Structure scoring and validation MCQs With Answer

Structure scoring and validation MCQs With Answer

Introduction: Structure scoring and validation are essential in computational biology and drug design to evaluate the plausibility and reliability of predicted biomolecular models. For M.Pharm students, understanding scoring functions, validation metrics, and common pitfalls helps in judging homology models, docking poses, and refined structures before progressing to in vitro experiments. This collection of MCQs covers theoretical foundations (force-field, knowledge-based and empirical scores), common metrics (RMSD, Ramachandran, MolProbity, ProSA, QMEAN, DOPE, VERIFY3D), and practical validation strategies (decoy discrimination, consensus scoring, enrichment, and cross-validation). Use these questions to deepen conceptual knowledge and improve practical assessment skills for structure-based drug discovery.

Q1. What is the primary goal of structure scoring and validation in computational structural biology?

• To generate the highest number of conformations regardless of quality
• To quantitatively assess and discriminate the quality and plausibility of predicted structures
• To replace experimental structure determination completely
• To always minimize RMSD as the only criterion

Correct Answer: To quantitatively assess and discriminate the quality and plausibility of predicted structures

Q2. Which statement best describes the limitation of RMSD as a validation metric?

• RMSD measures stereochemical quality like bond lengths and angles
• RMSD is insensitive to global fold differences and only measures side chains
• RMSD can be dominated by local differences and may not reflect correct functional regions or overall fold quality
• RMSD directly estimates binding affinity between ligand and protein

Correct Answer: RMSD can be dominated by local differences and may not reflect correct functional regions or overall fold quality

Q3. What property does a Ramachandran plot primarily evaluate?

• Hydrogen bond occupancy of side chains
• Backbone dihedral angles phi and psi distribution for residues
• Electrostatic potential around the protein
• Solvent accessibility per residue

Correct Answer: Backbone dihedral angles phi and psi distribution for residues

Q4. MolProbity is commonly used in validation because it provides which of the following?

• Only an energy function for loop modeling
• All-atom contact analysis including clashscore, rotamer and geometry checks
• A statistical potential derived solely from membrane proteins
• Quantum mechanical energy minimization for small ligands

Correct Answer: All-atom contact analysis including clashscore, rotamer and geometry checks

Q5. Knowledge-based scoring functions are characterized by which principle?

• They derive potentials from observed frequency of structural features in experimentally solved structures
• They rely exclusively on ab initio quantum calculations for each atom
• They always outperform physics-based potentials in all systems
• They compute scores only from solvent-accessible surface area

Correct Answer: They derive potentials from observed frequency of structural features in experimentally solved structures

Q6. What does the DOPE score (Discrete Optimized Protein Energy) primarily provide?

• A per-residue and global statistical potential for assessing homology models
• A kinetic rate for protein folding
• A measure of membrane insertion propensity
• An estimate of ligand binding free energy using explicit water

Correct Answer: A per-residue and global statistical potential for assessing homology models

Q7. QMEAN is best described as which type of validation tool?

• A composite scoring function that gives both global and local quality estimates by combining multiple structural descriptors
• A tool that only checks side-chain rotamer frequencies
• A purely physics-based molecular dynamics simulator
• A server for experimental NMR peak assignment

Correct Answer: A composite scoring function that gives both global and local quality estimates by combining multiple structural descriptors

Q8. In ProSA, the Z-score indicates what about a protein model?

• The number of stereochemical restraints violated
• How the model’s overall energy compares to energies of experimentally determined structures of similar size
• The RMSD between model and template in homology modeling
• The predicted pKa values of active-site residues

Correct Answer: How the model’s overall energy compares to energies of experimentally determined structures of similar size

Q9. What does VERIFY3D assess in model validation?

