# How to Interpret AlphaFold DB Confidence Scores: A Guide to pLDDT and PAE for Structural Bioinformatics

## Direct Answer and Reader Context

AlphaFold DB provides predicted protein structures with two confidence metrics that determine whether a model is usable for downstream analysis: pLDDT (predicted Local Distance Difference Test) and PAE (Predicted Aligned Error). pLDDT scores each residue's local confidence on a 0 to 100 scale, while PAE provides a pairwise matrix estimating the positional error between any two residues after alignment. For researchers retrieving structures from AlphaFold DB, interpreting these scores correctly separates reliable structural hypotheses from artifacts that can mislead docking studies, mutagenesis planning, and functional annotation. This guide explains what each metric measures, how to visualize them in the database interface, and how to apply them as decision criteria in structure-based analyses.

AlphaFold DB stores predicted structures for proteins across major model organisms, and the database interface displays both pLDDT and PAE for every entry. The pLDDT value appears as a color gradient on the 3D structure, ranging from blue for high confidence to orange and red for low confidence regions. The PAE matrix appears as a 2D plot where each cell represents the expected positional error between two residues. Understanding these visualizations requires knowing what the underlying algorithms compute and what biological interpretations are justified.

The practical outcome of this guide is a decision framework: you will know when a predicted structure supports molecular docking, when it supports domain-level analysis only, and when it should be treated as a low-confidence model requiring experimental validation. The guidance applies to single-chain predictions from AlphaFold DB and extends to complex predictions generated with AlphaFold-Multimer or AlphaFold 3, with appropriate caveats about the newer versions.

## What AlphaFold DB Provides and What It Does Not

AlphaFold DB is a public repository of protein structure predictions generated by the AlphaFold2 system. The database covers proteins from sequenced genomes, with the most complete coverage for human and major model organisms. Each entry includes the predicted atomic coordinates, a per-residue confidence score called pLDDT, and a pairwise confidence metric called PAE. The database also provides the multiple sequence alignment used for the prediction, which is essential for understanding why certain regions received low confidence scores.

The AlphaFold initiative has had a transformative impact on structural biology, with the 2024 Nobel Prize recognizing this contribution. AlphaFold2 achieved near-experimental accuracy for single-chain protein folding, and AlphaFold3 expanded predictions to protein-ligand, protein-nucleic acid, and protein-protein complexes. The AlphaFold Database has made these predictions accessible, accelerating translational research in structure-based drug discovery and the study of macromolecular assemblies. The architectural evolution from AlphaFold1 through AlphaFold2 to AlphaFold3 reflects different neural network designs: AlphaFold1 used deep neural networks, AlphaFold2 employed the Evoformer to model evolutionarily related sequences, and AlphaFold3 applies the Pairformer for pairwise amino acid interactions [8].

Despite these advances, AlphaFold predictions have known limitations. The models do not capture protein dynamics or multiple conformational states. A predicted structure represents one static conformation, and proteins that undergo large conformational changes upon ligand binding or post-translational modification will not be fully represented. The confidence scores provide information about prediction reliability, but they do not indicate whether the predicted conformation is the biologically relevant one.

For researchers using AlphaFold DB, the practical implication is that confidence scores must be interpreted in the context of the biological question. A high-confidence prediction of a folded domain supports hypotheses about that domain's structure. A low-confidence prediction of a loop region does not support residue-level claims about that loop. The distinction between local confidence and pairwise error is central to this interpretation.

## pLDDT: Per-Residue Local Confidence

### What pLDDT Measures

pLDDT is a per-residue score ranging from 0 to 100 that estimates how confident the model is in the predicted position of that residue. The score is calibrated against the Local Distance Difference Test, which compares the predicted structure to experimental structures. A pLDDT above 90 indicates very high confidence, comparable to experimental accuracy. Scores between 70 and 90 indicate confident predictions suitable for most analyses. Scores between 50 and 70 indicate low confidence, where the backbone is generally predicted but side chain positions may be unreliable. Scores below 50 indicate very low confidence, often corresponding to disordered regions or domains that are difficult to predict.

The pLDDT score is not a direct measure of structural accuracy. It is a model's self-assessment of confidence, calibrated on training data. In practice, high pLDDT regions tend to be accurate, and low pLDDT regions tend to be inaccurate, but the relationship is statistical instead of deterministic. Researchers should treat pLDDT as a reliability indicator instead of an error measurement.

### Visualizing pLDDT in AlphaFold DB

The AlphaFold DB structure viewer colors the predicted structure by pLDDT. The color gradient runs from dark blue for high confidence through cyan, yellow, and orange to red for low confidence. This visualization allows immediate identification of well-predicted domains and poorly predicted regions. The per-residue pLDDT values are also available in the downloadable files, either in the B-factor column of the PDB file or in the JSON format file.

