Active site prediction MCQs With Answer

Introduction: Active site prediction MCQs With Answer is a focused learning resource tailored for M.Pharm students aiming to strengthen their grasp of computational approaches used to identify enzyme and protein binding sites. This set covers theoretical basis and practical tools—from sequence conservation and homology modeling to structure-based pocket detection, machine learning, fragment hotspot mapping, and dynamics-informed strategies. Each question emphasizes concepts relevant to drug design, ligandability assessment, catalytic residue identification, and validation metrics so students can apply these methods in pharmacology and biopharmaceutical research. The questions are designed to prepare students for exams and translational projects by testing both conceptual understanding and method selection for active site prediction.

Q1. Which approach primarily relies on the 3D structure of a protein to identify cavities that could serve as active or binding sites?

• Sequence alignment and motif scanning
• Structure-based pocket detection
• Phylogenetic profiling
• Gene expression correlation

Correct Answer: Structure-based pocket detection

Q2. Which of the following tools is specifically designed to compute and characterize pockets and cavities from a protein 3D structure?

• PROSITE
• CASTp
• BLAST
• COXEN

Correct Answer: CASTp

Q3. Conservation-based methods for active site prediction assume that residues involved in catalysis or ligand binding are often:

• Highly variable across homologs
• Moderately hydrophobic only
• Highly conserved across evolutionary homologs
• Located exclusively in transmembrane regions

Correct Answer: Highly conserved across evolutionary homologs

Q4. Which algorithm or server maps evolutionary conservation onto a protein structure to help identify functional residues?

• ConSurf
• AutoDock
• Clustal Omega
• Phyre2

Correct Answer: ConSurf

Q5. Fragment hotspot mapping identifies regions prone to bind fragments primarily by analyzing:

• Genomic synteny
• Hydrogen-bonding, hydrophobic and polar interaction hotspots on the protein surface
• mRNA stability
• Protein-protein interaction databases only

Correct Answer: Hydrogen-bonding, hydrophobic and polar interaction hotspots on the protein surface

Q6. Which metric is most appropriate for evaluating binary classification of predicted active vs non-active residues, balancing true/false positives and negatives?

• Root mean square deviation (RMSD)
• Matthews correlation coefficient (MCC)
• B-factor
• Sequence identity percentage

Correct Answer: Matthews correlation coefficient (MCC)

Q7. When no experimental structure exists, which approach is commonly used before applying structure-based active site prediction tools?

• Homology modeling to build a 3D model
• Gene knockout experiments
• Direct use of primary sequence in CASTp
• Mass spectrometry of the protein alone

Correct Answer: Homology modeling to build a 3D model

Q8. DoGSiteScorer and SiteMap are examples of tools that provide which of the following outputs useful for drug design?

• Predicted phosphorylation sites only
• Pocket volume, hydrophobic/hydrophilic score and druggability estimates
• Predicted mRNA expression levels
• Protein solubility in different buffers

Correct Answer: Pocket volume, hydrophobic/hydrophilic score and druggability estimates

Q9. Evolutionary Trace (ET) methods help locate functional sites by ranking residues according to:

• Their chemical modification state in mass spectrometry
• Degree of evolutionary variation correlated with protein function
• Their B-factor in X-ray structures
• mRNA translation speed

Correct Answer: Degree of evolutionary variation correlated with protein function

Q10. Which of the following best describes a limitation of purely geometry-based pocket detection methods?

• They always require ligand-bound structures to work
• They may identify many cavities that are not biologically relevant or druggable
• They cannot be used on homology models
• They provide direct kinetic parameters of enzymatic activity

Correct Answer: They may identify many cavities that are not biologically relevant or druggable

Q11. Machine learning models for active site prediction commonly integrate which types of features?

• Only DNA methylation patterns
• Sequence conservation, structural environment, physicochemical properties, and pocket geometry
• Only protein expression levels from RNA-seq
• Chromosomal location and intron count

Correct Answer: Sequence conservation, structural environment, physicochemical properties, and pocket geometry

Q12. In active site prediction, ligandability is best defined as:

• The ability of a residue to be post-translationally modified
• The propensity of a pocket to bind small, drug-like molecules with high affinity
• The transcriptional regulation strength of the gene
• The solubility of the entire protein

Correct Answer: The propensity of a pocket to bind small, drug-like molecules with high affinity

Q13. Which experimental technique is most commonly used to validate computational active site predictions by identifying residues involved in catalysis?

• Northern blotting
• Site-directed mutagenesis followed by activity assays
• Chromatin immunoprecipitation (ChIP)
• 2D gel electrophoresis of mRNA

Correct Answer: Site-directed mutagenesis followed by activity assays

Q14. ConSurf assigns conservation scores to residues by analyzing a multiple sequence alignment and mapping results to structure. High conservation near a pocket most strongly suggests:

• Structural packing only, never functional importance
• Potential functional importance such as binding or catalytic roles
• That the pocket is an artifact of crystallization
• Exclusive involvement in protein-protein interactions, not ligand binding

Correct Answer: Potential functional importance such as binding or catalytic roles

Q15. Dynamic information from molecular dynamics (MD) simulations can improve active site prediction by revealing:

• Only the primary amino acid sequence
• Transient or cryptic pockets and conformational flexibility important for binding
• The codon usage bias of the gene
• The tertiary structure without time-dependent changes

Correct Answer: Transient or cryptic pockets and conformational flexibility important for binding

Q16. MetaPocket is an example of a consensus method that improves prediction accuracy by:

• Averaging experimental enzyme kinetics
• Combining results from multiple pocket detection algorithms
• Using only one best pocket detection method
• Predicting mRNA splicing patterns

Correct Answer: Combining results from multiple pocket detection algorithms

Q17. Catalytic residue prediction often focuses on identifying residues with specific physicochemical signatures. Which residue pair is commonly implicated in classical catalytic triads of serine proteases?

• Lys-Asp-His
• Ser-His-Asp
• Gly-Pro-Ala
• Met-Val-Leu

Correct Answer: Ser-His-Asp

Q18. Which validation metric plots true positive rate against false positive rate to evaluate a continuous predictor of active site residues or pockets?

• Precision-Recall curve only
• ROC curve (Receiver Operating Characteristic)
• Hydropathy plot
• Ramachandran plot

Correct Answer: ROC curve (Receiver Operating Characteristic)

Q19. Druggability scores incorporate multiple factors. Which factor is NOT typically part of druggability assessment?

• Pocket size and enclosure
• Hydrophobic and polar balance
• Presence of suitable anchor residues for ligand interactions
• mRNA half-life in the cell

Correct Answer: mRNA half-life in the cell

Q20. Hotspot residue identification methods such as alanine scanning (computational or experimental) identify residues whose mutation significantly affects binding free energy. A residue that, when mutated to alanine, dramatically decreases binding affinity is considered:

• Non-essential
• Binding hotspot
• Structurally irrelevant
• Always distant from the ligand

Correct Answer: Binding hotspot

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