PubMed · 7003346
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J Sy. 1980. ppGpp, a single molecule.. https://pubmed.ncbi.nlm.nih.gov/7003346/
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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 use of side chains as catalytic cofactors for protein mediated redox chemistry raises significant mechanistic issues as to how these amino acids are activated toward radical chemistry in a controlled manner. De novo protein design has been used to examine the structural basis for the creation and maintenance of a tryptophanyl radical in a three-helix bundle protein maquette. Here we report the detailed structural analysis of the protein by multidimensional NMR methods. An interesting feature of the structure is an apparent pi-cation interaction involving the sole tryptophan and a lysine side chain. Hybrid density functional calculations support the notion that this interaction raises the reduction potential of the W degrees /WH redox pair and helps explain the redox characteristics of the protein. This model protein system therefore provides a powerful model for exploring the structural basis for controlled radical chemistry in protein.
Effects of high pressure on enzymatic reactions are poised to revolutionize enzyme kinetics. The reason for this is that experimental designs are at hand to separate effects on equilibria between reactant states from effects on catalytic transition states and both yield new information. The first of the former runs contrary to Pauling's hypothesis that substrates are bound more tightly in the transition state, while the latter penetrates the 'black box' of catalysis, the stabilized transition state itself, and returns a precise measure of a physical parameter, deltaV. This in turn opens the door to new forms of structure-activity relationships. The first of these has been described, the effect of pressure on isotope effects, with the surprising finding that the entire isotope effect comes from a transition state phenomenon such as quantum mechanical hydrogen tunneling.