Back

Title: Catalytic rate constant for the utilization of biopolymers

Udema, I. I.

2026-05-29 biochemistry
10.64898/2026.05.29.728646 bioRxiv
Show abstract

The catalytic rate constant (kcat) for product formation is considered a turnover number. Therefore, it is often mistakenly believed that kcat equals the turnover number and the number of substrate molecules changed per unit of time. Therefore, the aim of this study is to show that the rate constant for product synthesis and release is not always the same as the rate constant [Formula] for substrate utilization. To determine the precise substrate concentration at which these two rate constants are identical, it is appropriate to derive equations that allow the computation of [Formula]. In the end, the study will provide the most likely concentration of enzymes that can guarantee minimal or no recycling. An analysis of the literature on invertase (EC 3.2.1.26) and the Bernfeld method of generating Michaelian kinetic parameters for human salivary alpha-amylase (HSAA, EC 3.2.1.1) revealed that all kinetic parameters except [Formula] increased with substrate concentration. Meanwhile, the values for invertase decreased from 0.0697 to 0.0361/min, and the values for HSAA decreased from 5,802.4687 to 3,213.0124/min. The magnitude of [Formula] for each substrate concentration ([ST]) is not always equal, except when [ST] is determined post-assay by computation or extrapolation. The lower [ST] at which [Formula] and kcat for [HSAA] are equal is 3.667540128 g/L (5.682584642 M), which is similar to the molarity of HSAA (5.6101967709 M). The kcat for HSAA was 11,930.9885/min. Future assays should aim to generate large amounts of data for a robust statistical analysis. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=164 SRC="FIGDIR/small/728646v1_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@c49b65org.highwire.dtl.DTLVardef@1b60655org.highwire.dtl.DTLVardef@159ba67org.highwire.dtl.DTLVardef@1dce0af_HPS_FORMAT_FIGEXP M_FIG C_FIG

Matching journals

The top 15 journals account for 50% of the predicted probability mass.

1
PLOS ONE
4510 papers in training set
Top 21%
8.5%
2
ACS Omega
90 papers in training set
Top 0.1%
8.5%
3
Biochemistry
130 papers in training set
Top 0.3%
3.6%
4
Analytical Chemistry
205 papers in training set
Top 0.8%
3.6%
5
Analytical Biochemistry
26 papers in training set
Top 0.1%
3.6%
6
Food Chemistry
12 papers in training set
Top 0.1%
3.6%
7
Biotechnology and Bioengineering
49 papers in training set
Top 0.2%
3.6%
8
Biochimie
23 papers in training set
Top 0.1%
3.3%
9
RSC Advances
18 papers in training set
Top 0.4%
2.1%
10
International Journal of Molecular Sciences
453 papers in training set
Top 5%
2.1%
11
Frontiers in Chemistry
14 papers in training set
Top 0.1%
1.8%
12
Protein Science
221 papers in training set
Top 0.8%
1.7%
13
Metabolic Engineering Communications
20 papers in training set
Top 0.2%
1.3%
14
FEBS Open Bio
29 papers in training set
Top 0.2%
1.3%
15
International Journal of Biological Macromolecules
65 papers in training set
Top 2%
1.3%
50% of probability mass above
16
eLife
5422 papers in training set
Top 49%
1.2%
17
Scientific Reports
3102 papers in training set
Top 65%
1.2%
18
Synthetic and Systems Biotechnology
10 papers in training set
Top 0.3%
1.2%
19
Applied and Environmental Microbiology
301 papers in training set
Top 2%
1.2%
20
Biochemical and Biophysical Research Communications
78 papers in training set
Top 0.9%
1.2%
21
Applied Sciences
24 papers in training set
Top 0.5%
1.1%
22
Metabolites
50 papers in training set
Top 0.8%
1.0%
23
Molecules
37 papers in training set
Top 1%
0.9%
24
The Journal of Nutritional Biochemistry
13 papers in training set
Top 0.3%
0.9%
25
RNA
169 papers in training set
Top 0.4%
0.9%
26
BMC Bioinformatics
383 papers in training set
Top 6%
0.9%
27
Journal of Virological Methods
36 papers in training set
Top 0.6%
0.8%
28
Talanta
12 papers in training set
Top 0.6%
0.8%
29
PeerJ
261 papers in training set
Top 13%
0.8%
30
Computational and Structural Biotechnology Journal
216 papers in training set
Top 8%
0.8%