ArticleProteins2020
Structural compliance: A new metric for protein flexibility.
Article in Proteins, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Efficient sampling of large-scale transition pathways and intermediate conformations in sub-mesoscopic protein complexes.Nature communications · 2026Article
- Einstein Model of a Graph to Characterize Protein Folded/Unfolded States.Molecules (Basel, Switzerland) · 2023Article
- Article
- Uses and Abuses of the Atomic Displacement Parameters in Structural Biology.Methods in molecular biology (Clifton, N.J.) · 2022Article
- PACKMAN-Molecule: Python toolbox for structural bioinformatics.Bioinformatics advances · 2022Article
- Low-Frequency Harmonic Perturbations Drive Protein Conformational Changes.International journal of molecular sciences · 2021Article
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Authors and funding
5 authors.
Funding
Abstract
Proteins are the active players in performing essential molecular activities throughout biology, and their dynamics has been broadly demonstrated to relate to their mechanisms. The intrinsic fluctuations have often been used to represent their dynamics and then compared to the experimental B-factors. However, proteins do not move in a vacuum and their motions are modulated by solvent that can impose forces on the structure. In this paper, we introduce a new structural concept, which has been called the structural compliance, for the evaluation of the global and local deformability of the protein structure in response to intramolecular and solvent forces. Based on the application of pairwise pulling forces to a protein elastic network, this structural quantity has been computed and sometimes is even found to yield an improved correlation with the experimental B-factors, meaning that it may serve as a better metric for protein flexibility. The inverse of structural compliance, namely the structural stiffness, has also been defined, which shows a clear anticorrelation with the experimental data. Although the present applications are made to proteins, this approach can also be applied to other biomolecular structures such as RNA. This present study considers only elastic network models, but the approach could be applied further to conventional atomic molecular dynamics. Compliance is found to have a slightly better agreement with the experimental B-factors, perhaps reflecting its bias toward the effects of local perturbations, in contrast to mean square fluctuations. The code for calculating protein compliance and stiffness is freely accessible at https://jerniganlab.github.io/Software/PACKMAN/Tutorials/compliance.
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