1. Mr. Briner Unit 8.1 Metabolism
ASM
DP Biology
Unit 8.1
Metabolism

2. 8.1.S1
Calculating and plotting rates of reaction from raw experimental results.

3. Enzymes Rates of reaction Rate measures how fast something happens
Mr. Briner
ASM
Enzymes
Rates of reaction
Rate measures how fast something happens
Need (1) measurement and (2) time

4. Enzymes Rates of reaction “Factors effecting enzyme activity”
Use the results from a lab to calculate the rate of reaction
The rate of reaction can be calculated using the formula:
Rate of reaction = unit / time taken (s)

5. 8.1.U1
Metabolic pathways consist of chains and cycles of enzyme-catalysed reactions.

6. Enzymes Metabolic Pathways Metabolic pathways
Consist of chains and cycles of enzyme catalyzed reactions

7. Enzymes Metabolic Pathways Metabolic pathways
Chemical changes in living things often happen in many steps
Several intermediate stages
Each stage has its own enzyme
Catabolic pathways breakdown molecules
Anabolic pathways build up molecules

8. Enzymes Metabolic Pathways Chain pathways Enzyme (1): Enzyme (2):
Substrate 1  Product 1
Enzyme (2):
Substrate 2  Product 2
Enzyme (3):
Substrate 2  Product 3
Product 3 is called the 'End product'.
e.g. Glycolysis

9. Enzymes
Metabolic Pathways
Cyclic pathways

10. Enzymes Metabolic Pathways Cyclic pathways Enzyme (1): Enzyme (2):
Intermediate 4 + Substrate  Intermediate 1
Enzyme (2):
Intermediate 1 Intermediate 2
Enzyme (3):
Intermediate 2  Intermediate 3
Enzyme (4):
Intermediate 3  Intermediate 4

11. Enzymes Induced-fit Model
Lock-and-key model does not fully explain the binding of substrate to active site!
As substrate binds to active site, shape of the active site changes!
Only then does the active site become complementary to shape of the substrate

12. Enzymes
Induced-fit Model

13. Enzymes
Induced-fit Model

14. Enzymes Induced-fit Model Substrate induces the active site to change
Weakening bonds in the substrate during the process
Thus reducing activation energy
Induced fit model helps explain the broad specificity of some enzymes

15. Enzymes
Induced-fit Model

16. Enzymes Induced-fit Model
Explains the broad specificity of some enzymes

17. When should we accept something as true?
TOK THOUGHT:
Power of PREDICTION
Scientific truths are often pragmatic. We accept them as true because they give us predictive power, that is, they work.
The German scientist Emil Fischer introduced the lock-and-key model for enzymes and their substrates in It was not until 1958 that Daniel Koshland in the US suggested that the binding of the substrate to the active site caused a conformational change, hence the induced-fit model.
When should we accept something as true?

18. 8.1.U2
Enzymes lower the activation energy of the chemical reactions that they catalyse.

19. Enzymes Activation energy
Enzymes lower the activation energy of the chemical reaction that they catalyze
Chemical reaction:
Reactants converted into products

20. Enzymes Activation energy Activation energy:
Must be exceeded for any reaction to occur
Allows breaking of bonds in exergonic reactions
Allows formation of bonds in endergonic reactions
Different reactions have different activation energies

21. Enzymes Activation energy Enzymes reduce activation energy
In the activated complex, energy is put into the substrate to weaken the structure
Allows the reaction to occur with a minimal amount of additional energy required 

22. Enzymes
Activation energy

23. Enzymes
Activation energy

24. Enzymes Activation energy
Normal activation energy would cause damage to the proteins of the cell
Thus reduced activation energy make these reactions possible in a cell
After the product is formed energy is released
Exergonic reactions release more energy than the activation energy

25. Enzymes
Activation energy

26. Enzymes Activation energy
Net energy of the reaction is not changed by the enzyme
Only reduces activation energy
Net energy released in exergonic reactions, or taken in by endergonic reactions, stays the same

27. Enzymes
Activation energy

28. MAJOR SOURCES Brent Cornell (Melbourne, AU)
Thank you to my favorite sources of information when making these lectures!
Chris Paine (Shanghai, CH) www. bioknowledgy.weebly.com
John Burrell (Bangkok, TH)
Dave Ferguson (Kobe, JA)
Brent Cornell (Melbourne, AU)
Andrew Allott – Biology for the IB Diploma
C. J.Clegg – Biology for the IB Diploma
Weem, Talbot, Mayrhofer – Biology for the International Baccalaureate
Howard Hugh’s Medical Institute –
Mr. Hoye’s TOK Website –
And all the contributors at

29. Mr. Briner Unit 8.1 Metabolism
ASM
DP Biology
Unit 8.1
Metabolism

30. 8.1.U3 8.1.S2 Enzyme inhibitors can be competitive or non-competitive.
Distinguishing different types of inhibition from graphs at specified substrate concentration.

31. Enzymes
Inhibition
Inhibitors are substances that reduce or completely stop the action of an enzyme
Inhibition can act on the active site or on another region of the enzyme molecule

32. Enzymes Competitive Inhibition
Substrate and inhibitor are chemically very similar
Inhibitor binds to the enzyme’s active site
Inhibitor occupies the active site
Prevents substrate from binding
Activity of the enzyme is prevented until the inhibitor dissociates

33. Enzymes
Competitive Inhibition

34. Enzymes Competitive Inhibition However:
If substrate concentration is increased it occupies more active sites than inhibitor
Substrate out-competes the inhibitor for the active sites
Rate of reaction will increase again

35. Enzymes
Competitive Inhibition
However:

36. Enzymes Competitive Inhibition Example: Folic acid synthase
Folic acid synthase is a bacterial enzyme
Produces folic acid from PABA and other substrates
Antibiotics called sulfanilamides bind to the active site of folic acid synthase
Blocks access of the similarly shaped PABA
Without folic acid, the bacteria die
Infection is overcome

