Secondary structure prediction MCQs With Answer

Secondary structure prediction MCQs With Answer

Introduction: Secondary structure prediction is a core topic in bioinformatics with direct applications in M.Pharm research, including homology modeling, rational drug design, and understanding protein–ligand interactions. This set of MCQs focuses on methods, evaluation metrics, underlying principles, and practical considerations for predicting helices, sheets and coils from sequence data. Questions cover classical algorithms (Chou–Fasman, GOR), profile- and machine learning-based predictors (PSIPRED, deep learning), databases and annotation standards (PDB, DSSP), and metrics used to judge prediction quality (Q3, SOV, MCC). The quiz is designed to deepen conceptual understanding and improve practical competence for advanced pharmacy students.

Q1. What is protein secondary structure?

• The 3D arrangement of all atoms in a protein including side chains
• The local backbone conformation stabilized by hydrogen bonds, such as alpha helices and beta sheets
• The overall spatial arrangement of multiple polypeptide chains forming a complex
• The sequential order of amino acids in a polypeptide chain

Correct Answer: The local backbone conformation stabilized by hydrogen bonds, such as alpha helices and beta sheets

Q2. Which classical secondary structure prediction method relies primarily on amino acid propensities for helix, sheet and coil?

• PSIPRED
• Chou–Fasman
• TMHMM
• Hidden Markov Models (HMMs)

Correct Answer: Chou–Fasman

Q3. The GOR method for secondary structure prediction is best described as:

• A purely physics-based simulation of folding using molecular dynamics
• A predictor using residue propensity tables without considering neighbors
• An information-theoretic method that uses local sequence windows to estimate state probabilities
• A transmembrane helix predictor

Correct Answer: An information-theoretic method that uses local sequence windows to estimate state probabilities

Q4. Which predictor uses position-specific scoring matrices (PSSMs) from multiple sequence alignments together with neural networks to achieve high accuracy?

• GOR
• Chou–Fasman
• PSIPRED
• DSSP

Correct Answer: PSIPRED

Q5. DSSP assigns secondary structure states based primarily on what feature derived from PDB coordinate files?

• Hydrophobicity index of residues
• Predicted B-factor values
• Hydrogen-bonding patterns and geometrical criteria between backbone atoms
• Sequence identity to known templates

Correct Answer: Hydrogen-bonding patterns and geometrical criteria between backbone atoms

Q6. What does the Q3 accuracy metric measure in secondary structure prediction?

• Segment overlap accuracy between predicted and observed secondary structure segments
• Per-residue fraction correctly predicted among three states (helix, sheet, coil)
• The quality of tertiary structure models built from predicted secondary structure
• Transmembrane helix prediction sensitivity

Correct Answer: Per-residue fraction correctly predicted among three states (helix, sheet, coil)

Q7. Which of the following is a typical three-state secondary structure prediction accuracy for modern profile-based predictors on non-redundant test sets?

• Around 50%
• Around 65%
• Around 80%
• Near 98%

Correct Answer: Around 80%

Q8. Which backbone torsion angles primarily determine local secondary structure conformations?

• Chi1 and Chi2 side-chain angles
• Omega only
• Psi and Phi backbone angles
• Torsion angles between nonpolar side chains

Correct Answer: Psi and Phi backbone angles

Q9. Profile-based predictors improve secondary structure accuracy mainly by incorporating which of the following?

• Physicochemical simulations of solvent dynamics
• Position-specific evolutionary information from multiple sequence alignments
• Only single-sequence amino acid propensities
• Pairwise docking energies

Correct Answer: Position-specific evolutionary information from multiple sequence alignments

Q10. Which secondary structure element typically has approximately 3.6 residues per turn?

• Beta strand
• Alpha helix
• Random coil
• 310 helix

Correct Answer: Alpha helix

Q11. Between parallel and antiparallel beta sheets, which generally has hydrogen bonds that are more linear and considered stronger?

• Parallel beta sheets
• Antiparallel beta sheets
• Both have identical hydrogen bond geometry
• Beta turns, not sheets, determine hydrogen bond strength

Correct Answer: Antiparallel beta sheets

Q12. Which specialized prediction tool is designed primarily to detect transmembrane helices rather than general secondary structure?

• PSIPRED
• GOR
• TMHMM
• Chou–Fasman

Correct Answer: TMHMM

Q13. A key limitation of the original Chou–Fasman algorithm is:

• Its reliance on deep neural networks requiring large training sets
• That it accounts for tertiary packing explicitly
• It ignores evolutionary profiles and long-range sequence context, limiting accuracy
• It only predicts transmembrane regions

Correct Answer: It ignores evolutionary profiles and long-range sequence context, limiting accuracy

Q14. What does the SOV (Segment OVerlap) score emphasize compared to per-residue Q3?

• It measures only coil prediction accuracy
• It evaluates segment-level agreement, penalizing boundary misplacements less harshly
• It is a measure of tertiary structure RMSD
• It counts only the number of predicted alpha helices

Correct Answer: It evaluates segment-level agreement, penalizing boundary misplacements less harshly

Q15. Recent improvements in secondary structure prediction accuracy are most strongly attributed to which approach?

• More extensive use of Chou–Fasman propensities alone
• Higher-resolution X-ray crystallography of membrane proteins
• Deep learning methods (CNNs, RNNs, transformers) trained on profiles and large datasets
• Manual curation of single-sequence rules

Correct Answer: Deep learning methods (CNNs, RNNs, transformers) trained on profiles and large datasets

Q16. DSSP annotates secondary structure using how many distinct states before reduction to three-state schemes?

• 3 states
• 5 states
• 8 states
• 20 states

Correct Answer: 8 states

Q17. When dealing with imbalanced datasets for secondary structure classes, which metric provides a balanced measure of binary classification performance?

• Accuracy (percentage correct)
• Matthews correlation coefficient (MCC)
• Simple recall for helix only
• Number of predicted residues

Correct Answer: Matthews correlation coefficient (MCC)

Q18. In protein annotation, what is a “coil” region?

• A region with repeating beta turns in a hairpin
• A region with regular hydrogen-bonded helix geometry
• An irregular or loop region lacking regular helix or sheet hydrogen-bond patterns
• A transmembrane alpha helix only

Correct Answer: An irregular or loop region lacking regular helix or sheet hydrogen-bond patterns

Q19. Which amino acid is generally considered to have the highest intrinsic propensity to form alpha helices?

• Proline
• Glycine
• Alanine
• Valine

Correct Answer: Alanine

Q20. How is accurate secondary structure prediction most directly useful in pharmaceutical research and drug design?

• It replaces the need for any experimental structural data
• It provides detailed atomic interactions for covalent inhibitor design
• It aids homology modeling, epitope mapping, and identification of structured binding regions to guide ligand design
• It predicts metabolic stability of small molecules

Correct Answer: It aids homology modeling, epitope mapping, and identification of structured binding regions to guide ligand design

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