Small peptide modeling methods MCQs With Answer

Introduction: This quiz collection on Small Peptide Modeling Methods MCQs With Answer is designed specifically for M.Pharm students studying Bioinformatics and Computational Biotechnology. It covers theoretical concepts and practical approaches for modeling short peptides, including force fields, sampling techniques, solvent models, docking strategies, and validation metrics. Questions emphasize method selection, common pitfalls, enhanced sampling, cyclic and stapled peptides, and interpretation of computational results in a drug design context. Use these MCQs to reinforce classroom learning, prepare for exams, and gain deeper insight into how computational tools guide peptide design, optimization, and prediction of peptide–target interactions.

Q1. Which statement best describes the main advantage of replica-exchange molecular dynamics (REMD) for peptide modeling?

• It improves sampling by running multiple replicas at different temperatures to overcome energy barriers.
• It uses quantum mechanics to calculate peptide electronic states.
• It reduces system size by coarse-graining atoms to beads.
• It enforces experimental NMR restraints during dynamics.

Correct Answer: It improves sampling by running multiple replicas at different temperatures to overcome energy barriers.

Q2. Which force field is commonly used and parametrized for accurate backbone and side-chain behavior in peptide simulations?

• AMBER family (e.g., ff14SB)
• UFF (Universal Force Field)
• MARTINI coarse-grained force field
• CHARMM-GUI webserver

Correct Answer: AMBER family (e.g., ff14SB)

Q3. In peptide docking to a protein receptor, which challenge is most critical compared to small-molecule docking?

• Peptide conformational flexibility and induced fit of both peptide and receptor
• Insufficient parameterization of small-molecule torsions
• Lack of available receptor crystal structures
• High computational cost of quantum calculations

Correct Answer: Peptide conformational flexibility and induced fit of both peptide and receptor

Q4. What is the primary purpose of using implicit solvent models (e.g., GB/SA) for peptide simulations?

• To speed up simulations by approximating solvent effects without explicit water molecules
• To model explicit hydrogen-bond networks between peptide and solvent
• To simulate membrane-embedded peptides accurately
• To perform quantum mechanical calculations on solvent molecules

Correct Answer: To speed up simulations by approximating solvent effects without explicit water molecules

Q5. Which method is most appropriate for de novo prediction of a short linear peptide’s three-dimensional structure without homologs?

• Ab initio fragment assembly (e.g., Rosetta/PEP-FOLD)
• Comparative homology modeling using distant templates
• Molecular docking into a ligand-binding pocket
• MM/PBSA free energy calculations

Correct Answer: Ab initio fragment assembly (e.g., Rosetta/PEP-FOLD)

Q6. When modeling disulfide-rich peptides, what special consideration must be included in the protocol?

• Explicitly define cystine connectivity and include disulfide bond restraints during sampling
• Ignore cysteines because they are not important for structure
• Always reduce disulfide bonds to free thiols before simulation
• Use coarse-grained models that do not represent sulfur atoms

Correct Answer: Explicitly define cystine connectivity and include disulfide bond restraints during sampling

Q7. Which enhanced sampling technique adds a history-dependent bias to overcome free energy barriers during peptide folding simulations?

• Metadynamics
• Steered molecular dynamics (SMD)
• Normal mode analysis
• Energy minimization

Correct Answer: Metadynamics

Q8. For evaluating modeled peptide structures against an experimental NMR ensemble, which metric is commonly used?

• RMSD (root-mean-square deviation) of backbone atoms
• Hydrophobic surface area only
• Partition coefficient (logP)
• pKa prediction error

Correct Answer: RMSD (root-mean-square deviation) of backbone atoms

Q9. Which approach is best for predicting protonation states and their effect on small peptide conformation at physiological pH?

• pKa calculations combined with constant-pH molecular dynamics
• Using a static neutral model for all ionizable residues
• Ignoring titratable groups in implicit solvent
• Applying coarse-grained models exclusively

Correct Answer: pKa calculations combined with constant-pH molecular dynamics

Q10. In peptide structure refinement, what is the main role of side-chain rotamer libraries?

