ArticlebioRxiv : the preprint server for biology2026
A Deep-Learning Atlas of XPO1-Mediated Nuclear Export at Proteome Scale.
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.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors.
Funding
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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.