Research Mission and Profile
Molecular Signal Processing
Bioorganic Chemistry
Biochemistry of Plant Interactions
Cell and Metabolic Biology
Independent Junior Research Groups
Program Center MetaCom
Publications
Good Scientific Practice
Research Funding
Networks and Collaborative Projects
Symposia and Colloquia
Alumni Research Groups
Publications
Publications - Cell and Metabolic Biology
Publications
Sesquiterpene lactones (STLs) are bitter tasting plant specialized metabolites derived from farnesyl pyrophosphate (FPP) that contain a characteristic lactone ring. STLs can be found in many plant families that are distantly related to each other and outside the plant kingdom. They are especially prevalent in the plant families Apiaceae and Asteraceae, the latter being one of the largest plant families besides the Orchidaceae. The STL diversity is especially large in the Asteraceae, which made them an ideal object for chemosystematic studies in these species. Many STLs show a high bioactivity, for example as protective compounds against herbivory. STLs are also relevant for pharmaceutical applications, such as the treatment of malaria with artemisinin. Recent findings have dramatically changed our knowledge about the biosynthesis of STLs, as well as their developmental, spatial, and environmental regulation. This review intents to update the currently achieved progress in these aspects. With the advancement of genome editing tools such as CRISPR/Cas and the rapid acceleration of the speed of genome sequencing, even deeper insights into the biosynthesis, regulation, and enzyme evolution of STL can be expected in the future. Apart from their role as protective compounds, there may be a more subtle role of STL in regulatory processes of plants that will be discussed as well.
Books and chapters
Searching and mining nuclear magnetic resonance (NMR)-spectra of naturally occurring substances is an important task to investigate new potentially useful chemical compounds. Multi-dimensional NMR-spectra are relational objects like documents, but consists of continuous multi-dimensional points called peaks instead of words. We develop several mappings from continuous NMR-spectra to discrete text-like data. With the help of those mappings any text retrieval method can be applied. We evaluate the performance of two retrieval methods, namely the standard vector space model and probabilistic latent semantic indexing (PLSI). PLSI learns hidden topics in the data, which is in case of 2D-NMR data interesting in its owns rights. Additionally, we develop and evaluate a simple direct similarity function, which can detect duplicates of NMR-spectra. Our experiments show that the vector space model as well as PLSI, which are both designed for text data created by humans, can effectively handle the mapped NMR-data originating from natural products. Additionally, PLSI is able to find meaningful ”topics” in the NMR-data.
Books and chapters
Searching and mining nuclear magnetic resonance (NMR)-spectra of naturally occurring products is an important task to investigate new potentially useful chemical compounds. We develop a set-based similarity function, which, however, does not sufficiently capture more abstract aspects of similarity. NMR-spectra are like documents, but consists of continuous multi-dimensional points instead of words. Probabilistic semantic indexing (PLSI) is an retrieval method, which learns hidden topics. We develop several mappings from continuous NMR-spectra to discrete text-like data. The new mappings include redundancies into the discrete data, which proofs helpful for the PLSI-model used afterwards. Our experiments show that PLSI, which is designed for text data created by humans, can effectively handle the mapped NMR-data originating from natural products. Additionally, PLSI combined with the new mappings is able to find meaningful ”topics” in the NMR-data.
This page was last modified on 27 Jan 2025 .