When examining a predicted structure, look for the overall color distribution. A protein with mostly blue and cyan regions has high overall confidence. A protein with large orange and red regions has substantial low-confidence areas that require caution. The color pattern often reveals domain boundaries, with linkers and terminal regions showing lower confidence than folded cores.

### Interpreting pLDDT Values

For structure-based analyses, pLDDT provides the first filter for reliability. Residues with pLDDT above 90 can be used for detailed structural claims, including side chain orientation and interaction analysis. Residues with pLDDT between 70 and 90 support backbone-level claims and are suitable for domain-level comparisons. Residues with pLDDT below 70 should not be used for residue-level claims, and regions with pLDDT below 50 should be treated as unreliable.

The pLDDT score also correlates with structural disorder. Regions with very low pLDDT often correspond to intrinsically disordered regions that do not adopt a stable folded structure. This correlation is useful for identifying disordered segments, but it is not definitive. Some low-confidence regions may be folded domains that the model could not predict accurately due to insufficient evolutionary information.

In the context of vaccine design and immunoinformatics, pLDDT plays a practical role. A multi-epitope vaccine candidate against Pseudomonas aeruginosa was designed using an integrative in silico pipeline that included de novo and refined 3D modeling. The final construct was evaluated for compactness and tractability, with a molecular weight of 12.7 kDa and an instability index below 40. The study used molecular dynamics to support thermal stability and docking to predict TLR4/MD-2 engagement [7]. For such applications, the confidence in the predicted structure directly affects the credibility of epitope presentation claims.

## PAE: Predicted Aligned Error for Domain Relationships

### What PAE Measures

PAE is a pairwise metric that estimates the positional error between two residues after the predicted structure is aligned on one of them. The PAE matrix is a 2D plot where the x and y axes represent residue positions. Each cell shows the expected error in angstroms between residue i and residue j when the structure is aligned on residue i. Low PAE values indicate that the relative positions of the two residues are predicted with high confidence. High PAE values indicate uncertainty in the relative arrangement.

PAE is particularly useful for assessing domain relationships. Two domains that have low PAE values between them are predicted to have a well-defined relative orientation. Two domains with high PAE values between them may be correctly folded individually, but their relative arrangement is uncertain. This distinction is critical for interpreting multi-domain proteins.

### Reading the PAE Matrix

The PAE plot in AlphaFold DB shows a color gradient from green for low error to white and red for high error. The diagonal of the matrix represents each residue compared to itself, which always has zero error. Off-diagonal regions reveal the confidence in relative positions.

A well-predicted protein shows a mostly green PAE matrix with low values throughout. A protein with uncertain domain arrangement shows green blocks along the diagonal corresponding to individual domains, with red or white regions between the blocks indicating uncertainty in inter-domain orientation. This pattern is common for multi-domain proteins where the domains are connected by flexible linkers.

### Using PAE to Distinguish Domain Arrangements

PAE provides information that pLDDT alone cannot. A protein can have high pLDDT for each domain but high PAE between domains, indicating that the individual domains are well predicted but their relative arrangement is unreliable. Conversely, low PAE between domains supports claims about the overall architecture of the protein.

For molecular docking studies, PAE is essential for determining whether a predicted structure can be used as a docking receptor. If the PAE between the binding site and the rest of the protein is high, the binding site's position relative to the protein is uncertain, and docking results should be interpreted with caution. If the PAE is low, the binding site is well positioned within the overall structure.

The experimental assessment of AI-based interactome mapping provides context for these limitations. High-confidence predictions from AlphaFold were found to be on par with established experimental approaches for protein-protein interactions. However, in proteome-wide screening, AI approaches underperformed in discovering strictly novel interactions compared to experimental reference maps. AlphaFold provided structural models for many experimentally identified interactions that were missed by virtual screens [10]. This finding suggests that AI predictions are most valuable for providing structural models for experimentally identified interactions instead of for discovering new interactions de novo.

## At a Glance: Confidence Score Decision Table

| pLDDT Range | Confidence Level | Recommended Use | Caution Level |
|-------------|------------------|-----------------|---------------|
| 90 to 100 | Very high | Residue-level structural claims, side chain analysis, docking receptor preparation | Low |
| 70 to 90 | High | Backbone-level analysis, domain comparisons, fold identification | Moderate |
| 50 to 70 | Low | Domain-level presence, secondary structure elements, not for residue-level claims | High |
| Below 50 | Very low | Disorder prediction, flexible region identification, not for structural claims | Very high |

| PAE Pattern | Interpretation | Recommended Use |
|-------------|----------------|-----------------|
| Low PAE throughout | Well-defined overall structure | Full structure for docking and interaction analysis |
| Low PAE within domains, high between domains | Domains well predicted, relative arrangement uncertain | Individual domains for analysis, not full assembly |
| High PAE throughout | Poorly defined structure | Avoid structure-based claims, consider experimental validation |

## Practical Workflow for Assessing AlphaFold DB Structures

### Step 1: Retrieve the Structure and Confidence Data

Access the AlphaFold DB entry for your protein of interest. The entry page displays the predicted structure with pLDDT coloring and provides access to the PAE plot. Download the structure file in PDB or mmCIF format and the JSON file containing per-residue pLDDT values and the PAE matrix. The downloadable files allow offline analysis and integration with other bioinformatics tools.

