Evaluating Enzymatic Productivity

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This entry is adapted from the peer-reviewed paper 10.3390/ijms23136908

Kinetic productivity analysis is critical to the characterization of enzyme catalytic performance and capacity. Enzymatic productivity is a measure of product formation or substrate disappearance over time, at a prescribed temperature under specified reaction conditions. It is the only measure which reliably summarizes the durability and reaction yield (a measure of the conversion of substrate) of an enzymatic process. Kinetic productivity analysis can be employed to assess the catalytic capacity of genetically and chemically modified variants, whole cells, the effect of immobilization carriers on productivity, difference between isoforms isolated from a range of organisms or tissues, and the effect of reaction solution additives.

industrial biotechnology enzyme productivity stability kinetics

1. Typical Enzyme Characterization Methods and Factors That Influence Enzymatic Productivity

Routinely determined conventional parameters in enzyme characterization include activity-based initial reaction rates using Michaelis–Menten kinetics (V_max, k_cat, K_m, K_i) and measures of stability (T_opt, t_1/2, and T_m) (Box) ^[1].

Box 1. Glossary.

Activity-based parameters:V_max: Maximal velocity of enzyme catalyzed reaction; k_cat (turnover number, V_max/[E]): Number of substrate molecules converted to product by each catalytic site per unit time; K_m: Enzyme-substrate affinity; k_cat/K_m (specificity)
constant or catalytic efficiency: How efficient an enzyme can be on two different substrates; K_i: Enzyme-inhibitor affinity depicting extent and types of inhibition (competitive, non-competitive).
Stability-based parameters: T_opt: Optimum temperature of activity; t_1/2: Half-life of irreversible thermal inactivation; T_m: Melting temperature at which 50% of protein structure and/or activity is lost.

However, since the time required to measure them is considerably shorter than the time required to prepare a productivity curve, these metrics do not predict how the enzyme will perform with extended and repeated use under specific reaction conditions, such as temperature, pH, additives, and substrate and enzyme concentrations. Enzyme reaction rates over time are continuously influenced by rapidly changing reaction compositions and conditions, such as decreasing substrates and increasing product concentrations as well as their effect on enzyme activation and inhibition. Additionally, productivity is affected by the progressive unfolding of the enzyme over the course of the reaction and modification of key amino acids. These can all affect final reaction yields, a particularly important factor in commercial bioprocesses (Figure 1, left) ^[2].

/media/item_content/202206/62bd6abad264fijms-23-06908-g001.png

Figure 1. Concept and factors that influence enzymatic productivity. (Left): Complex dependency of productivity on the enzyme function, stability, and mass transfer parameters. (Right): Hypothetical basic productivity curves showing the formation of product (filled square) or consumption of substrate (open squares) as a function of time under optimal reaction conditions. [E]: Enzyme; [S]: Substrate; [P]: Product concentrations.

Temperature impacts enzyme activity in a complex manner (Figure 1, left). For example, temperature optimum (T_opt) is influenced by the duration of the enzyme assay. Additionally, the melting temperature (T_m) is based on the structural unfolding (denaturation) of the enzyme and depends on the method used to detect this property. Far-UV circular dichroism is used to follow secondary structure, whereas intrinsic fluorescence, near-UV CD, and differential scanning calorimetry are employed to follow tertiary structure unfolding. In all these methods, the extent of stability (T_m) depends upon the scan rate. On the other hand, t_1/2 measures the activity regained after the enzyme has been cooled and refolded and can impact productivity. The increase in temperature enhances enzyme activity up to a certain limit. However, further increase in temperature might result in decreased productivity due to enzyme inactivation ^[3].

Another important factor related to productivity stems from the mass transfer limitation that can influence the rate of supply of the substrate to the enzyme active site (Figure 1, left). Mass transfer depends on several factors, including the type of substrate (simple vs. polymeric), the enzyme form, such as heterogeneous formulation (whole cells and immobilized), viscosity of the reaction medium that can change during the reaction with the product formation, reaction components which are not properly mixed, and how the substrate is dosed.

