Kinetic and physicochemical modeling of <i>β</i>-galactosidase from <i>Rhynchophorus palmarum</i> larvae

Figures
Abstract
Palm weevil (Rhynchophorus palmarum L.) is a significant pest that has been identified as a threat to palm trees in tropical regions. Beyond its agricultural impact, its digestive system represents a promising source of biocatalysts. The present study investigates the catalytic activity of β-galactosidase extracted from the digestive juice of R. palmarum larvae. o-nitrophenyl-β-D-galactopyranoside (oNPG) was utilized as the substrate in this investigation. The purified enzyme exhibited optimal activity at 330.0 ± 1.2 K and pH = 5.0 ± 0.1, as determined by empirical and mechanistic models. The activation energy (Ea) was estimated at 56.3 ± 9 kJ mol-1 using mechanistic models. Furthermore, the pK values for the enzyme-substrate complex were determined to be 4.0 ± 0.1 for the nucleophile and 6.2 ± 0.2 for the proton donor, which provides insight into the catalytic residues. Kinetic analysis through nonlinear regression yielded a catalytic constant (kcat) of 4.9 × 103 s-1 with Vmax and Km values of 49 ± 2 U mg-1 and 0.77 ± 0.08 mM, respectively. The results obtained provide novel insights into the physicochemical properties of this enzyme. The findings of this study demonstrated that the insect digestive system is a promising and largely untapped source of robust β-galactosidases with considerable potential for industrial biocatalytic applications.
Citation: Kambiré SM, Gnanwa MJ, Boa D, Kouadio NEJP, Karamoko BA (2026) Kinetic and physicochemical modeling of β-galactosidase from Rhynchophorus palmarum larvae. PLoS One 21(7): e0354469. https://doi.org/10.1371/journal.pone.0354469
Editor: Rafael Vazquez-Duhalt, Universidad Nacional Autonoma de Mexico Centro de Nanociencias y Nanotecnologia, MEXICO
Received: March 30, 2026; Accepted: July 8, 2026; Published: July 22, 2026
Copyright: © 2026 Kambiré et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data can be found in the manuscript and its supplementary files.
Funding: The author(s) received no specific funding for this work.
Competing interests: Les auteurs déclarent n’avoir aucun conflit d’intérêts.
1. Introduction
Enzymes play a fundamental role in biological systems by catalyzing biochemical reactions with high efficiency and specificity. Among them, β-galactosidases are widely studied due to their involvement in carbohydrate metabolism, particularly with regard to the cleavage of β-glycosidic bonds in substrates such as lactose [1]. These enzymes are produced by microorganisms, plants, and animals (including insects) [2–4], reflecting their ubiquity in nature. β-galactosidase deficiency in mammals, especially in humans, causes many digestive disorders [5]. Despite the extensive literature on microbial and plant β-galactosidases, those derived from insects have received relatively little attention, particularly with regard to their kinetic and physicochemical properties [6,7]. Current research has mainly focused either on identifying new sources of β-galactosidases or on improving the performance of existing enzymes for biotechnological applications.
Rhynchophorus palmarum (R. palmarum) is an insect pest widely distributed in the tropical regions and in agrosystems exploiting oil palms [6]. Owing to their diverse complex carbohydrate-degrading enzymes [8–12], natural abundance, large biomass, and rearing feasibility, R. palmarum larvae represent prime candidates for bioprospecting.
Previous studies have reported the biochemical characterization of β-galactosidase from R. palmarum larvae [11]. Interesting results were also obtained using a synthetic substrate, p-nitrophenyl-β-D-galactopyranoside (pNPG). These studies also investigated the influence of pH and temperature on enzyme activity which are among the important factors in the control of bioprocesses in biotechnology [6,13–15]. However, no comprehensive physicochemical modeling based on empirical and mechanistic approaches has been reported. Kinetic modeling allows the determination of key parameters such as activation energy (Ea), essential for assessing enzyme efficiency in industrial bioprocesses. Key kinetic parameters, including Km, Vmax, kcat and catalytic efficiency (kcat/Km), provide essential information on substrate affinity and catalytic performance. Accurate estimation of these parameters through empirical and mechanistic modeling allows for a more reliable characterization of the enzyme and supports the rational optimization of enzyme-based biotechnological processes [15]. Therefore, investigating the kinetic and physicochemical properties of β-galactosidase from R. palmarum is essential for understanding its catalytic behavior and assessing its biotechnological potential. In this work, enzymatic hydrolysis of o-nitrophenyl-β-D-galactopyranoside (oNPG) in the presence of β-galactosidase from R. palmarum was performed. In this context, the present study aims to provide a comprehensive physicochemical and kinetic characterization of this enzyme using empirical, mechanistic models and nonlinear regression. The effects of temperature and pH on enzyme activity were examined and key kinetic parameters were determined. This integrated approach provides a more accurate description of enzyme behaviour and provides valuable insights for potential biotechnological applications.
