IPB Halle: Leibniz-Institut für Pflanzenbiochemie Deutsch

Lange Nacht, die Wissen schafft

4. Juli 2025 am IPB
IPB-Newsletter | April 2025
Wir verstehen Pflanzen!

Der Film zum Institut
Molekulare Signalverarbeitung

Prof. Dr. Steffen Abel
Wie Pflanzen ihrer Umwelt trotzen.
Natur- und Wirkstoffchemie

Prof. Dr. Ludger Wessjohann
Die Chemie der Natur – Basis für Wirkstoffe zur Gesunderhaltung von Mensch und Pflanze.
Biochemie pflanzlicher Interaktionen

Prof. Dr. Tina Romeis
Wechselwirkungen in, von und mit Pflanzen.
Stoffwechsel- und Zellbiologie

Prof. Dr. Alain Tissier
Sekundär jedoch nicht zweitrangig - Die biologischen Funktionen pflanzlicher Sekundärmetaboliten.
Unabhängige Nachwuchsgruppe

Dr. Mariana Schuster

Aktuelles

Unser 10. Leibniz Plant Biochemistry Symposium am 7. und 8. Mai war ein großer Erfolg. Thematisch ging es in diesem Jahr um neue Methoden und Forschungsansätze der Naturstoffchemie. Die exzellenten Vorträge über Wirkstoffe…

Omanische Heilpflanze im Fokus der Phytochemie
IPB-Wissenschaftler und Partner aus Dhofar haben jüngst die omanische Heilpflanze Terminalia dhofarica unter die phytochemische Lupe genommen. Die Pflanze ist reich an…

Geschmack ist vorhersagbar: Mit FlavorMiner.
FlavorMiner heißt das Tool, das IPB-Chemiker und Partner aus Kolumbien jüngst entwickelt haben. Das Programm kann, basierend auf maschinellem Lernen (KI), anhand der…

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Datum: 14.05.2025 Referenznummer: 10/2025

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Publikation

Herrera-Rocha, F.; Fernández-Niño, M.; Duitama, J.; Cala, M. P.; Chica, M. J.; Wessjohann, L. A.; Davari, M. D.; Barrios, A. F. G.; FlavorMiner: a machine learning platform for extracting molecular flavor profiles from structural data J. Cheminform. 16 140 (2024) DOI: 10.1186/s13321-024-00935-9

Flavor is the main factor driving consumers acceptance of food products. However, tracking the biochemistry of flavor is a formidable challenge due to the complexity of food composition. Current methodologies for linking individual molecules to flavor in foods and beverages are expensive and time-consuming. Predictive models based on machine learning (ML) are emerging as an alternative to speed up this process. Nonetheless, the optimal approach to predict flavor features of molecules remains elusive. In this work we present FlavorMiner, an ML-based multilabel flavor predictor. FlavorMiner seamlessly integrates different combinations of algorithms and mathematical representations, augmented with class balance strategies to address the inherent class of the input dataset. Notably, Random Forest and K-Nearest Neighbors combined with Extended Connectivity Fingerprint and RDKit molecular descriptors consistently outperform other combinations in most cases. Resampling strategies surpass weight balance methods in mitigating bias associated with class imbalance. FlavorMiner exhibits remarkable accuracy, with an average ROC AUC score of 0.88. This algorithm was used to analyze cocoa metabolomics data, unveiling its profound potential to help extract valuable insights from intricate food metabolomics data. FlavorMiner can be used for flavor mining in any food product, drawing from a diverse training dataset that spans over 934 distinct food products.Scientific Contribution FlavorMiner is an advanced machine learning (ML)-based tool designed to predict molecular flavor features with high accuracy and efficiency, addressing the complexity of food metabolomics. By leveraging robust algorithmic combinations paired with mathematical representations FlavorMiner achieves high predictive performance. Applied to cocoa metabolomics, FlavorMiner demonstrated its capacity to extract meaningful insights, showcasing its versatility for flavor analysis across diverse food products. This study underscores the transformative potential of ML in accelerating flavor biochemistry research, offering a scalable solution for the food and beverage industry.

Publikation

Ortmann, S.; Marx, J.; Lampe, C.; Handrick, V.; Ehnert, T.-M.; Zinecker, S.; Reimers, M.; Bonas, U.; Lee Erickson, J.; A conserved microtubule-binding region in Xanthomonas XopL is indispensable for induced plant cell death reactions PLOS Pathog. 19 e1011263 (2023) DOI: 10.1371/journal.ppat.1011263

Pathogenic Xanthomonas bacteria cause disease on more than 400 plant species. These Gram-negative bacteria utilize the type III secretion system to inject type III effector proteins (T3Es) directly into the plant cell cytosol where they can manipulate plant pathways to promote virulence. The host range of a given Xanthomonas species is limited, and T3E repertoires are specialized during interactions with specific plant species. Some effectors, however, are retained across most strains, such as Xanthomonas Outer Protein L (XopL). As an ‘ancestral’ effector, XopL contributes to the virulence of multiple xanthomonads, infecting diverse plant species. XopL homologs harbor a combination of a leucine-rich-repeat (LRR) domain and an XL-box which has E3 ligase activity. Despite similar domain structure there is evidence to suggest that XopL function has diverged, exemplified by the finding that XopLs expressed in plants often display bacterial species-dependent differences in their sub-cellular localization and plant cell death reactions. We found that XopL from X. euvesicatoria (XopLXe) directly associates with plant microtubules (MTs) and causes strong cell death in agroinfection assays in N. benthamiana. Localization of XopLXe homologs from three additional Xanthomonas species, of diverse infection strategy and plant host, revealed that the distantly related X. campestris pv. campestris harbors a XopL (XopLXcc) that fails to localize to MTs and to cause plant cell death. Comparative sequence analyses of MT-binding XopLs and XopLXcc identified a proline-rich-region (PRR)/α-helical region important for MT localization. Functional analyses of XopLXe truncations and amino acid exchanges within the PRR suggest that MT-localized XopL activity is required for plant cell death reactions. This study exemplifies how the study of a T3E within the context of a genus rather than a single species can shed light on how effector localization is linked to biochemical activity.