2. How Productivity Curves Can Be Prepared

Productivity curves can be readily generated by incubating equal or known amounts of enzymes (from two or more different organisms, isoforms or wild-type and genetically and chemically modified, immobilized enzymes or whole cells containing enzyme/s) with substrate/s in the presence or absence of additives at a specific temperature under optimum reaction conditions. Aliquots are withdrawn at regular intervals throughout the reaction, which is eventually quenched by any method that denatures or inactivates the enzyme. The formation of product or the disappearance of substrate (no matter which is more convenient) is then plotted as a function of time after correcting for any non-enzymatic reaction (Figure 1, right) ^[2]. The quantification of product or substrate can be followed by any suitable measure, such as change in absorbance, fluorescence or viscosity, radiometric, manometric, polarimetric, chromatographic, electrophoretic, electrochemical or mass spectrometric methods depending on the availability of equipment and consumables, convenience, simplicity, speed, safety, and cost ^[4]. Productivity is generally expressed as volumetric productivity (amount of product formed per reaction volume per unit time) or specific volumetric productivity (volumetric productivity per mg or g of enzyme) ^[5]. Whereas enzyme assays are based on the initial rate of substrate utilization in the absence of product formation and are usually completed within minutes, the duration of productivity analysis can extend to hours with significant depletion in substrate concentration and accumulation of product.

3. Significance and Applications of Productivity Analysis

Productivity curves monitor yields throughout a reaction process under specific conditions (pH, temperature, ionic strength, substrate and enzyme concentrations). Therefore, the amount of reaction product at the end of an extended period of time is dependent upon the irreversible inactivation of enzyme due to thermal unfolding and/or substrate/product inhibition. In this way, different forms of an enzyme, such as native vs. modified, soluble vs. immobilized ^[2]^[6]^[7]^[8] can be evaluated and more efficient enzymes can be identified and compared across studies. Moreover, productivity can be maximized in the presence of an additive ^[9] or by varying other reaction conditions, such as ionic strength, pH, and [S] and [E] concentrations ^[6].

Higher productivities of cold-adapted and mesophilic lipases have been achieved by chemical modification of enzymes using benzoic anhydride, Ficoll, and 5 kDa of PEG. Higher productivities of all lipases were due to their higher protein stability and resistance to thermal unfolding at higher temperature. Moreover, the modified lipases retained better activity in paint emulsions after 20 weeks of incubation at 25 °C, indicating that they may have a potentially superior value for industrial applications ^[10].

Guanidinoacetate (GAA) is used as feed/food additive in pharmaceutical industry. The bacterium was genetically modified by introducing the arginine:glycine amidinotransferase (AGAT, EC 2.1.4.1) gene that catalyzes the following reaction:

L-arginine + glycine → L-ornithine + guanidinoacetate

The gene expression of AGAT was optimized and the genes responsible for glycine and arginine degradation were knocked out. Additionally, the ornithine pathway was optimized to reduce the arginine waste and prevent product inhibition of AGAT by ornithine. With the use of 20 g L⁻¹ of glycine and arginine at 30 °C and 220 rpm shaking, a volumetric productivity of 4.26 g/L was achieved in 20 h with a maximum productivity of 1.58 g/L/h. The productivity (g L⁻¹ h⁻¹) decreased with time up to 20 h, accounting for cell death and degradation ^[11].

Theanine is an FDA approved health drink additive. The addition of monovalent cations (Na⁺, Cs⁺) in the presence of Mg²⁺ to γ-glutamyl transpeptidase (GGT) from Bacillus licheniformis enhanced the production of theanine by 15% relative to the absence of metal additives ^[12]. The productivity analysis was carried out in a reaction mixture containing 25 μg mL⁻¹ GGT, 250 mM L-Gln as donor, 600 mM ethylamine as acceptor, ±200 mM Na⁺ or ±Cs⁺ as well as ±200 mM Mg²⁺ in 50 mM borate buffer (pH 10.5) at 37 °C for 4 h. The reason for the higher productivity in the presence of monovalent cations was found to be an increase in both the V_max (from 17 to 28 µM min⁻¹) and thermostability (t_1/2 at 60 °C, from 16 to 74 min⁻¹).

