with 412 and 2835 TCR sequences respectively. Our results confirm the presence of structural distinct TCR groups that target identical epitopes. In addition, we put forward several recommendations to perform unsupervised T-cell receptor sequence clustering.
    Availability and implementation: Source code implemented in Python 3 available at https://github.com/pmeysman/TCRclusteringPaper.
    Supplementary information: Supplementary data are available at Bioinformatics online.
    Mesh-Begriff(e) Cluster Analysis ; Epitopes ; Receptors, Antigen, T-Cell/immunology ; Software
    Chemische Substanzen Epitopes ; Receptors, Antigen, T-Cell
    Sprache Englisch
    Erscheinungsdatum 2018-09-07
    Erscheinungsland England
    Dokumenttyp Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 1422668-6
    ISSN 1367-4811 ; 1367-4803
    ISSN (online) 1367-4811
    ISSN 1367-4803
    DOI 10.1093/bioinformatics/bty821
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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    Verfügbar in ZB MED Köln/Königswinter

    Zs.A 2374: Hefte anzeigen Standort:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
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    ab Jg. 2022: Lesesaal (EG)

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