Enzymes Lecture 5 Principles of Microbiology for Engineers Dr - - PowerPoint PPT Presentation

Enzymes

• Lecture 5
• Principles of Microbiology for Engineers
• Dr Charles W Knapp BSc MSc PhD FHEA

Enzymes

• Biochemical/cellular activity

 Synthesis  Transformation/degradation reactions

Applications (e.g.):

 Microbial induced calcite precipitation  Bio-remediation / water treatment  Bio-synthesis  Energy production  Soil fertility

Catalytic proteins that speed up the rate of biochemical reactions Reactants in a chemical reaction must first be activated before the reaction can take place Enzymes are highly specific in the reactions they catalyze

Enzymes

Enzymes

• Enzymes do not do anything that is

thermodynamically impossible

• They affect rates.
• Net ΔG’ < 0

Enzymes

• The un-catalysed carboxylation of orotidine 5-

monophosphate has a half-life of 78 million years

• With enzyme orotidine 5-phosphate

decarboxylase, reaction takes 18 milliseconds

E+S ES E+P

Enzymes

• Protein structure

 (apo-protein)

• Co-factor

 Organic (heme/flavin)  Inorganic (metal/sulphur)  Co-enzymes (vitamins,

NADH/NADPH)

Active site

Enzyme kinetics

Enzyme kinetics (Michaelis Menten)

• V= velocity (rate of reaction per substrate concentration)

 Based on linear regression of initial reaction rates

• Vmax = maximum reaction rate (saturation)
• Km= Michaelis Menten constant

 Substrate concentration to yield: Vmax/2  Represents the enzyme’s affinity for the substrate

Enzyme kinetics

10 100 1000 0.01 0.1 1 10

Electrical conductivity (mS/cm) Time (s)

0.11M 0.22M 0.33M 0.44M 0.55M 0.88M 1.11M 1.54M 1.76M 1.98M

• 10

100 1000 0.01 0.1 1 10

Electrical conductivity (mS/cm) Time (s)

Molarity (M) 0.11 0.22 0.33 0.44 0.55 0.88 1.11 1.54 1.76 1.98

• (a)

1) Calculate substrate conditions versus time. Linear regression (may trim “tails”) Represent dC/dt = rate

Shashank, et al (2017 submitted)

Enzyme kinetics

0.0 0.5 1.0 1.5 2.0 1 2 3 4 5 6 7 BHI- Filtration BHI-Centrifugation

Rate of urea hydrolysis, RUH, (x10

• 4) (mS/cm/s)

Urea Concentration (M) CCrt

• 2) Plot “rates” versus “concentration”

Shashank, et al (2017 submitted)

Enzyme kinetics

3) Calculate Km and Vmax values estimate from graph non-linear regression (e.g., Monod model) Lineweaver Burke transformation Hanes-Woolf transformation / plot

Lineweaver Burke plot

• Based on the inverse
• f ‘V’ and ‘S’

(1/V and 1/[s])

• Used for

determination of Vmax and Km

• Good for

determining inhibition

Hanes-Woolf plot

• Good

representation of data

• Rapid calculation
• f Km and Vmax
• Not good

statistically

Inhibition

• Enzymes can be inhibited

 Analogous substance / substrate  “drug” (e.g., aspirin)  Poisons (e.g., cyanide)  Regulatory feedback (e.g., too much product)

Inhibition

• Competitive Inhibition

 Inhibitor and substrate compete

for enzyme

 Common resemblance  Maximum rates are not affected  Km (affinities) are affected.

Public Domain, https://en.wikibooks.org/w/index.php?curid=177294

Inhibition

• Non-competitive inhibition

 Inhibitor can bind to enzyme at

the same time as the substrate, but not at active site

 Km (affinity) is not affected

(substrate is still bound)

 Vmax is affected (inactivated

enzyme)

 Cannot be reversed by higher

substrate concentrations.

Public Domain, https://en.wikibooks.org/w/index.php?curid=177294

Enzyme inhibition

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