2. Materials and methods
2.1. Chemicals
oNPG and all other analytical grade reagents were purchased from Merck KGaA® (Darmstadt, Germany). Chromatographic media, including DEAE-Sepharose CL-6B, Sephacryl S-100 HR and Phenyl Sepharose CL-6B, were obtained from Pharmacia-LKB Biotech (Uppsala, Sweden). All the chemicals used were of analytical grade and from Merck KGaA®.
2.2. Enzyme samples
Enzymatic crude extract was obtained following the method described by Yapi et al. [11]. R. palmarum larvae were rinsed with distilled water and wrung out on filter paper. The digestive tract was isolated in potassium chloride solution (0.9%; w/v) using forceps and emptied of its contents. The digestive juice thus obtained was diluted (1/1; v/v) in a potassium chloride solution (0.9%; w/v). Centrifugation was then carried out at 6000 rpm for 30 min at 277.15 K, using a UNICEN ALRESA brand refrigerated centrifuge. The supernatant obtained was diluted (1/1; v/v) in the sodium acetate buffer at 100 mM pH = 5.6 and centrifuged again at 9000 rpm under the same conditions. The final supernatant was used as the crude enzyme extract.
2.3. Enzymatic assay
β-galactosidase activity was measured using a UV-visible spectrophotometer (Pioway Medical Lab. Equipment Co., Ltd. 5100). The reaction mixture contained 75 µL of 5 mM oNPG, 150 µL of sodium acetate buffer (100 mM, pH = 5.6) and 50 μL of enzyme solution. The reaction was incubated at 310.15 K for 10 min and stopped by adding 3 mL of 1 M sodium carbonate. Absorbance was measured at 420 nm. A control without enzyme was included. One unit (U) of enzyme activity was defined as the amount of enzyme required to release 1 μmol of o-nitrophenol (oNP) per minute under the assay conditions. Specific activity was expressed as U mg −1 of protein.
2.4. Protein assay and purification procedures
Protein concentration was measured using Lowry et al. [16] method, with bovine serum albumin as the standard. The purification was carried out in three chromatographic steps involving anion exchange chromatography, molecular exclusion gel fractionation and hydrophobic interaction chromatography (see Yapi et al. [11] for further details).
2.5. Influence of temperature and pH on enzyme activity
2.5.1. Experimental.
The influence of pH on enzyme activity is evaluated through assays conducted at 310.15 K using sodium acetate buffer (100 mM, 3.6 ≤ pH ≤ 5.6) and sodium phosphate buffer (100 mM, 5.6 ≤ pH ≤ 8.0). The influence of temperature is examined by measuring enzyme activity at pH = 5.6 (100 mM sodium acetate buffer) over a temperature range of 303.15 K to 353.15 K.
2.5.2. Models describing the effect of temperature on R. palmarum β-galactosidase activity.
When β-galactosidase activity is plotted against temperature, a bell-shaped curve is observed: activity increases with rising temperature, reaches a peak, and then declines [11]. Several empirical and mechanistic models developed for biological growth rates [17–19] can be applied to study the effect of temperature on enzyme activity. For more details, see the previous work [20].
Empirical models (Eqs. (1) and (2)) and mechanistic models (Eq. (3)), which explicitly determine Topt, were employed to analyze the experimental data.
(1) (2)- With β the Blanchard parameter.
- Wojcik and Miłek model (WMM) [18]
- R is the gas constant, T is the absolute temperature in Kelvin, Ed is the activation energy for enzyme deactivation, β is the Wojcik and Miłek parameter.
- The activation energy Ea is given by (Eq. (4)):
- Alexandrov and Yamagata model (AYM) [19]
- Where T is absolute temperature, Ha and Hd are interpreted as the levels of activation energy below and above enzyme’s optimal temperature (Topt), respectively.
2.5.3. Models describing the effect of pH on β-galactosidase activity.
As observed for temperature, the effect of pH on β-galactosidase activity follows a characteristic bell-shaped curve [11]. This behavior, where activity increases from near zero in highly acidic conditions to a maximum value before dropping back to zero in strongly alkaline conditions, is common to most enzymes [23]. Considering that only the native enzyme remains catalytically active, it has been demonstrated that enzyme activity under substrate-saturation conditions can be influenced by pH according to Eqs. (7) and (8) [24,25].
