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.
Friction analysis is proposed as the application of general control analysis to single enzymes to describe the control of elementary kinetic steps on the overall catalytic rate. For each transition, a friction coefficient is defined that measures the sensitivity of the turnover rate to the free energy of the transition state complex of the transition. The latter is captured in a single property of the transition, termed friction, as the geometrical mean of the inverse of the forward and backward rate constants. By definition, the friction coefficient measures the relative change in the turnover rate in response to a small change in the friction. The friction coefficient is the sum of the flux control coefficients of the forward and backward rate constants from general control theory and measures the extent to which an elementary step is rate determining. Two basic rules apply to the friction coefficients: (i) the summation theorem states that summation of the friction coefficients over all the steps in a scheme results in a value of 1, and (ii) the group rule states that grouping of rate constants of similar transitions results in a friction coefficient for the group that is the sum of the friction coefficients of the individual steps in the group. The friction coefficients are derived for a number a kinetic schemes taking the rate equations as the starting point and both rules are demonstrated. In fully coupled systems the friction coefficients of individual steps lie between 0 and 1. In partially uncoupled systems the summation theorem applies to all the rates in the system, however, the summation of subsets of friction coefficients may exceed the value of one, implying negative values for other steps in the scheme. The values of individual friction coefficients lie between -1 and 1. The friction coefficient is redefined in a numerical treatment of the steady state of more complex enzymatic schemes.
An established method to determine flux control coefficients is the enzyme titration method in which the change in pathway flux upon a change in the enzyme concentration is measured. In this study, the application of this method to a simple reconstituted pathway was investigated by simulated measurements. The pathway was assumed to be in the quasi-steady-state, which is the experimental realization of the mathematical construct 'steady state'. It was shown that flux control coefficients, calculated in a way that mimics their experimental determination, were strongly time dependent. Initially, the calculated flux control coefficient was high for the enzyme adjacent to the reaction monitoring the flux, and the steady-state value was overestimated. Likewise, flux control coefficients were underestimated for enzymes further away from the monitoring reaction. The observed time course of simulated flux control coefficients was shown to reflect the fact that experimental systems are not steady state but quasi-steady-state. For a pathway in the quasi-steady-state, some of the problems with enzyme titration experiments can be overcome by allowing the system to relax for a time interval that is large compared with the turnover time of the pooled pathway intermediates.