Publications - Cell and Metabolic Biology
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This page was last modified on 27 Jan 2025 .
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Publications - Cell and Metabolic Biology
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Rational re-design of the substrate pocket of phenylpropanoid-flavonoid O-methyltransferase (PFOMT) from Mesembryanthe-mum crystallinum, an enzyme that selectively methylates the 3’-position (= meta-position) in catechol-moieties of flavonoids to guiacol-moieties, provided the basis for the generation of variants with opposite, i. e. 4’- (para-) regioselectivity and enhanced catalytic efficiency. A double variant (Y51R/N202W) identified through a newly developed colorimetric assay efficiently modified the para-position in flavanone and flavano-nol substrates, providing access to the sweetener molecule hesperetin and other rare plant flavonoids having an isovanil-loid motif.
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 .