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.
S. Butterworth, Ashley L. Gin, Shikha Shikha et al.· bioRxiv· 0 citations
Many proteins' biological functions rely on interconversions between multiple conformations occurring at micro- to millisecond (µs-ms) timescales. A lack of standardized, large-scale experimental data has hindered obtaining a more predictive understanding of these motions. After curating >100 Nuclear Magnetic Resonance (NMR) relaxation datasets, we realized an observable for µs-ms dynamics might be hiding in plain sight. Millisecond dynamics can cause NMR signals to broaden beyond detection, leaving some residues not assigned in the chemical shift datasets of ~10,000 proteins deposited in the Biological Magnetic Resonance Data Bank (BMRB) 1. We made the bold assumption that residues missing assignments are exchange-broadened due to µs-ms motions and trained various deep learning models to predict missing assignments. Strikingly, these models also predict exchange measured via NMR relaxation experiments, indicative of µs-ms dynamics. The best of these models, which we named Dyna-1, leverages an intermediate layer of the multimodal language model ESM-32. Notably, dynamics directly linked to biological function, including enzyme catalysis and ligand binding, are particularly well predicted by Dyna-1, which parallels our findings that residues experiencing µs-ms exchange are more conserved. We anticipate the datasets and models presented here will be transformative in unlocking the common language of dynamics and function.
Hannah K. Wayment-Steele, Gina El Nesr, Ramith Hettiarachchi et al.· Nature· 0 citations
Multiple sequence alignment (MSA) Pairformer is presented, a protein language model that builds on AlphaFold2/3's bidirectional refinement between sequence and pairwise residue representations to accurately model the evolution of protein-protein interactions, despite training exclusively on individual chains.
Yo Akiyama, Zhidian Zhang, Olivia Tang et al.· Cell· 3 citations
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