Chapter 7: Blind Docking & Cavity Detection: CB-Dock2, fpocket & Allosteric Pocket Discovery
Chapter 7: Blind Docking & Cavity Detection: CB-Dock2, fpocket & Allosteric Pocket Discovery
Targeted vs blind docking paradigms; detecting binding pockets on uncharacterized or orphan proteins; Curvature-Based Docking (CB-Dock2) and fpocket algorithms; automated grid box sizing; and ranking candidate allosteric cavities by Vina affinity score and volume.
1. Targeted vs. Blind Docking Paradigms
In standard targeted docking, the user defines a tight 3D grid box ($\sim 20 \times 20 \times 20\text{ \AA}^3$) around a known orthosteric binding site or co-crystallized ligand. However, in many scenarios, the binding pocket is unknown:
- The protein is an uncharacterized orphan enzyme or newly discovered viral target.
- Searching for allosteric binding sites away from the primary catalytic pocket to achieve subtype selectivity and avoid active-site mutation resistance.
- Investigating off-target polypharmacology or predicting drug toxicity targets.
Attempting blind docking with a single massive grid box covering the entire macromolecule using AutoDock Vina dramatically degrades search efficiency, as the optimizer wastes sampling cycles in the solvent shell or shallow surface valleys.
2. Cavity Detection with CB-Dock2 & fpocket
Modern blind docking utilizes cavity detection algorithms to identify concave solvent-accessible clefts, followed by focused local docking:
Curvature-Based Docking (CB-Dock2)
Calculates macromolecular surface curvature. Identifies candidate binding pockets based on local curvature extrema and automatically sizes adaptive grid boxes proportional to cavity volume ($V$).
fpocket Geometric Tessellation
Utilizes Voronoi tessellation and alpha spheres (spheres contacting 4 protein atoms without containing any). Clusters polar and hydrophobic alpha spheres to calculate a Druggability Score ($D_{\text{score}}$).
# Detect cavities on receptor PDB
fpocket -f receptor.pdb
# Inspect generated cavity ranking and druggability scores
cat receptor_out/receptor_info.txt | grep -E "Pocket|Druggability Score|Volume"Common Pitfalls & Troubleshooting Checklist
- Java Heap Exhaustion (GATK / Picard): If processes terminate with
java.lang.OutOfMemoryError, set your environment variable explicitly:export _JAVA_OPTIONS="-Xmx16g -Djava.io.tmpdir=/tmp". - Index Timestamp Mismatch: If tools complain that a BAM index is missing or out of date, regenerate with
samtools index -@ 4 sample.bam. - Conda Channel Conflicts: Avoid ABI incompatibilities between bioconda and conda-forge by setting strict priority:
conda config --set channel_priority strict. - Chromosome Naming Contamination: Always verify whether reference FASTA headers use
chr1or1; divergent naming causes silent alignment drops.