Independent Effects of Protein Core Size and Expression on Residue-Level Structure-Evolution Relationships

Recently, we demonstrated that yeast protein evolutionary rate at the level of individual amino acid residues scales linearly with degree of solvent accessibility. This residue-level structure-evolution relationship is sensitive to protein core size: surface residues from large-core proteins evolve much faster than those from small-core proteins, while buried residues are equally constrained independent of protein core size. In this work, we investigate the joint effects of protein core size and expression on the residue-level structure-evolution relationship. At the whole-protein level, protein expression is a much more dominant determinant of protein evolutionary rate than protein core size. In contrast, at the residue level, protein core size and expression both have major impacts on protein structure-evolution relationships. In addition, protein core size and expression influence residue-level structure-evolution relationships in qualitatively different ways. Protein core size preferentially affects the non-synonymous substitution rates of surface residues compared to buried residues, and has little influence on synonymous substitution rates. In comparison, protein expression uniformly affects all residues independent of degree of solvent accessibility, and affects both non-synonymous and synonymous substitution rates. Protein core size and expression exert largely independent effects on protein evolution at the residue level, and can combine to produce dramatic changes in the slope of the linear relationship between residue evolutionary rate and solvent accessibility. Our residue-level findings demonstrate that protein core size and expression are both important, yet qualitatively different, determinants of protein evolution. These results underscore the complementary nature of residue-level and whole-protein analysis of protein evolution.     

Quantifying the impact of protein tertiary structure on molecular evolution

To investigate the evolutionary impact of protein structure, the experimentally determined tertiary structure and the protein-coding DNA sequence were collected for each of 1,195 genes. These genes were studied via a model of sequence change that explicitly incorporates effects on evolutionary rates due to protein tertiary structure. In the model, these effects act via the solvent accessibility environments and pairwise amino acid interactions that are induced by tertiary structure. To compare the hypotheses that structure does and does not have a strong influence on evolution, Bayes factors were estimated for each of the 1,195 sequences. Most of the Bayes factors strongly support the hypothesis that protein structure affects protein evolution. Furthermore, both solvent accessibility and pairwise interactions among amino acids are inferred to have important roles in protein evolution. Our results also indicate that the strength of the relationship between tertiary structure and evolution has a weak but real correlation to the annotation information in the Gene Ontology database. Although their influences on rates of evolution vary among protein families, we find that the mean impacts of solvent accessibility and pairwise interactions are about the same.     

Dissecting the roles of local packing density and longer‐range effects in protein sequence evolution

What are the structural determinants of protein sequence evolution? A number of site‐specific structural characteristics have been proposed, most of which are broadly related to either the density of contacts or the solvent accessibility of individual residues. Most importantly, there has been disagreement in the literature over the relative importance of solvent accessibility and local packing density for explaining site‐specific sequence variability in proteins. We show that this discussion has been confounded by the definition of local packing density. The most commonly used measures of local packing, such as contact number and the weighted contact number, represent the combined effects of local packing density and longer‐range effects. As an alternative, we propose a truly local measure of packing density around a single residue, based on the Voronoi cell volume. We show that the Voronoi cell volume, when calculated relative to the geometric center of amino‐acid side chains, behaves nearly identically to the relative solvent accessibility, and each individually can explain, on average, approximately 34% of the site‐specific variation in evolutionary rate in a data set of 209 enzymes. An additional 10% of variation can be explained by nonlocal effects that are captured in the weighted contact number. Consequently, evolutionary variation at a site is determined by the combined effects of the immediate amino‐acid neighbors of that site and effects mediated by more distant amino acids. We conclude that instead of contrasting solvent accessibility and local packing density, future research should emphasize on the relative importance of immediate contacts and longer‐range effects on evolutionary variation. Proteins 2016; 84:841–854. © 2016 Wiley Periodicals, Inc.     

Protein evolution with dependence among codons due to tertiary structure

Markovian models of protein evolution that relax the assumption of independent change among codons are considered. With this comparatively realistic framework, an evolutionary rate at a site can depend both on the state of the site and on the states of surrounding sites. By allowing a relatively general dependence structure among sites, models of evolution can reflect attributes of tertiary structure. To quantify the impact of protein structure on protein evolution, we analyze protein-coding DNA sequence pairs with an evolutionary model that incorporates effects of solvent accessibility and pairwise interactions among amino acid residues. By explicitly considering the relationship between nonsynonymous substitution rates and protein structure, this approach can lead to refined detection and characterization of positive selection. Analyses of simulated sequence pairs indicate that parameters in this evolutionary model can be well estimated. Analyses of lysozyme c and annexin V sequence pairs yield the biologically reasonable result that amino acid replacement rates are higher when the replacements lead to energetically favorable proteins than when they destabilize the proteins. Although the focus here is evolutionary dependence among codons that is associated with protein structure, the statistical approach is quite general and could be applied to diverse cases of evolutionary dependence where surrogates for sequence fitness can be measured or modeled.     

