Direct coevolutionary couplings reflect biophysical residue interactions in proteins

Coucke, Alice; Uguzzoni, Guido; Oteri, Francesco; Cocco, Simona; Monasson, Rémi; Weigt, Martin

doi:10.1063/1.4966156

Cited by 23 publications

(20 citation statements)

References 37 publications

(40 reference statements)

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“…Of note, residues that are in physical proximity are more strongly coupled because the interactions are more direct. In fact, coevolving pairs reflect the thermodynamics of the interaction between the two amino acids [2]. In addition, the role a residue plays in protein function or structure determines how strongly coupled a specific residue may be to other nearby residues.…”

Section: Introduction To Sequence Coevolution Methodsmentioning

confidence: 99%

Applications of sequence coevolution in membrane protein biochemistry

Nicoludis¹,

Gaudet²

2018

Biochimica et Biophysica Acta (BBA) - Biomembranes

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Recently, protein sequence coevolution analysis has matured into a predictive powerhouse for protein structure and function. Direct methods, which use global statistical models of sequence coevolution, have enabled the prediction of membrane and disordered protein structures, protein complex architectures, and the functional effects of mutations in proteins. The field of membrane protein biochemistry and structural biology has embraced these computational techniques, which provide functional and structural information in an otherwise experimentally-challenging field. Here we review recent applications of protein sequence coevolution analysis to membrane protein structure and function and highlight the promising directions and future obstacles in these fields. We provide insights and guidelines for membrane protein biochemists who wish to apply sequence coevolution analysis to a given experimental system.

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Section: Introduction To Sequence Coevolution Methodsmentioning

confidence: 99%

Applications of sequence coevolution in membrane protein biochemistry

Nicoludis¹,

Gaudet²

2018

Biochimica et Biophysica Acta (BBA) - Biomembranes

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“…Statistical energies have been used to predict sequence-dependent fitnesses [8], enzymatic rates [9], melting temperature[6, 10], and mutation effects [11]. Potts models can also be used to predict contacts in protein structure, as the coupling parameters of the model can indicate which position pairs have the strongest “direct” statistical dependencies, which are found to be good predictors of contacts [12]. This contact information has been found to be enough to perform accurate ab initio protein structure prediction from sequence variation data alone [13, 14].…”

Section: Introductionmentioning

confidence: 99%

Influence of multiple-sequence-alignment depth on Potts statistical models of protein covariation

Haldane

Levy

2019

Phys. Rev. E

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Potts statistical models have become a popular and promising way to analyze mutational covariation in protein multiple sequence alignments (MSAs) in order to understand protein structure, function and fitness. But the statistical limitations of these models, which can have millions of parameters and are fit to MSAs of only thousands or hundreds of effective sequences using a procedure known as inverse Ising inference, are incompletely understood. In this work we predict how model quality degrades as a function of the number of sequences N, sequence length L, amino-acid alphabet size q, and the degree of conservation of the MSA, in different applications of the Potts models: in “fitness” predictions of individual protein sequences, in predictions of the effects of singlepoint mutations, in “double mutant cycle” predictions of epistasis, and in 3-D contact prediction in protein structure. We show how as MSA depth N decreases an “overfitting” effect occurs such that sequences in the training MSA have overestimated fitness, and we predict the magnitude of this effect and discuss how regularization can help correct for it, using a regularization procedure motivated by statistical analysis of the effects of finite sampling. We find that as N decreases the quality of point mutation effect predictions degrade least, fitness and epistasis predictions degrade more rapidly, and contact predictions are most affected. However, overfitting becomes negligible for MSA depths of more than a few thousand effective sequences, as often used in practice, and regularization becomes less necessary. We discuss the implications of these results for users of Potts covariation analysis.

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“…The study of residue-residue coevolutive networks by statistic coupling is useful for analyzing conserved protein families [82][83][84][85] and identifying important residues in protein folding and stability [83,[85][86][87][88].…”

Section: Thermostabilizing Mutations Are Positioned In a Coevolutive mentioning

confidence: 99%

Glutantβase: a database for improving the rational design of glucose-tolerant β-glucosidases

Mariano

Pantuza

Santos

et al. 2020

BMC Mol and Cell Biol

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Β-glucosidases are key enzymes used in second-generation biofuel production. They act in the last step of the lignocellulose saccharification, converting cellobiose in glucose. However, most of the β-glucosidases are inhibited by high glucose concentrations, which turns it a limiting step for industrial production. Thus, β-glucosidases have been targeted by several studies aiming to understand the mechanism of glucose tolerance, pH and thermal resistance for constructing more efficient enzymes. In this paper, we present a database of β-glucosidase structures, called Glutantβase. Our database includes 3842 GH1 β-glucosidase sequences collected from UniProt. We modeled the sequences by comparison and predicted important features in the 3D-structure of each enzyme. Glutantβase provides information about catalytic and conserved amino acids, residues of the coevolution network, protein secondary structure, and residues located in the channel that guides to the active site. We also analyzed the impact of beneficial mutations reported in the literature, predicted in analogous positions, for similar enzymes. We suggested these mutations based on six previously described mutants that showed high catalytic activity, glucose tolerance, or thermostability (A404V, E96K, H184F, H228T, L441F, and V174C). Then, we used molecular docking to verify the impact of the suggested mutations in the affinity of protein and ligands (substrate and product). Our results suggest that only mutations based on the H228T mutant can reduce the affinity for glucose (product) and increase affinity for cellobiose (substrate), which indicates an increment in the resistance to product inhibition and agrees with computational and experimental results previously reported in the literature. More resistant βglucosidases are essential to saccharification in industrial applications. However, thermostable and glucose-tolerant β-glucosidases are rare, and their glucose tolerance mechanisms appear to be related to multiple and complex factors. We gather here, a set of information, and made predictions aiming to provide a tool for supporting the rational design of more efficient β-glucosidases. We hope that Glutantβase can help improve second-generation biofuel production. Glutantβase is available at http://bioinfo.dcc.ufmg.br/glutantbase.

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Direct coevolutionary couplings reflect biophysical residue interactions in proteins

Cited by 23 publications

References 37 publications

Applications of sequence coevolution in membrane protein biochemistry

Applications of sequence coevolution in membrane protein biochemistry

Influence of multiple-sequence-alignment depth on Potts statistical models of protein covariation

Glutantβase: a database for improving the rational design of glucose-tolerant β-glucosidases

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