### Step 2: Examine the pLDDT Distribution

Open the structure in a molecular viewer and assess the color distribution. Identify regions with high confidence (blue and cyan) and low confidence (orange and red). Record the pLDDT values for functionally important residues, such as active site residues, binding interfaces, or epitope regions. If your analysis depends on specific residues, their pLDDT values determine whether residue-level claims are justified.

### Step 3: Examine the PAE Matrix

Open the PAE plot and assess the overall pattern. Look for green blocks along the diagonal that indicate well-predicted domains. Examine the regions between domains to determine whether their relative arrangement is confident. If you plan to use the full structure for docking or interaction analysis, the PAE between the interaction site and the rest of the protein must be low.

### Step 4: Compare with Experimental Data

If experimental structures exist for the protein or homologous proteins, compare the predicted structure with the experimental data. The comparison provides an empirical check on prediction accuracy. The NCBI provides access to experimental structures through the Protein Data Bank and related resources, allowing direct comparison of predicted and experimental coordinates [1].

### Step 5: Document Confidence Scores in Your Analysis

Record the pLDDT and PAE values in your analysis documentation. Include the confidence assessment in any figures or tables that present the predicted structure. This documentation allows others to assess the reliability of your structural claims and supports reproducible research practices.

## Options and Tradeoffs in Using Predicted Structures

### Full Structure versus Domain-Level Analysis

The choice between using the full predicted structure or individual domains depends on the PAE pattern. If PAE is low throughout the structure, the full structure supports domain-level and residue-level analysis. If PAE is high between domains, use individual domains for analysis and avoid claims about the overall architecture. This tradeoff is common in multi-domain proteins where flexible linkers connect well-folded domains.

### AlphaFold DB versus Custom Predictions

AlphaFold DB provides precomputed predictions for many proteins, which is convenient for well-annotated genomes. For proteins not in the database, or for predictions with custom parameters, running AlphaFold locally or through a cloud service provides more control. Custom predictions allow modification of the multiple sequence alignment, which can improve confidence for proteins with sparse evolutionary information. The tradeoff is computational cost and the need for bioinformatics infrastructure.

### Single-Chain versus Complex Predictions

AlphaFold DB primarily provides single-chain predictions. For protein-protein complexes, AlphaFold-Multimer or AlphaFold 3 can predict multimeric structures. The confidence metrics for complexes follow the same principles, but the interpretation requires additional caution. The experimental assessment of AI-based interactome mapping found that high-confidence predictions were comparable to experimental approaches, but novel interaction discovery was limited [10]. For complex predictions, validate the interaction with experimental methods before drawing conclusions.

### Structure Prediction versus Experimental Determination

Predicted structures are hypotheses, not experimental facts. For critical applications, such as drug design or vaccine development, experimental validation is essential. The Pseudomonas aeruginosa vaccine study used molecular dynamics and docking to evaluate the predicted construct, providing additional confidence beyond the initial structure prediction [7]. This multi-method approach is appropriate for applications where structural accuracy directly impacts biological conclusions.

## Observations and Measurements for Confidence Assessment

### Per-Residue pLDDT Records

Maintain a record of pLDDT values for functionally important residues. For each residue of interest, record the pLDDT value and the confidence category. This record supports decisions about which residues can be used for detailed analysis and which require experimental validation.

### PAE Matrix Records

Record the PAE values between domains or between functional regions. The PAE between an active site and the rest of the protein determines whether the active site's position is reliable. The PAE between two interaction partners determines whether the relative orientation is trustworthy.

### Comparison with Experimental Structures

When experimental structures are available, record the root-mean-square deviation between the predicted and experimental structures for high-confidence regions. This comparison provides an empirical measure of prediction accuracy and helps calibrate expectations for proteins without experimental structures.

### Multiple Sequence Alignment Quality

The quality of the multiple sequence alignment directly affects prediction confidence. Proteins with deep multiple sequence alignments, meaning many diverse homologous sequences, tend to have higher confidence predictions. Proteins with shallow alignments, meaning few homologous sequences, tend to have lower confidence. Record the number of sequences in the alignment and the diversity of species represented.

## Records and Documentation Standards

### Structure Analysis Documentation

Document the following for each predicted structure used in your analysis: the AlphaFold DB identifier, the pLDDT values for key residues, the PAE pattern, the version of AlphaFold used for the prediction, and the date of retrieval. This documentation supports reproducibility and allows others to verify your confidence assessment.

