The network of stabilizing contacts in proteins studied by coevolutionary data

Abstract

The primary structure of proteins, that is their sequence, represents one of the most abundant set 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 networks of interactions which stabilizes the native conformation of some well-studied proteins, showing that it display different properties than the associated contact network. * Electronic address: guido.tiana@unimi.it 1 arXiv:1307.1583v1 [q-bio.BM]

Toward rationally redesigning bacterial two-component signaling systems using coevolutionary information

“…with high statistical significance. 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). We formulate a metric called the direct information score (DIS) from the direct couplings (Eqs.…”

A many-body term improves the accuracy of effective potentials based on protein coevolutionary data

“…In Ref. 12, for example, besides the calculation of mutational ∆∆G, it was used to identify the frustrated regions of the protein. In doing so, the fields h i (σ) were regarded just as chemical potential meant to fix the average concentration of the twenty types of amino acids.…”

“…12 to design an effective, non-portable two-body contact potential, assuming that the native conformation of the protein is known. This potential proved successful in back-calculating residue-residue interactions in families of proteins generated by simulated evolution.…”

Connecting the Sequence-Space of Bacterial Signaling Proteins to Phenotypes using Coevolutionary Landscapes

“…The Potts model (eq. 3) obtained from DCA has been related to the theory of evolutionary sequence selection as well as mutational changes in protein stability (Lui and Tiana 2013;Morcos et al 2014;Contini and Tiana 2015). Additional work has applied DCA to protein folding to predict the effect of point mutations on the folding rate (Mallik et al 2016) as well as construct a statistical potential for native contacts in a structure-based model of a protein (Cheng et al 2016) to better capture the transition state ensemble.…”

“…Assuming that the sequence diversity is completely due to stability considerations, HðsÞ ¼ bEðsÞ where EðsÞ is the energy of the folded protein with respect to the unfolded state and b ¼ ðk B T sel Þ À1 is the inverse of the evolutionary selection temperature from protein folding theory (Pande et al 1997(Pande et al , 2000Morcos et al 2014). Several studies have reported strong linear correlation between mutational changes in HðsÞ with mutational changes in protein stability (Lui and Tiana 2013;Morcos et al 2014;Contini and Tiana 2015). However, HðsÞ ¼ bEðsÞ may not be an appropriate approximation for proteins that have evolved with interacting partners, for which sequence selection is plausibly influenced by additional factors such as binding affinities as well as binding/unbinding rates.…”