Understanding the complex network of protein–protein interactions (PPIs) is essential for deciphering cellular processes in Chlamydomonas reinhardtii, a model unicellular green alga with growing importance in bioenergy, biotechnology, and basic plant biology. Despite advances in genome annotation and functional genomics in Chlamydomonas, comprehensive PPI maps remain largely incomplete. This project aims to generate an all-to-all PPI prediction for the Chlamydomonas proteome using AlphaFold2, an advanced deep learning model originally designed for protein structure prediction. By leveraging recent adaptations of AlphaFold2 for protein complex modeling, we will computationally predict the structures and interaction interfaces of all pairwise protein combinations within the Chlamydomonas reference proteome. The resulting interactome will be analyzed to identify high-confidence interactions, conserved protein complexes, and novel regulatory modules. Integration with existing transcriptomic, proteomic, and functional datasets will provide a systems-level perspective on cellular organization and signaling pathways in Chlamydomonas. This study not only advances the molecular understanding of algal biology but also establishes a scalable framework for high-throughput PPI prediction in non-model organisms.