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Literature summary extracted from

  • Stolarczyk, K.; Rogalski, J.; Bilewicz, R.
    NAD(P)-dependent glucose dehydrogenase applications for biosensors, bioelectrodes, and biofuel cells (2020), Bioelectrochemistry, 135, 107574 .
No PubMed abstract available

Application

EC Number Application Comment Organism
1.1.1.47 energy production NAD(P)-dependent glucose dehydrogenases have high potential for use in various systems to generate electricity from biological sources for applications in implantable biomedical devices, wireless sensors, and portable electronic devices. Application in biosensors and biofuel cells. Challenges for successful implementation of biofuel cells include increasing the stability of NAD+ and NADP+, and improving the binding of these cofactors in the glucose dehydrogenase enzyme. State-of-the-art fuel cells containing NAD(P)+-dependent GDH usually need an additional unbound cofactor supply from the solution. If the cofactor could be encapsulated in a small volume close to the enzyme or connected via a small linker into the carrier matrix, its reoxidation could be facilitated. Such an encapsulation together with the enzyme could be more effective for improving the fuel cell efficiency relative to the direct electrode binding schemes. Replacing the original cofactor in the enzyme molecule with a modified nicotinamide cofactor analogue would also help to retain the enzyme activity and make the NAD+- and NADP+-dependent enzymes more attractive for applications in fuel cells and sensing devices Aspergillus niger
1.1.1.47 energy production NAD(P)-dependent glucose dehydrogenases have high potential for use in various systems to generate electricity from biological sources for applications in implantable biomedical devices, wireless sensors, and portable electronic devices. Application in biosensors and biofuel cells. Challenges for successful implementation of biofuel cells include increasing the stability of NAD+ and NADP+, and improving the binding of these cofactors in the glucose dehydrogenase enzyme. State-of-the-art fuel cells containing NAD(P)+-dependent GDH usually need an additional unbound cofactor supply from the solution. If the cofactor could be encapsulated in a small volume close to the enzyme or connected via a small linker into the carrier matrix, its reoxidation could be facilitated. Such an encapsulation together with the enzyme could be more effective for improving the fuel cell efficiency relative to the direct electrode binding schemes. Replacing the original cofactor in the enzyme molecule with a modified nicotinamide cofactor analogue would also help to retain the enzyme activity and make the NAD+- and NADP+-dependent enzymes more attractive for applications in fuel cells and sensing devices Priestia megaterium
1.1.1.47 energy production NAD(P)-dependent glucose dehydrogenases have high potential for use in various systems to generate electricity from biological sources for applications in implantable biomedical devices, wireless sensors, and portable electronic devices. Application in biosensors and biofuel cells. Challenges for successful implementation of biofuel cells include increasing the stability of NAD+ and NADP+, and improving the binding of these cofactors in the glucose dehydrogenase enzyme. State-of-the-art fuel cells containing NAD(P)+-dependent GDH usually need an additional unbound cofactor supply from the solution. If the cofactor could be encapsulated in a small volume close to the enzyme or connected via a small linker into the carrier matrix, its reoxidation could be facilitated. Such an encapsulation together with the enzyme could be more effective for improving the fuel cell efficiency relative to the direct electrode binding schemes. Replacing the original cofactor in the enzyme molecule with a modified nicotinamide cofactor analogue would also help to retain the enzyme activity and make the NAD+- and NADP+-dependent enzymes more attractive for applications in fuel cells and sensing devices Thermoplasma acidophilum

Molecular Weight [Da]

EC Number Molecular Weight [Da] Molecular Weight Maximum [Da] Comment Organism
1.1.1.47 29000
-
-
 Priestia megaterium
1.1.1.47 38000
-
-
 Thermoplasma acidophilum

Organism

EC Number Organism UniProt Comment Textmining
1.1.1.47 Aspergillus niger
-
-
-
1.1.1.47 Priestia megaterium
-
-
-
1.1.1.47 Thermoplasma acidophilum
-
-
-

Temperature Optimum [°C]

EC Number Temperature Optimum [°C] Temperature Optimum Maximum [°C] Comment Organism
1.1.1.47 25
-
-
 Priestia megaterium
1.1.1.47 70
-
-
 Thermoplasma acidophilum

pH Optimum

EC Number pH Optimum Minimum pH Optimum Maximum Comment Organism
1.1.1.47 10
-
-
 Thermoplasma acidophilum

pI Value

EC Number Organism Comment pI Value Maximum pI Value
1.1.1.47 Thermoplasma acidophilum
-
-
 5.2
1.1.1.47 Priestia megaterium
-
-
 6