Functional Approaches to Discover New Compounds via Enzymatic Modification: Predicted Data Mining Approach and Biotransformation-Guided Purification
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 emer...
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MDPI AG
2025-05-01
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| Series: | Molecules |
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| author | Te-Sheng Chang |
| author_facet | Te-Sheng Chang |
| author_sort | Te-Sheng Chang |
| collection | DOAJ |
| description | 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 discovery. This research demonstrates that both approaches effectively allow for the discovery of novel bioactive molecules from natural sources, the enhancement of the bioactivity and solubility of existing compounds, and the development of alternatives to traditional methods. These findings highlight the potential of integrating traditional medicinal knowledge with modern enzymatic and computational tools to advance drug discovery and development. |
| format | Article |
| id | doaj-art-5ebf7e04e79d4695b6f7f81b8a2e1e7f |
| institution | OA Journals |
| issn | 1420-3049 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Molecules |
| spelling | doaj-art-5ebf7e04e79d4695b6f7f81b8a2e1e7f2025-08-20T02:33:51ZengMDPI AGMolecules1420-30492025-05-013010222810.3390/molecules30102228Functional Approaches to Discover New Compounds via Enzymatic Modification: Predicted Data Mining Approach and Biotransformation-Guided PurificationTe-Sheng Chang0Department of Biological Sciences and Technology, National University of Tainan, Tainan 70005, TaiwanIn 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 discovery. This research demonstrates that both approaches effectively allow for the discovery of novel bioactive molecules from natural sources, the enhancement of the bioactivity and solubility of existing compounds, and the development of alternatives to traditional methods. These findings highlight the potential of integrating traditional medicinal knowledge with modern enzymatic and computational tools to advance drug discovery and development.https://www.mdpi.com/1420-3049/30/10/2228biotransformationenzymatic synthesisglycosylationglycosyltransferaseglycoside hydrolaseflavonoids |
| spellingShingle | Te-Sheng Chang Functional Approaches to Discover New Compounds via Enzymatic Modification: Predicted Data Mining Approach and Biotransformation-Guided Purification Molecules biotransformation enzymatic synthesis glycosylation glycosyltransferase glycoside hydrolase flavonoids |
| title | Functional Approaches to Discover New Compounds via Enzymatic Modification: Predicted Data Mining Approach and Biotransformation-Guided Purification |
| title_full | Functional Approaches to Discover New Compounds via Enzymatic Modification: Predicted Data Mining Approach and Biotransformation-Guided Purification |
| title_fullStr | Functional Approaches to Discover New Compounds via Enzymatic Modification: Predicted Data Mining Approach and Biotransformation-Guided Purification |
| title_full_unstemmed | Functional Approaches to Discover New Compounds via Enzymatic Modification: Predicted Data Mining Approach and Biotransformation-Guided Purification |
| title_short | Functional Approaches to Discover New Compounds via Enzymatic Modification: Predicted Data Mining Approach and Biotransformation-Guided Purification |
| title_sort | functional approaches to discover new compounds via enzymatic modification predicted data mining approach and biotransformation guided purification |
| topic | biotransformation enzymatic synthesis glycosylation glycosyltransferase glycoside hydrolase flavonoids |
| url | https://www.mdpi.com/1420-3049/30/10/2228 |
| work_keys_str_mv | AT teshengchang functionalapproachestodiscovernewcompoundsviaenzymaticmodificationpredicteddataminingapproachandbiotransformationguidedpurification |