AlphaFold has revolutionized protein structure prediction, but validating its output is essential before using the model in downstream applications like docking or molecular dynamics. Here’s a step-by-step guide to assess the quality and reliability of AlphaFold-predicted structures.

🧬 How to Validate AlphaFold Structures

🔍 1. Check Prediction Confidence

AlphaFold provides two key metrics:

• pLDDT (per-residue confidence):

  • 90: Very high confidence

  • 70–90: Confident
  • <70: Low confidence (often in loops or disordered regions)
• PAE (Predicted Aligned Error):
  • Lower is better.
  • Useful to assess domain-level flexibility or uncertainty in multi-domain proteins.

📌 Tip: You can visualize both scores in the AlphaFold DB or with tools like PyMOL or ChimeraX.

🧫 2. Visual Inspection

Use molecular visualization tools to manually inspect the structure:

• PyMOL
• UCSF Chimera / ChimeraX
• Mol* (web-based viewer)

Look for:

• Disordered loops
• Steric clashes
• Abnormal bond angles
• Missing secondary structure elements

🧪 3. Structure Quality Assessment Tools

You can evaluate the stereochemistry and structural quality using these tools:

📦 Most of these tools accept PDB files and provide web or local versions.

🔬 4. Compare with Experimental Structures

If homologous structures exist:

• Align the AlphaFold model with known crystal/NMR structures using TM-align, PyMOL, or Chimera.
• Compute RMSD to assess structural similarity.

⚙️ 5. Functional and Dynamic Validation

• Dock known ligands to check active/binding site geometry.
• Perform Molecular Dynamics (MD) simulations to test structure stability over time.
  • Use GROMACS, AMBER, or NAMD.

✅ Summary

📚 References

• Jumper et al., Nature (2021): AlphaFold paper
• AlphaFold Protein Structure Database: https://alphafold.ebi.ac.uk/
• MolProbity: http://molprobity.biochem.duke.edu/