### Confidence Score Reporting

When presenting predicted structures in publications or reports, include the confidence assessment. State the pLDDT range for the regions used in your analysis and describe the PAE pattern for multi-domain proteins. This reporting standard allows readers to assess the reliability of your structural claims.

### Version Tracking

AlphaFold models are updated periodically, and predictions may change between versions. Record the AlphaFold version used for each prediction. If you compare structures across versions, note that differences may reflect model improvements instead of biological differences.

## Common Failure Patterns in Confidence Interpretation

### Overinterpreting Low-Confidence Regions

The most common error is treating low-confidence regions as reliable structural features. Residues with pLDDT below 70 should not be used for residue-level claims. Side chain orientations in low-confidence regions are particularly unreliable. If your analysis depends on a low-confidence region, obtain experimental validation before drawing conclusions.

### Ignoring PAE for Multi-Domain Proteins

A second common error is using the full structure of a multi-domain protein without checking PAE between domains. High PAE between domains means the relative arrangement is uncertain, even if each domain has high pLDDT. Docking studies using the full structure may produce misleading results if the domain arrangement is wrong.

### Assuming High Confidence Means Biological Relevance

High confidence indicates that the model is certain about the predicted structure, not that the predicted structure is the biologically relevant conformation. Proteins can adopt multiple conformations, and the predicted structure may represent one state. The AlphaFold system does not predict conformational dynamics, and the predicted structure may not capture the active conformation.

### Confusing pLDDT with Experimental Accuracy

pLDDT is a model confidence score, not a direct measurement of accuracy. While high pLDDT correlates with accuracy, the correlation is statistical. Individual residues can have high pLDDT but be inaccurate, particularly in regions with unusual conformations or strong interactions with ligands.

### Using Predicted Structures for Novel Interaction Discovery

The experimental assessment of AI-based interactome mapping found that AI approaches underperform in discovering strictly novel protein-protein interactions compared to experimental methods [10]. Predicted structures are valuable for modeling known interactions but should not be the primary tool for discovering new interactions. Experimental screening methods remain essential for interaction discovery.

## Limitations of AlphaFold Confidence Scores

### Static Structure Representation

AlphaFold predictions represent a single static conformation. Proteins are dynamic molecules that sample multiple conformations, and the predicted structure may not represent the dominant conformation in solution. Confidence scores do not provide information about conformational dynamics or the population of different states.

### Limited Coverage of Conformational States

The AlphaFold system does not predict multiple conformational states. Proteins that undergo large conformational changes upon ligand binding, phosphorylation, or other modifications will not be fully represented by a single prediction. The confidence scores indicate prediction reliability, not conformational diversity.

### Dependence on Evolutionary Information

AlphaFold predictions depend on the multiple sequence alignment. Proteins with few homologous sequences have lower prediction confidence, particularly in regions that are not conserved. The confidence scores reflect the availability of evolutionary information, and low confidence may indicate insufficient evolutionary signal instead of structural disorder.

### Challenges with Protein Dynamics

The review of AlphaFold's evolution identifies predicting protein dynamics and multiple conformational states as remaining challenges [8]. The current models do not capture the full range of protein behavior, and confidence scores should not be interpreted as measures of dynamic behavior.

### Complex Prediction Limitations

For protein-protein complexes, AlphaFold-Multimer and AlphaFold 3 provide predictions, but the confidence metrics require careful interpretation. The experimental assessment of AI-based interactome mapping found that high-confidence predictions are on par with experimental approaches, but the discovery of novel interactions is limited [10]. Complex predictions should be validated experimentally before being used for detailed claims.

## Quality Controls and Validation Approaches

### Cross-Validation with Experimental Structures

When experimental structures are available for the protein or close homologs, compare the predicted structure with the experimental data. The comparison provides an empirical check on prediction accuracy and helps identify systematic errors in the prediction.

### Molecular Dynamics Validation

Molecular dynamics simulations can assess the stability of predicted structures. The Pseudomonas aeruginosa vaccine study used molecular dynamics to support thermal stability up to 350 K and to evaluate linker flexibility [7]. This approach provides additional confidence beyond the initial structure prediction.

### Docking Validation

Docking studies can assess whether predicted structures support productive interactions. The vaccine study predicted a 1420 square angstrom interface with a binding free energy of -10.2 kcal per mole, suggesting productive TLR4/MD-2 engagement [7]. Docking results provide functional validation of predicted structures.

### Experimental Verification

For critical applications, experimental verification is essential. Techniques such as X-ray crystallography, cryo-electron microscopy, and nuclear magnetic resonance provide definitive structural information. The choice of experimental method depends on the protein's properties and the research question.

