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…
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…
Becker, M.; Kasprowicz, D.; Kurkina, T.; Davari, M. D.; Gipperich, M.; Gramelsberger, G.; Bergs, T.; Schwaneberg, U.; Trauth, D.;Toward antifragile manufacturing: Concepts from nature and complex human-made systems to gain from stressors and volatilityLetmathe, P., Balleer, A., Breuer, W., Gramelsberger, G.Transformation Towards Sustainability425-448(2024)ISBN:978-3-031-54699-0DOI: 10.1007/978-3-031-54700-3_16
Taleb coined the term “antifragility” to describe systems that benefit from stressors and volatility. While nature provides several examples of systems with antifragile behavior, manufacturing has so far only aimed to avoid or absorb stressors and volatility. This article surveys existing examples of antifragile system behavior in biology, biotechnology, software engineering, risk management, and manufacturing. From these examples, components of antifragile systems and principles to implement these components are derived and organized in a framework. The framework intends to serve as guidance for practitioners as well as starting point for future research on the design of antifragile systems in manufacturing.
Publikation
Illig, A.-M.; Siedhoff, N. E.; Davari, M. D.; Schwaneberg, U.;Evolutionary probability and stacked regressions enable data-driven protein engineering with minimized experimental effortJ. Chem. Inf. Model.646350-6360(2024)DOI: 10.1021/acs.jcim.4c00704
Protein engineering through directed evolution and (semi)rational approaches is routinely applied to optimize protein properties for a broad range of applications in industry and academia. The multitude of possible variants, combined with limited screening throughput, hampers efficient protein engineering. Data-driven strategies have emerged as a powerful tool to model the protein fitness landscape that can be explored in silico, significantly accelerating protein engineering campaigns. However, such methods require a certain amount of data, which often cannot be provided, to generate a reliable model of the fitness landscape. Here, we introduce MERGE, a method that combines direct coupling analysis (DCA) and machine learning (ML). MERGE enables data-driven protein engineering when only limited data are available for training, typically ranging from 50 to 500 labeled sequences. Our method demonstrates remarkable performance in predicting a protein’s fitness value and rank based on its sequence across diverse proteins and properties. Notably, MERGE outperforms state-of-the-art methods when only small data sets are available for modeling, requiring fewer computational resources, and proving particularly promising for protein engineers who have access to limited amounts of data.
Publikation
Zulfiqar, M.; Crusoe, M. R.; König-Ries, B.; Steinbeck, C.; Peters, K.; Gadelha, L.;Implementation of FAIR practices in computational metabolomics workflows—A case studyMetabolites14118(2024)DOI: 10.3390/metabo14020118
Scientific workflows facilitate the automation of data analysis tasks by integrating various software and tools executed in a particular order. To enable transparency and reusability in workflows, it is essential to implement the FAIR principles. Here, we describe our experiences implementing the FAIR principles for metabolomics workflows using the Metabolome Annotation Workflow (MAW) as a case study. MAW is specified using the Common Workflow Language (CWL), allowing for the subsequent execution of the workflow on different workflow engines. MAW is registered using a CWL description on WorkflowHub. During the submission process on WorkflowHub, a CWL description is used for packaging MAW using the Workflow RO-Crate profile, which includes metadata in Bioschemas. Researchers can use this narrative discussion as a guideline to commence using FAIR practices for their bioinformatics or cheminformatics workflows while incorporating necessary amendments specific to their research area.
Publikation
Pourhassan, Z. N.; Cui, H.; Muckhoff, N.; Davari, M. D.; Smits, S. H. J.; Schwaneberg, U.; Schmitt, L.;A step forward to the optimized HlyA type 1 secretion system through directed evolutionApplied Microbiology and Biotechnology1075131-5143(2023)DOI: 10.1007/s00253-023-12653-7
Secretion of proteins into the extracellular space has great advantages for the production of recombinant proteins. Type 1 secretion systems (T1SS) are attractive candidates to be optimized for biotechnological applications, as they have a relatively simple architecture compared to other classes of secretion systems. A paradigm of T1SS is the hemolysin A type 1 secretion system (HlyA T1SS) from Escherichia coli harboring only three membrane proteins, which makes the plasmid-based expression of the system easy. Although for decades the HlyA T1SS has been successfully applied for secretion of a long list of heterologous proteins from different origins as well as peptides, but its utility at commercial scales is still limited mainly due to low secretion titers of the system. To address this drawback, we engineered the inner membrane complex of the system, consisting of HlyB and HlyD proteins, following KnowVolution strategy. The applied KnowVolution campaign in this study provided a novel HlyB variant containing four substitutions (T36L/F216W/S290C/V421I) with up to 2.5-fold improved secretion for two hydrolases, a lipase and a cutinase.
Key points
• An improvement in protein secretion via the use of T1SS
• Reaching almost 400 mg/L of soluble lipase into the supernatant
• A step forward to making E. coli cells more competitive for applying as a secretion host
Graphical Abstract