All models (mechanistic and empirical) employed in this study to describe the relationship between enzyme activity and pH are presented below.
- Cardinal pH model with inflection (CPMI) by Rosso et al. [21,22,26]
- To improve convergence of non-linear regression, Dantigny et al. [26] modified the original equation by swapping pHmin and pHmax. This revised expression is adopted in the present study.
- represents the maximum velocity (activity) at optimum pH.
2.6. Kinetic parameters determination
Kinetic parameters of β-galactosidase from R. palmarum larvae were determined by measuring the initial velocity (V₀) of oNPG hydrolysis at various substrate concentrations ranging from 0 to 2.3 mM in the standard reaction mixture (at 310.15 K in 100 mM sodium acetate buffer, pH = 5.6). The apparent Michaelis constant (Km) and maximum velocity (Vmax) were estimated using the hyperbolic Michaelis-Menten equation (Eq. (9)), as well as three linearized models: Lineweaver-Burk plot (Eq. (10)), Eadie-Hofstee plot (Eq. (11)), and Hanes-Woolf plot (Eq. (12)).
(9)(10)(11)(12)Where V0 is the initial velocity of reaction (U mg-1) and is the concentration of substrate (mM).
2.7. Statistical analysis and non-linear regression
To determine statistical significance, a one-way analysis of variance (ANOVA) was performed at a significance level of 0.05. If the ANOVA indicates a statistically significant result, a Duncan post-hoc test is conducted. All assays were performed in at least three independent replicates, and the results are reported as mean ± standard deviation (SD). Statistical analyses were conducted using IBM SPSS software, version 22. Non-linear regressions were performed using SigmaPlot version 15.0 with 200 iterations, a step size of 1 and a tolerance of 10-12.
3. Results and discussion
3.1. Purification of β-galactosidase
The purification process of β-galactosidase from R. palmarum is achieved to a level of 86.88-fold purification, yielding an overall enzyme yield of 2.22% and a specific activity of 28.67 U mg-1 (see Table 1). These values fall within the range reported for insect β-galactosidase (23.84 to 39.73) U mg-1 [11,27], indicating that the enzyme preparation is comparable to those described in literature. However, as reported by Fuerst et al. [28], a purification factor of 5200 and a specific activity of 780 U mg-1 was achieved for β-galactosidase from Drosophila melanogaster. The results obtained demonstrate that R. palmarum larvae represent a significant biological source of glycosidases which possess noteworthy catalytic properties.
3.2. Influence of temperature on β-galactosidase activity
The temperature dependence of β-galactosidase activity exhibited a typical bell-shaped profile (Fig 1), reflecting the balance between increasing catalytic rates and thermal denaturation at elevated temperatures [29,30]. The optimal temperature (Topt = 330 K) (Table 2) was consistently estimated across all models, thereby demonstrating the robustness of the fitting approaches. Despite the superior statistical compatibility of empirical models, mechanistic models were favored on account of their enhanced biochemical pertinence [20], thus ensuring a higher degree of realism. Empirical models provide a superior statistical fit (higher R2 and lower AICc), with their parameters primarily describing the mathematical form of the activity profile. Consequently, these parameters should be interpreted as descriptive quantities. For instance, Tmin, Tmax, pHmin, and pHmax delineate the activity limits, while the Blanchard and Wojcik-Miłek coefficients principally regulate the asymmetry and slope of the curve, without direct physicochemical significance. In contrast, mechanistic models are based on the kinetic and thermodynamic principles of enzymes. Consequently, the estimated parameters are associated with explicit physicochemical significance: Ea reflects the energy barrier associated with catalysis, Ed is representative of thermal inactivation, and pKES1 and pKES2 correspond to the apparent ionization constants of the catalytic residues in the enzyme-substrate complex. While mechanistic models may occasionally exhibit a slightly lower statistical fit than purely empirical models, their parameters are interpretable within biological context. Consequently, mechanistic models are more appropriate for the comprehension of enzyme function and for purpose of comparison with values documented in the enzymatic literature.
