Mapping a interactome by crosslinking mass spectrometry and machine learning.

, a widespread human parasite, persists in hosts through complex molecular interactions. Protein-protein interactions (PPIs) underpin essential biological processes, including parasite-host interactions and cellular invasion. Herein, we utilized advanced crosslinking mass spectrometry (XL-MS) techniques to map a tachyzoite cytosolic extract interactome. By integrating MS-cleavable and non-cleavable analysis, we identified a total of 196 unique PPIs at medium confidence (false discovery rate [FDR] < 5%) and 171 at high confidence (FDR < 1%), revealing both known and novel interactions within critical cellular complexes such as the ribosome, proteasome, and dense granule proteins. Structural validation confirmed spatial proximity of crosslinked residues, while comparative analyzes against existing data sets (hyperLOPIT, ToxoNET, and STRING) corroborated the biological relevance of identified interactions. Furthermore, we introduced a machine learning approach leveraging biological annotations and experimental data to significantly enhance the detection and validation of PPIs. Our findings not only provide a refined view of 's molecular architecture but also highlight the utility of XL-MS coupled with computational tools in dissecting complex parasite proteomes. The XL-MS interactome map provides a new valuable resource for understanding parasite biology and developing targeted therapeutic strategies.IMPORTANCEOur work presents a novel application of crosslinking mass spectrometry (XL-MS) integrated with machine learning to systematically characterize the cytosolic protein-protein interactions in -a pathogen of significant clinical and epidemiological interest. This study addresses an important gap in microbial proteomics by leveraging advanced XL-MS techniques to capture transient and novel interactions, which are often challenging to detect with conventional methods. By combining both MS-cleavable and non-cleavable strategies with a robust machine learning approach, we were able to significantly enhance the identification of genuine protein interactions. The methodology described not only improves the depth and accuracy of interactome analysis but also offers a framework that can be applied to other complex microbial systems. We believe that the insights gained from our study will be of great interest to the microbiology community, particularly researchers focusing on host-pathogen interactions and the molecular mechanisms underlying parasitic infections.