Publikation

Zulfiqar, M.; Gadelha, L.; Steinbeck, C.; Sorokina, M.; Peters, K.; MAW: the reproducible Metabolome Annotation Workflow for untargeted tandem mass spectrometry J. Cheminform. 15 32 (2023) DOI: 10.1186/s13321-023-00695-y

Mapping the chemical space of compounds to chemical structures remains a challenge in metabolomics. Despite the advancements in untargeted liquid chromatography-mass spectrometry (LC–MS) to achieve a high-throughput profile of metabolites from complex biological resources, only a small fraction of these metabolites can be annotated with confidence. Many novel computational methods and tools have been developed to enable chemical structure annotation to known and unknown compounds such as in silico generated spectra and molecular networking. Here, we present an automated and reproducible Metabolome Annotation Workflow (MAW) for untargeted metabolomics data to further facilitate and automate the complex annotation by combining tandem mass spectrometry (MS2) input data pre-processing, spectral and compound database matching with computational classification, and in silico annotation. MAW takes the LC-MS2 spectra as input and generates a list of putative candidates from spectral and compound databases. The databases are integrated via the R package Spectra and the metabolite annotation tool SIRIUS as part of the R segment of the workflow (MAW-R). The final candidate selection is performed using the cheminformatics tool RDKit in the Python segment (MAW-Py). Furthermore, each feature is assigned a chemical structure and can be imported to a chemical structure similarity network. MAW is following the FAIR (Findable, Accessible, Interoperable, Reusable) principles and has been made available as the docker images, maw-r and maw-py. The source code and documentation are available on GitHub (https://github.com/zmahnoor14/MAW). The performance of MAW is evaluated on two case studies. MAW can improve candidate ranking by integrating spectral databases with annotation tools like SIRIUS which contributes to an efficient candidate selection procedure. The results from MAW are also reproducible and traceable, compliant with the FAIR guidelines. Taken together, MAW could greatly facilitate automated metabolite characterization in diverse fields such as clinical metabolomics and natural product discovery.

Publikation

Schymanski, E. L.; Kondić, T.; Neumann, S.; Thiessen, P. A.; Zhang, J.; Bolton, E. E.; Empowering large chemical knowledge bases for exposomics: PubChemLite meets MetFrag J. Cheminform. 13 19 (2021) DOI: 10.1186/s13321-021-00489-0

Compound (or chemical) databases are an invaluable resource for many scientific disciplines. Exposomics researchers need to find and identify relevant chemicals that cover the entirety of potential (chemical and other) exposures over entire lifetimes. This daunting task, with over 100 million chemicals in the largest chemical databases, coupled with broadly acknowledged knowledge gaps in these resources, leaves researchers faced with too much—yet not enough—information at the same time to perform comprehensive exposomics research. Furthermore, the improvements in analytical technologies and computational mass spectrometry workflows coupled with the rapid growth in databases and increasing demand for high throughput “big data” services from the research community present significant challenges for both data hosts and workflow developers. This article explores how to reduce candidate search spaces in non-target small molecule identification workflows, while increasing content usability in the context of environmental and exposomics analyses, so as to profit from the increasing size and information content of large compound databases, while increasing efficiency at the same time. In this article, these methods are explored using PubChem, the NORMAN Network Suspect List Exchange and the in silico fragmentation approach MetFrag. A subset of the PubChem database relevant for exposomics, PubChemLite, is presented as a database resource that can be (and has been) integrated into current workflows for high resolution mass spectrometry. Benchmarking datasets from earlier publications are used to show how experimental knowledge and existing datasets can be used to detect and fill gaps in compound databases to progressively improve large resources such as PubChem, and topic-specific subsets such as PubChemLite. PubChemLite is a living collection, updating as annotation content in PubChem is updated, and exported to allow direct integration into existing workflows such as MetFrag. The source code and files necessary to recreate or adjust this are jointly hosted between the research parties (see data availability statement). This effort shows that enhancing the FAIRness (Findability, Accessibility, Interoperability and Reusability) of open resources can mutually enhance several resources for whole community benefit. The authors explicitly welcome additional community input on ideas for future developments.

Mehr erhalten

Lange Nacht, die Wissen schafft

IPB-Newsletter | April 2025

Wir verstehen Pflanzen!

Molekulare Signalverarbeitung

Natur- und Wirkstoffchemie

Biochemie pflanzlicher Interaktionen

Stoffwechsel- und Zellbiologie

Unabhängige Nachwuchsgruppe

Aktuelles

Gelungenes Symposium

+++ Newsticker Wissenschaft #182 +++ Naturstoffe +++

+++ Newsticker Wissenschaft #181 +++ Maschinelles Lernen +++

Stellenausschreibungen

Referent*in der Geschäftsführung (m/w/d) (10/2025)

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