The network of stabilizing contacts in proteins studied by coevolutionary data.
By S. Lui, G. Tiana. 2013
33 citations recorded by the source corpus.
The primary structure of proteins, that is their sequence, represents one of the most abundant sets of experimental data concerning biomolecules. The study of correlations in families of co-evolving proteins by means of an inverse Ising-model approach allows to obtain information on their native conformation. Following up on a recent development along this line, we optimize the algorithm to calculate effective energies between the residues, validating the approach both back-calculating interaction energies in a model system, and predicting the free energies associated to mutations in real systems. Making use of these effective energies, we study the network of interactions which stabilizes the native conformation of some well-studied proteins, showing that it displays different properties than the associated contact network.
Subfields: Molecular Biology, Statistical and Nonlinear Physics
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Molecular Biology
- Martin Weigt
- Faruck Morcos
- José N. Onuchic
- Ryan R. Cheng
- Herbert Levine
- Matteo Figliuzzi
- Alexander Schug
- Samuel Coulbourn Flores
In Statistical and Nonlinear Physics
Citations in the corpus
- Coevolutionary Landscape Inference and the Context-Dependence of Mutations in Beta-Lactamase TEM-1 (2015, background): “Further more, coevolutionary patterns in protein families were recently found to be closely related to protein energetics and folding landscapes [71, 72].”
- Fast pseudolikelihood maximization for direct-coupling analysis of protein structure from many homologous amino-acid sequences (2014, methodology): “Numerous freshly conceived methods expand the concepts and applicability of DCA in various directions [30, 31, 32, 33, 34, 35, 36, 37, 38].”
- Connecting the Sequence-Space of Bacterial Signaling Proteins to Phenotypes Using Coevolutionary Landscapes (2016, background): “Several studies have reported strong linear correlation between mutational changes in H ð s Þ with mutational changes in protein stability (Lui and Tiana 2013; Morcos et al. 2014; Contini and Tiana 2015).”
- Toward rationally redesigning bacterial two-component signaling systems using coevolutionary information (2014, background): “Furthermore, mutational changes in the direct couplings have been shown to correlate well with the experimental mutational changes in the free energy for individual proteins (38).”