Understanding pLDDT Confidence Scores

When AlphaFold2 predicts a protein structure, it doesn't just give you a 3D model—it also provides a confidence score for every single amino acid. This metric, called pLDDT (predicted Local Distance Difference Test), is your key to understanding which parts of the structure are reliable and which might need experimental validation.

In this comprehensive guide, we'll explore what pLDDT scores mean, how to interpret them, and how to use this information to make better research decisions.

#What is pLDDT?

pLDDT is AlphaFold2's per-residue confidence metric. It predicts how accurately the model has positioned each amino acid in 3D space, scored from 0 to 100.

#How to Interpret pLDDT Scores

Understanding the score ranges is crucial for making informed decisions about your predicted structure:

Very High Confidence (pLDDT > 90)

• Typical regions: Well-folded domains, α-helices, β-sheets, and structured loops
• Expected accuracy: Better than 1.5 Å RMSD (root mean square deviation)
• Use cases: Drug design, protein engineering, detailed structural analysis

Confident (pLDDT 70-90)

These regions are generally correct in their overall fold but may have some positional uncertainty. The backbone is likely accurate, but side-chain positions might vary.

• Typical regions: Some loops, domain interfaces, flexible regions
• Expected accuracy: 1.5-4 Å RMSD
• Caution: Use for general structural insights, but validate before detailed analysis

Low Confidence (pLDDT < 70)

• Typical regions: Intrinsically disordered regions (IDRs), missing templates, long flexible loops
• Expected accuracy: Greater than 4 Å RMSD (unreliable)
• What to do: Consider experimental methods (NMR, SAXS) or disorder prediction tools

#Practical Example: Analyzing Your Results

Let's walk through a real example of interpreting pLDDT scores. Here's how you'd analyze a typical prediction:

Step 1: Color-Coded Visualization

Protogen Bio automatically colors your structure by pLDDT score using the standard color scheme:

Step 2: Identify Problematic Regions

Look for continuous stretches of low-confidence residues (orange/red). These might be:

• Disordered regions: Genuinely flexible parts with no fixed structure
• Missing homologs: Novel folds without good templates in the database
• Membrane-spanning regions: May require specialized prediction methods
• Long loops: Difficult to model without experimental constraints

Step 3: Cross-Reference with Biology

Always interpret pLDDT in the context of known biology:

# Example: Analyzing pLDDT scores programmatically
import json
import numpy as np

# Load pLDDT data from Protogen Bio results
with open('plddt_scores.json', 'r') as f:
    plddt = json.load(f)['plddt_per_residue']

# Calculate domain statistics
core_domain = plddt[10:150]  # Your known structured domain
print(f"Core domain mean pLDDT: {np.mean(core_domain):.1f}")
print(f"Core domain min pLDDT: {np.min(core_domain):.1f}")

# Identify low-confidence regions
low_conf_regions = np.where(np.array(plddt) < 70)[0]
print(f"Low confidence residues: {len(low_conf_regions)}")
print(f"Positions: {low_conf_regions.tolist()}")

#Common Pitfalls to Avoid

Don't Trust pLDDT for Oligomeric States

pLDDT scores are calculated per-chain and don't account for multi-chain interfaces. For protein complexes, use PAE (Predicted Aligned Error) matrices instead to assess interface confidence.

Low pLDDT ≠ Wrong Structure

Some regions genuinely lack a fixed structure (intrinsically disordered proteins). Low pLDDT might indicate true disorder, not model failure. Use disorder prediction tools like IUPred to confirm.

Don't Ignore Context

A single low-confidence residue in an otherwise confident region is often fine. Focus on continuous stretches of low pLDDT, not isolated dips.

#Best Practices for Using pLDDT

#Wrapping Up

pLDDT scores are your guide to understanding AlphaFold2 predictions. By learning to interpret these confidence metrics, you can make informed decisions about which parts of your structure to trust, which to validate, and which might be genuinely disordered.