Using multiple interdependency
to separate functional from phylogenetic correlations in protein alignments

Tillier, Elisabeth R. M.; Lui, Thomas

doi:10.1093/bioinformatics/btg072

Cited by 118 publications

(124 citation statements)

References 17 publications

Supporting

Mentioning

120

Contrasting

Order By: Relevance

“…Simulation studies: We tested the sensitivity of CAPS using simulated data that allow for the control of the extent of coevolution and the evolutionary history of each site in the alignment. We also compared CAPS with other nonparametric methods that use the information theory or a Bayesian approximation, including the method of Korber [herein called the mutual information criterion (MICK) implemented in our program PIMIC and available on request; Korber et al 1993] and the method of Tillier and Lui (2003) implemented in the program Dependency, as well as with the parametric method of Pollock et al (1999) implemented in the program lnLCorr. While parametric methods can be more powerful than nonparametric ones, incorrect assumptions in the model can yield a high number of false positives (Dimmic and Hubisz 2005).…”

Section: Methodsmentioning

confidence: 99%

“…This hypothesis states that, at any given time, some sites are invariable due to their functional or structural constraints but, as mutations are fixed elsewhere in the sequence, these constraints may change. Various methods for identifying covariant amino acid pairs at the molecular level have been previously developed (e.g., Korber et al 1993;Gö bel et al 1994;Shindyalov et al 1994;Taylor and Hatrick 1994;Tillier and Collins 1995;Chelvanayagam et al 1997;Pollock and Taylor 1997;Lockhart et al 1998;Tuffley and Steel 1998;Pollock et al 1999;Pritchard et al 2001;Tillier and Lui 2003;Ané et al 2004;Galtier 2004;Dutheil et al 2005). The main limitation of many of these methods has been their inability to separate phylogenetic linkage from functional and structural coevolution.…”

mentioning

confidence: 99%

“…In other methods there was not an assessment of the random noise caused by a limited number of sequences in the alignment or by high pairwise distance. Tillier and Lui (2003) attempted to remove the phylogenetic coevolution; however, their analysis was biased toward those positions covarying with at most a few others. Gloor et al (2005) partially corrected these effects although their method requires alignments of at least 125 sequences to remove stochastic covariation.…”

mentioning

confidence: 99%

See 2 more Smart Citations

A Novel Method for Detecting Intramolecular Coevolution: Adding a Further Dimension to Selective Constraints Analyses

2006

View full text Add to dashboard Cite

Protein evolution depends on intramolecular coevolutionary networks whose complexity is proportional to the underlying functional and structural interactions among sites. Here we present a novel approach that vastly improves the sensitivity of previous methods for detecting coevolution through a weighted comparison of divergence between amino acid sites. The analysis of the HIV-1 Gag protein detected convergent adaptive coevolutionary events responsible for the selective variability emerging between subtypes. Coevolution analysis and functional data for heat-shock proteins, Hsp90 and GroEL, highlight that almost all detected coevolving sites are functionally or structurally important. The results support previous suggestions pinpointing the complex interdomain functional interactions within these proteins and we propose new amino acid sites as important for interdomain functional communication. Three-dimensional information sheds light on the functional and structural constraints governing the coevolution between sites. Our covariation analyses propose two types of coevolving sites in agreement with previous reports: pairs of sites spatially proximal, where compensatory mutations could maintain the local structure stability, and clusters of distant sites located in functional domains, suggesting a functional dependency between them. All sites detected under adaptive evolution in these proteins belong to coevolution groups, further underlining the importance of testing for coevolution in selective constraints analyses.

show abstract

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

A Novel Method for Detecting Intramolecular Coevolution: Adding a Further Dimension to Selective Constraints Analyses

2006

View full text Add to dashboard Cite

show abstract

“…The problem has been discussed extensively in the literature (10,(23)(24)(25)(26). In this study, we implemented a simple sampling correction, by counting sequences with more than 80% identity and reweighting them in the frequency counts.…”

mentioning

confidence: 99%

Direct-coupling analysis of residue coevolution captures native contacts across many protein families

