How to look at AlphaFold structure results? From prediction to experimental decisions

When researchers first obtain an AlphaFold-predicted protein structure, many instinctively open the PDB file to view the 3D model. But the real challenge often comes after viewing the model—is this structure reliable? Which regions can be used with confidence? Which parts require caution?

As of 2025, the AlphaFold Protein Structure Database has been used by over 3 million researchers across more than 190 countries; AlphaFold Server has assisted in generating over 8 million structure predictions (according to Google DeepMind official data). These predicted structures are widely applied in drug design, enzyme engineering, protein-protein interaction analysis, and other scenarios. But a prediction remains a prediction—knowing how to interpret the results is more important than knowing how to run the prediction. This article systematically outlines the complete workflow for interpreting AlphaFold structure results, from confidence metrics to structural analysis, and how to translate predictions into actionable research insights.

I. Core Output Files from AlphaFold

From Structure to Application

After running AlphaFold (whether locally deployed or via AlphaFold Server), the following output files are typically generated:

l Model file (.cif or .pdb): Contains the predicted 3D structure coordinates, which can be opened directly in molecular viewers such as PyMOL or ChimeraX.

l Confidence summary file (summary_confidences.json): Contains overall confidence scores (pTM, ipTM, etc.).

l Detailed confidence file (full_data.json): Contains complete data, including the full PAE matrix.

l Ranking information: AlphaFold Server generates 5 prediction models per run, ranked by confidence from highest to lowest (0 being the best).

II. pLDDT: Confidence Per Amino Acid

To understand a structure, the first step is not to look at the model—it is to look at the confidence metrics.

l pLDDT (Predicted Local Distance Difference Test) is AlphaFold's per-residue local confidence score, ranging from 0 to 100. This score is visually mapped onto the 3D structure using four colors:

l Dark blue (pLDDT > 90) indicates very high confidence. The prediction confidence for this region is extremely high, with both backbone and side-chain conformations highly reliable—suitable for detailed structural analysis.

l Light blue (70 < pLDDT ≤ 90) indicates high confidence. The backbone conformation is generally very reliable, and side-chain conformations are usable in most cases.

l Yellow (50 < pLDDT ≤ 70) indicates low confidence. These regions typically correspond to flexible loops or locally disordered areas. The backbone trace is roughly correct, but side-chain conformations and local details are unreliable and should be interpreted with caution.

l Orange (pLDDT < 50) indicates very low confidence. These regions are typically intrinsically disordered regions (IDRs). The predicted structure is unreliable and should not be interpreted as a true structure.

On AlphaFold DB, each protein entry displays pLDDT distribution statistics (e.g., "46.6% very high confidence, 34% high confidence…"), allowing a quick assessment of overall prediction quality. In PyMOL or ChimeraX, structures are colored according to these categories, making it immediately apparent which regions are reliable and which require caution.

Key insight: low pLDDT ≠ prediction error. Low pLDDT can reflect true disorder or flexibility, but may also arise from insufficient homologous sequences or "fold-upon-binding" regions. Conversely, high pLDDT does not necessarily mean complete rigidity—conditionally folded regions may still be dynamic. Flexible loops and intrinsically disordered regions are biologically dynamic by nature; AlphaFold's "lack of confidence" in these regions accurately reflects their true biological state.

Ⅲ. PAE: Confidence in Relative Positioning Between Domains

PAE Matrix Analysis

If pLDDT answers "how accurate is each amino acid position," then PAE (Predicted Aligned Error) answers "how accurate is the relative positioning between different regions"—which is crucial for studying inter-domain relationships and protein-protein interactions.

PAE is presented as a matrix:

l Low PAE (< 5 Å): AlphaFold is very confident about the relative positioning of these two regions.

l Medium PAE (5–15 Å): Relative positioning is generally reliable, but with some uncertainty.

l High PAE (> 15 Å): Relative positioning is unreliable and should not be used as a basis for structural analysis.

How to read a PAE matrix?

When viewing a protein on AlphaFold DB, the PAE plot is interactive:

l Dark green blocks in the matrix indicate that the relative positions of residues within that region are reliable (typically corresponding to a structural domain).

l High PAE regions (light green or white) between different dark green blocks indicate that the relative positioning between these two domains is unreliable.

Using human GNE protein as an example: this protein has two independent domains, clearly visible as two dark green "blocks" on the PAE plot. The high PAE region between the two blocks indicates that while AlphaFold can predict the folding of each domain separately, it does not know their relative positioning.

This means: if you are interested in the internal structure of a single domain, you can use it with confidence; but if you are concerned about the spatial relationship between two domains (e.g., the conformation of a flexible linker region), that part of the prediction may be unreliable.

