Confidence Metrics - AlphaFold 3

Overview

AlphaFold 3 provides multiple confidence metrics to assess prediction quality. These metrics help you understand:

Local accuracy: How confident is each atom/residue?
Global accuracy: Is the overall fold correct?
Interface quality: Are chain-chain interactions accurate?
Relative positions: How accurate are token-token distances?

Similar to AlphaFold 2 and AlphaFold-Multimer, but with enhanced metrics for ligands, nucleic acids, and multi-chain complexes.

pLDDT: Per-Atom Confidence

predicted Local Distance Difference Test

Range: 0-100 (higher = more confident)Granularity: Per-atom (more detailed than AlphaFold 2’s per-residue)What it measures: Predicted accuracy of local structure

Understanding pLDDT Values

Confidence Ranges
Polymer-Specific

pLDDT Score	Interpretation	Typical Usage
90-100	Very high confidence	Well-ordered regions, reliable for detailed analysis
70-90	Confident	Generally accurate, suitable for most applications
50-70	Low confidence	Use with caution, may be flexible/disordered
< 50	Very low confidence	Likely disordered, unreliable coordinates

Accessing pLDDT Values

In the confidences JSON:

{
  "atom_plddts": [95.2, 94.8, 93.1, ...],  // num_atoms array
  "atom_chain_ids": ["A", "A", "A", ...]   // maps atoms to chains
}

Visualization

Most structure viewers can color by B-factor. AlphaFold 3 stores pLDDT in the B-factor column:

PyMOL

# Load and color by pLDDT (stored in B-factor)
load model.cif
spectrum b, blue_white_red, minimum=50, maximum=90

PAE: Predicted Aligned Error

Positional Uncertainty

Range: 0-32 Ångströms (lower = more confident)Granularity: Per-token pairWhat it measures: Error in relative position/orientation between two tokens

How PAE Works

PAE(i, j) estimates the error in token j’s position when the structure is aligned using token i’s coordinate frame.

Alignment

Align predicted structure to true structure using token i’s frame

Measurement

Measure positional error of token j after alignment

Prediction

PAE predicts this error in Ångströms

Interpreting PAE Plots

PAE is visualized as a 2D heatmap where both axes represent token indices.

Low PAE (dark blue): Confident in relative positions

Diagonal blocks: Well-structured domains
Off-diagonal blocks: Confident interfaces/contacts

High PAE (yellow/red): Uncertain relative positions

Between domains: Flexible linkers
Between chains: Uncertain/no interaction

Frame Construction

Proteins & Nucleic Acids
Small Molecules

Frames constructed from backbone atoms:

Proteins: Same as AlphaFold 2 (Cα, N, C)
Nucleic acids: Backbone phosphate groups

Accessing PAE Values

In the confidences JSON:

{
  "pae": [[0.5, 1.2, 15.3, ...],    // num_tokens × num_tokens
          [1.3, 0.4, 14.8, ...],
          [15.1, 14.9, 0.6, ...]],
  "token_chain_ids": ["A", "A", "B", ...]  // maps tokens to chains
}

Use Cases

Domain Organization

Low within-domain PAE, high between-domain PAE suggests multi-domain protein with flexible linkers

Interaction Confidence

Low off-diagonal PAE between chains indicates confident protein-protein interface

Binding Assessment

High PAE between protein and ligand suggests uncertain binding pose

Relative Orientation

PAE reveals which regions have well-defined spatial relationships

pTM: Predicted Template Modeling Score

Global Structure Confidence

Range: 0-1 (higher = more confident)Granularity: Global scalarWhat it measures: Accuracy of entire predicted structure

Interpretation Guidelines

Overall Quality
Size Considerations

pTM Score	Interpretation
> 0.8	Very confident, high-quality prediction
0.5-0.8	Decent confidence, likely correct overall fold
< 0.5	Low confidence, fold may be incorrect

TM-Score Background

The Template Modeling score measures structural similarity:

\text{TM-score} = \max \left[ \frac{1}{L} \sum_{i=1}^{L} \frac{1}{1 + (d_i / d_0)^2} \right]

Where:

L: Length of target sequence
d_i: Distance between aligned residues
d_0: Normalization factor (length-dependent)

References: Zhang & Skolnick, 2004; Xu & Zhang, 2010

Accessing pTM

In summary confidences JSON:

{
  "ptm": 0.84,
  "chain_ptm": [0.89, 0.76, 0.92]  // per-chain pTM
}

ipTM: Interface Predicted TM Score

Interface Quality

Range: 0-1 (higher = more confident)Granularity: Global or per-chain-pairWhat it measures: Accuracy of predicted interfaces between chains

Interpretation Guidelines

ipTM Score	Interpretation	Action
> 0.8	Confident, high-quality interface	Trust binding mode
0.6-0.8	Uncertain region	Verify with experiments
< 0.6	Low confidence	Likely incorrect interface

ipTM only considers inter-chain interactions. For single-chain predictions, use pTM instead.

Types of ipTM Metrics

Global ipTM
Per-Chain ipTM
Pair-wise ipTM

Overall confidence in all interfaces:

{
  "iptm": 0.72  // All chain-chain interfaces
}

Use for ranking predictions of the full complex.