## Safety and Regulatory Context for Structure-Based Applications

### Drug Discovery Applications

Predicted structures used in drug discovery require careful validation. The AlphaFold review highlights the acceleration of translational research in structure-based drug discovery, but regulatory approval requires experimental confirmation of binding and efficacy [8]. Predicted structures can guide drug design but cannot replace experimental data.

### Vaccine Development Applications

The Pseudomonas aeruginosa vaccine study demonstrates the use of predicted structures in vaccine development. The study used an integrative pipeline combining epitope discovery, safety filters, physicochemical profiling, and 3D modeling [7]. For vaccine candidates, experimental validation of immunogenicity and safety is required before clinical use.

### Clinical Translation Considerations

Structure-based predictions can inform clinical research, but clinical decisions require experimental evidence. The confidence scores provide information about prediction reliability, but they do not provide evidence of biological function or clinical efficacy. Researchers should clearly distinguish between computational predictions and experimental findings.

## Professional Escalation Criteria

### When to Seek Experimental Structural Data

Escalate to experimental structural determination when your analysis depends on low-confidence regions, when the PAE indicates uncertain domain arrangements, or when the biological conclusions are sensitive to structural details. Experimental methods provide definitive structural information that predicted structures cannot provide.

### When to Consult Structural Biology Experts

Consult structural biology experts when interpreting complex PAE patterns, when planning experimental validation, or when the predicted structure conflicts with experimental observations. Experts can provide guidance on the appropriate interpretation and validation strategies.

### When to Use Alternative Prediction Methods

Consider alternative prediction methods when AlphaFold confidence is low or when the protein has unusual properties. Comparative modeling using experimental structures of homologs may provide better predictions for some proteins. The comparative analysis of protein folding prediction models emphasizes the importance of evaluating diverse outputs from deep learning models [9].

### When to Question the Biological Relevance of a Prediction

Question the biological relevance of a prediction when the predicted structure conflicts with experimental data, when the protein is known to undergo large conformational changes, or when the prediction does not explain known functional properties. The predicted structure is a hypothesis that must be tested against experimental observations.

## Building a Confidence Score Decision Log for Multi-Structure Projects

### Why a Structured Decision Log Matters

Researchers often retrieve dozens or hundreds of AlphaFold DB structures for a single project, such as a secretome-wide screen, a protein family analysis, or a vaccine candidate pipeline. The challenge is not interpreting a single pLDDT or PAE plot but maintaining consistent, defensible decisions across many structures. Without a standardized record system, confidence assessments become subjective, and downstream analyses may mix high-confidence and low-confidence structures without clear documentation. A decision log solves this problem by creating a repeatable workflow for recording confidence metrics, applying consistent thresholds, and documenting the rationale for including or excluding each structure in subsequent analyses.

The need for such a system is evident in comparative structural studies. A structure-guided secretome analysis of gall-forming microbes used AlphaFold2 to predict the 3D structures of secreted proteins from plasmodiophorid, oomycete, and fungal pathogens. The study clustered protein folds based on structural homology and identified novel sequence-unrelated but structurally similar effector clusters [11]. For this type of project, the researchers needed a systematic way to decide which predicted structures were reliable enough for fold clustering and which should be excluded. A decision log provides that systematic framework.

### Core Components of a Confidence Decision Log

#### Structure Identification Fields

Each entry in the decision log must begin with unambiguous identification information. Record the AlphaFold DB identifier, the UniProt accession or gene name, the organism, and the date of retrieval. Also record the AlphaFold version used for the prediction, since models are updated periodically and predictions may change between versions. This information supports reproducibility and allows other researchers to retrieve the exact structure you analyzed.

The NCBI provides access to sequence databases and associated resources that can help you verify the protein identifier and its biological context [1]. Cross-referencing the AlphaFold DB entry with NCBI records ensures that you are analyzing the correct protein isoform and that the sequence matches your experimental construct.

#### pLDDT Summary Statistics

For each structure, record summary statistics for the pLDDT distribution instead of only noting individual residues. Calculate the mean pLDDT across all residues, the percentage of residues in each confidence category (above 90, 70 to 90, 50 to 70, below 50), and the pLDDT values for functionally important residues such as active site residues, binding interfaces, or epitope regions. These summary statistics provide a quick reference for comparing structures within a project.

The percentage of residues in each confidence category is particularly useful for project-level decisions. A structure with 80 percent of residues above 70 may be suitable for domain-level analysis, while a structure with only 30 percent above 70 requires more caution. Recording these percentages allows you to sort structures by overall confidence and prioritize those with the strongest support.

#### PAE Pattern Classification

Record the PAE pattern for each structure using a standardized classification scheme. Classify the PAE matrix as one of three patterns: low PAE throughout, low PAE within domains with high PAE between domains, or high PAE throughout. For multi-domain proteins, record the specific PAE values between domains of interest. This classification directly informs whether the full structure or individual domains should be used for downstream analysis.

