to Week 2 - edX...Linest in Google Docs spreadsheet video Please watch the online video (3 minutes 43 seconds). Linest in OpenOffice spreadsheet video Please watch the online video

WelcometoWeek2

StartingweektwovideoPlease watch the online video (1 minutes 24 seconds).

OPTIONAL‐Please participate in the online discussion forum.

Chapter3–ProteinStructure

IntroductiontoChapter3Chapter 3 contains two subsections.

Intro to Structure Part 1

Amino Acids, Primary Structure, and Secondary Structure

Intro to Structure Part 2

Tertiary Structure, Quaternary Structure, and X‐Ray Crystallography

At the conclusion of this chapter, you should understand how the ordering of individual amino acids

in a protein can affect the localized and global folding and function of the entire protein. You should

further have some appreciation of how x‐ray crystallographic data is used to determine the

structure of proteins.


3.1IntrotoStructurePt1

AminoacidstosecondarystructurevideoPlease watch the online video (8 minutes, 15 seconds).

A condensed summary of this video can be found in the Video summary page.


WorkingwithProteinDataBankentriesBackground: The structural information freely available in the Protein Data Bank is immense. It

allows anyone to be able to study protein structure and function.

Instructions: Use the tools in the PDB to examine the structures mentioned below and answer the

assessment questions.

Learning Goals: To learn how to manipulate proteins and identify their structural elements with the

tools in the PDB.

Please complete the online exercise.


FindingPDBentrycodesBackground: Searching a specific entry in the Protein Data Bank requires one to know the

corresponding PDB code.

Instructions: Read the passage below concerning how PDB codes can be determined.

Learning Goals: To learn how to find specific, useful information within the vast PDB database.

The Protein Data Bank is only useful if one can find information of interest. As we work through this

chapter, some students might wonder how to find other proteins. In the videos, I start with a PDB

code. What if you do not already have a code?

Searching the PDB requires one to have an idea of what is being sought. For the different examples

in this course, I knew that I wanted to find proteins that are rich in one type of secondary structure. I

performed an online search using terms like "proteins rich in alpha‐helices" and found a few names

of proteins that seemed to match what I wanted. I then searched on those particular proteins within

the PDB. Any single protein may have multiple entries in the PDB. I then looked through the

different entries until I found a specific PDB entry that demonstrated the properties I want to

highlight.

Some websites on the internet include PDB codes. One example is SCOP: Structural Classification of

Proteins, an extensive site that can be searched by a number of keywords that correspond to

common traits of proteins. Most proteins in SCOP are linked to a PDB entry.


3.2IntrotoStructurePt2

TertiarystructuretoX‐raycrystallographyvideoPlease watch the online video (7 minutes, 41 seconds).



ValidatingproteinstructuresBackground: Most protein structures are determined based on x‐ray crystallographic data. The

primary sequence of the protein is matched with the electron density map, and the individual amino

acids are placed within the structure as closely as possible. After all the amino acids are positioned,

the quality of the assigned structure can be measured with several tools. One of the most common

tools is the Ramachandran plot.

Instructions: Read the passage below about the use of Ramachandran plots to validate protein

structural assignments.

Learning Goal: To learn how Ramachandran plots graphically represents dihedral angles of

individual amino acids to predict the validity of a proposed protein structure and folding.

The Ramachandran plot is one of the primary methods for validating proposed protein structures

based on x‐ray crystallographic data. The plot compares selected dihedral angles in each amino acid

found within the proposed protein. The key dihedral angles for each amino acid are located along

the backbone of the protein and are labeled φ (phi), ψ (psi), and ω (omega). To repeat, each amino

acid residue contributes three rotatable bonds and three distinct dihedral angles to the backbone of

a peptide chain.

In theory, all dihedral angles can range in value from −180° to +180°. In practice, within a protein,

the dihedral angles tend to fall in well‐defined ranges. Because of interactions between the

nitrogen and carbonyl, ω is either 0 or 180°, typically 180°. φ has a value near ‐50° in an α‐helix and

ranges between ‐50 and ‐160° in a β‐sheet. ψ also has a value near ‐50° in an α‐helix but ranges

from +100 and +180° in a β‐sheet. When ψ is plotted against φ for each amino acid, most amino

acids fall within tightly defined regions bounded by the angle ranges above and another small area

for a specific subtype of α‐helix. Such a graph is called a Ramachandran plot, shown below.