Average interface confidence per chain:

{
  "chain_iptm": [0.85, 0.68, 0.91]
}

Element i = average ipTM between chain i and all other chains.Use case: Ranking predictions when interested in specific chain (e.g., ligand binding)

Interface confidence for each chain pair:

{
  "chain_pair_iptm": [
    [0.89, 0.72, 0.15],  // Chain A to A,B,C
    [0.72, 0.84, 0.20],  // Chain B to A,B,C
    [0.15, 0.20, 0.78]   // Chain C to A,B,C
  ]
}

Diagonal (i,i): pTM for chain i
Off-diagonal (i,j): ipTM for i-j interface

Use case: Antibody-antigen, known interacting pairs

Ranking Strategies

Full Complex

Use global iptm or composite ranking_score

Specific Interface

Use chain_pair_iptm[i][j] for known interacting chains i and j

Specific Chain

Use chain_iptm[i] when you care about chain i’s interactions but don’t know partners

Chain Structure

Use chain_ptm[i] when only the structure of chain i matters

Ranking Score

A composite score combining multiple metrics:

ranking_score = 0.8 × ipTM + 0.2 × pTM + 0.5 × disorder - 100 × has_clash

Use ranking_score ONLY for ranking predictions, not for absolute quality assessment.

Components

ipTM

number

Interface confidence (0-1) - weighted 0.8

pTM

number

Overall structure confidence (0-1) - weighted 0.2

disorder

number

Fraction disordered (0-1) - penalizes spurious helices (+0.5)

has_clash

boolean

Significant clashing atoms - strong penalty (-100)

When to Use

Use Ranking Score For
Don't Use Ranking Score For

Selecting best prediction from multiple samples
Comparing predictions with same input
Automated prediction pipelines
Initial filtering before manual inspection

Additional Metrics

Contact Probabilities

{
  "contact_probs": [[0.95, 0.12, ...],  // num_tokens × num_tokens
                     [0.12, 0.98, ...]]
}

Probability that two tokens are within 8Å (representative atoms). Use cases:

Identifying likely contacts before structure determination
Assessing binding likelihood
Filtering potential interfaces

Chain-Pair PAE Min

{
  "chain_pair_pae_min": [
    [0.5, 15.2, 18.7],
    [15.1, 0.4, 19.2],
    [18.9, 19.5, 0.6]
  ]
}

Lowest PAE value from chain i to chain j.

Correlates with binding: Low chain_pair_pae_min between chains suggests they interact. Can distinguish binders from non-binders.

Disorder Fraction

{
  "fraction_disordered": 0.23
}

Fraction of structure classified as disordered (0-1), measured by accessible surface area. Interpretation:

High disorder: Flexible regions, loops, or prediction artifacts
Used in ranking score to penalize spurious helices

Clash Detection

{
  "has_clash": false
}

Boolean indicating significant clashing atoms:

More than 50% of any chain has clashes, OR
More than 100 clashing atoms in any chain

Action: If true, consider re-running with different seeds or inspect manually.

Practical Guidelines

Initial Assessment

Check ranking_score, ptm, and iptm in summary JSON

pTM > 0.5: Likely correct fold
ipTM > 0.8: Confident interface
has_clash = false: No major issues

Visual Inspection

Load structure colored by pLDDT (B-factor)

Blue regions (>90): High confidence
Red regions (<50): Disordered/uncertain

Interface Analysis

Examine PAE plot

Low PAE blocks between chains: Good interface
High PAE: Uncertain interaction

Chain-Specific Quality

Check per-chain metrics

chain_ptm[i]: Structure of chain i
chain_iptm[i]: Chain i’s interfaces
chain_pair_pae_min[i][j]: Likely interaction between i and j

Domain Analysis

For multi-domain proteins:

Low within-domain PAE: Well-structured domains
High between-domain PAE: Flexible linkers

Additional Resources

AlphaFold 3 Paper

Detailed description of confidence metrics

EBI Training

Practical guide to confidence scores (AlphaFold 2 focused, still applicable)

Next Steps

Output Format

Learn about output file structure

Performance

Optimize predictions for better results

Getting Started

Core Concepts

User Guides

Advanced Usage

Resources

​Overview

​pLDDT: Per-Atom Confidence

predicted Local Distance Difference Test

​Understanding pLDDT Values

​Proteins

​DNA/RNA

​Ligands

​Accessing pLDDT Values

​Visualization

​PAE: Predicted Aligned Error

Positional Uncertainty

​How PAE Works

​Interpreting PAE Plots

​Frame Construction

​Accessing PAE Values

​Use Cases

Domain Organization

Interaction Confidence

Binding Assessment

Relative Orientation

​pTM: Predicted Template Modeling Score

Global Structure Confidence

​Interpretation Guidelines

​TM-Score Background

​Accessing pTM

​ipTM: Interface Predicted TM Score

Interface Quality

​Interpretation Guidelines

​Types of ipTM Metrics

​Ranking Strategies

​Ranking Score

​Components

​When to Use

​Additional Metrics

​Contact Probabilities

​Chain-Pair PAE Min

​Disorder Fraction

​Clash Detection

​Practical Guidelines

​Additional Resources

AlphaFold 3 Paper

EBI Training

​Next Steps

Output Format

Performance

Build docs developers (and LLMs) love

Overview

pLDDT: Per-Atom Confidence

Understanding pLDDT Values

Proteins

DNA/RNA

Ligands

Accessing pLDDT Values

Visualization

PAE: Predicted Aligned Error

How PAE Works

Interpreting PAE Plots

Frame Construction

Accessing PAE Values

Use Cases

pTM: Predicted Template Modeling Score

Interpretation Guidelines

TM-Score Background

Accessing pTM

ipTM: Interface Predicted TM Score

Interpretation Guidelines

Types of ipTM Metrics

Ranking Strategies

Ranking Score

Components

When to Use

Additional Metrics

Contact Probabilities

Chain-Pair PAE Min

Disorder Fraction

Clash Detection

Practical Guidelines

Additional Resources

Next Steps