Free Biomolecular Modeling and Simulations Certification Assessment | StemSkills Lab
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Free Biomolecular Modeling and Simulations 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. Download it as a PDF and add it to your LinkedIn profile. It is free.

Biomolecular Modeling and Simulations certification assessment

1
Take the quiz
20 questions on biomolecular modeling and simulations. About 20 minutes, at your own pace. No sign-up needed to start.
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One click with Google. Your score is saved to your account so you can see whether you passed.
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Pass? Get certified
Score 70% or more and download your verifiable certificate, then add it to LinkedIn.
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Biomolecular Modeling and Simulations Certification Assessment

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

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