While keeping track of dihedral angles in a protein may seem complex, the Ramachandran plot

makes the process very simple. Since most amino acid residues correspond to points that fall within

predicted ranges, those amino acids can be ignored. The important ones are those that fall beyond

the anticipated regions. Indeed, if a protein has over 5% of its amino acids as outliers, then the

structure of that protein may be reasonably suspected as being improperly assigned. For this

reason, the Ramachandran plot is a simple, visual tool for quickly checking the validity of an assigned

protein structure.


EvaluatingRamachandranplotsBackground: Ramachandran plots are a simple, visual tool for validating proposed protein

structures. A website that allows users to generate Ramachandran plots from many PDB entries is

the Ramachandran Server at Uppsala University in Sweden.

Instructions: Compare the Ramachandran plots below to answer the questions.

Learning Goals: To learn how to read and compare data from Ramachandran plots.



Chapter4–Enzymes

IntroductiontoChapter4Chapter 4 contains three subsections.

Michaelis‐Menten Kinetics

Enzyme Inhibition

Measuring Inhibition

At the conclusion of this chapter, you should understand how enzyme kinetics data are presented

graphically. You should also understand how different inhibitors affect enzymes, and how the

inhibition is quantified.

4.1Michaelis‐MentenKinetics

TheoryofactionvideoPlease watch the online video (7 minutes, 8 seconds).



WorkingwithconcentrationsBackground: Data in medicinal chemistry, including enzyme kinetics data, rely upon numbers with

various concentration units. Being comfortable with interconverting different concentration units is

a basic skill for one to have.

Instructions: Read the passage below and use the information to answer the subsequent

assessment questions.

Learning Goal: To become comfortable working with the different types of units commonly

encountered in medicinal chemistry.

The goal of a drug discovery program is generally to find a molecule that binds a target protein at

very low concentrations. As has been mentioned before (in Chapter 2), the binding is normally

determined in a biochemical assay, often in the form of a dissociation equilibrium constant (KD). For

a drug, the values for KD are very small, indicating the drug and target bind very tightly and do not

readily dissociate. Ideal KD values are in the nanomolar (nM) range, but during development

observed KD values are much higher. Hits in an early screen might have KD values in the micromolar

(µM) range. The table below shows the concentrations regularly encountered in a drug discovery

program.

name description unit relation to molarity

molar moles / liter M 1

millimolar millimoles / liter mM 10‐3

micromolar micromoles / liter µM 10‐6

nanomolar nanomoles / liter nM 10‐9

picomolar picomoles / liter pM 10‐12

Beyond the reporting of binding data (pharmacodynamics), the units in the table above are also

found throughout pharmacokinetics, especially in reports of the concentration of drugs in blood.



GraphingenzymekineticsdataBackground: Interpreting enzyme kinetics data requires one to be able to graph the information.

The previous unit contained a video which provided instructions on how to generate graphs from

enzyme kinetics data as a saturation plot (Michaelis‐Menten equation) or in linear form

(Lineweaver‐Burk equation).

Instructions: Use the videos on graphing kinetics data in Google Docs, Apache OpenOffice, and

Microsoft Excel to help you answer the assessment question below. Note that a sample calculation

is available in the next component. If the question gives you trouble, consider peeking ahead to see

one way to approach this problem.

Learning Goals: To learn how to graph kinetics data and use the resulting plot to understand the

activity of an enzyme.

LinestinGoogleDocsspreadsheetvideoPlease watch the online video (3 minutes 43 seconds).

LinestinOpenOfficespreadsheetvideoPlease watch the online video (4 minutes 9 seconds).

LinestinMicrosoftExcelvideoPlease watch the online video (3 minutes 45 seconds).



Samplecalculation‐Lineweaver‐BurkplotBackground: Lineweaver‐Burk plots depict 1/V vs. 1/[S]. These are very useful for determining the

nature of enzyme‐substrate interactions.

Instructions: Review the sample calculation demonstrating the use of V‐[S] data points to generate a

Lineweaver‐Burk plot.

Learning Goal: To learn how to use V‐[S] data points to determine Vmax and Km of an enzyme‐

substrate system.

Task: Determine Vmax and Km based on the provided data points.

V (mmol/min) [S] (mmol)

1.0 20

2.8 60

5.5 210

8 480

Solution:

Start by loading the V‐[S] data points into a spreadsheet (Apache Open Office shown). One column

is for [S] and the other for V.

For the Lineweaver‐Burk plot, we need the reciprocal of both V and [S].

We can use LINEST to perform the regression of the 1/V‐1/[S] data points. The format in OpenOffice

is LINEST(data_y;data_x;linear_type;stats). For data_y, highlight the cells for 1/V. For data_x,

highlight the cells for [S]. For linear_type, enter a 1. For stats, enter a 0. Remember to finish the

function properly based on your spreadsheet application. In OpenOffice, press ctrl‐shift‐enter to

execute the function.

