Trypanosoma cruzi is the causative agent of Chagas disease, a neglected illness with outdated treatments. Bromodomain factors (BDFs) are essential proteins that recognize acetylated lysines and have strong therapeutic potential. They form part of epigenetic complexes that regulate chromatin accessibility and, therefore, gene expression. However, little is known about their structure in trypanosomatids. Here, we used a combination of experimental and bioinformatic approaches to infer the stoichiometry and structure of T. cruzi bromodomain-containing complexes. By reconstructing the proximity networks of five BDFs using TurboID-directed proximity labeling, we identified highly interconnected components that assemble into the CRKT and NuA4 complexes. Using novel structure prediction strategies that systematically explore the stoichiometric space, we inferred that CRKT assembles into three distinct modules and NuA4 in two, with different degrees of interaction dynamics. The core module of CRKT contains two copies of each component, including BDF3, BDF5, and BDF8, arranged in a subcomplex with central symmetry. The catalytic module of CRKT has three subunits, including the h istone a cetyltransferase 2 (HAT2), while the BET ( b romodomain and e xtra-terminal) module has one unit of both BDF4 and BDF1. The catalytic module of NuA4 closely resembles the yeast piccolo-NuA4 module and contains HAT1, while the TINTIN module associates with the catalytic module via the C-terminal domain of BDF6. These insights shed light on the structure and composition of epigenetic complexes in trypanosomatids, opening new avenues for rational drug design aimed at disrupting their function.

Advances in artificial intelligence (AI) have transformed the field of protein structure, notably with the accuracy level reached by AlphaFold in the prediction of monomeric and multimeric protein structures. However, while cloud implementations have broadened access to these methods, there is still a lack of non-AI tools to systematically interpret and analyze the resulting data, especially under varying modeling contexts. Here, we present MultimerMapper, a pipeline and software suite designed to extract, integrate, analyze, and visualize the behavior of multimeric systems in ensembles of overlapping protein structure predictions. It utilizes statistics and graph theory, combined with novel approaches, to interpret how protein-protein interfaces and protein conformation behave across different modeling contexts. Starting from a list of input sequences, MultimerMapper guides users through the generation and interpretation of structure prediction ensembles. It produces interactive 2D and 3D visualizations, highlights higher-order subassemblies, infers probable stoichiometries, and identifies alternative interaction modes and conformational changes associated with the presence or absence of specific proteins. MultimerMapper is cross-platform, freely available, and easily integrates with existing workflows. It offers a new perspective on the dynamic nature of predicted protein complexes, supporting researchers in the exploration of functional mechanisms and assembly paths.

Background: In recent years, compounds known as Proteolysis Targeted Chimeras (PROTACs) have revitalized the field of bioactive molecule design. These compounds promote proteolysis of therapeutic targets by recruiting them to ubiquitin ligases. One of the most commonly used classes of compounds in the synthesis of PROTACs are immunomodulatory imides (IMiDs), such as thalidomide (TLD), which interact with the E3 ligase CRL4^CRBN via the CULT domain of the cereblon protein (CRBN). This domain has been identified in proteins across various phylogenetic groups, including trypanosomatids, leading to the hypothesis that IMiD-derived PROTACs should be active in these organisms. Methods: The trypanocidal activity of the PROTAC dBET1 and its separated components (JQ1 and TLD) were assayed using a T. cruzi strain expressing β-glalactosidase. Potential CRL4-E3L complexes from humans and trypanosomatids were assembled in silico with MultimerMapper. The IMiD-binding site of HsCRBN and its trypanosomatid homologs were analyzed using molecular dynamics and docking simulations. Results: We demonstrate that the compound dBET1 does not function as a PROTAC in Trypanosoma cruzi. In silico structural analysis of CRL4-E3L complex orthologs revealed that the trypanosomal CULT-containing protein is not part of such a complex. Molecular dynamics simulations showed that the pocket of this CULT domain is smaller than that of mammalian CRBN and cannot accommodate IMiDs within. Conclusions: We underscore the importance of functional and structural validation in drug discovery, particularly when extrapolating mechanisms between evolutionarily distant species. While PROTACs hold promise in human therapeutics, our work advocates for re-evaluating the rationale behind thalidomide-based PROTACs in trypanosomatid research.

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-multimer to predict direct interactions and validated them using yeast two and three-hybrid assays. We focused on MRG domain-containing proteins and their interactions, typically found in histone acetyltransferase/deacetylase complexes. The results identified a structurally conserved complex, Tc TINTIN, which is orthologous to human and yeast TINTIN complexes; and another trimeric complex involving an MRG domain, only seen in trypanosomatids. The identification of a key component of Tc TINTIN, Tc MRGBP, 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 Tc BDF6. 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 AlphaFold2-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 AlphaFold2 models for the determination of multiprotein complexes illuminates the power of using such artificial intelligence-derived tools in the future development of biology. Abstract Figure:

Aim: Kinetoplastids are flagellated protozoa encompassing multiple parasitic species responsible for severe neglected diseases. Although treatments exist, therapeutic failure and toxic side effects underscore the need for innovative drug development. Recent advances in protein design have accelerated the creation of small protein modules with specific functions, known as miniproteins, with broad pharmacological applications. However, their intrinsic inability to cross biological membranes limits their use against intracellular targets. This work aims to propose and computationally explore a modular delivery strategy that exploits the flagellar pocket (FP) as an entry route to deliver protein-based therapeutics into the parasites. Methods: Using experimentally determined structures of three FP receptors, we applied a motif-scaffolding pipeline combining RFdiffusion, ProteinMPNN, and AlphaFold2-multimer to design de novo miniprotein modules capable of mimicking the natural cargo recognized by each receptor. Candidate designs were evaluated using a scoring function integrating minimum interaction predicted aligned error (miPAE) and backbone root mean square deviation (RMSD) across five predicted models per design. Results: The design campaign yielded different outcomes depending on the target. For the transferrin receptor, 67 candidates surpassed the established in silico success thresholds, a pool expected to contain multiple experimentally validated binders. For the invariable surface glycoprotein 65, 17 candidates met the criteria, constituting a tractable experimental panel. Lastly, the haptoglobin-hemoglobin receptor proved a challenging target, with no candidates clearly surpassing both thresholds, likely due to the hydrophilic nature of its binding interfaces and the requirement for direct heme coordination. Conclusions: This work provides a structural rationale for a novel receptor-mediated intracellular delivery paradigm in kinetoplastid parasites, offering a computational pipeline for generating miniprotein modules ready for experimental validation. We further outline how these delivery modules could be integrated into modular protein-based drugs incorporating protease recognition sequences, cell-penetrating peptides, and subcellular localization signals, laying the conceptual ground for a new therapeutic approach against these neglected diseases.