To improve the economics of the reaction, enzymes have been immobilized on magnetic nanomaterials to simplify the recycling process by overcoming fouling issues, which commonly arise with the more traditional use of membrane separation of the enzyme from the reaction liquor ^[13]. However, all forms of immobilization techniques can positively or negatively impact an enzyme’s reaction kinetics and overall productivity. An example of where magnetic immobilization has been shown to provide productivity improvement potential ^[14] is in the manufacture of industrial ethanol from lignocellulosic biomass ^[15]. During the cellulase and β-glucosidase catalyzed pretreatment of lignocellulose, vanillin and formic acids are formed as by-products. Both of these are inhibitory to the enzymes’ activities.

γ-Glutamyl cysteine (GGC) is currently marketed as a precursor to boost the synthesis of glutathione in the body ^[16]^[17]. GGT catalyzes the transfer of γ-glutamyl group to various acceptors and is a key enzyme for the commercial production of GGC and theanine. Glutathione and its precursors are potential novel adjunct therapies to improve the survival of patients with COVID-19 pneumonia. The mechanism of action is to inhibit or ameliorate the cytokine storm, which is induced by viral mediated cellular glutathione depletion during infection ^[18]. The industrial processes for the manufacture of GGC and theanine are impacted by the high price of the purified GGT enzymes, which can be extracted from various organisms. Therefore, it is critical for manufacturers to consider the use of productivity curves to maximize the synthesis of GGC and theanine under optimum reaction conditions and in the most cost-effective manner. Under optimized conditions (37 °C; pH 9; Bacillus licheniformis GGT, 1 U ml⁻¹), 85–87% synthesis of theanine was achieved from ethylamine (600 mM) and glutamine (80 mM) within 4 h. Immobilized GGT did not show any enhancement in productivity compared with the free enzyme, as the increase in the thermostability of the immobilized enzyme was accompanied by a 60% decrease in its specific activity. However, the immobilized enzyme could be reused 5 times with only 10% loss in activity up to 3 cycles, thereby minimizing manufacturing costs ^[17].

4. Basis for Higher Productivity

Although the productivity analysis will reveal which form of enzyme (native vs. modified, soluble vs. immobilized) or enzyme source provides the higher yield under optimal reaction conditions, the analysis does not inform the basis of its higher productivity. Higher productivity can be due to many factors, such as enhanced thermal stability, higher intrinsic activity, reduced inhibition of the enzyme or mass transfer. To dissect the basis for the higher yield of an enzyme, its Michaelis–Menten kinetics (V_max/k_cat, K_i, k_cat/K_m), and thermostability assays (t_1/2, T_opt, T_m) should be carried out. Table 1 lists the variables that can affect enhanced productivity.

Table 1. Activity, stability, inhibition or substrate concentration: Basis for enhanced productivity.

Enzyme	Modification/ Additive	Activity	Stability	[Substrate]	Inhibition	Reference
α-amylase	Native vs. CM	Dec.	Incr.	NA	NA	^[8]
Lipase Lipase	Native vs. CM Im	Dec. Nd	Incr. Nd	NA 5–25%	NA Nd	^[10] ^[19]
Savinase	Native vs. CM	Incr.	Dec.	NA	Dec.	^[7]
β-galactosidase	Native vs. Im	Dec.	Incr.	NA	Dec.	^[20]
Metalloprotease	Native vs. +Ca²⁺	Incr.	Incr.	NA	NA	^[9]
Penicillin acylase	Im vs. Im	NA	NA	Incr. 30–200 mM	NA	^[6]
*GGT (Bl) GGT (Bl) GGT (E.coli)	Native vs. Im ±Additives Native	Dec. Incr. Nd	Incr. Incr. Nd	NA NA [donor:acceptor]	NA NA NA	^[16] ^[12] ^[21]
Cellulase Cellulosome	Native vs. Im ^@ GM: Meso- vs. thermophilic	Incr. Var.	Incr. Incr.	NA NA	Dec. NA	^[14] ^[22]

