Using the Mutation-Selection Framework to Characterize Selection on Protein Sequences

Teufel, Ashley I.; Ritchie, Andrew M.; Wilke, Claus O.; Liberles, David A.

doi:10.3390/genes9080409

Cited by 13 publications

(17 citation statements)

References 64 publications

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“…This was initially done by using mixtures of substitution models (Koshi and Goldstein 1995 ) and models that did not assume the same mutational process for all sites in a mutation-selection framework (Halpern and Bruno 1998 ; Lartillot and Philippe 2004 ). The CAT models have been extended to include temporal shifts in amino acid fitnesses (CAT-BP) and have spawned work on the related mutation-selection models, including towards relaxing assumptions of an equilibrium process (Blanquart and Lartillot 2008 ; Teufel et al 2018 ). Variants of the mutation-selection framework remain at the cutting edge of amino acid substitution models, but have not been widely used for ancestral sequence reconstruction yet.…”

Section: Methodological Improvementsmentioning

confidence: 99%

“…Overall, the state of the art of protein models has progressed from PAM-style models of increasing sophistication (Dayhoff et al 1978 ; Jones et al 1992 ; Whelan and Goldman 2001 ; Le and Gascuel 2008 ) to CAT models (Lartillot and Philippe 2004 ) to CAT models with breakpoints (Zhou et al 2010 ) to mutation-selection models (Teufel et al 2018 ). Breakpoints and covarion-type models enable rates to shift at a site over a tree (Wang et al 2007 ).…”

Section: Methodological Improvementsmentioning

confidence: 99%

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Ancestral Sequence Reconstruction: From Chemical Paleogenetics to Maximum Likelihood Algorithms and Beyond

2021

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As both a computational and an experimental endeavor, ancestral sequence reconstruction remains a timely and important technique. Modern approaches to conduct ancestral sequence reconstruction for proteins are built upon a conceptual framework from journal founder Emile Zuckerkandl. On top of this, work on maximum likelihood phylogenetics published in Journal of Molecular Evolution in 1996 was one of the first approaches for generating maximum likelihood ancestral sequences of proteins. From its computational history, future model development needs as well as potential applications in areas as diverse as computational systems biology, molecular community ecology, infectious disease therapeutics and other biomedical applications, and biotechnology are discussed. From its past in this journal, there is a bright future for ancestral sequence reconstruction in the field of evolutionary biology.

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Section: Methodological Improvementsmentioning

confidence: 99%

Section: Methodological Improvementsmentioning

confidence: 99%

Ancestral Sequence Reconstruction: From Chemical Paleogenetics to Maximum Likelihood Algorithms and Beyond

2021

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“…One of their main applications has been to characterize the genes, sites (Nielsen and Yang, 1998;Yang et al, 2005;Murrell et al, 2012) or lineages (Zhang and Nielsen, 2005;Kosakovsky Pond et al, 2011) having experienced positive selection (Murrell et al, 2015;Enard et al, 2016). More generally, these models highlight the respective contributions of mutation, selection, genetic drift (Teufel et al, 2018) and biased gene conversion (Pouyet and Gilbert, 2020;Kosiol and Anisimova, 2019), and the causes of their variation between genes (Zhang and Yang, 2015) or across species (Seo et al, 2004;Popadin et al, 2007;Lartillot and Poujol, 2011).…”