With execution of the function, two cells are filled. The one on the left is the slope, and the one on

the right is the y‐intercept. I manually added the slope and Y‐intercept labels for clarity.

From the y‐intercept, Vmax can be directly determined. The slope can then be used to determine Km.

Vmax is 12.3 mmol/min, and Km is 224 mmol.

A graph can be more satisfying than the LINEST function. The graph below shows the data in a

Lineweaver‐Burk format. Right click on a data point, select a Format Trend Line..., select a Linear

regression and Show Equation, and the graph will display the best‐fit equation for the data

points. The slope and y‐intercept should match the output of the LINEST function.

4.2EnzymeInhibition

ReversibleinhibitorsvideoPlease watch the online video (7 minutes 12 seconds).



DistinguishingtypesofenzymeinhibitorsBackground: Inhibitors can be readily distinguished by visually inspecting plots of V vs. [S] at varying

inhibitor concentrations ([I]) and noting the effect of the inhibitor upon Km and Vmax. In the previous

video clip, we observed how an inhibitor changes the shape of a traditional Michaelis‐Menten‐type

plot.

Instructions: Use the Lineweaver‐Burk plots below to classify the type of inhibitor present in each

system.

Learning Goal: To learn how to interpret effects of an inhibitor upon Lineweaver‐Burk plots.



4.3MeasuringInhibition

IC50andKivideoPlease watch the online video (6 minutes 41 seconds).



DetermininginhibitionBackground: Measuring the binding of an inhibitor to an enzyme is a fundamental part of

preliminary screening in a drug discovery program. The enzyme inhibition of molecule is normally

quantified as either a Ki or IC50 value. Both are related to the potency of an inhibitor. A

smaller Ki or IC50 value indicates a stronger inhibitor.

Instructions: Use what you know concerning Ki and IC50 values as well as the Cheng‐Prussoff

equation to answer the questions below.

Learning Goal: To learn how to work through enzyme inhibition data.



Chapter5–Receptors

IntroductiontoChapter5Chapter 5 contains three subsections.

Types of Receptors

Ligands

Occupancy Theory

At the conclusion of this chapter, you should know the different types of receptors and what

structural features distinguish one from another. You should also know the types of receptor

ligands and be able to identify each based on a response vs. log [L] graph. Finally, you should

understand the fundamental assumptions of occupancy theory and situations in which the

assumptions fail.


5.1TypesofReceptors

ReceptorsuperfamiliesvideoPlease watch the online video (6 minutes 26 seconds).



PDBexampleofanionchannelBackground: Ligand‐gated ion channels are membrane‐bound receptors. All membrane‐bound

receptors are characterized by a large number of parallel α‐helices. The α‐helices allow the protein

to criss‐cross the cell membrane and anchor itself in place.

Instructions: In a separate window, bring up the Protein Data Bank website and look up entry 4HFH.

Learning Goals: To appreciate the overall structure of ligand‐gated ion channels and gain more

experience examining proteins in the Protein Data Bank.

Please access the online Protein Data Bank website.



PDBexampleofaG‐protein‐coupledreceptorBackground: G‐Protein‐coupled receptors are membrane‐bound receptors. All membrane‐bound

receptors are characterized by a large number of parallel α‐helices. The α‐helices allow the protein to

criss‐cross the cell membrane and anchor itself in place.

Instructions: In a separate window, bring up the Protein Data Bank website and look up entry 2RH1.

Learning Goals: To appreciate the overall structure of G‐protein‐coupled receptors and gain more

experience examining proteins in the Protein Data Bank.

Please access the online Protein Data Bank website.



5.2Ligands

LigandtypesvideoPlease watch the online video (7 minutes 40 seconds).



DiggingdeeperintoantagonistsBackground: Antagonists are ligands that block the action of an agonist without causing a response

from a receptor. Different subtypes of antagonists are known to exist.

Instructions: Read the passage below on competitive and noncompetitive antagonists.

Learning Goals: To learn about the two types of antagonists and understand how they are

distinguished in response curves.

The two types of antagonist, competitive and noncompetitive, are distinguished by how the

antagonist binds the receptor. A competitive antagonist binds at the same site as an agonist. A

competitive antagonist increases the EC50 of any agonist present without decreasing the maximum

response. By increasing the EC50 of the agonist, the antagonist decreases the agonist's potency.