IV. Overall Confidence Scores: pTM and ipTM

For monomeric proteins, pTM (predicted TM-score) is a global assessment of overall structure quality—higher is better. For multimers or complexes, ipTM (interface predicted TM-score) evaluates the confidence of inter-chain interfaces.

AlphaFold Server uses a ranking_score to sort the 5 predicted models, taking into account pTM, ipTM, and penalties (such as atomic clashes or erroneous helices in disordered regions). The model ranked 0 is typically the best overall.

V. From Confidence to Structure: How to Apply in Practice

Step 1: Rapid overall quality screening. First, examine the pLDDT distribution (displayed at the top of the AlphaFold DB page). If most regions have pLDDT > 70, the overall structure is reliable; if large areas have pLDDT < 50, use with caution or consider alternative methods.

Step 2: Identify reliable regions. Use color mapping to see which regions are dark blue/light blue (high confidence) and which are yellow/orange (low confidence). High-confidence regions can be used for detailed structural analysis (e.g., active sites, binding pockets); low-confidence regions may correspond to flexible loops or disordered regions and should not be overinterpreted.

Step 3: Examine inter-domain relationships. Open the PAE plot and check for high PAE regions between different domains. If present, relative positioning is unreliable and should not be used to infer spatial relationships between domains.

Step 4: Cross-validation and experimental design. High-confidence regions can be directly used for downstream work such as molecular docking, virtual screening, and mutagenesis design. Low-confidence regions require further experimental validation (e.g., X-ray crystallography, NMR, cross-linking mass spectrometry).

Special considerations for AlphaFold 3: When predicting multi-component complexes, AlphaFold 3 may exhibit novel error types, such as predicting spurious ordered structures in disordered regions (hallucinations) or atomic clashes. Therefore, for AlphaFold 3-predicted complex structures, it is particularly important to carefully examine confidence metrics and the physical plausibility of the structure.

VI. MatwingsVenus™ (Xiaowu™): Integrating Structure Interpretation into Full-Chain R&D

MatwingsVenus™

In April 2026, Matwings Technology officially launched the conversational protein R&D agent MatwingsVenus™ (Xiaowu™) . In July 2026, MatwingsVenus™ stood out among hundreds of exhibiting products to be selected for the "Treasure of the Hall" award at the World Artificial Intelligence Conference (WAIC), becoming the only AI for Science product to receive this honor.

In the practical research scenario of AlphaFold structure interpretation, users typically face the following questions: after obtaining a predicted structure, how do you quickly determine which regions are reliable? How do you associate structural information with functional annotations? How do you design the next experiment based on the structure? What MatwingsVenus™ provides is not just another single-point analysis tool, but a one-stop platform that embeds structure interpretation into the complete R&D workflow.

In June 2026, the platform added the Protenix model to its "Protein Generation" module, which can predict the 3D structures of multi-component systems including proteins, DNA/RNA, small molecule ligands, and ions, and further analyze intermolecular binding conformations and interaction interfaces. Users can input protein-related queries in natural language to complete integrated analysis from structure prediction to functional annotation—the platform integrates 30+ databases, covering 400+ specialized tools, and automatically performs parallel multi-database retrieval, data integration, and standardized output.

Furthermore, MatwingsVenus™ is equipped with two core capabilities—AI-directed evolution and AI enzyme discovery—which extend AlphaFold structure interpretation directly into protein engineering and functional optimization. After completing structural analysis, users can initiate AI-driven protein design on the same platform without switching tools or re-entering sequences. The platform supports billion-scale real-labeled protein data retrieval, integrates 200+ protein design tools, and compresses traditional protein R&D cycles from 2–5 years down to 2–6 months.

As Matwings Technology noted at an industry forum: the true bottleneck in AI-driven protein R&D is not model accuracy, but whether the full pipeline from structure prediction to functional validation can be closed.

VII. Summary

Workflow from AlphaFold Prediction to Experimental Research

When you obtain AlphaFold structure results, the correct order is: first look at confidence, then look at the structure. Specifically:

1. Examine pLDDT → assess local reliability of each region, distinguishing "reliable regions" from "caution-required regions"

2. Examine the PAE matrix → determine whether relative positioning between domains is reliable

3. Examine overall scores → pTM (monomer) or ipTM (complex) to judge overall quality

4. Make decisions based on confidence → high-confidence regions can be confidently used for downstream analysis; low-confidence regions require experimental validation or should be avoided

AlphaFold is a powerful structure prediction tool, but what it provides is ultimately a prediction, not an experimental fact. Mastering the interpretation of confidence metrics is the key step to translating predicted results into reliable scientific information. The value of MatwingsVenus™ (Xiaowu™) lies in integrating structure interpretation, functional annotation, and protein design into a complete intelligent R&D pipeline—from "seeing the structure" to "using the structure well," enabling AI to truly serve the full spectrum of protein research.