PubMed · 8749374
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1995. Proteins.. https://doi.org/10.1016/0959-440x(95)80020-4
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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.
The original yeast two-hybrid system and its variants have proven to be effective tools for identification and analysis of protein-protein, protein-DNA and protein-RNA interactions. The two-hybrid assay is being applied to the entire complement of proteins of the yeast Saccharomyces cerevisiae to characterize the network of protein-protein interactions in the eukaryotic cell. The development of nontranscriptional cytosolic and membrane-associated two-hybrid methods has made it possible to detect and examine a number of protein-protein interactions in their normal cellular locations. Small-molecule hybrid systems have been developed which can be used to study protein-ligand interactions and to activate cellular processes by forcing protein associations.
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