Computional Structural Biology

Question 1

Select the correct statement regarding the use of protein crystals in X-ray diffraction.

A Proteins cannot scatter X-rays unless crystallized.
B Crystallization ensures proteins adopt their native conformations.
C Crystals eliminate background noise in the diffraction pattern.
D Crystallization creates a lattice that produces measurable diffraction patterns

Answer 1

D Crystallization creates a lattice that produces measurable diffraction patterns

Question 2

In protein structure determination, electron density maps:

A Directly display the exact positions of all atoms in the protein.
B Are derived from processed diffraction data.
C Are used exclusively for small proteins.
D Represent theoretical electron distributions predicted from atomic models.

Answer 2

B Are derived from processed diffraction data.

Question 3

In X-ray crystallography, when will constructive interference occur? (Select all that apply)

A X-ray waves diffracted from parallel crystal planes meet Bragg’s Law conditions.
B The path difference between scattered waves equals half a wavelength.
C The crystal lattice spacing is larger than the incident X-ray wavelength.
D The amplitude of diffracted waves cancels out.

Answer 3

A X-ray waves diffracted from parallel crystal planes meet Bragg’s Law conditions.

Question 4

The process of building a protein structure from electron density requires:

A Iterative refinement of atomic coordinates against experimental data and geometric restraints.
B Direct mapping of amino acid side chains based on characteristic electron density shapes.
C Sequential tracing of the backbone followed by automated side chain placement.
D Real-time modification of atomic positions guided by difference density maps.

Answer 4

A Iterative refinement of atomic coordinates against experimental data and geometric restraints.

Question 5

Select all advantages that Cryo-EM has over X-ray crystallography:

A Enables visualization of proteins in their native environment.
B Enables capture of multiple conformational states.
C Allows study of larger macromolecular complexes.
D Does not require solidifying the sample.
E Can always achieve superior resolution.
F Captures membrane proteins in lipid environments.

Answer 5

A Enables visualization of proteins in their native environment.
B Enables capture of multiple conformational states.
C Allows study of larger macromolecular complexes.
F Captures membrane proteins in lipid environments.

Question 6

At which structural level are hydrogen bonds between backbone atoms primarily responsible for stabilizing regular conformations?

A Primary structure.
B Secondary structure.
C Tertiary structure.
D Both primary and tertiary structure

Answer 6

B Secondary structure.

Question 7

In Single Particle Analysis (SPA) for Cryo-EM, three-dimensional reconstruction requires:
A Determination of particle orientations through projection matching and angular assignment.
B Averaging of all particle images regardless of their conformational states.
C Sequential merging of 2D class averages based on sample tilting angles.
D Direct conversion of 2D micrographs into 3D volumes using Fourier transforms

Answer 7

A Determination of particle orientations through projection matching and angular assignment.

Question 8

Explain the technical challenges of studying intrinsically disordered proteins (IDPs) versus well-ordered proteins using experimental techniques.

Answer 8

IDPs lack a stable 3D structure under physiological conditions, unlike well-ordered proteins that fold
into specific shapes essential for their function. This inherent flexibility means that IDPs exist as
dynamic ensembles of conformations rather than a fixed structure.

X-ray crystallography requires the formation of well-ordered crystals, which is nearly impossible with
IDPs due to their structural heterogeneity.

Nuclear magnetic resonance (NMR) spectroscopy
faces difficulties because the multitude of overlapping signals from rapidly interconverting conformations complicates data interpretation.

Cryo-EM relies on averaging multiple images to resolve structures, but the conformational variability of IDPs leads to blurred results

Question 9

Which energy landscape feature presents the main difficulty for predicting protein structures?

A The large number of possible conformations that increases exponentially with protein length.
B The flat energy landscape lacking significant energy barriers between conformations.
C The complex, rugged energy surface with numerous low-energy structures.
D The influence of temperature on the stability of different conformational states

Answer 9

C The complex, rugged energy surface with numerous low-energy structures.

Question 10

When is homology modeling expected to give the most accurate structural predictions?

A When the template and target share over 90% sequence identity across conserved regions.
B When the template and target share less than 30% sequence identity but have similar functions.
C When the template and target share >65% sequence identity across the full protein length.
D When the template and target share 50% sequence identity with significant gaps and insertions.

Answer 10

C When the template and target share >65% sequence identity across the full protein length.

Question 11

When would protein threading be the most appropriate approach for structure prediction?

A When the target has 15-25% sequence identity with known structures but predicted secondary
structure elements match existing folds.
B When the target sequence shows strong conservation of hydrophobic packing patterns despite
low overall sequence identity.
C When multiple sequence alignments reveal conserved structural motifs within a protein family.
D When remote homologs exist but their evolutionary relationship cannot be detected by sequence
comparison alone.

