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Biomolecular Modeling & Simulations — Free Certification Assessment

StemSkills Lab > Biomolecular Modeling & Simulations — Free Certification Assessment

Biomolecular Modeling & Simulations — Free Certification Assessment

Test your knowledge of biomolecular modeling — homology modeling, solvent models, force fields, and validation. Pass at 70% to earn a verifiable StemSkills certificate you can download as a PDF and add to your LinkedIn profile. Free.

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Biomolecular Modeling & Simulations — Certification Assessment

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Free certification assessment. Pass at 70% to earn a verifiable StemSkills certificate.

  • Questions count: 20
  • Passing grade: 70%
  • Time limit: 20 minutes
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1.
Homology (comparative) modeling builds a 3D model using:
A related protein of known structure as a template
A DNA gel
A mass spectrum
Only the target sequence with no templates
2.
A prerequisite for reliable homology modeling is:
A random template
Sufficient sequence identity to a suitable template (generally higher = better)
0% sequence identity to any known structure
No alignment
3.
The Protein Data Bank (PDB) primarily stores:
Gene expression levels
Experimentally determined 3D biomolecular structures
Reaction kinetics tables
Codon usage
4.
A Ramachandran plot evaluates:
Ligand affinity
Charge distribution
Solvent density
Backbone φ/ψ dihedral angles (stereochemical quality)
5.
Explicit solvent models water as:
A single point charge for the whole box
Individual water molecules in the system
A uniform dielectric constant only
Vacuum
6.
Implicit solvent (e.g., GB/PB) approximates water as:
Ice
A protein
Explicit molecules
A continuum dielectric medium
7.
Energy minimization finds:
The sequence
The global maximum energy
The melting temperature
A nearby local energy minimum of the structure
8.
A force field in biomolecular simulation is:
A parameterized potential energy function for the molecular system
A microscope
An alignment score
A magnetic device
9.
Which is an example of a common protein force field family?
Clustal
ImageJ
BLAST
AMBER / CHARMM / OPLS / GROMOS
10.
Model validation tools (e.g., PROCHECK/MolProbity) primarily assess:
mRNA levels
Stereochemical/geometry quality of a structure or model
Gene function
Ligand solubility
11.
SASA (solvent-accessible surface area) quantifies:
The net charge
The number of chains
The timestep
How much surface is exposed to solvent
12.
Coarse-grained models (e.g., MARTINI) improve efficiency by:
Grouping several atoms into single interaction beads
Using quantum mechanics for all atoms
Adding more atoms
Removing the force field
13.
Quantum mechanics/molecular mechanics (QM/MM) is used when:
No chemistry occurs
Only water matters
The protein is ignored
Part of the system (e.g., a reaction center) needs quantum treatment while the rest is classical
14.
Loop modeling is often the hardest part of homology modeling because loops:
Are always helical
Never contact solvent
Are variable/flexible and poorly conserved between template and target
Contain no atoms
15.
A multiple sequence alignment (MSA) contributes to modeling by:
Removing water
Measuring temperature
Setting the barostat
Identifying conserved residues and guiding template/target alignment (and contacts)
16.
Which statement about simulation timescales is correct?
MD can always reach seconds trivially
All biological processes occur within 1 fs
Many functional motions exceed typical all-atom MD reach, motivating enhanced sampling
Timescale is irrelevant
17.
Enhanced-sampling methods (e.g., metadynamics, REMD) aim to:
Slow down sampling
Delete the solvent
Overcome energy barriers and sample rare events more efficiently
Fix the sequence
18.
Free-energy methods (e.g., FEP, MM/PBSA) are used to estimate:
The crystal color
The gene promoter
The camera angle
Relative/absolute binding or solvation free energies
19.
A key reason to run replicas or repeat simulations is to:
Waste compute deliberately
Change the force field mid-run
Avoid any analysis
Assess reproducibility and statistical significance of observations
20.
Before trusting any model or simulation result, a good practice is to:
Ignore the force field
Remove all hydrogens
Publish immediately
Validate against experimental data and check convergence/quality metrics
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