• Compatibility of the 3D environment of each residue with its amino acid type using a 1D–3D profile
• Only the backbone hydrogen bonding pattern
• The presence of ligand electron density in crystallographic maps
• The secondary structure predicted from sequence alone

Correct Answer: Compatibility of the 3D environment of each residue with its amino acid type using a 1D–3D profile

Q10. EMRinger is a validation metric particularly useful for which experimental modality?

• Circular dichroism spectroscopy
• Cryo-electron microscopy for assessing side-chain fit to density maps
• X-ray small-angle scattering curve fitting
• Fluorescence resonance energy transfer distance validation

Correct Answer: Cryo-electron microscopy for assessing side-chain fit to density maps

Q11. In the context of decoy discrimination and scoring function performance, what does ROC AUC measure?

• The absolute binding free energy in kcal/mol
• The ability of a scoring method to distinguish actives from decoys across all score thresholds
• The number of hydrogen bonds in the active site
• The average RMSD of decoy set to native structure

Correct Answer: The ability of a scoring method to distinguish actives from decoys across all score thresholds

Q12. Which approach is most effective at reducing false positives in virtual screening?

• Using a single rigid receptor and a single scoring function always
• Consensus scoring that combines multiple scoring functions and possibly receptor conformations
• Discarding docking scores and selecting compounds at random
• Only selecting the highest molecular weight compounds

Correct Answer: Consensus scoring that combines multiple scoring functions and possibly receptor conformations

Q13. What is the purpose of cross-validation when developing or calibrating scoring functions?

• To maximize training accuracy by fitting parameters to the same data repeatedly
• To estimate predictive performance on unseen data and reduce overfitting
• To calculate experimental binding constants directly
• To increase the number of features without penalty

Correct Answer: To estimate predictive performance on unseen data and reduce overfitting

Q14. Rotamer analysis in validation primarily checks what aspect of a structure?

• Backbone phi-psi angles for glycines only
• Side-chain conformations against common rotamer libraries to identify outliers
• The presence of metal ions in the binding site
• Long-range electrostatic interactions

Correct Answer: Side-chain conformations against common rotamer libraries to identify outliers

Q15. Clashscore as reported by MolProbity is defined as which of the following?

• The number of serious steric overlaps per 1000 atoms in the model
• The fraction of residues in allowed Ramachandran regions
• The average B-factor across all atoms
• The count of hydrogen bonds per residue

Correct Answer: The number of serious steric overlaps per 1000 atoms in the model

Q16. Local DOPE profiles provide what useful information for model refinement?

• Residue-wise energetic score indicating potentially misfolded or poorly modeled regions
• Experimental NMR distance restraints directly
• The ligand solubility in water
• The predicted membrane spanning segments only

Correct Answer: Residue-wise energetic score indicating potentially misfolded or poorly modeled regions

Q17. When comparing two scoring methods for rank correlation of predicted binding poses, which statistic is preferred if the relationship is non-linear?

• Pearson correlation coefficient
• Spearman rank correlation coefficient
• Mean squared error
• Standard deviation of scores

Correct Answer: Spearman rank correlation coefficient

Q18. What does the enrichment factor (EF) quantify in virtual screening validation?

• How many decoys have better scores than actives on average
• The improvement in finding actives in the early part of the ranked list versus random selection
• The RMSD between docked poses and crystal structures
• The average molecular weight of the top-ranked compounds

Correct Answer: The improvement in finding actives in the early part of the ranked list versus random selection

Q19. A high atomic B-factor in an X-ray structure generally indicates what?

• High certainty and precisely determined positions
• Increased atomic displacement or disorder and lower confidence in atomic positions
• That the residue is part of a canonical secondary structure
• That the atom is covalently modified

Correct Answer: Increased atomic displacement or disorder and lower confidence in atomic positions

Q20. Which widely used validation program focuses primarily on stereochemical parameters such as bond lengths, angles and Ramachandran analysis?

• PROCHECK
• ProSA Z-score
• EMRinger
• QMEAN

Correct Answer: PROCHECK

Download