PubMed · 13222343
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J A BARCLAY, I D SINGH. 1954-11-29. The isolated glomerulus.. https://pubmed.ncbi.nlm.nih.gov/13222343/
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MOTIVATION: Predicting enzyme function from its sequence is still an unsolved problem in the life sciences. Moreover, with the explosion of annotated genome data, we are inundated with potential enzymatic sequences that have not yet been biochemically characterized. While it is not possible to assign a not-yet-existing label to such a sequence, there is high value in placing the sequence as accurately as possible in known function space. Doing so can help provide more accurate falsifiable hypotheses for experimentalists wishing to characterize enzymes from specific functional families. RESULTS: Here we present a contrastive learning algorithm for predicting enzyme function from sequence. Our method, EnzPlacer, predicts the third, second, and first EC numbers for a protein whose fourth EC number is not in the training corpus. This novel prediction mechanism accurately places a protein sequence within a narrowed-down functional context, even if the precise function remains unknown. AVAILABILITY AND IMPLEMENTATION: EnzPlacer and data is available at https://github.com/drxiangma/EnzPlacer under a GPL3 license.
Use of ultrasonication as a pretreatment before aqueous oil extraction and aqueous enzymatic oil extraction was found to be useful in the case of extraction of oil from the seeds of Jatropha curcas L. The use of ultrasonication for 10 min at pH 9.0 followed by aqueous oil extraction gave a yield of 67%. However, the maximum yield of 74% was obtained by ultrasonication for 5 min followed by aqueous enzymatic oil extraction using an alkaline protease at pH 9.0. Use of ultrasonication also resulted in reducing the process time from 18 to 6 h.
In the postgenomic era, emphasis is shifting from protein identification to protein functional analysis. Enzyme function can be characterized by measuring activity across series of substrates, which generates an activity profile or fingerprint. Activity fingerprinting is particularly useful to differentiate closely related enzymes. Previously reported fingerprinting methods use series of parallel measurements, which are complex and difficult to reproduce. Here we report a new method for fingerprinting enzyme activities based on using mixtures of substrates, or substrate cocktails, in a single reaction that is then analyzed by HPLC. The fingerprints produced are highly reproducible and allow functional differentiation and classification of closely related enzymes, as demonstrated for a series of lipases and esterases. The method is practical, general, and flexible in terms of reaction conditions and can be adapted to any reaction type.