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

In this blog post, we’ll explore Chai-1, a reproduction of AlphaFold3 built under an Apache-2 license. Chai-1 is a state-of-the-art multi-modal foundation model for molecular structure prediction, capable of unified predictions across proteins, small molecules, DNA, RNA, glycosylations, and more.

One key advantage of Chai-1 is its ability to perform well without relying on multiple sequence alignments (MSAs). This not only speeds up predictions but also maintains high accuracy. Additionally, Chai-1 can incorporate experimental data, such as wet-lab restraints, to improve prediction quality, making it a powerful tool for applications like drug discovery.

At Neurosnap, we’ve integrated Chai-1 into our platform with a straightforward Web UI and clear visualizations. Metrics like pLDDT, pTM, and PAE are displayed alongside intuitive graphics, giving you a clear picture of the confidence and reliability of your results.

Whether you’re just getting started with Chai-1 or looking to deepen your expertise, this post will guide you through the essential metrics and visualizations available in Neurosnap’s Chai-1 platform. For a deeper dive into Chai-1’s background and capabilities, check out our recent blog posts:

• Introducing Chai-1: A Commercial Alternative to AlphaFold3
• AlphaFold3 Advancing Antibody Docking and Improving Accuracy

Understanding the Core Metrics of Chai-1 Predictions

Chai-1 evaluates biomolecular structure predictions using a comprehensive set of metrics. These metrics help assess both local and global accuracy, ensuring a clear understanding of each model's performance.

Below is a breakdown of the key metrics used:

1. Metrics Aggregate Score

A single, high-level number representing the overall quality of the prediction. A higher score indicates greater confidence in the global fold accuracy of the protein structure.

2. Mean pLDDT (Predicted Local Distance Difference Test)

Definition: The average per-residue confidence score across the entire protein structure.

Significance: Higher values reflect the model's trust in the precise placement of individual atoms.

Interpretation:

• pLDDT > 0.9: Very high confidence.
• 0.7 ≤ pLDDT ≤ 0.9: High confidence.
• 0.5 ≤ pLDDT < 0.7: Low confidence.
• pLDDT < 0.5: Very low confidence.

3. pTM (Predicted TM-Score)

Definition: An integrated measure of how well the model has predicted the overall structure of a complex. It represents the predicted TM score for a superposition between the predicted structure and the hypothetical true structure.

Significance: A TM score provides insights into the reliability of the predicted global fold:

• pTM > 0.5: The overall predicted fold might resemble the true structure.
• pTM ≤ 0.5: The predicted structure is likely incorrect.

Note: pTM scores should be interpreted carefully. For example, if one protein in a complex is much larger and accurately predicted, it can dominate the score even if a smaller interacting partner is poorly modeled. Thus, while a pTM score above 0.5 suggests structural similarity, it does not guarantee accurate modeling of all components.

4. ipTM (Interface Predicted TM-Score)

Definition: A metric that specifically evaluates the accuracy of the predicted relative positions of subunits in a complex. It assesses how well the interactions and spatial relationships between chains are modeled.

Significance: ipTM is often more useful to users than pTM because it provides direct insight into the quality of subunit positioning within the complex. Accurate subunit positions (reflected in a high ipTM score) strongly correlate with the correctness of the entire complex:

• ipTM > 0.8: High-confidence, high-quality prediction.
• 0.6 ≤ ipTM ≤ 0.8: A "grey zone" where predictions could be correct or incorrect.
• ipTM < 0.6: Indicates a likely failed prediction.

Note: Factors such as disordered regions or low pLDDT scores can reduce ipTM values, even for correctly predicted complexes. Additionally, when using settings optimized for speed (e.g., minimal recycling steps in large-scale interaction screenings), thresholds as low as 0.3 may be used for initial analysis, though such predictions require further examination to confirm accuracy.

5. Inter-Chain Clashes

Definition: Identifies physical overlaps or steric clashes between chains in the predicted structure.

Impact: The presence of inter-chain clashes indicates potential errors in the spatial arrangement of chains, reducing model reliability.

6. Per-Chain pTM

Definition: A detailed breakdown of pTM scores for individual chains in a complex structure.

Utility: Helps pinpoint chains modeled with higher accuracy, even if the overall complex has lower global confidence.

7. Overall Quality Score

In addition to standard metrics, Neurosnap’s Overall Quality Score provides a holistic assessment of the predicted structure by integrating multiple confidence parameters into a single label.

This allows users to: - Quickly evaluate the reliability of a prediction. - Use the score as a starting point for deeper analysis with individual metrics and structural visualizations.

Visualizing Chai-1 Predictions

When using Chai-1 to predict protein or protein complex structures, several visual outputs can help you assess the model’s reliability. Below is an overview of each plot we offer and how it informs structural interpretation.

1. pLDDT (Predicted Local Distance Difference Test)

This plot provides a residue-by-residue confidence score:

• High pLDDT values suggest well-defined local structure, indicating that the backbone geometry in those regions is likely correct.
• Lower pLDDT values often correspond to flexible linkers, or other less-structured regions where the model is more uncertain.

2. Per Chain Pair ipTM Values

This matrix captures how confident Chai-1 is in the interfaces between different chains:

• Elevated ipTM values suggest strongly defined interactions and accurate subunit positioning.
• Decreased ipTM values imply uncertainty in inter-chain orientation, which may indicate flexibility or weaker interfaces.

3. Predicted Aligned Error (PAE)

This plot reveals how the model positions and orients different parts of the structure:

• Lower PAE values between domains indicate stable relative placement. If two regions have low PAE, the model is quite confident they’re arranged correctly.
• Higher PAE values in certain areas mean Chai-1 is more ambiguous about how those parts fit together, signaling caution when interpreting their positions.

4. Predicted Distance Error (PDE)

The PDE heatmap focuses on the confidence in inter-residue distances:

• Lower PDE values mean Chai-1 is fairly certain about those residue-residue distances, which supports a more reliable 3D arrangement.
• Higher PDE values highlight regions where distance predictions are less certain, recommending closer scrutiny of those regions.

By examining all four plots together, you can quickly identify well-resolved parts of your predicted structure, flag flexible or disordered regions, and focus on key interfaces. This ensures you make the most of Chai-1’s predictions—balancing high-confidence areas with the spots that need additional validation or follow-up experiments.

Making Sense of Chai-1 Predictions

By combining the above metrics and visualizations, you can comprehensively evaluate your Chai-1 predictions:

1. Start with Global Metrics

• Aggregate Score: Gives a quick snapshot of overall model quality.
• pTM: Provides an initial sense of whether the global fold is likely correct.

2. Look at Local Detail

• Mean pLDDT & Per-Residue pLDDT Plot: Identify the regions with high or low confidence. High pLDDT indicates well-defined areas; low pLDDT spots may need further scrutiny or indicate flexibility.

3. Check Interfaces and Multi-Chain Arrangements

• ipTM: Focuses on the accuracy of inter-chain contacts. A high ipTM means the subunits are likely positioned correctly.
• Per-Chain pTM: Helps pinpoint which chains are confidently modeled if the overall structure has mixed accuracy.

4. Assess Domain Positions

• Predicted Aligned Error (PAE): Reveals how confidently Chai-1 arranges different domains or protein regions. Low PAE between domains indicates a reliable spatial arrangement.
• Predicted Distance Error (PDE): Highlights the certainty in residue-residue distances, guiding you to areas that may need extra confirmation.

By following this step-by-step approach—checking global metrics first, then focusing on local scores and inter-chain relationships—you can confidently interpret high-quality regions and identify areas requiring additional validation or complementary experimental data.

Ready to Try It?

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