ArticleBiophysical journal2009
An energetic representation of protein architecture that is independent of primary and secondary structure.
Article in Biophysical journal, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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
6 citing papers in PubMed.
- Statistical Thermodynamics of the Protein Ensemble: Mediating Function and Evolution.Annual review of biophysics · 2025Review
- A Thermodynamic Atlas of Proteomes Reveals Energetic Innovation across the Tree of Life.Molecular biology and evolution · 2022Article
- The role of negative selection in protein evolution revealed through the energetics of the native state ensemble.Proteins · 2016Article
- Prediction of protein relative enthalpic stability from molecular dynamics simulations of the folded and unfolded states.Biophysical journal · 2012Article
- The role of protein conformational fluctuations in allostery, function, and evolution.Biophysical chemistry · 2011Review
- Investigating homology between proteins using energetic profiles.PLoS computational biology · 2010Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Protein fold classification often assumes that similarity in primary, secondary, or tertiary structure signifies a common evolutionary origin. However, when similarity is not obvious, it is sometimes difficult to conclude that particular proteins are completely unrelated. Clearly, a set of organizing principles that is independent of traditional classification could be valuable in linking different structural motifs and identifying common ancestry from seemingly disparate folds. Here, a four-dimensional ensemble-based energetic space spanned by a diverse set of proteins was defined and its characteristics were contrasted with those of Cartesian coordinate space. Eigenvector decomposition of this energetic space revealed the dominant physical processes contributing to the more or less stable regions of a protein. Unexpectedly, those processes were identical for proteins with different secondary structure content and were also identical among different amino-acid types. The implications of these results are twofold. First, it indicates that excited conformational states comprising the protein native state ensemble, largely invisible upon inspection of the high-resolution structure, are the major determinant of the energetic space. Second, it suggests that folds dissimilar in sequence or structure could nonetheless be energetically similar if their respective excited conformational states are considered, one example of which was observed in the N-terminal region of the Arc repressor switch mutant. Taken together, these results provide a surface area-based framework for understanding folds in energetic terms, a framework that may eventually yield a means of identifying common ancestry among structurally dissimilar proteins.
Indexed as
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.