• To propose energetically favorable side-chain conformations for packing and optimization
• To predict backbone dihedral angles phi and psi
• To determine peptide primary sequence from structure
• To provide explicit solvent configurations

Correct Answer: To propose energetically favorable side-chain conformations for packing and optimization

Q11. Which statement about cyclic peptide modeling is true?

• Cyclization constrains conformational space, often improving binding affinity but requires special bond formation and sampling protocols.
• Cyclization always destabilizes peptide structure and should be avoided computationally.
• Cyclic peptides can be modeled with the same parameters as linear peptides without modification.
• Cyclization eliminates all backbone flexibility so dynamics are unnecessary.

Correct Answer: Cyclization constrains conformational space, often improving binding affinity but requires special bond formation and sampling protocols.

Q12. Which scoring approach is commonly used to estimate binding affinity of peptide–protein complexes after docking?

• MM/PB(GB)SA rescoring of snapshots from molecular dynamics
• Counting hydrogen bonds only
• Using sequence identity score instead of energy
• Ignoring solvent contributions entirely

Correct Answer: MM/PB(GB)SA rescoring of snapshots from molecular dynamics

Q13. What is the key limitation of homology modeling for peptides compared to proteins?

• Few high-quality short-peptide templates and high conformational variability reduce reliability.
• Peptides have no secondary structure and therefore cannot be modeled.
• Homology modeling always yields perfect peptide structures.
• Sequence alignment algorithms do not work on peptides.

Correct Answer: Few high-quality short-peptide templates and high conformational variability reduce reliability.

Q14. For modeling membrane-active peptides, which environment representation is most appropriate?

• Explicit lipid bilayer with appropriate force-field and water
• Implicit solvent only without lipids
• Vacuum simulations to speed up calculation
• Coarse-grained water box without lipids

Correct Answer: Explicit lipid bilayer with appropriate force-field and water

Q15. Which metric measures per-residue atomic fluctuation during a peptide MD simulation?

• RMSF (root-mean-square fluctuation)
• Global RMSD only
• Hydrophobic moment
• Sequence alignment score

Correct Answer: RMSF (root-mean-square fluctuation)

Q16. When integrating NMR NOE restraints into peptide modeling, what is the main benefit?

• NOE restraints guide conformational sampling toward experimentally observed interproton distances, improving accuracy.
• They eliminate the need for any force field during simulation.
• NOEs allow prediction of pKa values directly from structure.
• NOE restraints replace the need for solvent in calculations.

Correct Answer: NOE restraints guide conformational sampling toward experimentally observed interproton distances, improving accuracy.

Q17. Which computational method is best for rapidly generating plausible backbone conformations of short peptides for downstream docking?

• Fragment-based assembly combined with Monte Carlo sampling
• Quantum mechanical geometry optimization of the whole peptide
• Homology modeling using full protein templates only
• Purely coarse-grained simulation without backmapping

Correct Answer: Fragment-based assembly combined with Monte Carlo sampling

Q18. In coarse-grained models for peptides, what is the primary trade-off compared to all-atom models?

• Lower computational cost at the expense of atomic detail and fine interactions
• Higher accuracy in hydrogen-bond networks but slower speed
• Ability to predict pKa values precisely
• No need for parameterization or validation

Correct Answer: Lower computational cost at the expense of atomic detail and fine interactions

Q19. Which technique helps to identify metastable conformational states of a peptide from MD trajectories?

• Markov state models (MSMs) built from clustered trajectory data
• Simple RMSD averaging over the whole trajectory without clustering
• Counting only the number of hydrogen bonds per frame
• Using only energy minimization snapshots

Correct Answer: Markov state models (MSMs) built from clustered trajectory data

Q20. For computational peptide design aiming to increase helicity, which modification is commonly modeled and validated in silico?

• Stapling (hydrocarbon cross-link) to stabilize alpha-helix
• Replacing all alanines with glycine to increase rigidity
• Removing all charged residues to ignore electrostatics
• Converting peptide to DNA sequence

Correct Answer: Stapling (hydrocarbon cross-link) to stabilize alpha-helix

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