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

Abstract

Articles you may be interested inThe study of correlated mutations in alignments of homologous proteins proved to be successful not only in the prediction of their native conformation but also in the development of a two-body effective potential between pairs of amino acids. In the present work, we extend the effective potential, introducing a many-body term based on the same theoretical framework, making use of a principle of maximum entropy. The extended potential performs better than the two-body one in predicting the energetic effect of 308 mutations in 14 proteins (including membrane proteins). The average value of the parameters of the many-body term correlates with the degree of hydrophobicity of the corresponding residues, suggesting that this term partly reflects the effect of the solvent. C 2015 AIP Publishing LLC.[http://dx

Inferring repeat-protein energetics from evolutionary information

“…Nevertheless, these methods do not take into account the chemical nature of the amino acids, which can be codifying inhomogeneities in the energetic distribution that are crucial for the activity of repeat-proteins [28, 29]. On this basis, different approaches have been proposed recently to include chemical details in the correlation analyses [30], trying to predict folding stability [31], conformational heterogeneity [23, 32, 33], mutational effect in the interaction in two-component signaling proteins [27] or the global effect on antibiotic resistance from sequences of β -lactamases [34, 35]. As many other tools, these were optimized to perform well on globular proteins, and their application to repeat proteins is not straightforward.…”

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

“…Assuming that the sequence diversity is completely due to stability considerations, $H(s)=\beta E(s)$ where $E(s)$ is the energy of the folded protein with respect to the unfolded state and $\beta ={(k_B{T}_{sel})}^{-1}$ is the inverse of the evolutionary selection temperature from protein folding theory (Pande et al 1997, 2000; Morcos et al 2014). Several studies have reported strong linear correlation between mutational changes in $H(s)$ with mutational changes in protein stability (Lui and Tiana 2013; Morcos et al 2014; Contini and Tiana 2015). However, $H(s)=\beta E(s)$ 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.…”

“…The Potts model (eq. 3) obtained from DCA has been related to the theory of evolutionary sequence selection (Morcos et al 2014) 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.…”

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.…”