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Protein-protein interactions: how to push forward the limits of the revolutionary AlphaFold2 programme?

​AlphaFold2 is revolutionising protein structure prediction and structural biology practices. However, it may prove less effective for certain protein assemblies, particularly when they depend on intrinsically disordered regions. In an article published in Nature Communications, researchers from the I2BC show that applying a fragmentation strategy to the protein partners of such assemblies very significantly improves AlphaFold2's prediction capacity.

Published on 29 January 2024

AlphaFol​​d2 and its limits

Mapping protein-protein interaction networks is essential for understanding the dynamics of cellular functions and their cross-regulation. Precise knowledge of interaction sites makes it possible to specifically perturb the proteins in these networks and understand the synergies and competitions that ensure cell function.

Unfortunately, a great amount of structural information is still lacking to provide a detailed understanding of the organisation of interaction networks. The AlphaFold2 artificial intelligence programme has demonstrated a remarkable ability to predict the structures of protein assemblies that have co-evolved over long time scales. Its performance remained poorly characterised for assemblies involving intrinsically disordered regions, which often mediate transient and dynamic interactions during evolution.

Protein fra​​​gmentation strategy

In a study published in the journal Nature Communications, researchers from the AMIG team at the I2BC department  have shown that AlphaFold2 performs poorly if large disordered regions are used directly for prediction (40% success rate). A protein fragmentation strategy was found to be particularly well adapted to predicting the interfaces between folded domains and small protein motifs that fold on contact with the partner. Applied on a large scale on more than 900 complexes, this strategy achieved a success rate of almost 90%, a very encouraging result for the systematic screening of protein interaction networks. Nevertheless, the study calls for vigilance with regard to the risks of detecting false positives, which will be at the heart of future developments in artificial intelligence strategies such as AlphaFold2.


Contacts ​​Institut CEA-Joliot:

Jessica Andr​​eani (

Raphaël Gu​érois (

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