CM: Chemically modified; Im: Immobilized; both GGT (γ-glutamyl transpeptidase); Bl: B. licheniformis; * no net increase in productivity of free vs. immobilized enzyme; ^@: Immobilized on Fe₃O₄-metal organic framework nanomaterial; NA: Not applicable; Nd: Not determined; GM: Genetically modified; Dec.: Decreased; Incr.: Increased; Var.: Variable.

5. Optimization of Parameters for Enhancing Productivity

Once the most efficient form of an enzyme has been identified, the reaction conditions (temperature, pH, substrate and enzyme concentrations, additives, etc.) should be optimized to maximize the reaction yield in the shortest time. If the basis of the effect of increased productivity is related to kinetic or stability improvements, then genetic or chemical modification can be considered for improving k_cat, K_m, t_1/2 of inactivation and/or T_opt. If the substrate and product are unfavorably impacting productivity, their concentrations can be controlled via substrate feeding or product removal. Problems related to mass transfer can be overcome by reactor design and configuration (substrate introduction and transport, cosolvent selection). Another key factor that impacts productivity is enzyme formulation and choice between whole cell biocatalysts, crude or purified enzyme, soluble or immobilized enzyme ^[19].

6. Conclusions

Due to the practical significance of enzyme catalyzed reactions in biochemical manufacturing, productivity curves are suggested to be generated under various conditions (pH, temperature, [S] and [E] concentrations) to assist in the optimization of reaction yields (Figure 1, left; Table 1) and minimize operational costs.

Productivity maximization of enzyme catalyzed manufacturing process also requires optimization of other key reaction parameters ^[1]. These involve the selection of appropriate enzyme sources and formulations, yield improving enzyme modifications, choice of reaction solvents and additives, and enzyme reactor designs and modes of operation. This should all be considered in the initial planning of the process, with subsequent optimizations prioritized in the following hierarchy: enzyme activity and form, reaction conditions, and reactor design (Figure 2).

/media/item_content/202206/62bd6b054f04fijms-23-06908-g008.png

Figure 2. Hierarchical strategy to maximize reaction productivity based on a prioritized optimization of enzymes, solvents, additives, and enzyme reactors. Bottom to top: Step 1., Preferred enzyme activity profile depicted by the direction of arrows: Source options include mesophiles or extremophiles that offer enzymes with activities that are psychrophilic, thermophilic, acidophilic, alkaliphilic, halophilic, piezophilic, and/or organic-solvent tolerant ^[23]. Step 2., Enzyme source and optimization: Unmodified, genetically ^[1]^[2]^[24] or chemically modified ^[7]^[8] variants. Step 3., Enzyme formulations: Free, inclusion body ^[5] or immobilized ^[13]^[15]^[25]. Step 4., Reaction solvent: Carrying aqueous or non-aqueous ((e.g., organic solvent ^[1]^[6], ionic liquid or super-critical CO₂ ^[26]). Step 5., Varying [E] and [S] concentrations ^[19]. Additives or co-factors that improve yields, activity, and productivity ^[9]^[26]. Step 6., Reaction mode and reactor design: For example, batch, continuous, solid state or plug flow ^[27].

Productivity is a measure of critical importance to the translational and commercial use of enzymes and the process that utilizes them. Consequently, overlooking this method of measurement during enzyme characterization risks missing not only important data, but may also ignore important cost-effective processes and commercial opportunities ^[3].

