Speaker
Description
Molecular mimicry, where pathogen proteins structurally resemble host antigens, is a central hypothesis in autoimmune pathology. However, current detection methods are largely limited to sequence homology or domain-level alignment, failing to identify the subtle, discontinuous structural "patches" that often drive antibody cross-reactivity. To resolve this computational bottleneck, we present JumpMASTER, a heavily optimized evolution of the state-of-the-art MASTER structural motif-matching algorithm. JumpMASTER achieves more than two orders of magnitude acceleration over existing standards, enabling the detection of 20 angstrom radii structural mimics for hundreds of pathogen queries across the entire human proteome in seconds.
We integrated this high-speed structural scanning with AlphaFold3 to computationally model antibody cross- reactivity from pathogen epitopes to predicted human mimics. Applying this pipeline to major viral proteomes—including SARS-CoV-2, HIV, RSV, and Epstein-Barr Virus (EBV)—we uncovered a broad landscape of both previously characterized and novel host-pathogen structural homologies, some under various levels of evolutionary selection. As a prominent example, we identified high-confidence mimicry motifs within both surface-exposed and intracellular targets, such as vesicular trafficking proteins. We propose that these intracellular mimics become accessible to circulating antibodies via viral-induced cellular stress or ectopic surface expression, leading to more inflammation.
These findings demonstrate JumpMASTER’s utility in unmasking non-obvious, structurally conserved epitopes that evade sequence-based detection. By providing a scalable framework to map the 'structural mimicry interactome,' this work generates new hypotheses regarding the etiology of post-viral syndromes and highlights new classes of potential therapeutic targets.
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