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As shown in Figure 3, predictions of enzyme performance perf(r*, S i) for the list of candidate enzymes entered into our decision flowchart in order to score the sequences in Equation 1.
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Quadratic mathematical model equations were derived for the prediction of enzyme activity.
Prediction of enzyme enantioselectivity in silico could be of major utility for avoiding the expensive and time-consuming experiments.
Using central composite design (CCD) a quadratic mathematical model equation was derived for the prediction of enzyme activity.
As mentioned above, the prediction of enzyme family and subfamily class is an imbalance multi-class classification problem.
However, this prediction of enzyme function should be taken with care as it is based on functional analysis of very few members of the gene family only.
Thus, genetic prediction of enzyme activity is best possible for the poor and ultra-rapid genotypes, but poor or ultra-rapid metabolizing activity can also be caused by enzyme inhibitors or inducers [ 30].
The expanded model, hereafter referred to as iJL1678-ME, allows for de novo prediction of enzyme abundances and their cellular location as well as the constraining effects of membrane production.
Genome-based prediction of enzymes for the denitrification pathway in strain HdN1.
Arg biosynthesis seems to be localised predominantly in plastids, with some ambiguous localisation prediction of enzymes in the cytosol [ 3].
When there is no sequence information in databases about enzymes catalyzing the desired reaction r*, we must rely on the prediction of enzymes as putative candidates to process other substrates (multispecificity) or to catalyze a promiscuous reaction other than their native ones [ 49].
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