Section: Introductionmentioning

confidence: 99%

An improved codon modeling approach for accurate estimation of the mutation bias

Latrille

Lartillot

2021

Preprint

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Nucleotide composition in protein-coding sequences is the result of the equilibrium between mutation and selection. In particular, the nucleotide composition differs between the three coding positions, with the third position showing more extreme composition than the first and the second positions. Yet, phylogenetic codon models do not correctly capture this phenomenon and instead predict that the nucleotide composition should be the same for all 3 positions of the codons. Alternatively, some models allow for different nucleotide rates at the three positions, a problematic approach since the mutation process should in principle be blind to the coding structure and homogeneous across coding positions. Practically, this misconception could have important consequences in modelling the impact of GC-biased gene conversion (gBGC) on the evolution of protein-coding sequences, a factor which requires mutation and fixation biases to be carefully disentangled. Conceptually, the problem comes from the fact that phylogenetic codon models cannot correctly capture the fixation bias acting against the mutational pressure at the mutation-selection equilibrium. To address this problem, we present an improved codon modeling approach where the fixation rate is not seen as a scalar anymore, but as a tensor unfolding along multiple directions, which gives an accurate representation of how mutation and selection oppose each other at equilibrium. Thanks to this, this modelling approach yields a reliable estimate of the mutational process, while disentangling fixation probabilities in different directions.

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“…Evolutionary emergence has been explored by studying gene duplication [1–3], de novo gene emergence [4–7, 40, 53], open reading frame extension [8, 9, 11], and sequence properties [10, 54], i.e. GC-content [12] and codon usage [13, 14]. However, whether a novel protein is deleterious or beneficial depends not only on its own sequence features but also the environmental context of interaction partners [15, 16].…”

Section: Introductionmentioning

confidence: 99%

A computational exploration of resilience and evolvability of protein-protein interaction networks

Klein

Holmér

Smith

et al. 2020

Preprint

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AbstractProtein-protein interaction (PPI) networks represent complex intra-cellular protein interactions, and the presence or absence of such interactions can lead to biological changes in an organism. Recent network-based approaches have shown that a phenotype’s PPI network’s resilience to environmental perturbations is related to its placement in the tree of life; though we still do not know how or why certain intra-cellular factors can bring about this resilience. One such factor is gene expression, which controls the simultaneous presence of proteins for allowed extant interactions and the possibility of novel associations. Here, we explore the influence of gene expression and network properties on a PPI network’s resilience, focusing especially on ribosomal proteins—vital molecular-complexes involved in protein synthesis, which have been extensively and reliably mapped in many species. Using publicly-available data of ribosomal PPIs for E. coli, S.cerevisae, and H. sapiens, we compute changes in network resilience as new nodes (proteins) are added to the networks under three node addition mechanisms—random, degree-based, and gene-expression-based attachments. By calculating the resilience of the resulting networks, we estimate the effectiveness of these node addition mechanisms. We demonstrate that adding nodes with gene-expression-based preferential attachment (as opposed to random or degree-based) preserves and can increase the original resilience of PPI network. This holds in all three species regardless of their distributions of gene expressions or their network community structure. These findings introduce a general notion of prospective resilience, which highlights the key role of network structures in understanding the evolvability of phenotypic traits.1Author SummaryProteins in organismal cells are present at different levels of concentration and interact with other proteins to provide specific functional roles. Accumulating lists of all of these interactions, complex networks of protein interactions become apparent. This allows us to begin asking whether there are network-level mechanisms at play guiding the evolution of biological systems. Here, using this network perspective, we address two important themes in evolutionary biology (i) How are biological systems able to successfully incorporate novelty? (ii) What is the evolutionary role of biological noise in evolutionary novelty? We consider novelty to be the introduction of a new protein, represented as a new “node”, into a network. We simulate incorporation of novel proteins into Protein-Protein Interaction (PPI) networks in different ways and analyse how the resilience of the PPI network alters. We find that novel interactions guided by gene expression (indicative of concentration levels of proteins) creates a more resilient network than either uniformly random interactions or interactions guided solely by the network structure (preferential attachment). Moreover, simulated biological noise in the gene expression increases network resilience. We suggest that biological noise induces novel structure in the PPI network which has the effect of making it more resilient.

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Using the Mutation-Selection Framework to Characterize Selection on Protein Sequences

Cited by 13 publications

References 64 publications

Ancestral Sequence Reconstruction: From Chemical Paleogenetics to Maximum Likelihood Algorithms and Beyond

Ancestral Sequence Reconstruction: From Chemical Paleogenetics to Maximum Likelihood Algorithms and Beyond

An improved codon modeling approach for accurate estimation of the mutation bias

A computational exploration of resilience and evolvability of protein-protein interaction networks

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