References

Burton, S.; Cowan, D.; Woodley, J. The search for the ideal biocatalyst. Nat. Biotechnol. 2002, 20, 37–45.
Fox, R.J.; Davis, S.C.; Mundorff, E.C.; Newman, L.M.; Gavrilovic, V.; Ma, S.K.; Chung, L.M.; Ching, C.; Tam, S.; Muley, S.; et al. Improving catalytic function by ProSAR-driven enzyme evolution. Nat. Biotechnol. 2007, 25, 338–344.
Miłek, J. Estimation of the kinetic parameters for H2O2 enzymatic decomposition and for catalase deactivation. Braz. J. Chem. Eng. 2018, 35, 995–1004.
Eisenthal, R.; Danson, M. (Eds.) Enzyme Assays: A Practical Approach, 2nd ed.; Oxford University Press: Oxford, UK, 2002.
Küsters, K.; Pohl, M.; Krauss, U.; Ölçücü, G.; Albert, S.; Jaeger, K.E.; Wiechert, W.; Oldiges, M. Construction and comprehensive characterization of an EcLDCc-CatIB set-varying linkers and aggregation inducing tags. Microb. Cell Factories 2021, 20, 49.
Illanes, A.; Wilson, L.; Altamirano, C.; Cabrera, Z.; Alvarez, L.; Aguirre-Cespedes, C.-A. Production of cephalexin in organic medium at high substrate concentrations with CLEA of penicillin acylase and PGA-450. Enzym. Microb. Technol. 2007, 40, 195–203.
Siddiqui, K.S.; Parkin, D.M.; Curmi, P.M.; Francisci, D.D.; Poljak, A.; Barrow, K.; Noble, M.H.; Trewhella, J.; Cavicchioli, R. A novel approach for enhancing the catalytic efficiency of a protease at low temperature: Reduction in substrate inhibition by chemical modification. Biotechnol. Bioeng. 2009, 103, 676–686.
Siddiqui, K.S.; Poljak, A.; De Francisci, D.; Guerriero, G.; Pilak, O.; Burg, D.; Raftery, M.J.; Parkin, D.M.; Trewhella, J.; Cavicchioli, R. A chemically modified α-amylase with a molten-globule state has entropically driven enhanced thermal stability. Protein Eng. Des. Sel. 2010, 23, 769–780.
Ertan, H.; Cassel, C.; Verma, A.; Poljak, A.; Charlton, T.; Aldrich-Wright, J.; Omar, S.M.; Siddiqui, K.S.; Cavicchioli, R. A new broad specificity alkaline metalloprotease from a Pseudomonas sp. isolated from refrigerated milk: Role of calcium in improving enzyme productivity. J. Mol. Catal. B Enzym. 2015, 113, 1–8.
Jayawardena, M.B.; Yee, L.H.; Poljak, A.; Cavicchioli, R.; Kjelleberg, S.J.; Siddiqui, K.S. Enhancement of lipase stability and productivity through chemical modification and its application to latex-based polymer emulsions. Process Biochem. 2017, 57, 131–140.
Yan, K.; Tian, R.; Zhang, L.; Lv, X.; Liu, L.; Liu, Y. Biosynthesis of Guanidinoacetate by Bacillus subtilis Whole-Cell Catalysis. Fermentation 2022, 8, 116.
Lin, L.-L.; Lu, B.-Y.; Chi, M.-C.; Huang, Y.-F.; Lin, M.-G.; Wang, T.-F. Activation and thermal stabilization of a recombinant γ-glutamyltranspeptidase from Bacillus licheniformis ATCC 27811 by monovalent cations. Appl. Microbiol. Biotechnol. 2022, 106, 1991–2006.
Shemsi, A.M.; Khanday, F.A.; Qurashi, A.; Khalil, A.; Guerriero, G.; Siddiqui, K.S. Site-directed chemically-modified magnetic enzymes: Fabrication, improvements, biotechnological applications and future prospects. Biotechnol. Adv. 2019, 37, 357–381.
Qi, B.; Luo, J.; Wan, Y. Immobilization of cellulase on a core-shell structured metal-organic framework composites: Better in-hibitors tolerance and easier recycling. Bioresour. Technol. 2018, 268, 577–582.