The relatively high optimal temperature suggests that the enzyme possesses notable thermotolerance, which is advantageous for industrial applications requiring elevated temperatures in order to enhance reaction rates and limit microbial contamination. This optimum is notably high for an insect-derived β-galactosidase. While this optimum exceeds the typical body temperature of the larvae, it demonstrates notable thermotolerance, which is likely indicative of an evolutionary adaptation to the exothermic conditions encountered when degrading plant tissues. Furthermore, studies by Sharifi et al. [7] on β-galactosidase extracted from the digestive tract of Xanthogaleruca luteola demonstrated an optimum temperature of 60 °C (333 K). In contrast, the optimal temperature for β-galactosidase from R. ferrugineus is 40 °C (313 K) [6]. With regard to catalytic performance, the β-galactosidase from R. palmarum larvae exhibited an optimal temperature of 330.0 K (57 °C), thus demonstrating remarkably high thermal tolerance in comparison to several recently characterized microbial homologs. For instance, the modified β-galactosidase from Bifidobacterium longum BIM B-813 D reported by Morozova et al. [31] had an optimal temperature range extending up to 50 °C. The comparatively higher optimum temperature observed for the R. palmarum enzyme highlights its robust catalytic capacity under high-temperature conditions, making it a promising candidate for biotechnological processes where temperature fluctuations occur.
The activation energy (Ea = 56.3 kJ. mol-1), determined through mechanistic modeling, has been found to be higher than the previously reported value (35.43 kJ. mol-1) [11]. This discrepancy is hypothesized to be attributable to the implementation of nonlinear regression analysis in the current study. This methodological approach provides more reliable parameter estimates than classical linearization techniques [20]. Indeed, the strength of the nonlinear regression approach used in this study, compared to traditional linearization of the Arrhenius plot, lies in its statistical robustness. Linearization methods frequently result in the distortion of the experimental error structure, thereby conferring an exaggerated significance upon specific data points [20].
3.3. Influence of pH on β-galactosidase activity
The pH-activity profile exhibited a bell-shaped curve (Fig 2), which is characteristic of enzyme systems involving ionizable catalytic residues [11,20]. The optimal pH (pHopt = 5.0) (Table 3) is consistent with acidic digestive environments and aligns with previous findings for insect β-galactosidases [11]. The reported pHopt values for β-galactosidase from R. ferrugineus (pH = 4.0) [6] and Xanthogaleruca luteola (pH = 3.0) [7] were lower than those determined in this study. This indicates that the R. palmarum enzyme functions optimally under less acidic conditions. In general, the pHopt of β-galactosidases from different sources varies from 1.5 to 7.0 [32–36]. Furthermore, β-galactosidases from R. palmarum and Bifidobacterium longum BIM B-813 D share a similar acidic optimal pH profile (pH = 5.0), reflecting a conserved adaptation of glycosyl hydrolases functioning in specific acidic microenvironments, whether in the specialized digestive tract of insect larvae or in specific bacterial metabolic niches [31].
The estimation of the apparent ionization constants of the enzyme-substrate complex was conducted through the utilization of mechanistic modeling (pKES1 = 4.0 and pKES2 = 6.2). In accordance with the well-established catalytic mechanism of β-galactosidases and other glycoside hydrolases, catalysis necessitates the presence of two catalytic carboxylate residues: a nucleophile that must remain deprotonated and a general acid/base catalyst (proton donor) that must remain protonated during the cleavage of the glycosidic bond. Consequently, the lower apparent pKES value corresponds to the catalytic nucleophile, while the higher value corresponds to the proton donor. Despite the indirect nature of these attributions, which is predicated upon a mechanistic interpretation as opposed to direct structural evidence, a full consistency with the reported catalytic mechanism for glycosidase retention is demonstrated [37,38].
From a biotechnological perspective, the optimal pH level for the activity of β-galactosidase from R. palmarum (pHopt = 5.0) is advantageous for industrial applications requiring enzyme activity under acidic conditions. Such applications include the hydrolysis of lactose in fermented dairy products (e.g., yogurt, pH = 4.0–4.5) or fruit-based beverages [39–41]. The specific activities of the enzyme (based on the best-fit model) at pH = 4 and 4.5 are 19.36 and 29.7 U mg-1, respectively. It is evident that acid-active β-galactosidases hold considerable industrial significance, primarily due to their ability to obviate the necessity for pH adjustment, thereby mitigating the risk of microbial contamination during processing [42].
