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A Protocol for Computer-Based Protein Structure and Function Prediction

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Published on: November 3, 2011

Assessing scoring metrics for AlphaFold2 and AlphaFold3 protein complex predictions.

Luca R Genz^1,2,3, Sanjana Nair^1,3, Natan Nagar^1,3,4

¹Research Department of Integrative Virology, Leibniz-Institut für Virologie (LIV), Germany.

Protein Science : a Publication of the Protein Society

|October 13, 2025

Summary

AI models like AlphaFold3 and ColabFold show promise in predicting protein structures. Interface-specific scores and the new C2Qscore offer improved assessment for protein complex modeling accuracy.

Keywords:

AlphaFold3 ChimeraX plug‐in ColabFold Interface scoring Protein complex modeling Protein structure prediction Scoring metrics

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Area of Science:

Structural biology
Computational biology
Biomolecular modeling

Background:

AI-driven protein structure prediction is rapidly advancing structural biology.
Accurate assessment of predicted protein complex models is crucial for understanding biomolecular interactions.

Purpose of the Study:

To evaluate widely used scoring metrics for AI-predicted protein complex models.
To benchmark assessment scores for ColabFold and AlphaFold3.
To develop an improved scoring metric for protein complex quality assessment.

Main Methods:

Benchmarking of scoring metrics using 223 heterodimeric protein structures and their predictions from ColabFold (with/without templates) and AlphaFold3.
Analysis of interface-specific vs. global scores for model reliability.
Development and application of a new weighted combined score, C2Qscore.

Main Results:

ColabFold with templates and AlphaFold3 demonstrate similar performance, outperforming ColabFold without templates.
Assessment scores are most effective for ColabFold without templates.
Interface-specific scores, particularly ipTM and model confidence, are more reliable for complex predictions.
The novel C2Qscore improves model quality assessment and reveals limitations of existing metrics for complex assemblies.

Conclusions:

AI models show significant progress in protein complex prediction, but careful score selection is vital.
Interface-specific scores and the C2Qscore enhance the reliability of protein complex model evaluation.
The C2Qscore provides a valuable tool for assessing protein complex models in realistic scenarios, including cryo-EM data.