The PAE pattern classification should also note whether the protein is predicted to be single-domain or multi-domain. This information affects the interpretation of both pLDDT and PAE. A single-domain protein with high pLDDT and low PAE throughout supports full-structure analysis. A multi-domain protein with high pLDDT in each domain but high PAE between domains supports domain-level analysis only.

#### Decision Outcome and Rationale

For each structure, record the decision outcome and the rationale. Possible outcomes include: use full structure for detailed analysis, use individual domains only, use for fold identification only, or exclude from structure-based analysis. The rationale should reference the specific pLDDT and PAE values that drove the decision. This documentation ensures that decisions are transparent and can be revisited if new information becomes available.

The decision outcome should also note any experimental validation that was performed or planned. If the structure was used for docking or interaction analysis, record whether the results were consistent with experimental observations. This feedback loop improves the calibration of confidence thresholds for future projects.

### Implementing the Decision Log in Practice

#### Step 1: Define Project-Specific Thresholds

Before retrieving structures, define the pLDDT and PAE thresholds that will guide your decisions. These thresholds depend on the type of analysis you plan to perform. For residue-level claims such as side chain orientation or specific interactions, require pLDDT above 90 for the relevant residues. For backbone-level analysis such as fold identification or domain comparison, require pLDDT above 70 for the regions of interest. For domain-level analysis, require low PAE within domains but accept high PAE between domains.

Document these thresholds in the decision log so that all structures are evaluated consistently. The thresholds should be based on the published guidance for pLDDT interpretation and the specific requirements of your analysis. The Galaxy Training Network provides accessible workflow training that can help you implement consistent analysis pipelines for structure assessment [4].

#### Step 2: Retrieve and Record Structure Data

For each protein in your project, retrieve the AlphaFold DB entry and download the structure file and the JSON file containing per-residue pLDDT values and the PAE matrix. Extract the summary statistics and record them in the decision log. This step is straightforward for proteins in AlphaFold DB but requires running local predictions for proteins not in the database.

The EMBL-EBI Training resources provide learning pathways for bioinformatics data analysis that include practical guidance on retrieving and interpreting structure prediction data [2]. These resources can help you build the skills needed to efficiently process multiple structures.

#### Step 3: Classify PAE Patterns

Examine the PAE plot for each structure and classify the pattern using the standardized scheme. For multi-domain proteins, identify the domain boundaries and record the PAE values between domains. This classification is the most time-consuming step but is essential for determining whether the full structure or individual domains should be used.

The PAE classification should be performed by someone familiar with the protein's biology. Knowing the expected domain structure from experimental data or sequence analysis helps interpret the PAE pattern. A predicted domain boundary that aligns with a known functional domain is more credible than one that appears only in the PAE plot.

#### Step 4: Apply Decision Criteria

Apply the project-specific thresholds to each structure and record the decision outcome. For structures that pass the thresholds, proceed with the planned analysis. For structures that fail the thresholds, document the reason and consider alternative approaches such as comparative modeling or experimental determination.

The comparative analysis of protein folding prediction models emphasizes the importance of evaluating diverse outputs from deep learning models and combining predictions from different approaches [9]. If a structure fails the confidence thresholds, consider whether another prediction method or an experimental structure of a homolog provides better support.

#### Step 5: Review and Update the Log

Review the decision log periodically as the project progresses. Update entries when new experimental data becomes available or when the analysis plan changes. The log should be a living document that reflects the current state of knowledge about each structure.

The nf-core documentation provides standards for reproducible workflow implementation that can be adapted to structure analysis projects [5]. Applying similar standards to your decision log ensures that the log is complete, consistent, and usable by other members of your research group.

### Common Failure Patterns in Decision Log Implementation

#### Inconsistent Threshold Application

A common failure is applying different thresholds to different structures within the same project. This inconsistency often occurs when structures are assessed at different times or by different researchers. The decision log prevents this problem by requiring that thresholds be defined before retrieval and applied uniformly to all structures.

#### Overlooking PAE for Multi-Domain Proteins

Another common failure is recording pLDDT but ignoring PAE for multi-domain proteins. A structure can have high pLDDT in each domain but high PAE between domains, making the full structure unsuitable for docking or interaction analysis. The decision log should require PAE classification for every structure, also those that appear to be multi-domain.

#### Failing to Document Negative Results

Researchers often document structures that pass confidence thresholds but fail to record structures that were excluded. This omission creates a biased record that does not reflect the full scope of the analysis. The decision log should include all structures considered, with the decision outcome and rationale for each.

#### Confusing Confidence with Biological Relevance

A decision log records prediction confidence, not biological relevance. A structure can have high pLDDT and low PAE but still represent a conformation that is not biologically relevant. The decision log should include a field for biological context, noting any known conformational dynamics or experimental observations that affect interpretation.