Answer 11

D When remote homologs exist but their evolutionary relationship cannot be detected by sequence
comparison alone.

Question 12

How does modern coevolutionary analysis identify meaningful residue-residue contacts in protein
structures?

A By detecting conserved residues that are identical across different species.
B By separating direct evolutionary couplings from indirect correlations using statistical methods.
C Through random sampling of residue pairs in protein sequences.
D By predicting contacts based on amino acid likelihood for certain secondary structures.

Answer 12

B By separating direct evolutionary couplings from indirect correlations using statistical methods.

Question 13

How do MD simulations enhance our understanding of protein dynamics?

A By providing static snapshots of proteins in their lowest energy states.
B By sampling the global energy minimum conformation of proteins.
C By using quantum mechanical methods to simulate bond-breaking and electron transfer events
within proteins.
D By generating time-resolved trajectories of each atom that capture both small-scale and largescale protein motions.

Answer 13

D By generating time-resolved trajectories of each atom that capture both small-scale and largescale protein motions.

Question 14

How are protein force field parameters determined and optimized?

A By iteratively adjusting them to match quantum mechanical calculations of small molecules and
from experimental thermodynamic data.
B By training machine learning models on experimental protein structures and spectroscopy.
C By fitting them to high-level theoretical calculations and experimental vibrational spectra data.
D By tuning parameters to align with protein folding and unfolding free energy measurements.

Answer 14

A By iteratively adjusting them to match quantum mechanical calculations of small molecules and
from experimental thermodynamic data.

Question 15

In force fields, chemical bonds are typically modeled as:

A Springs that can stretch and break, accounting for the energy needed to break bonds.
B Simple springs that can stretch and compress around their natural length.
C Connected springs that affect both bond lengths and angles together.
D Classical approximations based on quantum mechanical calculations.

Answer 15

B Simple springs that can stretch and compress around their natural length.

Question 16

Why is selecting an appropriate time step crucial in MD simulations?

A The time step must be smaller than the shortest vibrational period to accurately capture atomic
motions.
B Larger time steps allow for faster simulations by skipping intermediate calculations without affecting accuracy.
C The time step determines how efficiently the simulation explores the potential energy surface of
the molecular system.
D The time step does not have any impact on the physical accuracy of the simulation results.

Answer 16

A The time step must be smaller than the shortest vibrational period to accurately capture atomic
motions.

Question 17

How are dihedral angle potentials modeled in force fields?

A Using complex corrections based on quantum calculations between adjacent angles.
B With periodic energy functions that include patterns matching rotational symmetry.
C Through multiple cosine functions with optimized strengths and angles derived from detailed
energy profiles.
D By employing flexible spline functions that connect quantum reference points while maintaining
rotational consistency.

Answer 17

C Through multiple cosine functions with optimized strengths and angles derived from detailed
energy profiles.

Question 18

A researcher is simulating how a protein and a molecule bind together and notices that the molecule
quickly leaves the binding site during the simulation. What change to the simulation settings could
help keep the molecule in the binding area?

A Slowly lower the solvent’s ability to reduce electric charges to make the attraction between the
molecule and the binding site stronger.
B Use adaptive time steps that become smaller where there are strong forces around the binding
site.
C Apply restrictions on the molecule’s position or use advanced sampling methods to keep it in the
binding site.
D Increase the range for non-bonded interactions and update the nearby particles list more often.

Answer 18

C Apply restrictions on the molecule’s position or use advanced sampling methods to keep it in the
binding site.

Question 19

In simulations with periodic boundary conditions (PBC):

A The system uses elastic collisions at the edges to keep momentum but stops particles from escaping.
B Long-range forces are cut off at the box edges to save computing power and avoid errors from
particles interacting with themselves.
C Atoms that leave one side of the simulation box re-enter from the opposite side, keeping interactions continuous.
D Each particle interacts only with the nearest images of other particles within a set distance.

Answer 19

C Atoms that leave one side of the simulation box re-enter from the opposite side, keeping interactions continuous

Question 20

The minimum elements needed to simulate a protein inside a cell include:

A Protein structure, explicit water molecules, and ions to balance the system’s overall charge.
B Protein structure using a model that doesn’t show individual water molecules but includes normal salt levels.
C Protein structure, explicit water molecules, ions at physiological concentration, and any required
cofactors.
D Protein structure with specific water molecules closely surrounding it and a variable that adjusts
for the overall solvent effects.

Answer 20

C Protein structure, explicit water molecules, ions at physiological concentration, and any required
cofactors.

Question 21

Energy minimization is needed before running an MD simulation to:

A Optimize the system’s energy landscape to make sampling different shapes more efficient during
the main simulation.
B Relax local strains in bonds and angles while keeping the overall protein shape to start from a
stable configuration.
C Remove bad overlaps, unfavorable charge interactions, and high-energy shapes that could cause
calculation problems.
D Balance the water distribution around the protein while slowly removing restraints to keep the
system stable.

