The Plant Science Student Conference (PSSC) has been organised by students from the two Leibniz institutes, IPK and IPB, every year for the last 20 years. In this interview, Christina Wäsch (IPK) and Carolin Apel (IPB)…
Over 600 guests came to the IPB on July 4 for the Long Night of Sciences to learn lots of new things and put their knowledge to the test at our science quiz course. This year, our program was aimed equally at children and…
Our 10th Leibniz Plant Biochemistry Symposium on May 7 and 8 was a great success. This year's theme was new methods and research approaches in natural product chemistry. The excellent presentations on active substances and…
Herrera-Rocha, F.; Fernández-Niño, M.; Duitama, J.; P. Cala, M.; José Chica, M.; A. Wessjohann, L.; D. Davari, M.; Fernando González Barrios, A.;FlavorMiner: A Machine Learning Platform for Extracting Molecular Flavor Profiles from Structural DataChemRxiv(2024)DOI: 10.26434/chemrxiv-2024-821xm
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.
Publications
Müllers, Y.; Sadr, A. S.; Schenderlein, M.; Pallab, N.; D. Davari, M.; Glebe, U.; Reifarth, M.;Acrylate‐derived RAFT polymers for enzyme hyperactivation – boosting the α‐chymotrypsin enzyme activity using tailor‐made poly(2‐carboxyethyl)acrylate (PCEA)ChemCatChem16e202301685(2024)DOI: 10.1002/cctc.202301685
We study the hyperactivation of α‐chymotrypsin (α‐ChT) using the acrylate polymer poly(2‐carboxyethyl) acrylate (PCEA) in comparison to the commonly used poly(acrylic acid) (PAA). The polymers are added during the enzymatic cleavage reaction of the substrate N‐glutaryl‐L‐phenylalanine p‐nitroanilide (GPNA). Enzyme activity assays reveal a pronounced enzyme hyperactivation capacity of PCEA, which reaches up to 950% activity enhancement, and is significantly superior to PAA (revealing an activity enhancement of approx. 450%). In a combined experimental and computational study, we investigate α‐ChT/polymer interactions to elucidate the hyperactivation mechanism of the enzyme. Isothermal titration calorimetry reveals a pronounced complexation between the polymer and the enzyme. Docking simulations reveal that binding of polymers significantly improves the binding affinity of GPNA to α‐ChT. Notably, a higher binding affinity is found for the α‐ChT/PCEA compared to the α‐ChT/PAA complex. Further molecular dynamics (MD) simulations reveal changes in the size of the active site in the enzyme/polymer complexes, with PCEA inducing a more pronounced alteration compared to PAA, facilitating an easier access for the substrate to the active site of α‐ChT.