Probing the dark energy in the functional protein universe

We show how to localize and quantify the functional evolutionary constraints on natural proteins. Protein folding has been one of the strongest constraints in sequence evolution. The method we propose compares the perturbations caused by local sequence variants to the energetics of the protein folding process and to the corresponding change to the apparent selection landscape of sequences over the evolutionary time scale. The difference between the physical folding free energies and the evolutionary free energies can be called a "dark energy." We analyze various protein sets and thereby show that dark energy is largely localized at functional sites, which are also often energetically frustrated from the point of view of folding. Overall, we find that about 25% of the positions of the folded globular proteins display some significant dark energy. When a function relies on a free energy that can be thermodynamically quantified, such as the binding energy to a partner, the relationship of this physical free energy with dark energy can be used to define a functional selection temperature, just as there is a selection temperature for folding. We show that selection for folding and binding functions bear similar weights in specific protein-protein interactions.

Authors

Institutions

Publication Details

Journal
Proceedings of the National Academy of Sciences
Published
2026-01-22
DOI
https://doi.org/10.1073/pnas.2531111123
Citations
2
Primary Topic
Protein Structure and Dynamics
Type
article
Field-Weighted Citation Impact
44.47
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Probing the dark energy in the functional protein universe

Ignacio E. Sánchez, Peter G. Wolynes, Diego U. Ferreiro, Ezequiel A. Galpern et al.
2 citations
Proceedings of the National Academy of Sciences
Protein Structure and Dynamics
44.47
article

Probing the dark energy in the functional protein universe

Ignacio E. Sánchez, Peter G. Wolynes, Diego U. Ferreiro, Ezequiel A. Galpern, Carlos Bueno
article en
2 citations

Abstract

We show how to localize and quantify the functional evolutionary constraints on natural proteins. Protein folding has been one of the strongest constraints in sequence evolution. The method we propose compares the perturbations caused by local sequence variants to the energetics of the protein folding process and to the corresponding change to the apparent selection landscape of sequences over the evolutionary time scale. The difference between the physical folding free energies and the evolutionary free energies can be called a "dark energy." We analyze various protein sets and thereby show that dark energy is largely localized at functional sites, which are also often energetically frustrated from the point of view of folding. Overall, we find that about 25% of the positions of the folded globular proteins display some significant dark energy. When a function relies on a free energy that can be thermodynamically quantified, such as the binding energy to a partner, the relationship of this physical free energy with dark energy can be used to define a functional selection temperature, just as there is a selection temperature for folding. We show that selection for folding and binding functions bear similar weights in specific protein-protein interactions.

Proceedings of the National Academy of SciencesVol. 123(4)
Center for Theoretical Biological Physics (US), Fundación Ciencias Exactas y Naturales (AR), Rice University (US)
Affordable and clean energy
Openalex Percentile: Top 0%
Protein Structure and Dynamics
44.47
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.