3.4. Kinetic parameters
The kinetic parameters determined in this study indicate a high catalytic efficiency of β-galactosidase from R. palmarum (Table 4). The corresponding graphical representations can be found in Figs 3 and 4. No evidence of enzymatic inhibition was observed. The Km value (0.77 mM) reflects a strong affinity for the synthetic substrate oNPG, while the Vmax (49 U mg −1) indicates a high catalytic capacity. The values obtained were consistent across both nonlinear and linearized models, thereby confirming the reliability of the estimates. However, it should be noted that Km value reflects the affinity for a synthetic substrate and may differ from that observed with natural substrates in vivo. The Km value obtained in the present study is significantly lower than that proposed by Yapi et al. [11] (0.90 mM), which indicates a relatively high enzyme-substrate affinity in the present study. This discrepancy can be primarily attributed to the use of divergent test conditions, particularly with regard to substrate concentrations. Furthermore, when using insect larvae as an enzyme source, the Km may also depend on the age (maturity) of the larvae [43–45] and on the organ or tissue (midgut, salivary gland, liver, etc.) [46,47].
A review of the literature reveals an absence of studies on kinetic parameters (Km and Vmax) of insect-derived β-galactosidases. When considering non-insect sources, reported Km values range from 1.77 to 6.5 mM, while Vmax values vary between 0.073 U mg-1 and 49.3 U mg-1 [48–53]. The parameters Km and Vmax are conventionally employed to characterize the enzyme-substrate affinity and the maximum catalytic capacity of an enzyme, in accordance with the Michaelis-Menten model [54–56]. The catalytic constant (kcat = 4.9 × 103 s-1) and catalytic efficiency (kcat/Km = 6.36 × 106 M-1 s-1) of this enzyme are indicative of its place among highly efficient biocatalysts, approaching diffusion-controlled limits [57]. In comparison with β-galactosidases from non-insect sources, the enzyme demonstrates higher affinity for the substrate, emphasizing its potential for industrial applications [57].
A limitation of this study is that the kinetic and mechanistic analyses were performed exclusively using oNPG, a synthetic chromogenic substrate commonly used for β-galactosidase assays. Although oNPG has been shown to provide a reliable and sensitive means of characterizing enzyme activity and estimating kinetic parameters, it has been demonstrated that its catalytic behavior may not fully reflect that observed with the natural substrate, lactose, or with other galactosides.
Conclusion
The present study provides a physicochemical and kinetic characterization of β-galactosidase from Rhynchophorus palmarum larvae using advanced statistical modeling. The integration of empirical and mechanistic approaches in conjunction with nonlinear regression facilitated precise estimation of pivotal catalytic parameters, thereby unveiling an optimal temperature of 330.0 K and an optimal pH of 5.0. Mechanistic modeling further allowed determination of the apparent ionization constants of the enzyme–substrate complex (pKES1 = 4.0 and pKES2 = 6.2), thereby offering indirect insights into the protonation states of catalytic residues at the active site. A key limitation of this work is the exclusive use of the synthetic substrate o-nitrophenyl-β-D-galactopyranoside. It is recommended that future studies incorporate natural substrates such as lactose to validate these findings under more realistic conditions. In view of the high catalytic efficiency (kcat = 4.9 × 103 s-1), thermotolerance, and activity under mildly acidic conditions, immobilization of the enzyme on solid supports is a relevant perspective for the assessment of its stability and reusability in industrial applications. The results of this study demonstrate that insect digestive systems are a promising and underutilized source of robust enzymes for biotechnological applications.
Supporting information
S1 Table. Tables of raw experimental data for physicochemical and kinetic characterization of β-galactosidase from R. palmarum.
https://doi.org/10.1371/journal.pone.0354469.s001
(XLSX)
S2 Data. Model equations and optimized parameters used for curve fitting of β-galactosidase activity from R. palmarum larvae as a function of temperature and pH.
https://doi.org/10.1371/journal.pone.0354469.s002
(XLSX)
S3 Data. Data for linearized Michaelis-Menten plots (Lineweaver-Burk, Eadie-Hofstee, and Hanes-Woolf) of β-galactosidase from R. palmarum.
https://doi.org/10.1371/journal.pone.0354469.s003
(XLSX)
S4 File. Purification procedure and SDS-PAGE analysis of purified β-galactosidase from R. palmarum.
https://doi.org/10.1371/journal.pone.0354469.s004
(DOCX)
S5 File. Raw SDS-PAGE image of purified β-galactosidase from R. palmarum.
https://doi.org/10.1371/journal.pone.0354469.s005
(PNG)
S6. File. SDS-PAGE profile of purified β-galactosidase from R. palmarum.
Note: The SDS-PAGE experiment was performed in 2021 as part of the enzyme purification work. Given the time elapsed, the original uncropped acquisition file could not be retrieved despite our efforts.
https://doi.org/10.1371/journal.pone.0354469.s006
(DOCX)
References
- 1. Nivetha A, Mohanasrinivasan V. Mini review on role of β-galactosidase in lactose intolerance. IOP Conf Ser: Mater Sci Eng. 2017;263:022046.
