Coevolving protein residues: maximum likelihood identification and relationship to structure 1 1Edited by G. Von Heijne

Pollock, David D.; Taylor, William R.; Goldman, Nick

doi:10.1006/jmbi.1998.2601

Cited by 239 publications

(206 citation statements)

References 25 publications

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“…Several early studies identified within-protein correlations among amino acid positions but did not account for phylogenetic relationships (Korber et al 1993;Neher 1994). More recent studies have identified co-occurring substitutions within a phylogeny (Pollock 1999;Fukami-Kobayashi et al 2002;Dimmic et al 2005;Dutheil et al 2005;Yeang and Haussler 2007). Such patterns support within-protein epistatic interactions but do not distinguish among CN, CM, and CWS scenarios.…”

Section: Compensatory Protein Evolutionmentioning

confidence: 99%

Weak Selection and Protein Evolution

2012

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The "nearly neutral" theory of molecular evolution proposes that many features of genomes arise from the interaction of three weak evolutionary forces: mutation, genetic drift, and natural selection acting at its limit of efficacy. Such forces generally have little impact on allele frequencies within populations from generation to generation but can have substantial effects on long-term evolution. The evolutionary dynamics of weakly selected mutations are highly sensitive to population size, and near neutrality was initially proposed as an adjustment to the neutral theory to account for general patterns in available protein and DNA variation data. Here, we review the motivation for the nearly neutral theory, discuss the structure of the model and its predictions, and evaluate current empirical support for interactions among weak evolutionary forces in protein evolution. Near neutrality may be a prevalent mode of evolution across a range of functional categories of mutations and taxa. However, multiple evolutionary mechanisms (including adaptive evolution, linked selection, changes in fitness-effect distributions, and weak selection) can often explain the same patterns of genome variation. Strong parameter sensitivity remains a limitation of the nearly neutral model, and we discuss concave fitness functions as a plausible underlying basis for weak selection.U NDER the neutral model, newly arising mutations fall into two major fitness classes: strongly deleterious and selectively neutral (Kimura 1968;King and Jukes 1969). The first class is well supported by mutation accumulation experiments (reviewed in Simmons and Crow 1977;Halligan and Keightley 2009) and early DNA sequence comparisons (Grunstein et al. 1976;Kafatos et al. 1977) and is shared among competing evolutionary models. The novel and controversial aspect of the neutral theory was the proposition that, among mutations that go to fixation, the vast majority are selectively neutral. Advantageous substitutions, although important in phenotypic evolution, are sufficiently rare at the molecular level that they need not be considered to adequately model the process. Under the neutral theory, within-and between-species variation sample two aspects of a process of origination by mutation and changes in gene frequency dominated by drift and, for some mutations, negative selection (Kimura and Ohta 1971a). In contrast, polymorphism and divergence may be "uncoupled" under selection models (Gillespie 1987). Protein polymorphism and the neutral model: invariance of heterozygosityClear predictions for levels of polymorphism within populations and divergence among species are appealing aspects of the neutral model. However, within a few years of its proposal, the notion of drift-dominated evolution was challenged by overall patterns of allozyme polymorphism and contrasts between DNA and protein divergence.Although evolutionary geneticists were generally surprised by the extent of naturally occurring variation revealed by allozyme gel electrophoresis in the 197...

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Section: Compensatory Protein Evolutionmentioning

confidence: 99%

Weak Selection and Protein Evolution

2012

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“…Indeed, although some more sophisticated models have been proposed (e.g., refs. [45][46][47][48][49][50], all commonly used phylogenetic models of long-term protein evolution assume that epistasis is absent so that sites evolve independently (51)(52)(53)(54)(55)(56).…”

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confidence: 99%

Contingency and entrenchment in protein evolution under purifying selection

Shah

McCandlish

Plotkin

2015

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

180

236

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The phenotypic effect of an allele at one genetic site may depend on alleles at other sites, a phenomenon known as epistasis. Epistasis can profoundly influence the process of evolution in populations and shape the patterns of protein divergence across species. Whereas epistasis between adaptive substitutions has been studied extensively, relatively little is known about epistasis under purifying selection. Here we use computational models of thermodynamic stability in a ligand-binding protein to explore the structure of epistasis in simulations of protein sequence evolution. Even though the predicted effects on stability of random mutations are almost completely additive, the mutations that fix under purifying selection are enriched for epistasis. In particular, the mutations that fix are contingent on previous substitutions: Although nearly neutral at their time of fixation, these mutations would be deleterious in the absence of preceding substitutions. Conversely, substitutions under purifying selection are subsequently entrenched by epistasis with later substitutions: They become increasingly deleterious to revert over time. Our results imply that, even under purifying selection, protein sequence evolution is often contingent on history and so it cannot be predicted by the phenotypic effects of mutations assayed in the ancestral background.intragenic epistasis | coevolution | near neutrality | protein stability W hether a heritable mutation is advantageous or deleterious to an organism often depends on the evolutionary history of the population. A mutation that is beneficial at the time of its introduction may confer its beneficial effect only in the presence of other potentiating or permissive mutations (1-9). Thus, the fate of a mutation arising in a population may be contingent on previous mutations (10-13). Conversely, once a mutation has fixed in a population, the mutation becomes part of the genetic background onto which subsequent modifications are introduced. Because the beneficial effects of the subsequent modifications may depend on the focal mutation, as time passes reversion of the focal mutation may become increasingly deleterious, leading to a type of evolutionary conservatism, or entrenchment (14-18).In the context of protein evolution, the effects of contingency and entrenchment are most easily studied by considering a sequence of single amino acid changes (19) that extends both forward and backward in time from some focal substitution. To assess the roles of contingency and entrenchment we can study the degree to which each focal substitution was facilitated by previous substitutions, and the degree to which the focal substitution influences the subsequent course of evolution (Fig. 1A).Dependencies within a sequence of substitutions are closely connected to the concept of epistasis-that is, the idea that the phenotypic effect of a mutation at a particular genetic site may depend on the genetic background in which it arises (20-24). In the absence of epistasis, a mutation has the same effect ...

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“…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

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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.

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Coevolving protein residues: maximum likelihood identification and relationship to structure 1 1Edited by G. Von Heijne

Cited by 239 publications

References 25 publications

Weak Selection and Protein Evolution

Weak Selection and Protein Evolution

Contingency and entrenchment in protein evolution under purifying selection

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

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