Davidi, L.; Moraïs, S.; Artzi, L.; Knop, D.; Hadar, Y.; Arfi, Y.; Bayer, E.A. Toward combined delignification and saccharification of wheat straw by a laccase-containing designer cellulosome. Proc. Natl. Acad. Sci. USA 2016, 113, 10854–10859.
Bindal, S.; Gupta, R. Theanine synthesis using γ-Glutamyl Transpeptidase from Bacillus licheniformis ER-15. J. Agric. Food Chem. 2014, 62, 9151–9159.
Zarka, M.H.; Bridge, W.J. Oral administration of γ-glutamylcysteine increases intracellular glutathione levels above homeostasis in a randomised human trial pilot study. Redox Biol. 2017, 11, 631–636.
Horowitz, R.I.; Freeman, P.R.; Bruzzese, J. Efficacy of glutathione therapy in relieving dyspnea associated with COVID-19 pneumonia: A report of 2 cases. Respir. Med. Case Rep. 2020, 30, 101063.
Nordblad, M.; Gomes, M.D.; Meissner, M.P.; Ramesh, H.; Woodley, J.M. Scoping Biocatalyst Performance Using Reaction Trajectory Analysis. Org. Process Res. Dev. 2018, 22, 1101–1114.
Mateo, C.; Monti, R.; Pessela, B.C.; Fuentes, M.; Torres, R.; Manuel Guisán, J.; Fernández-Lafuente, R. Immobilization of lactase from Kluyveromyces lactis greatly reduces the inhibition promoted by glucose. Full hydrolysis of lactose in milk. Biotechnol. Prog. 2004, 20, 1259–1262.
Fukao, T.; Suzuki, H. Enzymatic Synthesis of γ-Glutamylvalylglycine Using Bacterial γ-Glutamyltranspeptidase. J. Agric. Food Chem. 2021, 69, 7675–7679.
Moraïs, S.; Stern, J.; Kahn, A.; Galanopoulou, A.P.; Yoav, S.; Shamshoum, M.; Smith, M.A.; Hatzinikolaou, D.G.; Arnold, F.H.; Bayer, E.A. Enhancement of cellulosome-mediated deconstruction of cellulose by improving enzyme thermostability. Biotechnol. Biofuels 2016, 9, 164.
Siddiqui, K.S.; Thomas, T. Protein Adaptations in Extremophiles; Nova Science Publishers Inc.: Hauppauge, NY, USA, 2008; p. 250.
Klaewkla, M.; Pichyangkura, R.; Charoenwongpaiboon, T.; Wangpaiboon, K.; Chunsrivirot, S. Computational design of oligosaccharide producing levansucrase from Bacillus licheniformis RN-01 to improve its thermostability for production of levan-type fructooligosaccharides from sucrose. Int. J. Biol. Macromol. 2020, 160, 252–263.
Kao, M.-R.; Kuo, H.-W.; Lee, C.-C.; Huang, K.-Y.; Huang, T.-Y.; Li, C.-W.; Chen, C.W.; Wang, A.H.-J.; Yu, S.-M.; Ho, T.-H.D. Chaetomella raphigera β-glucosidase D2-BGL has intriguing structural features and a high substrate affinity that renders it an efficient cellulase supplement for lignocellulosic biomass hydrolysis. Biotechnol. Biofuels 2019, 12, 258.
Siddiqui, K.S.; Shemsi, A.M.; Guerriero, G.; Najnin, T.; Taha; Ertan, H. Biotechnological Improvements of Cold-Adapted Enzymes: Commercialization via an Integrated Approach. In Psychrophiles: From Biodiversity to Biotechnology; Margesin, R., Ed.; Springer: Berlin/Heidelberg, Germany, 2017; pp. 477–512.
Poznansky, B.; Cleary, S.E.; Thompson, L.A.; Reeve, H.A.; Vincent, K.A. Boosting the productivity of H2-driven biocatalysis in a commercial hydrogenation flow reactor using H2 from water electrolysis. Front. Chem. Eng. 2021, 3, 718257.

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Khawar Sohail Siddiqui

Haluk Ertan

Anne Poljak

Wallace J. Bridge

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Update Date: 08 Jul 2022

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