- 2. Husain Q. Beta galactosidases and their potential applications: a review. Crit Rev Biotechnol. 2010;30(1):41–62. pmid:20143935
- 3. Katrolia P, Rajashekhara E, Yan Q, Jiang Z. Biotechnological potential of microbial α-galactosidases. Crit Rev Biotechnol. 2014;34(4):307–17. pmid:23937250
- 4. Flood MT, Kondo M. Toxicity evaluation of a beta-galactosidase preparation produced by Penicillium multicolor. Regul Toxicol Pharmacol. 2004;40(3):281–92. pmid:15546682
- 5. Ugidos-Rodríguez S, Matallana-González MC, Sánchez-Mata MC. Lactose malabsorption and intolerance: a review. Food Funct. 2018;9(8):4056–68. pmid:29999504
- 6. Riseh NS, Ghadamyari M, Motamediniya B. Biochemical characterisation of α- and β-glucosidases and α- and β-galactosidases from red palm weevil, Rhynchophorus ferrugineus (Olivier) (Col.: Curculionide). Plant Prot Sci. 2012;48(2):85–93.
- 7. Sharifi M, Ghadamyari M, Moghadam M, Saiidi F. Biochemical characterization of digestive carbohydrases from Xanthogaleruca luteola and inhibition of its α-amylase by inhibitors extracted from the common bean. Arch Biol Sci. 2011;63:705–16.
- 8. Hagley EAC. The Role of the Palm Weevil, Rhynchophorus palmarum, as a Vector of Red Ring Disease of Coconuts. I. Results of Preliminary Investigations1. Journal of Economic Entomology. 1963;56(3):375–80.
- 9. Oehlschlager AC, Chinchilla C, Castillo G, Gonzalez L. Control of red ring disease by mass trapping of rhynchophorus palmarum (Coleoptera: Curculionidae). Florida Entomologist. 2002;85(3):507–13.
- 10. Bédikou M, Ahi P, Koné M, Faulet B, Gonnety J, Kouamé P, et al. Purification and characterization of a novel thermoacidophilic and thermostable α-mannosidase from the digestive fluid of oil palm weevil Rhynchophorus palmarum (Coleoptera: Curculionidae) larvae. Eur J Entomol. 2009;106(2):185–91.
- 11. Yapi Dya, Niamke Sl, Kouame LP. Biochemical characterization of a strictly specific beta‐galactosidase from the digestive juice of the palm weevil Rhynchophorus palmarum larvae. Entomological Science. 2007;10(4):343–52.
- 12. Yapi Assoi Yapi D, Gnakri D, Lamine Niamke S, Patrice Kouame L. Purification and biochemical characterization of a specific beta-glucosidase from the digestive fluid of larvae of the palm weevil, Rhynchophorus palmarum. J Insect Sci. 2009;9:4. pmid:19611239
- 13. Daniel RM, Danson MJ. A new understanding of how temperature affects the catalytic activity of enzymes. Trends Biochem Sci. 2010;35(10):584–91. pmid:20554446
- 14. Daniel RM, Danson MJ, Eisenthal R. Erratum. Trends in Biochemical Sciences. 2001;26:401.
- 15. Patnaik PR. Temperature optima of enzymes: sifting fact from fiction. Enzyme and Microbial Technology. 2002;31(3):198–200.
- 16. Lowry OH, Rosebrough NJ, Farr AL, Randall RJ. Protein measurement with the Folin phenol reagent. J Biol Chem. 1951;193(1):265–75. pmid:14907713
- 17. Blanchard G, Guarini J, Richard P, Gros P, Mornet F. Quantifying the short-term temperature effect on light-saturated photosynthesis of intertidal microphytobenthos. Mar Ecol Prog Ser. 1996;134:309–13.
- 18. Wojcik M, Miłek J. A new method to determine optimum temperature and activation energies for enzymatic reactions. Bioprocess Biosyst Eng. 2016;39(8):1319–23. pmid:27066801
- 19. Alexandrov GA, Yamagata Y. A peaked function for modeling temperature dependence of plant productivity. Ecological Modelling. 2007;200(1–2):189–92.
