A Deep-Learning Atlas of XPO1-Mediated Nuclear Export at Proteome Scale
Sajina DhungeliD, Sihara de Zoysa, DeAnna Burns, Linden McGregor, Rajaian Pushpabai RajeshiD, Ridila Alam, Diya Arain, Vivaan Bhaskar, Jayden Jeong, Aarush Kikani, Eashan Kolli, Zain Mardini, Aanya Parasramka, Eden Potterton, Saachi Thomas, Chintan K. KikaniiD
Abstract
Exportin 1 (XPO1/CRM1) is the principal nuclear export receptor for cargos bearing hydrophobic nuclear export sequences (NESs). Dysregulation of XPO1-dependent export is implicated in cancer, neurodegeneration, and other diseases, yet a comprehensive view of XPO1 function remains limited by the poor reliability of sequence-based NES prediction. Existing predictors are largely derived from a small set of XPO1-cargo structures and are therefore biased toward canonical docking geometries, limiting their ability to detect NESs that engage XPO1 through noncanonical pocket-occupancy patterns. We hypothesized that deep learning-based structural modeling could overcome this limitation by directly sampling binding geometries. Using AlphaFold 3, we modeled full-length cargo-XPO1-RanGTP complexes for more than 4,000 human proteins and identified over 3,000 previously uncharacterized, high-confidence NESs. Integration of AlphaFold predictions with unsupervised structural geometry analysis and experimental validation identified both canonical NESs and noncanonical sequence patterns exhibiting atypical anchor-residue usage, expanding the structural language of XPO1-recognized NESs. Groove-resolved contact maps further revealed helix rotation within the export groove as a regulatory feature that can rewire pocket usage without altering the core NES sequence, enabling PTM- and cofactor-sensitive tuning of export strength. This exportome atlas resolves many previously ambiguous or unidentified NESs in disease-associated proteins and across major cellular systems, including centrosome organization, mRNA processing, ubiquitin signaling, kinase networks, ribosome quality control, and macroautophagy. We further identified recurrent NES-NLS tandem motifs encoded in primary sequence, suggesting coordinated regulation of nucleocytoplasmic transport. Together, our deep learning-based exportome atlas, integrated with NLS maps and accessible through a web-searchable resource, defines an expanded and regulatable code of nuclear transport at proteome scale and offers a framework for dissecting nuclear trafficking and its dysregulation in human disease.
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