Understanding AlphaFold Output Files

This document explains the output files generated by AlphaFold and their significance for protein structure prediction.

Output Directory Structure

The output directory contains several types of files for each model prediction:

1. Model Predictions

• unrelaxed_model_[1-5]_pred_0.pdb: Initial predicted structure in PDB format
• unrelaxed_model_[1-5]_pred_0.cif: Initial predicted structure in mmCIF format
• relaxed_model_[1-5]_pred_0.pdb: Relaxed (energy-minimized) structure in PDB format
• relaxed_model_[1-5]_pred_0.cif: Relaxed structure in mmCIF format

2. Confidence Metrics

• confidence_model_[1-5]_pred_0.json: Per-residue confidence scores
• Contains three key arrays:
  • residueNumber: Position in the sequence
  • confidenceScore: pLDDT score (0-100)
  • confidenceCategory: Confidence category (L=Low, M=Medium, H=High)
• pLDDT interpretation:
  • < 50: Low confidence (L)
  • 50-70: Medium confidence (M)
  • 70-90: High confidence (H)

  • 90: Very high confidence (H)

3. Ranking and Analysis

• ranked_[0-4].pdb/.cif: Models sorted by confidence, with 0 being the best
• ranking_debug.json: Details about the model ranking process
• relax_metrics.json: Metrics from the structure relaxation step
• timings.json: Performance metrics for different stages of prediction

4. Intermediate Data

• features.pkl: Input features extracted from the sequence
• result_model_[1-5]_pred_0.pkl: Raw prediction results
• msas/: Directory containing multiple sequence alignments used for prediction

Key Files for Analysis

1. Best Model: Always check ranked_0.pdb first - this is AlphaFold's best prediction
2. Confidence Assessment: Review confidence_model_[1-5]_pred_0.json to understand prediction reliability
3. High confidence scores (>90) suggest very reliable predictions
4. Lower scores may indicate flexible or disordered regions

File Formats

1. PDB Files (.pdb):
2. Standard format for protein structures
3. Easily viewable in molecular visualization software (PyMOL, VMD, etc.)

4. Contains atomic coordinates and basic metadata

5. mmCIF Files (.cif):

6. Modern format for protein structures
7. More detailed than PDB format
8. Better handles large structures and contains more metadata

Using the Output

1. Structure Analysis:
2. Use ranked_0.pdb for your primary analysis
3. Compare with other ranked models to assess structural variability

4. Pay attention to regions with high confidence scores

5. Quality Assessment:

6. Check confidence scores to identify reliable regions
7. Look for consistently high-confidence regions across models

8. Be cautious about interpreting low-confidence regions

9. Visualization:

10. Color structure by pLDDT score to highlight reliable regions
11. Compare multiple models to understand structural flexibility
12. Focus analysis on high-confidence regions

Visualizing PDB Files

To visualize the predicted protein structures (PDB files), you have several options:

1. Desktop Applications:

2. PyMOL: Professional-grade molecular visualization (recommended)

   • Download from: https://pymol.org/
   • Features:
   • High-quality rendering
   • Powerful analysis tools
   • Script automation support
   • Can color by B-factor (confidence scores in AlphaFold)

3. UCSF Chimera: Free academic visualization tool

   • Download from: https://www.cgl.ucsf.edu/chimera/
   • Good for structure analysis and comparison

4. VMD: Specialized in molecular dynamics

   • Download from: https://www.ks.uiuc.edu/Research/vmd/
   • Excellent for trajectory analysis

5. Web-Based Viewers:

6. Mol*: Modern web-based viewer

   • Access via: https://molstar.org/viewer/
   • Just drag and drop your PDB file

7. NGL Viewer: Lightweight web viewer

   • Access via: http://nglviewer.org/ngl/
   • Good for quick visualization

8. Analyzing ranked_0.pdb:

9. This is AlphaFold's best prediction
10. The B-factor column (last number in each ATOM record) contains pLDDT confidence scores

11. In PyMOL, you can color by B-factor to visualize confidence:
    python # PyMOL commands load ranked_0.pdb spectrum b, rainbow # Colors structure by confidence scores

12. Best Practices:

13. Always start by viewing ranked_0.pdb
14. Color the structure by confidence scores
15. Compare multiple models to understand flexibility
16. Look for regions with high confidence scores (>90)

17. Be cautious about interpreting low-confidence regions

18. Important Features to Look For:

19. Secondary structure elements (helices, sheets)
20. Overall fold and domain organization
21. Regions of high vs. low confidence
22. Potential flexible regions (varying between models)
23. Biologically important sites or motifs

Understanding Pickle (.pkl) Files

AlphaFold generates two types of pickle files that contain detailed prediction data:

1. result_model_[1-5]_pred_0.pkl:
   Contains the raw prediction outputs including:
2. distogram: Distance predictions between residue pairs
3. experimentally_resolved: Predictions about atom positions
4. masked_msa: Multiple sequence alignment information
5. predicted_lddt: Raw predictions for local confidence
6. structure_module: Final atom positions and masks
7. plddt: Per-residue confidence scores (0-100)

8. ranking_confidence: Overall model confidence

9. features.pkl:
   Contains input features used for prediction:

10. sequence: The input protein sequence
11. aatype: Amino acid type encodings
12. msa: Multiple sequence alignment data
13. template_*: Information about structural templates
14. residue_index: Numbering of residues
15. domain_name: Name of the protein domain

Key Metrics from Pickle Files

1. Confidence Scores (pLDDT):
2. Range: 0-100
3. Higher is better

4. Your results show:

   • Average score: 89.16 (Very good)
   • Range: 65.29 - 93.71
   • Most residues have high confidence (>70)

5. Multiple Sequence Alignment (MSA):

6. Your protein had 3,303 aligned sequences
7. This is a good number for prediction accuracy

8. More diverse alignments generally improve prediction quality

9. Structure Module:

10. Contains final atomic coordinates
11. Includes all backbone and side chain atoms
12. Shape: (34 residues, 37 atoms per residue, 3 coordinates)

Analyzing Pickle Files

To analyze these files, you can use the provided inspect_pkl.py script:

python inspect_pkl.py

This will show:
- Data structure of predictions
- Confidence scores
- Sequence information
- Template usage
- MSA statistics

Why Five Models?

AlphaFold generates five models for each prediction for several important reasons:

1. Sampling Different Conformations:
2. Proteins can exist in multiple stable conformations
3. Different models may capture different possible structural states

4. Helps identify flexible or dynamic regions of the protein

5. Confidence Assessment:

6. Agreement between models indicates prediction reliability

7. Regions that vary between models may be:

   • Naturally flexible
   • Have multiple possible conformations
   • Harder to predict accurately

8. Model Architecture:

9. Each model uses slightly different neural network parameters
10. Models are trained independently with different random seeds

11. This ensemble approach improves prediction robustness

12. Ranking System:

13. AlphaFold ranks the five models based on predicted confidence
14. ranked_0 represents the most confident prediction

15. Comparing ranks helps identify the most likely structure

16. Scientific Best Practice:

17. Multiple models follow the scientific principle of ensemble sampling
18. Helps avoid over-relying on a single prediction
19. Provides error estimates for the prediction

When analyzing results, it's important to:
- Start with the highest-ranked model (ranked_0)
- Compare models to identify consistent and variable regions
- Consider all models when the confidence scores are similar

Performance Metrics

The timings.json file provides detailed information about:
- MSA generation time
- Feature processing time
- Model prediction time
- Structure relaxation time

This can be useful for:
- Optimizing future runs
- Understanding computational requirements
- Identifying bottlenecks in the prediction pipeline