A noncompetitive antagonist binds at an allosteric site. Such binding does not affect the EC50 value

(potency) agonist, but it does diminish the response caused by the agonist.

Depending on the needs of the discovery program, one type of antagonist might be more effective

than the other. Competitive antagonists, however, tend to be more common. If the drug discovery

group knows the structure of a receptor's endogenous ligand, then the team often designs

antagonists to resemble the natural ligand. The designed antagonist will likely the bind the same

site as the endogenous ligand.


Interpretingdose‐responsegraphsBackground: Dose‐response relationships are commonly encountered in drug development

programs. The characteristic sigmoidal plots convey a large amount of information concisely and can

be readily interpreted.

Instructions: Look at the dose‐response plots below and interpret which scenario is most likely for

each.

Learning Goals: To gain experience analyzing dose‐response relationships and determining how

different types of ligands affect dose‐response graphs.



5.3OccupancyTheory

TheprosandconsoftheClarkmodelvideoPlease watch the online video (8 minutes 42 seconds).



DetermininganEC50valueBackground: The EC50 value for an agonist occurs at the inflection point of a sigmoidal curve.

Instructions: Read the passage below on the easiest method for determining an EC50 from a set of

experimental data. Then, use the data points to estimate the EC50 value of the ligand.

Learning Goals: To understand how to manipulate ligand‐response data and gain quantitative

information on the ligand.

Data points fitting a sigmoidal relationship are much more difficult to manipulate than linear

data. Unfortunately, software packages that best handle sigmoidal data are not freely available. In

the absence of a commercial data processing software package, linearizing ligand‐response data is a

viable option.

Because Clark's occupancy theory can be modeled with what is essentially the Michaelis‐Menten

equation, the ligand‐response data for many receptors can be linearized with what amounts to the

Lineweaver‐Burk equation. The linearized, double‐reciprocal version of Clark's equation is shown

below.

With this equation, any set of response (E) vs. [L] data points can be converted to their inverse

forms as 1/E vs. 1/[L], plotted, and matched to a best‐fit line with a spreadsheet function like

LINEST. The y‐intercept can be used to determine Emax and then KD can be derived from the slope of

the line. Remember that KD is equal to EC50, at least for agonists and partial agonists that follow the

Clark model.



EC50andKdrevisitedBackground: The inflection point of a log [L]‐response plot corresponds to the log EC50 value of the

ligand. At the EC50 value, half of the total receptor concentration is bound in the receptor‐ligand

complex (R‐L) and the other half remains as free receptor, R. At EC50, the ligand concentration is

equivalent to KD.

Instructions: Read the passage below that establishes the relationship between EC50 and KD.

Learning Goal: To more fully understand the equivalence of EC50 and KD in Clark's Occupancy Theory.

The graph below is a typical response vs. log [L] curve for a full agonist. The point of inflection of

this curve occurs at 50% maximum response. The ligand concentration required to achieve 50%

of Emax is called EC50, or the concentration required to achieve and effect of 50%.

The rest of the discussion assumes that [L] is equal to EC50. If we are at EC50, then the system is also

at 50% of Emax.

According to Clark's theory, the only way to achieve 50% of Emax is for half of the receptors to be

bound by the ligand (a full agonist) as the receptor‐ligand complex (R‐L). If half of the receptors are

bound, then an equal number (half) of the receptors are not bound and exist in the form of free

receptor (R).

Remember that the binding of a ligand to a receptor is a reversible process. This equilibrium binding

is quantified by the dissociation equilibrium constant (KD). The equation for KD is shown below.

As has been stated, at 50% Emax, [R‐L] = [R]. In the equation for the equilibrium dissociation

constant, two terms cancel and leave the relationship KD = [L].

At 50% E50, [L] also equals EC50. Through a simple substitution, EC50 = KD.

This relationship is important. Experimentally, KD is not easy to measure directly, but through a log

[L]‐response plot, EC50 is easy fairly easy to determine. Because EC50 and KD are equal, a log [L]‐

response plot is a relatively simple method for indirectly determining the equilibrium dissociation

constant (KD) of a receptor and its agonist or partial agonist.


UpregulationanddownregulationBackground: Clark's occupancy theory, while useful for modeling the behavior of many receptors,

fails to accommodate many observed qualities of ligand‐receptor interactions. Examples already

covered include spare receptors and constituently active receptors.

Instructions: Read the passage below concerning the concepts of upregulation and downregulation,

two ideas that Clark's occupancy theory in its simple form cannot explain.

Learning Goals: To learn about the ideas of upregulation and downregulation, which are frequently

encountered in drug discovery and clinical medicine.

