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

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…

Im Rahmen der Wirkstofftage am 9. April 2025 verlieh das Leibniz-Forschungsnetzwerk Wirkstoffe den Preis „Leibniz-Wirkstoff des Jahres“ 2025 an Dr. Ibrahim Morgan, Dr. Robert Rennert, Dr. Tuvshinjargal Budragchaa und Prof.…

Seit Februar 2021 bietet Wolfgang Brandt, ehemaliger Leiter der Arbeitsgruppe Computerchemie am IPB, sein Citizen Science-Projekt zur Pilzbestimmung an. Dafür hat er in regelmäßigen Abständen öffentliche Vorträge zur Vielfalt…

zeige öffentliche Seminare
IPB-Seminar
IPB Doctoral Training Course (DoCou)
Probevorlesung
Progress Report MetaCom
Seminare am Campus
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Stellenausschreibungen

Laborantin bzw. Technischer Assistent*in (m/w/d) (9/2025)

Datum: 08.04.2025 Referenznummer: 9/2025

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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

Méndez, Y.; Vasco, A. V.; Ebensen, T.; Schulze, K.; Yousefi, M.; Davari, M. D.; Wessjohann, L. A.; Guzmán, C. A.; Rivera, D. G.; Westermann, B.; Diversification of a novel α‐galactosyl ceramide hotspot boosts the adjuvant properties in parenteral and mucosal vaccines Angew. Chem. Int. Ed. 63 e202310983 (2024) DOI: 10.1002/anie.202310983

Publikation

Püllmann, P.; Homann, D.; Karl, T. A.; König, B.; Weissenborn, M. J.; Light‐controlled biocatalysis by unspecific peroxygenases with genetically encoded photosensitizers Angew. Chem. Int. Ed. 62 e202307897 (2023) DOI: 10.1002/anie.202307897

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.

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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

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

Preisverleihung zu den Wirkstofftagen: “Leibniz-Wirkstoff des Jahres 2025” geht an IPB-Team für Krebsforschungsprojekt

Pilzsachverständige in Sachsen-Anhalt: Jubiläumsstimmung zur Frühjahrstagung

Stellenausschreibungen

Laborantin bzw. Technischer Assistent*in (m/w/d) (9/2025)

Publikationen

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