- 20. Kambiré MS, Gnanwa JM, Boa D, Kouadio EJP, Kouamé LP. Modeling of enzymatic activity of free β-glucosidase from palm weevil, Rhynchophorus palmarum Linn. (Coleoptera: Curculionidae) larvae: Effects of pH and temperature. Biophys Chem. 2021;276:106611. pmid:34098161
- 21. Rosso L, Lobry JR, Bajard S, Flandrois JP. Convenient Model To Describe the Combined Effects of Temperature and pH on Microbial Growth, Appl. Environ. Microbiol. 1995;61:610–6. https://doi/abs/10.1128/aem.61.2.610-616.1995
- 22. Rosso L, Lobry JR, Flandrois JP. An unexpected correlation between cardinal temperatures of microbial growth highlighted by a new model. J Theor Biol. 1993;162(4):447–63. pmid:8412234
- 23. Bisswanger H. Enzyme assays. Perspectives in Science. 2014;1(1–6):41–55.
- 24. Lundemo P, Karlsson EN, Adlercreutz P. Eliminating hydrolytic activity without affecting the transglycosylation of a GH1 β-glucosidase. Appl Microbiol Biotechnol. 2017;101(3):1121–31. pmid:27678115
- 25. Jurado E, Camacho F, Luzón G, Vicaria JM. Kinetic models of activity for β-galactosidases: influence of pH, ionic concentration and temperature. Enzyme and Microbial Technology. 2004;34(1):33–40.
- 26. Dantigny P, Burgain A, Deniel F, Bensoussan M. A model for the effect of pH on the growth of chalk yeasts. Int J Food Microbiol. 2014;186:49–54. pmid:24995847
- 27. Aghaali N, Ghadamyari M, Ajamhasani M. Biochemical characterization of glucosidases and galactosidases from Rosaceae ranch borer, Oosphranteria coerulescens redt. (Col.: Cerambycidae). Romanian Journal of Biochemistry. 2012;49:125–37.
- 28. Fuerst TR, Knipple DC, MacIntyre RJ. Purification and characterization of β-galactosidase-1 from Drosophila melanogaster. Insect Biochemistry. 1987;17(8):1163–71.
- 29. Bisswanger H. Enzyme kinetics: principles and methods for determining catalytic parameters. Perspectives in Science. 2017.
- 30. Arcus VL, Prentice EJ, Hobbs JK, Mulholland AJ, Van der Kamp MW, Pudney CR, et al. On the Temperature Dependence of Enzyme-Catalyzed Rates. Biochemistry. 2016;55(12):1681–8. pmid:26881922
- 31. Моrozova A, Golovneva N, Kurchenko V, Evdokimov I, Lodygin A, Nagdalian A, et al. Enhanced production, activity and deregulation of β-galactosidase in a new mutant strain Bifidobacterium longum BIM B-813 D: Characterization and industrial potential. Int J Biol Macromol. 2026;369:152774. pmid:42214519
- 32. Chiba S, Yamada M, Isobe K. Novel acidophilic β-galactosidase with high activity at extremely acidic pH region from Teratosphaeria acidotherma AIU BGA-1. J Biosci Bioeng. 2015;120(3):263–7. pmid:25797715
- 33. Kong F, Wang Y, Cao S, Gao R, Xie G. Cloning, purification and characterization of a thermostable β-galactosidase from Thermotoga naphthophila RUK-10. Process Biochemistry. 2014;49(5):775–82.
- 34. Isobe K, Yamashita M, Chiba S, Takahashi N, Koyama T. Characterization of new β-galactosidase from acidophilic fungus, Teratosphaeria acidotherma AIU BGA-1. J Biosci Bioeng. 2013;116(3):293–7. pmid:23623895
- 35. Chen W, Chen H, Xia Y, Zhao J, Tian F, Zhang H. Production, purification, and characterization of a potential thermostable galactosidase for milk lactose hydrolysis from Bacillus stearothermophilus. J Dairy Sci. 2008;91(5):1751–8. pmid:18420605
- 36. Song C, Liu G-L, Xu J-L, Chi Z-M. Purification and characterization of extracellular β-galactosidase from the psychrotolerant yeast Guehomyces pullulans 17-1 isolated from sea sediment in Antarctica. Process Biochemistry. 2010;45(6):954–60.
