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

Enzymes 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 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) Active site  Co-factor  Organic (heme/flavin)  Inorganic (metal/sulphur)  Co-enzymes (vitamins, NADH/NADPH)

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 10 Molarity (M) 0.11M 0.11 0.22M 0.22 0.33M 0.33 0.44M Electrical conductivity (mS/cm) 0.44 Electrical conductivity (mS/cm) 0.55M 0.55 0.88M 1 0.88 1 1.11M 1.11 1.54M 1.54 1.76M 1.76 1.98M 1.98 0.1 0.1 0.01 0.01 10 100 1000 10 100 1000 � � Time (s) Time (s) (a) 1) Calculate substrate conditions versus time . Linear regression (may trim “tails”) Represent dC/dt = rate Shashank, et al (2017 submitted)

Enzyme kinetics 7 -4 ) (mS/cm/s) BHI- Filtration BHI-Centrifugation 6 5 Rate of urea hydrolysis, R UH , (x10 4 3 2 1 C Crt 0 0.0 0.5 1.0 1.5 2.0 Urea Concentration (M) � 2) Plot “rates” versus “concentration” Shashank, et al (2017 submitted)

Enzyme kinetics 3) Calculate K m and V max 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 of ‘V’ and ‘S’ (1/V and 1/[s])  Used for determination of V max and K m  Good for determining inhibition

Hanes-Woolf plot  Good representation of data  Rapid calculation of K m and V max  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 http://alevelnotes.com