A05

Be concise and focus on critical concepts. For each question or subpart, your response should be between 50 and 100 words.

Q01

Points: 8

Explain how X-ray crystallography is used to determine the electron density of a molecule. Describe how this information leads to fitting and refining an atomic model. Include the key steps in your explanation, from data collection to model refinement.

Solution

X-ray crystallography determines a molecule's electron density by directing X-rays at a crystallized sample. The X-rays scatter upon interacting with the electrons in the crystal, producing a diffraction pattern. By measuring the intensities and angles of these diffracted beams, scientists use Fourier transforms to calculate an electron density map, which reveals where electrons are likely to be located within the molecule.

This electron density information is crucial for fitting and refining an atomic model. Scientists fit atoms into regions of high electron density in the map, creating an initial model of the molecule's structure. They then refine this model by adjusting atomic positions and thermal factors to minimize discrepancies between the observed diffraction data and the model's predicted data.

Q02

Points: 8

Explain how X-rays scatter off electrons in a crystal. Discuss why this phenomenon is essential for determining protein structures using X-ray crystallography. In your answer, briefly describe the relationship between the crystal lattice and the resulting diffraction pattern.

Solution

X-rays scatter off electrons in a crystal through elastic interactions known as Thomson scattering. When X-rays encounter the electrons in a crystal lattice, they are deflected without a loss of energy. The electrons act as point scatterers, and due to the regular, repeating arrangement of atoms in the crystal, the scattered X-rays interfere constructively and destructively, creating a diffraction pattern.

This scattering phenomenon is essential for determining protein structures because the resulting diffraction pattern contains information about the electron density within the crystal. By analyzing the intensities and positions of the diffracted X-rays, researchers can compute an electron density map using Fourier transforms. This map reveals the locations of atoms in the protein, allowing scientists to build and refine an accurate three-dimensional model of its structure.

The relationship between the crystal lattice and the diffraction pattern is direct and fundamental. The periodic arrangement of atoms in the crystal lattice causes the scattered X-rays to interfere in specific directions, producing discrete spots in the diffraction pattern. According to Bragg's Law, the angles at which these spots appear are related to the spacing between lattice planes in the crystal. Thus, the geometry of the crystal lattice determines the positions and intensities of the diffraction spots observed.

Q03

Points: 8

Explain constructive and destructive interference in the context of X-ray diffraction patterns. Describe how these phenomena contribute to the formation of diffraction spots and the absence of certain reflections. Briefly discuss how understanding these concepts helps in interpreting X-ray diffraction data.

Solution

In X-ray diffraction, constructive and destructive interference arise from the superposition of X-rays scattered by electrons in a crystal lattice. Constructive interference occurs when the path difference between scattered waves leads them to be in phase, reinforcing each other and producing intense diffraction spots. Destructive interference happens when waves are out of phase, canceling each other out and resulting in the absence of reflections. These phenomena dictate the positions and intensities of diffraction spots, reflecting the crystal's internal structure. Understanding interference is crucial for interpreting diffraction data, as it allows scientists to calculate electron density maps and build accurate atomic models.

Q04

Points: 10

Compare and contrast Cryo-Electron Microscopy (Cryo-EM) and X-ray Crystallography as methods for determining the structures of biological macromolecules.

Solution

Cryo-Electron Microscopy (Cryo-EM) and X-ray Crystallography are techniques for determining macromolecular structures but differ in methodology and applications. X-ray crystallography requires crystallizing the sample, providing high-resolution structures but limited by the difficulty of crystallization for some proteins. Cryo-EM examines samples flash-frozen in vitreous ice without the need for crystals, capturing molecules in a more native state. Cryo-EM is advantageous for studying large complexes and membrane proteins, especially those difficult to crystallize. While X-ray crystallography often achieves higher resolution, Cryo-EM has improved significantly and is more suitable for heterogeneous or flexible samples.

Q05

a

Points: 4

Use UniProt to search for the Enoyl-[acyl-carrier-protein] reductase [NADH] (inhA) from Mycobacterium smegmatis Mycobacterium tuberculosis (strain ATCC 25618 / H37Rv) with the highest annotation score. Summarize the function of this protein and provide the UniProt ID.

Change

I did not proof read enough and the species should have been Mycobacterium tuberculosis (strain ATCC 25618 / H37Rv). I will accept structures from Mycobacterium smegmatis given the lateness of the correction.

Solution

UniProt ID: P9WGR1

The protein InhA is an enoyl-[acyl-carrier-protein] reductase in Mycobacterium tuberculosis, essential for mycolic acid biosynthesis, which is crucial for the bacterial cell wall. In the fatty acid elongation cycle, it catalyzes the NADH-dependent reduction of 2-trans-enoyl-[acyl-carrier protein]. This enzyme is a primary target of the tuberculosis drugs isoniazid and ethionamide, which inhibit its activity by forming an adduct with NAD. Mutations in inhA can lead to drug resistance, impacting tuberculosis treatment effectiveness.​

b

Points: 2

Provide the amino acid sequence of the protein you found in part (a).

