Functional Approaches to Discover New Compounds via Enzymatic Modification: Predicted Data Mining Approach and Biotransformation-Guided Purification
Te-Sheng Chang

TL;DR
This paper reviews new methods using enzymes and computing to discover bioactive compounds from plants more efficiently.
Contribution
The paper introduces and evaluates two novel approaches, PDMA and BGP, for efficient discovery of bioactive compounds.
Findings
PDMA predicts enzymatic biotransformation potential to identify new compounds computationally.
BGP combines enzymatic transformation with purification to isolate novel bioactive molecules.
Both methods enhance compound bioactivity and solubility while reducing traditional discovery efforts.
Abstract
In the field of biotechnology, natural compounds isolated from medicinal plants are highly valued; however, their discovery, purification, biofunctional characterization, and biochemical validation have historically involved time-consuming and laborious processes. Two innovative approaches have emerged to more efficiently discover new bioactive substances: the predicted data mining approach (PDMA) and biotransformation-guided purification (BGP). The PDMA is a computational method that predicts biotransformation potential, identifying potential substrates for specific enzymes from numerous candidate compounds to generate new compounds. BGP combines enzymatic biotransformation with traditional purification techniques to directly identify and isolate biotransformed products from crude extract fractions. This review examines recent research employing BGP or the PDMA for novel compound…
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Taxonomy
TopicsEnzyme Production and Characterization · Microbial Metabolism and Applications · Enzyme Catalysis and Immobilization
