Deciphering Divergent Trypanosomatid Nuclear Complexes by Analyzing Interactomic Datasets with AlphaFold2 and Genetic Approaches

RODRIGUEZ ARAYA, ELVIO; MERLI, MARCELO L.; CRIBB, PAMELA; DE SOUZA, VINICIUS C.; SERRA, ESTEBAN

ACS Infectious Diseases

American Chemical Society

Año: 2023 vol. 9 p. 1267 - 1282

Acetylation signaling pathways in trypanosomatids, a group of early branching organisms, are poorly understood due to highly divergent protein sequences. To overcome this challenge, we used interactomic datasets and AlphaFold2 (AF2)-multimer to predict direct interactions and validated them using yeast two and three-hybrid assays. We focused on MORF4 related gene (MRG) domain-containing proteins and their interactions, typically found in histone acetyltransferase/deacetylase complexes. The results identified a structurally conserved complex, TcTINTIN, which is orthologous to human and yeast trimer independent of NuA4 for transcription interaction (TINTIN) complexes; and another trimeric complex involving an MRG domain, only seen in trypanosomatids. The identification of a key component of TcTINTIN, TcMRGBP, would not have been possible through traditional homology-based methods. We also conducted molecular dynamics simulations, revealing a conformational change that potentially affects its affinity for TcBDF6. The study also revealed a novel way in which an MRG domain participates in simultaneous interactions with two MRG binding proteins binding two different surfaces, a phenomenon not previously reported. Overall, this study demonstrates the potential of using AF2-processed interactomic datasets to identify protein complexes in deeply branched eukaryotes, which can be challenging to study based on sequence similarity. The findings provide new insights into the acetylation signaling pathways in trypanosomatids, specifically highlighting the importance of MRG domain-containing proteins in forming complexes, which may have important implications for understanding the biology of these organisms and developing new therapeutics. On the other hand, our validation of AF2 models for the determination of multiprotein complexes illuminates the power of using such artificial intelligence-derived tools in the future development of biology.