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Bioorganic Chemistry
Biochemistry of Plant Interactions
Cell and Metabolic Biology
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Publications - Cell and Metabolic Biology
Publications
Plant S-adenosyl-l-methionine-dependent class I natural product O-methyltransferases (OMTs), related to animal catechol OMTs, are dependent on bivalent cations and strictly specific for the meta position of aromatic vicinal dihydroxy groups. While the primary activity of these class I enzymes is methylation of caffeoyl coenzyme A OMTs, a distinct subset is able to methylate a wider range of substrates, characterized by the promiscuous phenylpropanoid and flavonoid OMT. The observed broad substrate specificity resides in two regions: the N-terminus and a variable insertion loop near the C-terminus, which displays the lowest degree of sequence conservation between the two subfamilies. Structural and biochemical data, based on site-directed mutagenesis and domain exchange between the two enzyme types, present evidence that only small topological changes among otherwise highly conserved 3-D structures are sufficient to differentiate between an enzymatic generalist and an enzymatic specialist in plant natural product methylation.
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 .