37. Enzymes
Competitive Inhibition
Example: Folic acid synthetase

38. Enzymes Non-competitive Inhibition
Substrate and inhibitor are not similar
Inhibitor does not bind to active site
Binds to the enzyme at a different site
Allosteric site
Changes the conformation (3-D, tertiary structure) of the enzyme
Causes enough change to slow enzyme activity
Also called Allosteric inhibition

39. Enzymes
Non-competitive Inhibition

40. Enzymes Non-competitive Inhibition
Non-competitive inhibitor always significantly reduces the rate of reaction
Rate of reaction is always lower when the inhibitor is present
Increasing the substrate concentration increases the chance of a substrate colliding with an uninhibited enzyme
The rate can increase, but to a lower plateau

41. Enzymes
Non-competitive Inhibition

42. Enzymes Non-competitive Inhibition Example: Silver (Ag+)
Silver bonds with the -SH groups of cysteine
Amino acid which forms covalent disulfide bridges
Disruption of disulfide bridges alters the tertiary structure of the enzyme
Affecting its active site
Thus, Silver (and other heavy metals) act as metabolic poisons
Disrupt the activity of many enzymes

43. Enzymes
Inhibition

44. 8.1.U4
Metabolic pathways can be controlled by end-product inhibition.

45. Enzymes End-Product Inhibition
Enzyme pathways can be controlled by concentration of products from the end of the pathway
A form of allosteric inhibition
Works by negative feedback
Increase in the product, decreases the reaction
Decrease in the product, increase the reaction

46. Enzymes
End-Product Inhibition

47. Enzymes End-Product Inhibition
When too much of an end product is made, the excess interacts with enzymes at the beginning of the pathway
Decreasing the activity of the pathway until the end products are used up
Thus releasing the allosteric enzymes from inhibition
Allowing for the metabolic pathway to function again, producing more end products

48. Enzymes End-Product Inhibition Example: Phosphofructokinase
Glycolysis is controlled by ten enzymes which work to digest glucose and produce ATP
When ATP is in excess, it binds to an allosteric site of phosphofructokinase
Decreasing its activity and inhibiting glycolysis

49. Enzymes End-Product Inhibition Example: Phosphofructokinase
Binding of ATP to allosteric site is reversible
When ATP is in shortage, it is released from the allosteric site and used up
Phosphofructokinase is no longer inhibited
Allowing glycolysis to proceed and produce more ATP

52. Enzymes
End-Product Inhibition

53. 8.1.A1
End-product inhibition of the pathway that converts threonine to isoleucine.

54. Enzymes End-Product Inhibition example Example: Threonine-Isoleucine
Bacteria synthesize isoleucine from threonine
Takes five enzyme-catalyzed steps
As concentration of isoleucine increases, some of it binds to the allosteric site of threonine deaminase
Isoleucine acts as a non-competitive inhibitor to threonine deaminase

56. Enzymes End-Product Inhibition example Example: Threonine-Isoleucine
The pathway is then turned off
Reducing isoleucine production.
If the concentration of isoleucine later falls (because it is used up) then the allosteric sites of threonine deaminase are emptied
Enzymes restarts converting threonine to isoleucine

57. 8.1.A2
Use of databases to identify potential new anti-malarial drugs.

58. Enzymes Bioinformatics and Malaria Bioinformatics
An approach to studying biology relying on crowd sharing results
Multiple research groups can add information to a database
Has facilitated research into metabolic pathways = chemogenomics

59. Enzymes Bioinformatics and Malaria Bioinformatics
Databases target genome are compared to protein sequence information
Can screen for possible interactions before starting lab research
Either based on previous experiments or on shapes and sequences that may match

60. Enzymes Bioinformatics and Malaria Bioinformatics
Massive libraries of chemicals are tested individually on a range of organisms
For each organism, a range of target sites in enzymes are identified
A range of chemicals which are known to work on those sites are tested
Protein structures are investigated

62. Enzymes Bioinformatics and Malaria Malaria
Disease caused by the pathogen: Plasmodium falciparum
This protozoan uses mosquitoes and humans as a host
Can be passed on by mosquito bites
In 2015
Roughly 214 million malaria cases
Estimated malaria deaths

64. Enzymes
Bioinformatics and Malaria
Malaria

65. Enzymes Bioinformatics and Malaria Malaria
Increasing drug resistance has lead to the use of bioinformatics to try and identify new drugs

66. Enzymes Bioinformatics and Malaria In one study:
Approx. 300,000 chemicals were screened against pathogen strains
Using a massive database of proteins
Related and unrelated organisms, including human cell lines, also screened
To see if something effects the pathogen without effecting humans

67. Enzymes Bioinformatics and Malaria In one study:
19 new chemicals that inhibit the enzymes targeted by anti-malarial drugs were identified
15 chemicals that bind to malarial proteins were identified
Could help test the presence of P. falciparum
These results indicate possible new directions for drug research

68. Enzymes Protein simulations http://www.drug-design-workshop.ch/

69. MAJOR SOURCES Brent Cornell (Melbourne, AU)
Thank you to my favorite sources of information when making these lectures!
Chris Paine (Shanghai, CH) www. bioknowledgy.weebly.com
John Burrell (Bangkok, TH)
Dave Ferguson (Kobe, JA)
Brent Cornell (Melbourne, AU)
Andrew Allott – Biology for the IB Diploma
C. J.Clegg – Biology for the IB Diploma
Weem, Talbot, Mayrhofer – Biology for the International Baccalaureate
Howard Hugh’s Medical Institute –
Mr. Hoye’s TOK Website –
And all the contributors at