ArticlebioRxiv : the preprint server for biology2026
Decoding epitope immunodominance in HIV Env using cryoEM and machine learning.
Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
18 authors.
Funding
Abstract
Viral surface glycoproteins, such as the HIV envelope protein (Env), present numerous antibody (Ab) epitopes, yet immune responses consistently focus on only a subset, a phenomenon known as immunodominance. Although structural studies have provided insights into Env antigenicity, our understanding of the molecular features that govern efficient Ab engagement remains incomplete, thereby limiting the predictive and rational design of vaccines. Here, we characterized the structural determinants of epitope immunodominance in HIV Env by integrating high-resolution cryoEM-based polyclonal epitope mapping (cryoEMPEM) across different clades with quantitative analyses of epitope topology, accessibility, and physicochemical properties. More than 70 new structures were resolved to assemble a library of >100 Env-antibody complexes. These data informed the development of a surface-centric, machine-learning model to predict relative
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Registered trials
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