### Records and Measurements for Project-Level Decisions

#### Confidence Score Distribution Records

Maintain a summary table of confidence score distributions across all structures in the project. This table should include the mean pLDDT, the percentage of residues in each confidence category, and the PAE pattern classification for each structure. This summary allows you to identify patterns in prediction quality across the protein family or secretome.

The structure-guided secretome analysis of gall-forming microbes provides an example of how such records support project-level decisions. The study identified species-specific expansions of protein folds and low abundance of known orphan effector families [11]. These conclusions depended on consistent assessment of prediction confidence across many structures.

#### Threshold Validation Records

Record how well the confidence thresholds predicted actual structural accuracy for structures that were subsequently validated experimentally. This validation record calibrates the thresholds for future projects. If structures with pLDDT above 70 consistently matched experimental structures, the threshold is well calibrated. If some structures with high pLDDT were inaccurate, the threshold may need adjustment.

#### Version Comparison Records

When AlphaFold models are updated, record the differences between versions for structures in your project. Note whether pLDDT values changed substantially and whether the PAE pattern classification changed. This record helps you decide whether to update your analysis when new predictions become available.

### Troubleshooting Common Decision Log Problems

#### Missing PAE Data

If the PAE data is missing from the AlphaFold DB entry, check whether you downloaded the correct files. The PAE matrix is provided in the JSON format file, not in the PDB or mmCIF structure file. If the JSON file is unavailable, consider running a local prediction to generate the PAE data.

#### Discrepancies Between pLDDT and PAE

When pLDDT and PAE suggest different conclusions, investigate the source of the discrepancy. High pLDDT with high PAE between domains indicates well-predicted individual domains with uncertain relative arrangement. Low pLDDT with low PAE is unusual and may indicate a prediction artifact. Record the discrepancy and consider whether the structure requires experimental validation.

#### Structures with Uniformly Low Confidence

For structures with uniformly low pLDDT, the prediction is unreliable and structure-based analysis is not justified. The decision log should record these structures as excluded and note the reason. Consider whether the protein is intrinsically disordered or whether the prediction failed due to insufficient evolutionary information.

### Professional Escalation Criteria for Decision Log Entries

#### Escalate When Confidence Thresholds Are Marginal

When a structure falls near the boundary of your confidence thresholds, escalate the decision to a second reviewer or consult structural biology expertise. Marginal cases require careful consideration of the specific analysis and the consequences of using an unreliable structure.

#### Escalate When Experimental Data Conflicts

When experimental observations conflict with the predicted structure, escalate to experimental validation. The predicted structure is a hypothesis that must be tested against experimental data. The decision log should note the conflict and the planned validation approach.

#### Escalate When Project-Level Patterns Emerge

When multiple structures in a project show similar confidence patterns, such as consistently low pLDDT in a particular domain family, escalate to a project-level review. This pattern may indicate a systematic limitation of the prediction method for that protein family, and alternative approaches may be needed.

The Carpentries lessons provide foundational training in computing and data analysis that can help you implement robust record-keeping practices for your decision log [6]. These skills support reproducible research and ensure that your confidence assessments are transparent and defensible.

### Integrating the Decision Log with Downstream Analysis

The decision log should connect directly to downstream analysis workflows. When a structure passes the confidence thresholds, the log entry should reference the specific analysis that was performed and the results obtained. When a structure fails the thresholds, the log entry should reference the alternative approach that was used instead.

This integration ensures that confidence assessment is not an isolated step but an ongoing part of the research process. The Bioconductor project provides reproducible genomic-analysis workflows that can be adapted to integrate structure confidence assessment with downstream analysis [3]. Applying similar principles to your decision log ensures that the log remains useful throughout the project lifecycle.

The decision log also supports publication and reporting. When presenting results, you can reference the decision log to show that structures were assessed consistently and that confidence thresholds were applied uniformly. This documentation strengthens the credibility of your structural claims and supports the reproducibility of your research.

## Frequently Asked Questions

### What is the difference between pLDDT and PAE?

pLDDT is a per-residue score from 0 to 100 that estimates the local confidence in each residue's predicted position. PAE is a pairwise matrix that estimates the positional error between any two residues after alignment. pLDDT tells you how confident the model is in each residue's position, while PAE tells you how confident the model is in the relative arrangement of residue pairs. For multi-domain proteins, PAE is essential for assessing whether the domain arrangement is reliable.

### How should I interpret a pLDDT score of 70?

A pLDDT score of 70 falls at the boundary between high and low confidence. Scores between 70 and 90 support backbone-level analysis and domain comparisons, but side chain positions may not be reliable. For residue-level claims, such as specific side chain interactions, a pLDDT above 90 is preferable. If your analysis depends on residues with pLDDT around 70, consider experimental validation.

