Scoring residue conservation

McCrae

Journal of General Virology

et al. 2016

The process by which eukaryotic viruses with segmented genomes select a complete set of genome segments for packaging into progeny virus particles is not understood. In this study a model based on the association of genome segments through specific RNA-RNA interactions driven by base pairing was formalized and tested in the Orbivirus genus of the Reoviridae family. A strategy combining screening of the genomic sequences for inter-segment complementarity with direct functional testing of inter-segment RNA-RNA interactions using reverse genetics is described in the type species of the Orbivirus genus, Bluetongue virus (BTV). Two examples, involving four of the ten BTV genomic segments, of specific intersegment interaction motifs whose maintenance is essential for the generation of infectious virus, were identified. Equivalent inter-segment complementarities were found between the identified regions of the orthologous genome segments of all orbiviruses, including phylogenetically distant species. Specific interaction of the participating RNA segments was confirmed in vitro using electrophoretic mobility shift assays, with the interactions inhibited using oligonucleotides complementary to the interaction motif of one of the interacting partners, and also through mutagenesis of the motifs. In each example, the base pairing rather than the absolute sequence was critical to the formation of a functional inter-segment interaction, with mutations only being tolerated in rescued virus if compensating changes were made in the interacting partner to restore uninterrupted base pairing. The absolute sequence of the complementarity motifs varied between species, indicating that this newly identified phenomenon may contribute to the observed lack of reassortment between Orbivirus species.

Section: Methodsmentioning

confidence: 99%

Inter-segment complementarity in orbiviruses: a driver for co-ordinated genome packaging in the Reoviridae?

McCrae

Journal of General Virology

et al. 2016

“…1B). A frequent and simpler measure of evolutionary importance is residue conservation (Livingstone and Barton 1993;Pei and Grishin 2001;Valdar 2002;Mihalek et al 2004), but conservation is an average rather than a derivative and is less accurate than ET in practice. In that light, prior ET studies have already shown the broad applications of evolutionary gradients: They identify functional sites and allosteric pathway residues (Yao et al 2003), guide mutations that block or reprogram function (Rodriguez et al 2010), and define structural motifs that predict function on a large scale (Ward et al 2009;Erdin et al 2010), such as substrate specificity (Amin et al 2013).…”

Section: A Genotype-phenotype Perturbation Equationmentioning

confidence: 99%

A formal perturbation equation between genotype and phenotype determines the Evolutionary Action of protein-coding variations on fitness

Katsonis¹,

2014

The relationship between genotype mutations and phenotype variations determines health in the short term and evolution over the long term, and it hinges on the action of mutations on fitness. A fundamental difficulty in determining this action, however, is that it depends on the unique context of each mutation, which is complex and often cryptic. As a result, the effect of most genome variations on molecular function and overall fitness remains unknown and stands apart from population genetics theories linking fitness effect to polymorphism frequency. Here, we hypothesize that evolution is a continuous and differentiable physical process coupling genotype to phenotype. This leads to a formal equation for the action of coding mutations on fitness that can be interpreted as a product of the evolutionary importance of the mutated site with the difference in amino acid similarity. Approximations for these terms are readily computable from phylogenetic sequence analysis, and we show mutational, clinical, and population genetic evidence that this action equation predicts the effect of point mutations in vivo and in vitro in diverse proteins, correlates disease-causing gene mutations with morbidity, and determines the frequency of human coding polymorphisms, respectively. Thus, elementary calculus and phylogenetics can be integrated into a perturbation analysis of the evolutionary relationship between genotype and phenotype that quantitatively links point mutations to function and fitness and that opens a new analytic framework for equations of biology. In practice, this work explicitly bridges molecular evolution with population genetics with applications from protein redesign to the clinical assessment of human genetic variations.

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

“…Conservation scores were calculated for each position using the trident scoring method (16), with the conservation scores adjusted to account for trivial composition effects as described in SI Materials (results are given in Table S1). Fig.…”

Section: Is Residue Burial An Important Factor Controlling Evolutionamentioning

confidence: 99%

Structural imperatives impose diverse evolutionary constraints on helical membrane proteins

Oberai

Joh

Pettit

et al. 2009

The amino acid sequences of transmembrane regions of helical membrane proteins are highly constrained, diverging at slower rates than their extramembrane regions and than water-soluble proteins. Moreover, helical membrane proteins seem to fall into fewer families than water-soluble proteins. The reason for the differential restrictions on sequence remains unexplained. Here, we show that the evolution of transmembrane regions is slowed by a previously unrecognized structural constraint: Transmembrane regions bury more residues than extramembrane regions and soluble proteins. This fundamental feature of membrane protein structure is an important contributor to the differences in evolutionary rate and to an increased susceptibility of the transmembrane regions to disease-causing single-nucleotide polymorphisms.disease mutation ͉ potassium channel ͉ protein folding ͉ protein stability ͉ single nucleotide polymorphisms E volutionary rates vary considerably in different cellular compartments (1). Membrane proteins have been found to diverge faster overall than soluble proteins (2, 3), but this increased rate is confined entirely to the rapidly evolving extramembrane regions. Transmembrane regions, on average, diverge much more slowly than the extramembrane regions more slowly than soluble proteins (1, 4-6).A major factor controlling protein sequence divergence is the need to preserve protein function by maintaining a folded structure (7). Because the physical forces that drive folding can change with environment, proteins in different cellular locations can be subject to distinct evolutionary constraints. Membrane proteins, in particular, must accommodate to a dramatically varied environment, ranging from hydrocarbon chains in the bilayer core to water as they emerge from the membrane (8, 9). It therefore seems possible that distinct structural imperatives found in different environments could be an important contributor to evolutionary rates. An obvious sequence adaptation is the hydrophobic matching of the protein exterior, reflected in an apolar transmembrane amino acid composition. Although amino acid diversity is more limited in the transmembrane segments, simple compositional differences do not explain the slower divergence rates of transmembrane regions (1, 4, 5).Here, we find that the transmembrane regions of membrane proteins bury more residues on average than soluble proteins and much more than extramembrane regions, a possible mechanism for increasing stabilization in the absence of the hydrophobic effect. Because buried residues evolve at slower rates than surface residues (10-12), the higher level of residue burial in the transmembrane regions leads to slower sequence divergence. Moreover, we find that higher residue burial may explain a higher prevalence of disease-causing mutations in the transmembrane region of membrane proteins compared with the extramembrane regions. Results and DiscussionTransmembrane Regions Bury More Residues. Fig. 1A shows plots of the fractional surface area buried per residue v...