While Clark's occupancy has a very appealing (and useful) simplicity, the fact is that receptors are

not simple. The different receptor superfamilies have completely different structures, and each

superfamily has its own unique behaviors and traits. These behaviors make the task of developing a

single, unified ligand‐receptor theory very challenging.

One trait of some receptors is downregulation. For some types of cells, if a receptor is continuously

stimulated to a high level by a ligand, the cell responds by decreasing its population of that

particular receptor. In other words, the concentration of that receptor is decreased. Because the

receptor concentration is lower, fewer receptors are available for stimulation and generation of a

response. If the ligand concentration is left unchanged, that same ligand will affect a smaller

response because fewer receptors are available. If the ligand is a drug, then the patient will receive

a smaller therapeutic effect from the same drug dosage. The patient is said to be desensitized to

the drug. Desensitization can often be observed in instances of downregulation. In order to

experience the same therapeutic effect from a drug that has caused desensitization, a patient must

increase his dosing levels.

If a desensitized patient quits taking a medication, then it is generally true that the cells will slowly

return to their original receptor concentration in a process called upregulation. Once the cells are

restored to their original state and original levels of receptor concentration, the patient will once

again be sensitized to the drug. If the patient begins taking the medication again, he must return to

the original dosing levels. If the patient takes elevated levels of medication, an overdose may be a

risk.

Desensitization is very common in patients who use opiates to manage pain. Advanced patients

become desensitized and require a higher dose to achieve their original level of pain relief.


Cheng‐PrussoffforreceptorsBackground: The Cheng‐Prussoff equation was introduced in Chapter 4 as a convenient method for

interconverting Ki and IC50 values of enzymes.

Instructions: Read the passage below on the use of the Cheng‐Prussoff equation in the area of

receptors.

Learning Goals: To appreciate the generality of the Cheng‐Prussoff equation and more fully

understand the different variables used to describe the activity of antagonists.

Most drugs that bind to a receptor are antagonists, generally competitive antagonists. The

competitive antagonist prevents the activation of the receptor by its endogenous ligand by binding

the receptor in the same position as the endogenous ligand. A competitive antagonist is therefore

much like the competitive inhibitors that we discussed in Chapter 4. Both the antagonist and

inhibitor prevent the action of a protein by blocking the protein's primary binding pocket.

The activity of competitive inhibitors can be quantified in two different ways: Ki, which is the

dissociation equilibrium constant of the enzyme‐inhibitor complex, or IC50, which is the

concentration of the inhibitor required to reduce the rate of an enzyme‐catalyzed reaction by

50%. Ki is a constant and an inherent property of the enzyme‐inhibitor complex. IC50, however,

varies depending on the concentration of the substrate used in the assay of the inhibitor's

activity. If the substrate concentration in the assay is known along with the Km of the enzyme and

substrate, then the Cheng‐Prussoff equation can be used to interconvert Ki and IC50.

Likewise, a competitive antagonist can be quantified in two different ways: Ki, which is the

dissociation equilibrium constant of the receptor‐antagonist complex, or IC50, which is the

concentration of the antagonist required to reduce the response caused by the action of an agonist

on a receptor by 50%. Ki is a constant and an inherent property of the receptor‐antagonist

complex. IC50 varies depending on the concentration of the agonist used in the assay of the

antagonist's activity. Not surprisingly, the Cheng‐Prussoff can also be used to interconvert these

two values.

When applied to receptors, the Cheng‐Prussoff equation requires the concentration of ligand, [L],

used in the assay. Also required is the KD of the receptor‐ligand complex. Specifically, that is the KD

of the receptor‐ligand complex without any antagonist present. The receptor version of the Cheng‐

Prussoff equation is shown below.

As with enzyme assays, studies of antagonists often report IC50 values for the antagonist. IC50 values

generated by different research groups cannot be directly compared unless the agonist

concentration in the various assays are identical. The Cheng‐Prussoff equation allows conversion

ofIC50 values to the directly comparable Ki values.


Examination1

FirstExaminationThe exam is open book and open note. All questions may be attempted only once, so be certain of

your answer before submitting it. There are ten questions. Each is its own component within the

Examination 1 subsection.

Remember that you are bound by the honor code. No postings to the forum concerning the exam

are allowed. Furthermore, you must work on the examination independently.

ProblemsPlease complete the online problems in Examination 1.

Documents

to Week 2 - edX...Linest in Google Docs spreadsheet video Please watch the online video (3 minutes 43 seconds). Linest in OpenOffice spreadsheet video Please watch the online video