Solution

MTGLLDGKRILVSGIITDSSIAFHIARVAQEQGAQLVLTGFDRLRLIQRITDRL
PAKAPLLELDVQNEEHLASLAGRVTEAIGAGNKLDGVVHSIGFMPQTGMGINPF
FDAPYADVSKGIHISAYSYASMAKALLPIMNPGGSIVGMDFDPSRAMPAYNWMT
VAKSALESVNRFVAREAGKYGVRSNLVAAGPIRTLAMSAIVGGALGEEAGAQIQ
LLEEGWDQRAPIGWNMKDATPVAKTVCALLSDWLPATTGDIIYADGGAHTQLL

c

Points: 4

Visit the Protein Data Bank (PDB) and find the best experimental structure for native inhA and provide the four-character PDB ID. Explain why you chose this structure (e.g., resolution, completeness, mutations). Download the structure in PDBXmmCIF Format.

Tip

To select the best native structure of Enoyl-[acyl-carrier-protein] reductase (inhA) from the Protein Data Bank (PDB), follow these steps:

1. Once the search results appear, you’ll see a list of structures associated with the protein. Each structure will have a unique PDB ID and may have different experimental conditions, resolutions, and details.
2. To select the most suitable structure, consider the following:
   • Structures with a lower resolution (in Ångstroms) are generally more accurate. Aim to select a structure with a resolution below 2.0 Å, as these offer higher detail. Structures with a resolution above 3.0 Å might be less precise.
   • Check the structure for any missing regions, such as loops or domains. A complete structure provides a more accurate representation of the protein, while structures missing important regions may limit its reliability.
   • Prefer structures solved using X-ray crystallography or Cryo-Electron Microscopy (Cryo-EM). X-ray crystallography typically yields high-resolution structures, but Cryo-EM can also provide important structural insights, especially for flexible regions.
   • Review the structure’s description and any bound ligands. Choose a structure that reflects the biologically relevant state of the protein. For example, if studying the active form of the protein, select a structure where a relevant ligand or cofactor is bound.
3. If available, examine the structure’s validation report, which provides insights into the accuracy and reliability of the structural data. Look for structures with good validation scores for quality assurance.

Q06

Points: 6

Describe the concept of homology modeling in protein structure prediction. Explain the main principles behind this method and why it's useful. Mention at least one limitation of this approach.

Solution

Homology modeling predicts a protein's three-dimensional structure based on its amino acid sequence similarity to a known structure (the template). The principle is that proteins with similar sequences tend to have similar structures. By aligning the target protein sequence with the template, a structural model is constructed by mapping residues accordingly. This method is useful for gaining structural insights when experimental data is unavailable, aiding in function prediction and drug design. A limitation is that the accuracy of the model heavily depends on the degree of similarity; low sequence identity between the target and template can result in unreliable models.

Q07

a

Points: 4

• Visit the SWISS-MODEL interactive workspace.

• Use the following amino acid sequence for prediction:

  > MTGLLDHKRILVSGIVTDSSIAYHVQAVAQEQGGELVLTGFDKLRVIQRVTDRL
  PSKAPLLELDAENEQHLASLAGKITEAIGAGNKIDGVTHSIGFMPRTGMGVNPF
  FDTPYADVTRGPHISAYSYTSMSKALLPIMNPGGTIVGMNFDPSRAMPVYNWMT
  AAKSAAESINRFVARETGKYGIRNNLVAAGPIRTLVMSAIVGGVLGAEAGAQIC
  LVEENWDQRTPMGWNMKDITPVCKTVCGLLSDWLPATTGDIIYHDGGAHTQLL
  

• After submitting the sequence, identify and record the five PDB IDs with the highest identity scores from the template search results.

• Build homology models using these templates.
• Explain what Global Model Quality Estimation (GMQE) is in the context of protein modeling.
• Note which PDB structure provided the highest GMQE score.
• Download the modelCIF file by clicking on the "Download Files" button associated with your best model.

b

Points: 2

• Navigate to AlphaFold 3 (AF3)
• Predict the structure of the inhA protein using the same amino acid sequence provided above.
• Once the prediction is complete, download and extract the results.
• Locate the file named _model_0.cif, which contains the predicted structure from AF3.

c

Points: 4

• Load the following structures into Mol*. You may use other programs such as PyMOL or ChimeraX. (I personally prefer ChimeraX over PyMOL. I suggested Mol* since it is completely web based.)
  • The experimental structure of the protein from Q05.
  • The predicted structure from SWISS-MODEL (modelCIF file).
  • The predicted structure from AlphaFold 3 (_model_0.cif file).
• Align the structures to compare their conformations.
• Take a screenshot of the aligned structures and include it in your submission.
• Discuss at least one key difference observed between the structures. This could relate to structural features such as folding patterns, active sites, or any notable deviations.

Programming+

These problems are not required and will not impact your BIOSC 1540 grade. The instructor will assess these separately to validate correctness without an assigned grade. Thus, you may work on these problems individually or in a team-based setting and "due" by the end of the semester. Happy coding!

