Dark energy: the cost of function in protein evolution
Abstract
The evolutionary fate of proteins is driven by both folding stability and biological function, dual constraints that often conflict, creating frustration and imposing functional costs beyond stability. These costs can be captured by a "dark energy": the difference between the evolutionary energy of protein sequences and their physical folding energy. Recent advances in deep mutational scanning, protein language models, and inverse-folding models have enabled the quantification of dark energy across the protein universe. We review the computational and experimental approaches that disentangle folding and function at scale, revealing a dark energy component and providing new insights into how biological information flows from sequence to structure to function and back to sequence.
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BibTeX
@misc{galpern2026dark,
title = {{Dark energy: the cost of function in protein evolution}},
author = {Ezequiel A. Galpern and Federico Caamaño and Ignacio E. Sánchez and Diego U. Ferreiro},
howpublished = {arXiv},
year = {2026},
month = aug,
doi = {10.48550/arxiv.2608.27711},
eprint = {2608.27711},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2608.27711},
note = {CC BY-NC-SA 4.0},
}
RIS
TY - UNPB
TI - Dark energy: the cost of function in protein evolution
AU - Ezequiel A. Galpern
AU - Federico Caamaño
AU - Ignacio E. Sánchez
AU - Diego U. Ferreiro
PY - 2026
DA - 2026/08/27
PB - arXiv
DO - 10.48550/arxiv.2608.27711
UR - https://arxiv.org/abs/2608.27711
AB - The evolutionary fate of proteins is driven by both folding stability and biological function, dual constraints that often conflict, creating frustration and imposing functional costs beyond stability. These costs can be captured by a "dark energy": the difference between the evolutionary energy of protein sequences and their physical folding energy. Recent advances in deep mutational scanning, protein language models, and inverse-folding models have enabled the quantification of dark energy across the protein universe. We review the computational and experimental approaches that disentangle folding and function at scale, revealing a dark energy component and providing new insights into how biological information flows from sequence to structure to function and back to sequence.
ER -