Interpreting Boltz-1 (AlphaFold3) Metrics and Visualizations on Neurosnap

In this blog post we are going to explore how to leverage and interpret the Boltz-1 implementation of AlphaFold3. Boltz-1 is a reproduction of the original AlphaFold3 model released by Google's DeepMind which offers a number of major advantages over the base AlphaFold3 model such as a commercial friendly MIT license, more advanced usage options, and improvements in accuracy for modeling many different types of molecules and complexes.

One of Boltz-1's standout features is its robust confidence metrics, which empower users to evaluate the reliability and plausibility of predicted structures. At Neurosnap, we’ve taken this further by integrating an array of intuitive visualizations into our online Boltz-1 tool. These visual aids help you interpret key metrics like pLDDT, pTM, ipTM, PDE, and PAE, offering a deeper understanding of structural predictions.

Whether you’re new to Boltz-1 or an experienced researcher, this post will walk you through these metrics and visualizations, helping you unlock the full potential of Neurosnap’s Boltz-1 service. For a deeper dive into Boltz-1’s background and capabilities, check out our recent blog posts.

• AlphaFold3 Advancing Antibody Docking and Improving Accuracy
  
• Achieving AlphaFold3-Level Accuracy with Open-Source Boltz-1

Understanding the Core Metrics

Aggregate Score:
A single, top-level number that represents an overall assessment of prediction quality. Higher scores mean the model is more confident in the global fold.

Mean pLDDT (Predicted Local Distance Difference Test):
Per-residue confidence values typically scaled 0–1. High pLDDT suggests the model strongly trusts its local atomic placement. Average pLDDT >0.9 often indicates a near-atomic-resolution prediction, suitable for detailed analysis.

Complex ipLDDT (Interface pLDDT):
Focuses on the residue-level confidence at chain-chain interfaces. High ipLDDT means the model is confident about how chains interact.

pTM (Predicted TM-Score):
A global measure of structural similarity to a native-like fold. Scores near 1.0 signal a model that likely captures the true topology of the protein.

ipTM (Interface Predicted TM-Score):
Similar to pTM but specifically evaluating the accuracy of how different chains interact. High ipTM values mean interfaces are accurately modeled.

Complex PDE & iPDE (Predicted Docking Error):
Quantify uncertainty in how chains are arranged relative to each other. Lower values mean more precise docking predictions.

Per Chain pTM:
For complexes, this breaks down pTM scores on a per-chain basis. It can highlight if one chain is modeled better than another within the same complex.

Overall Quality:
In addition to the standard metrics, Neurosnap provides an “Overall Quality” score—an aggregate measure calculated by combining various quality indicators. This holistic metric allows you to quickly gauge the general reliability of the predicted structure. By incorporating multiple confidence parameters into one concise label, Neurosnap’s Overall Quality score streamlines the interpretation process, offering a clear starting point before you delve deeper into the individual metrics and visualizations.

Visualizations to Guide Your Interpretation

Predicted LDDT per Position:
This shows a residue-by-residue confidence profile. Elevated, stable values indicate well-defined domains, while dips highlight regions that might be disordered or flexible.

ipTM Pairs:
This matrix displays ipTM scores for interfaces between chains. Higher values mean the model predicts robust, well-defined interactions. Lower values indicate uncertain or poorly defined interfaces.

Predicted Aligned Error (PAE) This heatmap assesses how confidently the model predicts the relative positions and orientations of residues across the protein. Lower PAE values between residues belonging to different domains indicate well-defined relative positions and orientations—suggesting stable domain-domain relationships. Conversely, higher PAE values for such residue pairs suggest uncertainty in how these domains are arranged relative to each other, indicating that their positions and orientations should not be taken as definitive.

Predicted Distance Error (PDE) The PDE heatmap focuses on the confidence in inter-residue distances. Lower PDE values indicate that the model predicts these distances with high precision, enhancing trust in the 3D positioning of amino acids and other structural elements. On the other hand, higher PDE values highlight regions where distance estimates are less certain, guiding you to treat these areas more tentatively when drawing structural conclusions.

Bringing It All Together

By combining these metrics and plots, you can comprehensively evaluate your predicted models:

1. Start with Global Metrics: Check the aggregate score and pTM for an instant quality appraisal.
2. Look at Local Detail: Use mean pLDDT to confirm backbone and sidechain confidence and consult the per-position LDDT plot to identify less certain regions.
3. Check Interfaces and Multi-Chain Arrangements: ipTM and ipLDDT highlight the reliability of inter-chain contacts. The ipTM pairs heatmap shows which interactions stand on solid ground.
4. Assess Domain Positions: PAE helps discern whether different domains are confidently placed or if their relative arrangement is uncertain, while PDE confirms that predicted distances are precise.

Ready to Try It?

If you’re eager to see these metrics and visualizations in action, explore our Boltz-1 (AlphaFold3) service.