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Computational Structural Biology Exam

When: Oct 14, 2024 at 4:00 pm. Points: 100

2024 Fall CSB Exam (Key)

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Review guide

This guide covers the major themes of the exam, providing a broad framework for your review. Since the exam is open note, concentrate on developing a deep understanding of the major concepts and approaches, rather than memorizing specific facts.

Lecture 11: Introduction to Structural Biology

  • Explain the roles of covalent bonds, such as peptide bonds, in stabilizing primary protein structures, and how noncovalent forces drive protein folding and dynamic interactions.
  • Describe the hierarchy of protein structure, including primary (sequence of amino acids), secondary (alpha helices, beta sheets), tertiary (3D shape of a polypeptide), and quaternary structures (multi-chain complexes).
  • Understand how electron density maps from techniques like X-ray crystallography are used to determine atomic positions in protein structures.
  • Explain how Cryo-Electron Microscopy (Cryo-EM) allows for protein structure determination without crystallization, and its importance in studying large, dynamic complexes in near-native states, especially for observing macromolecular assemblies.
  • Explain the characteristics of intrinsically disordered proteins (IDPs), including their lack of stable 3D structure under physiological conditions and the challenges they pose for structure prediction compared to well-ordered proteins.

Lecture 12: Protein Structure Prediction

  • Understand the importance of protein structure prediction in fields such as drug discovery, biotechnology, and the understanding of disease mechanisms.
  • Explain the challenges in protein structure prediction, including Levinthal's paradox and the complexity of efficiently navigating conformational space.
  • Describe the concept of rugged energy landscapes in protein folding and how proteins navigate these landscapes to achieve their functional conformations.
  • Explain the basics of homology modeling, including the sequence similarity thresholds.
  • Explain how coevolutionary analysis identifies pairs of residues that evolve together, indicating proximity in the 3D structure, and how this information informs structural predictions.
  • Describe how AlphaFold uses deep learning, coevolutionary data, and neural network-based pattern recognition to predict protein structures with high accuracy.

Lecture 13: Molecular Dynamics (MD) Principles

  • Explain the role of molecular dynamics (MD) simulations in studying protein folding, binding, and flexibility over time.
  • Understand why atoms are modeled as classical particles in MD simulations and the implications of ignoring quantum mechanical effects.
  • Apply Newton's laws of motion in the context of atomistic simulations within molecular dynamics.
  • Describe the components of force fields, including bond stretching, angle bending, and torsions, and explain how they contribute to the potential energy in MD simulations.
  • Identify scenarios in which quantum mechanical effects cannot be ignored and discuss the limitations of classical mechanics in these cases.
  • Explain the key noncovalent interactions—hydrogen bonding, electrostatic interactions, van der Waals forces, and hydrophobic effects—and their roles in molecular recognition.

Lecture 14: Molecular System Representations

  • Identify criteria for selecting high-quality experimental structures for simulations.
  • Explain strategies for adding missing residues or atoms in protein models prior to conducting MD simulations.
  • Describe the steps involved in protein preparation for simulations.
  • Evaluate the suitability of a protein structure for simulations based on factors such as completeness, functional state, and clash scores.
  • Explain the purpose of using periodic boundary conditions (PBC) in molecular simulations.

Lecture 15: Ensembles and Atomistic Insights

  • Define microstates and macrostates, and explain their significance in statistical mechanics and molecular simulations.
  • Discuss the importance of adequately sampling microstates in MD simulations to compute reliable ensemble averages.
  • Explain how thermostats (like Berendsen and Nosé-Hoover) and barostats are used to control temperature and pressure in MD simulations.
  • Distinguish between the equilibration (relaxation) phase and the production (data collection) phase in MD simulations.
  • Use Root Mean Square Deviation (RMSD) as a metric to assess conformational changes in proteins over time during simulations.
  • Use Root Mean Square Fluctuation (RMSF) to evaluate the flexibility of specific residues or regions in a protein during simulations.
  • Explain the relationship between energy and probability in molecular simulations.

Lecture 16: Structure-Based Drug Design

  • Describe the stages of the drug discovery pipeline and explain the role of computational methods.
  • Identify key factors in selecting protein targets for drug design.
  • Explain the role of virtual screening in narrowing down potential compounds from large chemical libraries during drug discovery.
  • Describe how Gibbs free energy (ΔG) determines the strength of protein-ligand interactions.
  • Explain the contributions of enthalpy (noncovalent interactions) and entropy (flexibility) to the thermodynamics of protein-ligand binding.
  • Explain the purpose of alchemical free energy simulations in calculating changes in free energy.
  • Describe how alchemical simulations work.
  • Explain the method of thermodynamic integration (TI) used in alchemical simulations.

Lecture 17: Docking and Virtual Screening

  • Describe the docking process in virtual screening, including how selecting one representative protein conformation simplifies protein-ligand binding prediction.
  • Explain the importance of choosing an appropriate protein conformation to account.
  • Describe methods for detecting binding pockets, such as alpha shape theory and grid-based techniques.
  • Discuss the challenges in detecting cryptic binding sites.
  • Explain the process of pose optimization in docking to optimize ligand positions within the binding site for accurate binding affinity prediction.

Lecture 18: Ligand-Based Drug Design

  • Explain how molecular descriptors like LogP, molecular weight, and topological polar surface area (TPSA) are used to predict bioactivity in ligand-based drug design.
  • Describe how Extended Connectivity Fingerprints (ECFPs) encode structural information for similarity comparisons in ligand-based drug design.
  • Explain the basics of how molecular fingerprints are generated by hashing atom-specific properties.
  • Discuss the challenges of efficiently exploring chemical space to find active compounds similar to known bioactive molecules.

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