PubMed · 13785321
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G N WILKINSON. 1961. Statistical estimations in enzyme kinetics.. https://doi.org/10.1042/bj0800324
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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 potential of protein microarrays in high-throughput screening (HTS) still remains largely unfulfilled, essentially because of the difficulty of extracting meaningful, quantitative data from such experiments. In the particular case of enzyme microarrays, low-molecular-weight fluorescent affinity labels (FALs) can function as ideally suited activity probes of the microarrayed enzymes. FALs form covalent bonds with enzymes in an activity-dependent manner and therefore can be used to characterize enzyme activity at each enzyme's address, as predetermined by the microarraying process. Relying on this principle, we introduce herein thematic enzyme microarrays (TEMA). In a kinetic setup we used TEMAs to determine the full set of kinetic constants and the reaction mechanism between the microarrayed enzymes (the theme of the microarray) and a family-wide FAL. Based on this kinetic understanding, in an HTS setup we established the practical and theoretical methodology for quantitative, multiplexed determination of the inhibition profile of compounds from a chemical library against each microarrayed enzyme. Finally, in a validation setup, K(i)(app) values and inhibitor profiles were confirmed and refined.
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