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

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

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

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)

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

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

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

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

Enzymes Metabolic Pathways Cyclic pathways

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

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

Enzymes Induced-fit Model

Enzymes Induced-fit Model

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

Enzymes Induced-fit Model

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

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 1890. 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?

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

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

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

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 

Enzymes Activation energy

Enzymes Activation energy

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

Enzymes Activation energy

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

Enzymes Activation energy

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) www.click4biology.info Dave Ferguson (Kobe, JA) http://canada.canacad.ac.jp/High/49 Brent Cornell (Melbourne, AU) http://ib.bioninja.com.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 – www.hhmi.org/biointeractive Mr. Hoye’s TOK Website – http://mrhoyestokwebsite.com And all the contributors at www.YouTube.com

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

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.

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

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

Enzymes Competitive Inhibition

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

Enzymes Competitive Inhibition However:

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

Enzymes Competitive Inhibition Example: Folic acid synthetase

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

Enzymes Non-competitive Inhibition

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

Enzymes Non-competitive Inhibition

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

Enzymes Inhibition

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

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

Enzymes End-Product Inhibition

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

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

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

Enzymes End-Product Inhibition

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

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

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

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

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

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

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

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 438 000 malaria deaths http://www.who.int/features/factfiles/malaria/en/

Enzymes Bioinformatics and Malaria Malaria

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

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

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

Enzymes Protein simulations http://www.drug-design-workshop.ch/ http://pdb101.rcsb.org/

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) www.click4biology.info Dave Ferguson (Kobe, JA) http://canada.canacad.ac.jp/High/49 Brent Cornell (Melbourne, AU) http://ib.bioninja.com.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 – www.hhmi.org/biointeractive Mr. Hoye’s TOK Website – http://mrhoyestokwebsite.com And all the contributors at www.YouTube.com