Coupling of oceanographic state to the dark proteome: a foundation for genome-informed marine productivity modeling
- 1Division of Science, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
Abstract
Phytoplankton drive ~46% of global primary production, yet whether their protein repertoires encode ocean conditions - and whether reference databases capture the environmentally responsive fraction - is unknown. We used a protein language model to classify 447.7 million proteins from 2,357 samples and analyzed domain profiles for 231.7 million algal proteins across 2,044 samples. Under spatial block cross-validation, environment predicted individual domain abundances at R2 up to 0.59, while domain profiles predicted sea surface temperature at R2 = 0.38. The strongest coupling lay beyond annotated sequence space: 33,950 families clustered from 201 million Pfam-dark proteins coupled to environment 2.29-fold more strongly than Pfam domains. Cross-taxonomic selection, protein language modeling, and AlphaFold 3 predictions of 138 well-folded, InterPro-unannotated representatives support the Pfam-dark proteome as a biologically structured evolutionary compartment. Thus, the proteins most tightly coupled to ocean state are those least represented in reference databases.
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BibTeX
@misc{nelson2026coupling,
title = {{Coupling of oceanographic state to the dark proteome: a foundation for genome-informed marine productivity modeling}},
author = {D. R. Nelson and M. Plouviez and S. Daakour and A. Jaiswal and W. Fu and S. A. Amin and K. Salehi-Ashtiani},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.23.753693},
url = {https://www.biorxiv.org/content/10.64898/2026.09.23.753693v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Coupling of oceanographic state to the dark proteome: a foundation for genome-informed marine productivity modeling
AU - D. R. Nelson
AU - M. Plouviez
AU - S. Daakour
AU - A. Jaiswal
AU - W. Fu
AU - S. A. Amin
AU - K. Salehi-Ashtiani
PY - 2026
DA - 2026/09/23
PB - bioRxiv
DO - 10.64898/2026.09.23.753693
UR - https://www.biorxiv.org/content/10.64898/2026.09.23.753693v1
AB - Phytoplankton drive ~46% of global primary production, yet whether their protein repertoires encode ocean conditions - and whether reference databases capture the environmentally responsive fraction - is unknown. We used a protein language model to classify 447.7 million proteins from 2,357 samples and analyzed domain profiles for 231.7 million algal proteins across 2,044 samples. Under spatial block cross-validation, environment predicted individual domain abundances at R2 up to 0.59, while domain profiles predicted sea surface temperature at R2 = 0.38. The strongest coupling lay beyond annotated sequence space: 33,950 families clustered from 201 million Pfam-dark proteins coupled to environment 2.29-fold more strongly than Pfam domains. Cross-taxonomic selection, protein language modeling, and AlphaFold 3 predictions of 138 well-folded, InterPro-unannotated representatives support the Pfam-dark proteome as a biologically structured evolutionary compartment. Thus, the proteins most tightly coupled to ocean state are those least represented in reference databases.
ER -