Welcome to Week 2 Starting week two video Please watch the online video (1 minutes 24 seconds). OPTIONAL‐Please participate in the online discussion forum. Chapter 3 – Protein Structure Introduction to Chapter 3 Chapter 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. OPTIONAL‐Please participate in the online discussion forum. 3.1 Intro to Structure Pt 1 Amino acids to secondary structure video Please watch the online video (8 minutes, 15 seconds). A condensed summary of this video can be found in the Video summary page. OPTIONAL‐Please participate in the online discussion forum. Working with Protein Data Bank entries Background: 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. OPTIONAL‐Please participate in the online discussion forum. Finding PDB entry codes Background: 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. OPTIONAL‐Please participate in the online discussion forum. 3.2 Intro to Structure Pt 2 Tertiary structure to X‐ray crystallography video Please watch the online video (7 minutes, 41 seconds). A condensed summary of this video can be found in the Video summary page. OPTIONAL‐Please participate in the online discussion forum. Validating protein structures Background: 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. OPTIONAL‐Please participate in the online discussion forum. Evaluating Ramachandran plots Background: 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. Please complete the online exercise. OPTIONAL‐Please participate in the online discussion forum. Chapter 4 – Enzymes Introduction to Chapter 4 Chapter 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. 4.1 Michaelis‐Menten Kinetics Theory of action video Please watch the online video (7 minutes, 8 seconds). A condensed summary of this video can be found in the Video summary page. OPTIONAL‐Please participate in the online discussion forum. Working with concentrations Background: 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. Please complete the online exercise. OPTIONAL‐Please participate in the online discussion forum. Graphing enzyme kinetics data Background: 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. 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 (4 minutes 9 seconds). Linest in Microsoft Excel video Please watch the online video (3 minutes 45 seconds). Please complete the online exercise. OPTIONAL‐Please participate in the online discussion forum. Sample calculation ‐ Lineweaver‐Burk plot Background: Lineweaver‐Burk plots depict 1/V vs. 1/[S]. These are very useful for determining the nature of enzyme‐substrate interactions.