Acceptable languages: Python v3.10+, Mojo v24.4+, Zig v0.13+, Rust v1.80+

Rewards

Engaging with these optional programming problems offers several valuable academic and professional growth opportunities.

• Consistent engagement with these Programming+ problems will allow me to write more detailed, compelling recommendation letters highlighting your computational skills. These personalized letters can significantly boost your applications for future academic programs, internships, or job opportunities.
• If there is enough interest, optional Friday recitations will be provided. This will give you individualized attention to accelerate learning and actionable feedback on your code and problem-solving approaches.
• Exceptional solutions may be featured on our course website with the students' permission. This is a way for us to recognize and appreciate your hard work and dedication to these problems.

Note

These problems would be similar to ones given in a major-only version of the class. Although, there would be more relevant instructions during class and would be given more than a week to complete.

P01: PDB File Parser

In the field of structural biology, the Protein Data Bank (PDB) file format is widely used to store three-dimensional structural data of molecules. Your task is to create a program that can read and extract basic information from a PDB file. This will give you hands-on experience with the kind of data processing often required in structural biology research.

Write a program that takes a PDB file as input and extracts the following information:

1. The total number of atoms in the structure
2. The number of amino acid residues

To accomplish this, you'll need to understand the structure of a PDB file. Each line in a PDB file represents a record, and different record types contain different information. For example, ATOM records contain information about individual atoms, while HELIX and SHEET records describe secondary structures.

You may find it helpful to use the Biopython library, specifically the Bio.PDB module, which provides tools for working with PDB files. If you choose to use Biopython, look into the PDBParser class for reading PDB files and the Structure class for accessing the parsed data.

Your program should output a summary of the extracted information in a clear, readable format.

P02: RMSD Calculator

Root Mean Square Deviation (RMSD) is a commonly used measure to compare protein structures. It quantifies the average distance between atoms of superimposed proteins. This metric is crucial in various applications, such as assessing the quality of predicted protein structures or analyzing conformational changes.

Your task is to write a program that calculates the RMSD between two protein structures. The program should:

1. Take two PDB files as input. These could represent the same protein in different states (e.g., experimental vs. predicted structures).
2. Align the structures (you may assume they are already roughly aligned).
3. Calculate the RMSD between corresponding atoms.

To calculate RMSD, you'll need to:

1. Extract the 3D coordinates of corresponding atoms from both structures.
2. Calculate the squared differences between these coordinates.
3. Take the average of these squared differences.
4. Take the square root of this average.

You may want to focus on comparing only the backbone atoms (N, Cα, C) or all heavy atoms. Be sure to specify in your output which atoms you're using for the comparison.

Consider using libraries like NumPy to handle the 3D coordinate data and perform calculations efficiently.

P03: Secondary Structure Predictor

Predicting protein secondary structure from amino acid sequence is a fundamental problem in bioinformatics. While modern methods use sophisticated machine learning techniques, simpler algorithms can still provide insights into the relationship between sequence and structure.

Your task is to implement a basic secondary structure prediction algorithm, such as the Chou-Fasman method. This algorithm assigns propensities for each amino acid to be in an alpha helix, beta sheet, or random coil, then uses these propensities to predict the most likely secondary structure for each residue.

Your program should:

1. Take a protein sequence as input (a string of one-letter amino acid codes).
2. Implement the Chou-Fasman algorithm or another simple prediction method.
3. Output the predicted secondary structure for each residue (e.g., H for helix, E for sheet, C for coil).

To implement the Chou-Fasman method:

1. Use a table of propensity values for each amino acid. You can find more information in the following papers: (Jiang et al., 1998 ; Chen et al., 2006)
2. Scan the sequence to find regions with high propensities for helices or sheets.
3. Apply rules for extending and terminating these structures.
4. Assign remaining regions as coils.

Remember, this is a simplified method and won't be as accurate as modern predictors, but it will give you insight into the principles of sequence-based structure prediction.

P04: Solvent-Accessible Surface Area Calculator

Solvent-Accessible Surface Area (SASA) is an important property in protein structure analysis. It represents the surface area of a protein that is accessible to a solvent molecule (typically water) and can provide insights into protein-protein interactions, ligand binding sites, and more.

Your task is to write a program that calculates the SASA for each residue in a protein structure. The program should:

1. Take a PDB file as input.
2. Calculate the SASA for each residue.
3. Output the SASA values in a clear, readable format.

To calculate SASA, you can:

1. Use the Biopython library, which provides SASA calculation functionality.
2. Use an external tool like FreeSASA, which you can call from your Python script.
3. Implement a simple SASA algorithm yourself (this is more challenging and less accurate, but educational).

If using Biopython or FreeSASA:

1. Read the documentation to understand how to use the SASA calculation functions.
2. Ensure you understand the units of the output (typically Ų) and any parameters you need to set (like probe radius).

If implementing your own algorithm:

1. Represent the protein as a set of spheres (atoms).
2. Use a probe sphere to "roll" over the surface of these spheres.
3. Calculate the area accessible to the probe.

Your output should list each residue along with its calculated SASA. Consider also providing summary statistics, like average SASA per residue type or total protein SASA.