Morcos

Pagnani

Lunt

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

1,372

2,225

View full text Add to dashboard Cite

The similarity in the three-dimensional structures of homologous proteins imposes strong constraints on their sequence variability. It has long been suggested that the resulting correlations among amino acid compositions at different sequence positions can be exploited to infer spatial contacts within the tertiary protein structure. Crucial to this inference is the ability to disentangle direct and indirect correlations, as accomplished by the recently introduced direct-coupling analysis (DCA). Here we develop a computationally efficient implementation of DCA, which allows us to evaluate the accuracy of contact prediction by DCA for a large number of protein domains, based purely on sequence information. DCA is shown to yield a large number of correctly predicted contacts, recapitulating the global structure of the contact map for the majority of the protein domains examined. Furthermore, our analysis captures clear signals beyond intradomain residue contacts, arising, e.g., from alternative protein conformations, ligand-mediated residue couplings, and interdomain interactions in protein oligomers. Our findings suggest that contacts predicted by DCA can be used as a reliable guide to facilitate computational predictions of alternative protein conformations, protein complex formation, and even the de novo prediction of protein domain structures, contingent on the existence of a large number of homologous sequences which are being rapidly made available due to advances in genome sequencing.statistical sequence analysis | residue-residue covariation | contact map prediction | maximum-entropy modeling

show abstract

“…Homologous proteins from different organisms are matched so that equivalent amino acids within the sequence are aligned in a column. The variation along this column is then quantified by calculating the mutual information content (27,28,31). The variation can also be quantified by estimating the pairwise amino acid distance using probabilistic approaches and then, the correlation between the distance patterns between amino acid columns from the same or different proteins (29,32,33).…”

Section: Molecular Coadaptation: Millions Of Years Of Trial-error Evomentioning

confidence: 99%

Protein coadaptation and the design of novel approaches to identify protein–protein interactions

Fares¹,

Ruiz-González²,

Labrador³

2011

IUBMB Life

View full text Add to dashboard Cite

SummaryProteins rarely function in isolation but they form part of complex networks of interactions with other proteins within or among cells. The importance of a particular protein for cell viability is directly dependent upon the number of interactions where it participates and the function it performs: the larger the number of interactions of a protein the greater its functional importance is for the cell. With the advent of genome sequencing and ''omics'' technologies it became feasible conducting large-scale searches for protein interacting partners. Unfortunately, the accuracy of such analyses has been underwhelming owing to methodological limitations and to the inherent complexity of protein interactions. In addition to these experimental approaches, many computational methods have been developed to identify protein-protein interactions by assuming that interacting proteins coevolve resulting from the coadaptation dynamics between the amino acids of their interacting faces. We review the main technological advances made in the field of interactomics and discuss the feasibility of computational methods to identify protein-protein interactions based on the estimation of coevolution. As proof-of-concept, we present a classical case study: the interactions of cell surface proteins (receptors) and their ligands. Finally, we take this discussion one step forward to include interactions between organisms and species to understand the generation of biological complexity. Development of technologies for accurate detection of proteinprotein interactions may shed light on processes that go from the fine-tuning of pathways and metabolic networks to the emergence of biological complexity.

show abstract

Using multiple interdependency to separate functional from phylogenetic correlations in protein alignments

Cited by 118 publications

References 17 publications

A Novel Method for Detecting Intramolecular Coevolution: Adding a Further Dimension to Selective Constraints Analyses

A Novel Method for Detecting Intramolecular Coevolution: Adding a Further Dimension to Selective Constraints Analyses

Direct-coupling analysis of residue coevolution captures native contacts across many protein families

Protein coadaptation and the design of novel approaches to identify protein–protein interactions

Contact Info

Product

Resources

About