### What does a red region in the AlphaFold DB structure viewer mean?

A red region in the structure viewer indicates a pLDDT score below 50, which is very low confidence. These regions often correspond to disordered segments or domains that the model could not predict accurately. Red regions should not be used for structural claims. They may indicate intrinsically disordered regions, but they may also indicate prediction failure due to insufficient evolutionary information.

### How do I use the PAE plot to decide if I can use the full structure for docking?

Examine the PAE values between the proposed binding site and the rest of the protein. If the PAE values are low, the binding site's position relative to the protein is well defined, and the full structure can be used for docking. If the PAE values are high, the binding site's position is uncertain, and docking results should be interpreted with caution. Consider using individual domains for docking if the domain arrangement is uncertain.

### Can I use AlphaFold DB structures for drug discovery?

AlphaFold DB structures can guide drug discovery by providing structural hypotheses for target proteins. However, the predictions are computational models, not experimental structures. The AlphaFold review highlights the acceleration of structure-based drug discovery, but regulatory approval requires experimental confirmation [8]. Use predicted structures to generate hypotheses and design experiments, but do not rely on them for final decisions.

### How do AlphaFold confidence scores compare to experimental structure quality?

High-confidence AlphaFold predictions with pLDDT above 90 are often comparable to experimental structures in accuracy. However, the confidence scores are model self-assessments, not direct measurements. The experimental assessment of AI-based interactome mapping found that high-confidence predictions are on par with established experimental approaches for protein-protein interactions [10]. For critical applications, experimental validation remains essential.

### What should I do if my protein has low pLDDT throughout?

If your protein has low pLDDT throughout, the prediction is unreliable, and structure-based analysis is not justified. Consider alternative approaches: search for experimental structures of homologs, use comparative modeling based on related proteins, or determine the structure experimentally. The comparative analysis of protein folding prediction models emphasizes the importance of evaluating diverse outputs from different models, and combining predictions from multiple approaches may provide more reliable information [9].

### How do I report AlphaFold confidence scores in my publication?

Report the AlphaFold DB identifier, the AlphaFold version, the pLDDT values for key residues, and the PAE pattern for multi-domain proteins. State the confidence level for the regions used in your analysis. This reporting standard allows readers to assess the reliability of your structural claims and supports reproducible research practices.

## Related Bioinformatics Guides

- [Alphafold Protein Ligand Docking: Structural Analysis and Computational Methodologies in Bioinformatics](/knowledge/bioinformatics/alphafold-protein-ligand-docking)
- [AlphaFold Structure Prediction Server: Structural Analysis and Computational Methodologies in Bioinformatics](/knowledge/bioinformatics/alphafold-structure-prediction-server)
- [How To Use Alphafold To Predict Structure: Structural Analysis and Computational Methodologies in Bioinformatics](/knowledge/bioinformatics/how-to-use-alphafold-to-predict-structure)
- [The Protein Data Bank (PDB): Archival Standards, Structural Validation Metrics, and Bioinformatics Integration Protocols](/knowledge/bioinformatics/protein-data-bank-archival-validation)
- [How to Interpret Gene Set Enrichment Analysis Results](/knowledge/bioinformatics/how-to-interpret-gene-set-enrichment-analysis-results)

## References and Further Reading

- [NCBI Data Resources](https://www.ncbi.nlm.nih.gov/). National Center for Biotechnology Information.
- [EMBL-EBI Training](https://www.ebi.ac.uk/training). European Bioinformatics Institute.
- [Bioconductor](https://bioconductor.org/). Bioconductor Project.
- [Galaxy Training Network](https://training.galaxyproject.org/). Galaxy Project.
- [nf-core Documentation](https://nf-co.re/docs). nf-core.
- [The Carpentries Lessons](https://carpentries.org/lessons). The Carpentries.
- [In Silico Design and Characterization of the Essential Outer-Membrane Lipoprotein LolB-Derived Multi-Epitope Vaccine Candidate Against &lt,i&gt,Pseudomonas aeruginosa&lt,/i&gt,.](https://doi.org/10.3390/mps9020052). 2026.
- [The transformative impact of AI-enabled AlphaFold 3: evolution, current status, and future prospects in structural biology.](https://doi.org/10.3389/frai.2026.1739303). 2026.
- [Unlocking the power of AI models: exploring protein folding prediction through comparative analysis.](https://doi.org/10.1515/jib-2023-0041). 2024.
- [Experimental assessment of AI-based interactome mapping.](https://doi.org/10.1038/s41467-026-70942-x). 2026.
- [Structure-guided secretome analysis of gall-forming microbes offers insights into effector diversity and evolution.](https://doi.org/10.7554/elife.105185). 2025.

> This article is educational and does not replace validated analysis plans, institutional policy, clinical interpretation, or specialist review.