- 37. Davies G, Henrissat B. Structures and mechanisms of glycosyl hydrolases. Structure. 1995;3(9):853–9. pmid:8535779
- 38. McCarter JD, Withers SG. Mechanisms of enzymatic glycoside hydrolysis. Curr Opin Struct Biol. 1994;4(6):885–92. pmid:7712292
- 39. Damin BIS, Kovalski FC, Fischer J, Piccin JS, Dettmer A. Challenges and perspectives of the β-galactosidase enzyme. Appl Microbiol Biotechnol. 2021;105(13):5281–98. pmid:34223948
- 40. Ruiz-Ramírez S, Jiménez-Flores R. Invited review: Properties of β-galactosidases derived from Lactobacillaceae species and their capacity for galacto-oligosaccharide production. J Dairy Sci. 2023;106(12):8193–206. pmid:37678769
- 41. Xavier JR, Ramana KV, Sharma RK. β-galactosidase: Biotechnological applications in food processing. J Food Biochem. 2018;42(5):e12564.
- 42. Zhou Y, Liu Y, Gao F, Xia Z, Zhang Z, Addai FP, et al. β‐Galactosidase: Insights into source variability, genetic engineering, immobilisation and diverse applications in food, industry and medicine. Int J of Dairy Tech. 2024;77(3):651–79.
- 43. Pankoke H, Bowers MD, Dobler S. Influence of iridoid glycoside containing host plants on midgut β-glucosidase activity in a polyphagous caterpillar, Spilosoma virginica Fabricius (Arctiidae). J Insect Physiol. 2010;56(12):1907–12. pmid:20727899
- 44. Ghadamyari M, Hosseininaveh V, Sharifi M. Partial biochemical characterization of alpha- and beta-glucosidases of lesser mulberry pyralid, Glyphodes pyloalis Walker (Lep.: Pyralidae). C R Biol. 2010;333(3):197–204. pmid:20338537
- 45. Yu SJ. β-Glucosidase in four phytophagous Lepidoptera. Insect Biochemistry. 1989;19(1):103–8.
- 46. Zibaee A, Bandani AR, Ramzi S. Enzymatic properties of alpha- and beta-glocusidases extracted from midgut and salivary glands of rice striped stem borer, Chilo suppressalis Walker (Lepidoptera: Pyralidae). C R Biol. 2009;332(7):633–41. pmid:19523603
- 47. Schomburg I, Chang A, Schomburg D. Standardization in enzymology—Data integration in the world׳s enzyme information system BRENDA. Perspectives in Science. 2014;1(1–6):15–23.
- 48. Alikunju AP, Joy S, Salam JA, Silvester R, Antony AC, Rahiman KMM, et al. Functional Characterization of a New Cold-Adapted β-Galactosidase from an Arctic Fjord Sediment Bacteria Enterobacter ludwigii MCC 3423. Catal Lett. 2018;148(10):3223–35.
- 49. Fan Y, Yi J, Hua X, Zhang Y, Yang R. Preparation and characterization of gellan gum microspheres containing a cold-adapted β-galactosidase from Rahnella sp. R3. Carbohydr Polym. 2017;162:10–5. pmid:28224885
- 50. Hussien SA, Doosh K. Extraction, purification and characterization of β-galactosidase from tomato (Lycopersicon esculentum) and its application in milk. Annals of Romanian Society for Cell Biology. 2021;25:11842–52.
- 51. Murphy J, Ryan MP, Walsh G. Purification and Characterization of a Novel β-Galactosidase From the Thermoacidophile Alicyclobacillus vulcanalis. Appl Biochem Biotechnol. 2020;191(3):1190–206. pmid:32002730
- 52. Murphy J, Walsh G. Purification and characterization of a novel thermophilic β-galactosidase from Picrophilus torridus of potential industrial application. Extremophiles. 2019;23(6):783–92. pmid:31549249
- 53. Vidya CH, Gnanesh Kumar BS, Chinmayee CV, Singh SA. Purification, characterization and specificity of a new GH family 35 galactosidase from Aspergillus awamori. Int J Biol Macromol. 2020;156:885–95. pmid:32277984
- 54. Murugan R. Theory on the rate equations of Michaelis-Menten type enzyme kinetics with competitive inhibition. PLoS One. 2024;19(7):e0302679. pmid:39024204
- 55. Singh D, Robin T, Urbakh M, Reuveni S. High-order Michaelis-Menten equations allow inference of hidden kinetic parameters in enzyme catalysis. Nat Commun. 2025;16(1):2739. pmid:40113771
- 56. Marius KS, Jacques GM, Kalifa M, Aristide KB. Thermal stability and kinetic characterization of insect-derived glycosidases using the Arrhenius model. Trop J Nat Prod Res. 2025;9(10):5010–5.
- 57. Maini PK, Schnell S. A Century of Enzyme Kinetics: Reliability of the K M and v max Estimates. Comments on Theoretical Biology. 2003;8(2–3):169–87.