Answer 21

C Remove bad overlaps, unfavorable charge interactions, and high-energy shapes that could cause
calculation problems.

Question 22

When preparing a protein structure, choosing the right protonation states is important because:

A They determine the strength of hydrogen bonds inside the protein and affect its local stability.
B They control electric interactions, affect the acidity levels of nearby parts, and can change how
the protein binds to other molecules.
C They change the local electric environment around certain parts and influence how protons move
in active sites.
D They manage the formation of salt bridges where the protein meets the solvent and affect the
overall charge in the simulation.

Answer 22

B They control electric interactions, affect the acidity levels of nearby parts, and can change how
the protein binds to other molecules.

Question 23

A complete protein structure determined by X-ray crystallography with a resolution of 3.0 Å is:

A Suitable for MD simulations after refining side chain positions and backbone geometry.
B Limited in reliability because atomic positions are uncertain and loops might be missing.
C Good for MD simulations but needs additional modeling and validation.
D Directly usable for MD if combined with other data like Cryo-EM or NMR.

Answer 23

A Suitable for MD simulations after refining side chain positions and backbone geometry.

Question 24

Observable properties in molecular systems are:

A Average values calculated from the system’s wave function over time for specific energy levels.
B Averages taken over all possible states, weighted by their likelihood at a stable temperature.
C Long-term averages of how variables change around their stable values.
D States sampled based on the assumption that all accessible states are equally likely

Answer 24

B Averages taken over all possible states, weighted by their likelihood at a stable temperature.

Question 25

The Nosé-Hoover thermostat differs from the Berendsen thermostat because it:

A Uses an extended system with a heat bath variable that ensures accurate temperature distribution through predictable movements.
B Applies a simple relaxation method that slowly reaches the desired temperature by slightly interacting with an external heat source.
C Maintains time-reversible motions by adjusting velocities, keeping both energy and momentum
balanced in the extended system.
D Creates regular temperature changes through a feedback system that ensures equal energy distribution among all movement types.

Answer 25

A Uses an extended system with a heat bath variable that ensures accurate temperature distribution through predictable movements.

Question 26

Multiple shorter simulations are often preferred over one long simulation because:

A They allow better exploration of different states through methods like parallel tempering and
replica exchange while keeping the system’s behavior consistent.
B They improve sampling of different shapes and structures by starting from various initial setups
and running independent paths, which helps verify statistical results.
C They reduce the build-up of errors in calculations by periodically resetting velocities based on
the Maxwell-Boltzmann distribution.
D They enable adaptive strategies that focus computing power on less-explored areas of the energy landscape.

Answer 26

B They improve sampling of different shapes and structures by starting from various initial setups
and running independent paths, which helps verify statistical results.

Question 27

The relationship between energy and probability in statistical mechanics means that:

A Higher energy states are less common because they require more energy to reach.
B States with higher energy are less likely to be occupied, following an exponential decrease.
C The system balances the tendency to spread out with the available energy.
D The system tries to occupy as many different states as possible while keeping its total energy
fixed.

Answer 27

B States with higher energy are less likely to be occupied, following an exponential decrease.

Question 28

Barostats maintain constant pressure by:

A Using an extended system with a virtual piston that interacts with volume changes through a
mass parameter.
B Dynamically adjusting the simulation box size to control internal forces while keeping external
pressure steady.
C Changing the system’s motion equations to include pressure-related terms that influence group
particle movements.
D Making periodic changes to the volume based on current pressure differences from the target
using a feedback system.

Answer 28

D Making periodic changes to the volume based on current pressure differences from the target
using a feedback system.

Question 29

Which interaction typically provides the strongest contribution to binding enthalpy?

A Short-range quantum mechanical forces and dispersion interactions that maximize contact area
between molecules.
B Electrostatic interactions between charged groups, influenced by the surrounding environment
and charge screening.
C Directional hydrogen bonds that form organized networks and are stabilized by polarization effects.
D Complex multipolar interactions, including higher-order electric moments and induced polarization between molecules.

Answer 29

B Electrostatic interactions between charged groups, influenced by the surrounding environment
and charge screening.

Question 30

The entropic contribution to binding:

A Results from changes in how molecules rotate and move, along with the rearrangement of water
molecules at the binding interface.
B Represents the change in the number of possible configurations the system can adopt when the
complex forms, including the movement of solvent molecules.
C Arises from the balance between decreased flexibility of the bound molecules and the favorable
release of water molecules due to the hydrophobic effect.
D Occurs through changes in the vibrational movements and quantum states of the bound complex
compared to the free molecules.

Answer 30

B Represents the change in the number of possible configurations the system can adopt when the
complex forms, including the movement of solvent molecules.

