16. AI-Driven 3D Biomolecular Structure Prediction & In Silico Docking
End-to-end atomic structure inference from sequence FASTA using ColabFold/AlphaFold, computing confidence metrics (pLDDT, PAE), and performing virtual screening for small-molecule chemical ligands via AutoDock Vina.
| Metric / Requirement | Specification | Target Configuration |
|---|---|---|
| Core Engine / Framework | ColabFold (AlphaFold2) + AutoDock Vina + PyMOL | Nextflow / Snakemake Compatible |
| Benchmark Runtime | ~15 – 30 minutes per target (GPU-accelerated) | 1× NVIDIA A100 / RTX 4090 GPU + 16 CPU cores |
| Standard Container | docker pull colabfold/colabfold:1.5.5-cuda12.2 | Docker • Singularity • Conda |
| Input → Deliverable | Amino Acid FASTA + Small Molecule SMILES → 3D PDB Structure, Docked Conformations, Binding ΔG | |
Step 1: GPU Batch Structure Prediction with ColabFold (AlphaFold2)
Generates fast multiple sequence alignments (MSAs) using MMseqs2 and runs AlphaFold2 evoformer recycling on GPU.
colabfold_batch target_proteins.fasta colabfold_results/ \
--amber --templates --num-recycle 3 --use-gpu-relax--amber applies Amber force field energy minimization to relieve steric clashes; --num-recycle 3 elevates resolution.Step 2: Confidence Metric Extraction (pLDDT & PAE Analysis)
Parses predicted local distance difference test (pLDDT) confidence and extracts top model coordinates.
import json
import glob
scores = glob.glob('colabfold_results/*_scores.json')
for s in scores:
with open(s) as f:
data = json.load(f)
print(f"{s}: Mean pLDDT = {sum(data['plddt'])/len(data['plddt']):.2f}")Step 3: Receptor & Chemical Ligand Preparation in PDBQT Format
Adds polar hydrogens, assigns Gasteiger charges, and defines rotatable ligand bonds.
# Convert ligand SMILES to 3D PDBQT with OpenBabel
obabel -:"CC(=O)Oc1ccccc1C(=O)O" -O ligand.pdbqt --gen3d -p 7.4
# Prepare Receptor PDBQT
prepare_receptor4.py -r colabfold_results/top_model_relaxed.pdb -o receptor.pdbqtStep 4: Molecular Docking & Affinity Grid Search with AutoDock Vina
Executes iterated Monte Carlo local searches to predict binding pose conformations and free energy of binding (ΔG in kcal/mol).
vina --receptor receptor.pdbqt \
--ligand ligand.pdbqt \
--center_x 15.2 --center_y 24.8 --center_z -8.4 \
--size_x 20 --size_y 20 --size_z 20 \
--out docked_conformations.pdbqt \
--cpu 16 --exhaustiveness 32--exhaustiveness 32 guarantees exhaustive conformational sampling of flexible ligand bonds.🚀 Automated Nextflow (nf-core) Production Execution
Execute this complete workflow across any SLURM, AWS Batch, or Google Cloud HPC cluster using the verified community container pipeline:
# Executed via ColabFold GPU container / SLURM cluster