Bio-based processes offer a solution to reduce reliance on polluting production processes and excessive land-use for food production. However, the design of such processes and effective microbial cell factories is hampered by the complexity of biological systems. The development of bioprocesses and microbial production strains can be accelerated by integrating concepts of resource allocation...
In view of the worsening climate crisis and increasing plastic waste pollution, scientific interest in the development of an environmentally friendly enzymatic degradation mechanism for plastics is growing. However, the bottleneck in the industrial application of enzymes for plastic waste recycling is their insufficient activity and partial lack of stability under industrial conditions.
To...
The Lipidomics Informatics for Life Science (LIFS) consortium, part of the German Network for Bioinformatics Infrastructure (de.NBI), pioneers advancements in lipidomics workflows through the development of a comprehensive suite of tools.
LipidCreator facilitates targeted LC-MS/MS assay generation and seamlessly integrates with Skyline for method file generation and data processing on...
Carbon-carbon bond-forming reactions are essential for organic synthesis. In conventional petrochemical-driven approaches, these reactions rely on organic solvents and hazardous starting materials. In contrast, biocatalysts offer an alternative pathway based on second-generation feedstocks. In particular, pyruvate decarboxylase from Acetobacter pasteurianus (ApPDC) offers a sustainable...
Molecular knowledge of enzyme-ligand complexes is crucial to understand their reactivity and thus design small molecules/protein mutations able to inhibit/modify enzyme activity. Complementing data science and other modeling approaches, Quantum Mechanics/Molecular Mechanics (QM/MM) simulations can provide further information on the enzymatic mechanism, thus facilitating such biomedical and...
It is essential to understand target enzyme function for applications in biomedicine and biotechnology. A good method to predict the function of new enzymes is the classification through neural networks in combination with large structural datasets. To keep computational requirements feasible for these large systems, we created a more sophisticated representation of an enzyme than the sequence...
Machine learning-guided optimization has become a driving force for recent improvements in protein engineering. In addition, new protein language models are learning the grammar of evolutionarily occurring sequences at large scales. This work combines both approaches to make predictions about mutational effects that support protein engineering. To this end, an easy-to-use software tool called...