Question 31

In alchemical free energy simulations:

A The system is changed in a series of small steps while measuring energy differences between
each step.
B A control parameter gradually turns interactions on or off in a smooth, reversible way.
C Multiple copies of the system are simulated at different states to improve sampling.
D Energy differences are calculated by comparing the initial and final states directly.

Answer 31

B A control parameter gradually turns interactions on or off in a smooth, reversible way.

Question 32

The Gibbs free energy of binding (Δ𝐺bind) represents:

A The potential of mean force integrated along the binding coordinate, incorporating solvent-mediated effective interactions between partners.
B The reversible work performed during the association process under constant temperature and
pressure conditions, including reorganization effects.
C The difference in chemical potential between bound and unbound states that combines enthalpic
interactions and entropic penalties at equilibrium.
D The ensemble-averaged energy change weighted by the ratio of partition functions for complexed
versus dissociated states.

Answer 32

B The reversible work performed during the association process under constant temperature and
pressure conditions, including reorganization effects.

Question 33

Docking simplifies binding free energy calculations by:

A Using empirical scoring methods that estimate energy contributions based on atomic interactions and simplified solvent models.
B Finding favorable binding positions by systematically rotating rigid molecules and ignoring complex entropy factors.
C Applying knowledge-based scoring from analyzing protein-ligand structures in databases.
D Breaking down interaction energies on a grid and assuming the protein is rigid.

Answer 33

A Using empirical scoring methods that estimate energy contributions based on atomic interactions and simplified solvent models.

Question 34

Stochastic algorithms in pose optimization:

A Use adaptive energy barriers to guide the sampling of molecular shapes while ensuring proper
transition probabilities.
B Group similar molecular shapes based on their structural differences to identify key representative structures.
C Explore the energy landscape by making random changes to molecular poses and accepting them
based on probability criteria.
D Apply evolutionary techniques that select and improve binding poses using fitness scores from
scoring functions.

Answer 34

C Explore the energy landscape by making random changes to molecular poses and accepting them
based on probability criteria.

Question 35

Scoring functions in docking:

A Break down binding interactions into distance-based atomic potentials calibrated with experimental and quantum data.
B Estimate binding energies using weighted terms that combine physical forces, statistical data,
and solvation effects.
C Use machine learning models trained on structural data to predict binding strengths based on
geometric and chemical features.
D Assess how well proteins and ligands fit together using energy calculations enhanced by combining multiple scoring methods.

Answer 35

B Estimate binding energies using weighted terms that combine physical forces, statistical data,
and solvation effects.

Question 36

Grid-based pocket detection involves:

A Examining local surface shapes and angles to find inward-facing areas.
B Dividing the protein space into a grid of cubes and categorizing points based on their relation to
protein atoms and solvent exposure.
C Using energy probes to map areas where binding is energetically favorable.
D Calculating shapes and spaces within the protein using computational topology

Answer 36

B Dividing the protein space into a grid of cubes and categorizing points based on their relation to
protein atoms and solvent exposure.

Question 37

Which molecular property is the best indicator of a molecule’s ability to cross cell membranes?

A The ratio of polar surface area to volume combined with the number of rotatable bonds.
B The octanol-water partition coefficient (LogP) along with ionization states.
C Dynamic surface area measurements that consider different shapes and hydrogen bonding sites.
D Molecular shape based on mass distribution and electrostatic potential maps.

Answer 37

B The octanol-water partition coefficient (LogP) along with ionization states.

Question 38

The Tanimoto similarity coefficient:

A Measures the normalized inner product of molecular fingerprints, accounting for bit density.
B Calculates the overlap of binary feature sets by dividing the number of shared features by the
total unique features.
C Uses weighted feature matching based on the frequency of specific substructural patterns.
D Computes distance metrics in chemical space by comparing pharmacophoric elements.

Answer 38

B Calculates the overlap of binary feature sets by dividing the number of shared features by the
total unique features.

Question 39

The purpose of hashing in generating molecular fingerprints is to:

A Convert various molecular substructures into fixed-size feature vectors.
B Assign unique, fixed-length codes to different molecular substructures.
C Reduce the dimensionality of complex molecular data while preserving structural similarities.
D Create consistent numerical identifiers for molecular fragments using encoding methods.

Answer 39

A Convert various molecular substructures into fixed-size feature vectors.

Question 40

In ECFP (Extended-Connectivity Fingerprints) generation, each subsequent iteration:

A Combines information from neighboring atoms while retaining stereochemical details.
B Updates atom identifiers by aggregating data from connected atoms using hashing functions.
C Applies information theory to retain important structural features in the molecular graph.
D Develops more complex connectivity patterns with atomic features within defined radius shells

Answer 40

B Updates atom identifiers by aggregating data from connected atoms using hashing functions.