Predicting the protein interaction landscape of a mycobacterial pathogen

Proteome-wide crosslinking mass spectrometry reveals novel components of essential complexes in Toxoplasma

Protein–protein interactions underpin nearly all cellular processes, yet systematic definition of these networks remains limited outside a few model organisms. As a result, the architectures of essential complexes in many divergent lineages remain poorly characterized. Here we developed a high-coverage crosslinking mass spectrometry framework to map the proteome-wide interactome of the model apicomplexan parasite Toxoplasma gondii. From 29,624 crosslinked peptide pairs, we resolved a network of 2,859 protein–protein interactions that we integrated with structural modeling to resolve interaction interfaces. We identified and validated previously unrecognized components of essential protein complexes, including a structurally distinct ATP synthase subcomplex containing a highly divergent, apicomplexan-specific α subunit essential for parasite fitness. Beyond revealing unexpected diversification of core mitochondrial machinery, these findings provide a general strategy to define the molecular architecture of divergent organisms and represent a foundational resource for hypothesis generation, structural inference, and discovery of lineage-specific vulnerabilities in pathogen biology.

The predicted interactome of the human mitochondrial proteome

Despite the fundamental importance of mitochondria in cellular metabolism, the molecular function(s) of many mitochondrial proteins remain unknown. Since protein function can be inferred from their interacting partners, we repurpose the protein structure prediction algorithm AlphaFold Multimer (AFM) as a classification model to predict protein-protein interactions of the entire human mitochondrial proteome. By screening 630,003 protein pairs, we create a compendium of 2,895 previously known and newly observed interactions, which include the interacting partner(s) of 85 uncharacterized mitochondrial proteins, thereby linking them to a known biochemical pathway. Extending the AFM-based analysis to 11 diverse eukaryotes identifies evolutionarily conserved interactions among human hits, including regulators of core bioenergetic pathways. Our experiments, guided by these predictions, nominate protein interactions that form the coenzyme Q metabolon and define the mitochondrial copper delivery pathway to cytochrome c oxidase. Our compendium represents a powerful resource for the systematic, structure-based functionalization of the human mitochondrial proteome.

HInt: interaction-based homology discovery through accelerated genome-scale AlphaFold screening

HInt (Homology by Interaction), an accelerated AlphaFold-based framework that enables practical proteome-scale PPI prediction through biologically informed pre-filtering and optimised high-throughput structure modelling, and provides a general framework for uncovering hidden homologues and expands the conceptual landscape of protein homology inference.

Computational Identification and Structural Analysis of Ustilago maydis Effector Proteins.

Identification of novel inhibitors of Mycobacterium smegmatis growth through genome-wide overexpression of Cluster P3 mycobacteriophage Xavia genes.

Bacteriophages encode numerous genes with no known function, many of which can affect essential cellular processes when expressed in the bacterial host. For mycobacteriophages, genome-wide overexpression in Mycobacterium smegmatis can be used to identify proteins that impair growth. To evaluate the cytotoxic potential of the Cluster P3 phage Xavia, we constructed a plasmid library containing 71 predicted Xavia genes under the anhydrotetracycline inducible promoter pTet and screened this library in a plate-based cytotoxicity assay to measure impacts on M. smegmatis growth. Two genes prevented transformants recovery, consistent with toxicity under basal promoter leakiness, and inducible expression of 18 additional genes impaired growth. These inhibitory proteins include structural components; factors involved in DNA metabolism, lysogeny, and lysis; and several proteins with no known function. These results extend functional screening into a lineage of actinobacteriophages that has not previously been characterized, and identify new proteins that warrant further mechanistic analysis.

GenomeProt: User friendly proteogenomics for canonical and non-canonical proteoform characterisation

Quantifying the diversity of RNAs and proteins produced by cells is fundamental to the biological and clinical sciences. However, many RNAs and proteins remain uncharacterised, especially proteins translated from alternate RNA isoforms; untranslated regions of mRNAs and non-coding RNAs, as well as the effects of DNA variation on protein sequences. Proteogenomics aims to characterise the complete proteome by integrating genomics and/or transcriptomics with proteomics, but current tools have limitations in useability, analysis features and visualisation of resulting data. To address these gaps, we developed GenomeProt, a user-friendly GUI-based tool for integrative proteogenomic analysis. We demonstrate its utility by integrating long-read RNA sequencing with mass-spectrometry-based proteomics to pinpoint proteoform expression generated by alternative splicing; discover novel, unannotated proteins in human brain samples; and quantify variant-containing peptides associated with treatment resistance in a melanoma xenograft model. GenomeProt brings the discovery power of proteogenomics to biologists, illuminating the hidden proteome.