Computed structures of core eukaryotic protein complexes

Area of Science:

• Molecular Biology
• Structural Biology
• Computational Biology

Background:

• Protein-protein interactions are fundamental to cellular processes, but many eukaryotic protein complex structures remain uncharacterized.
• Identifying and structurally modeling these complexes is crucial for understanding biological function.

Purpose of the Study:

• To systematically identify and model core eukaryotic protein complexes in the *Saccharomyces cerevisiae* proteome.
• To leverage advances in amino acid coevolution analysis and deep learning for structure prediction.

Main Methods:

• Utilized RoseTTAFold and AlphaFold for structure modeling.
• Screened 8.3 million protein pairs from yeast proteome using multiple sequence alignments.
• Applied amino acid coevolution analysis to predict interacting proteins.

Main Results:

• Identified 1505 yeast protein pairs likely to interact.
• Generated structure models for 106 previously unidentified protein assemblies.
• Provided structural models for 806 complexes lacking prior structural characterization.
• Characterized complexes with up to five subunits involved in essential cellular processes.

Conclusions:

• This study systematically mapped and structurally modeled numerous yeast protein complexes, including novel ones.
• The findings provide significant insights into eukaryotic cellular functions and protein complex organization.
• The integrated approach of coevolution analysis and deep learning is effective for discovering and modeling protein interactions.