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Experimental assessment of AI-based interactome mapping.

Luke Lambourne^1,2,3, Anupama Yadav^1,2,3, Yang Wang^1,2,3

¹Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.

Nature Communications

|April 4, 2026

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Summary

This summary is machine-generated.

Artificial intelligence (AI) shows promise in predicting protein structures but underperforms in discovering novel protein-protein interactions (PPIs) compared to experimental methods. AI models currently excel at providing structural insights for known PPIs.

Area of Science:

Molecular Biology
Computational Biology
Genetics

Background:

Genotype-phenotype relationships depend on macromolecular interactions.
Understanding the protein-protein interactome is crucial for genetics and cellular biology.
AI advancements, like AlphaFold, enable accurate tertiary protein structure prediction, reviving interest in computational interactome prediction.

Purpose of the Study:

To experimentally assess the impact and performance of AI-driven interactome predictions.
To compare AI predictions against experimental interactome maps for yeast and humans.
To evaluate the discovery potential of AI in identifying novel protein-protein interactions (PPIs).

Main Methods:

Development of a comprehensive experimental framework.

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Systematic assessment of AI-driven interactome predictions (e.g., AlphaFold).

Proteome-wide screening and comparison with experimental reference interactome maps.

Main Results:

High-confidence AI predictions match the quality of established experimental methods.
AI approaches underperform in discovering novel PPIs compared to experimental maps.
The yeast interactome map identified over 40-fold more novel PPIs than AI.
AlphaFold provided structural models for many experimentally identified PPIs missed by AI screens.

Conclusions:

AI's current strength lies in modeling the quaternary structures of experimentally identified PPIs.
Experimental methods remain superior for discovering novel PPIs at a proteome-wide scale.
AI and experimental approaches are complementary in interactome research.