Geometric Constraints Dominate the Antigenic Evolution of Influenza H3N2 Hemagglutinin

We have carried out a comprehensive analysis of the determinants of human influenza A H3 hemagglutinin evolution. We consider three distinct predictors of evolutionary variation at individual sites: solvent accessibility (as a proxy for protein fold stability and/or conservation), Immune Epitope Database (IEDB) epitope sites (as a proxy for host immune bias), and proximity to the receptor-binding region (as a proxy for one of the functions of hemagglutinin-to bind sialic acid). Individually, these quantities explain approximately 15% of the variation in site-wise dN/dS. In combination, solvent accessibility and proximity explain 32% of the variation in dN/dS; incorporating IEDB epitope sites into the model adds only an additional 2 percentage points. Thus, while solvent accessibility and proximity perform largely as independent predictors of evolutionary variation, they each overlap with the epitope-sites predictor. Furthermore, we find that the historical H3 epitope sites, which date back to the 1980s and 1990s, only partially overlap with the experimental sites from the IEDB, and display similar overlap in predictive power when combined with solvent accessibility and proximity. We also find that sites with dN/dS > 1, i.e., the sites most likely driving seasonal immune escape, are not correctly predicted by either historical or IEDB epitope sites, but only by proximity to the receptor-binding region. In summary, a simple geometric model of HA evolution outperforms a model based on epitope sites. These results suggest that either the available epitope sites do not accurately represent the true influenza antigenic sites or that host immune bias may be less important for influenza evolution than commonly thought.     

Understanding the Effect of Secondary Structures and Aggregation on Human Protein Folding Class Evolution

Using several model organisms it has been shown earlier that protein designability is related to contact density or fraction of buried residues and influence protein evolutionary rates dramatically. Here, using Homo sapiens as a model organism, we have analyzed two main folding classes (all-α and all-β) to examine the factors affecting their evolutionary rates. Since, secondary structures are the most fundamental components of the protein folding classes, we explored the effect of protein secondary structure composition on evolution. Our results show that sheet and helix fractions exhibit positive and negative correlations, respectively, with the rate of protein evolution. On dividing the secondary structure components according to solvent accessibility, linear regression model identified two factors namely buried sheet fraction and relative aggregation propensity. Both these factors together can explain about 13.4% variability in the rate of human protein evolution, while buried sheet residues can alone account to 9.9% variability.     

Non-catalytic Binding Sites Induce Weaker Long-Range Evolutionary Rate Gradients than Catalytic Sites in Enzymes

Enzymes exhibit a strong long-range evolutionary constraint that extends from their catalytic site and affects even distant sites, where site-specific evolutionary rate increases monotonically with distance. While protein–protein sites in enzymes were previously shown to induce only a weak conservation gradient, a comprehensive relationship between different types of functional sites in proteins and the magnitude of evolutionary rate gradients they induce has yet to be established. Here, we systematically calculate the evolutionary rate (dN/dS) of sites as a function of distance from different types of binding sites in enzymes and other proteins: catalytic sites, non-catalytic ligand binding sites, allosteric binding sites, and protein–protein interaction sites. We show that catalytic sites indeed induce significantly stronger evolutionary rate gradient than all other types of non-catalytic binding sites. In addition, catalytic sites in enzymes with no known allosteric function still induce strong long-range conservation gradients. Notably, the weak long-range conservation gradients induced by non-catalytic binding sites in enzymes is nearly identical in magnitude to those induced by ligand binding sites in non-enzymes. Finally, we show that structural determinants such as local solvent exposure of sites cannot explain the observed difference between catalytic and non-catalytic functional sites. Our results suggest that enzymes and non-enzymes share similar evolutionary constraints only when examined from the perspective of non-catalytic functional sites. Hence, the unique evolutionary rate gradient from catalytic sites in enzymes is likely driven by the optimization of catalysis rather than ligand binding and allosteric functions.     

Exploring the evolutionary rate differences of party hub and date hub proteins in Saccharomyces cerevisiae protein-protein interaction network

Evolutionary rates of party hub and date hub proteins of Saccharomyces cerevisiae are analyzed under the perspective of ordered/disordered ness of proteins and the three dimensional structural context such as the solvent accessibility of the amino acid residues. Our results suggest that the lowering of evolutionary rate of the party hub proteins than the date hub proteins is solely contributed by the ordered regions of the corresponding proteins. Moreover the slower evolutionary rate of the party hub proteins than the date hub counterparts can be attributed to the presence of buried amino acid residues. Thus, our work endeavors further into the understanding of the evolutionary rate differences of the two different types of hub proteins of S. cerevisiae.     

Structural mapping of protein interactions reveals differences in evolutionary pressures correlated to mRNA level and protein abundance

Genome-wide studies in Saccharomyces cerevisiae concluded that the dominant determinant of protein evolutionary rates is expression level: highly expressed proteins generally evolve most slowly. To determine how this constraint affects the evolution of protein interactions, we directly measure evolutionary rates of protein interface, surface, and core residues by structurally mapping domain interactions to yeast genomes. We find that mRNA level and protein abundance, though correlated, report on pressures affecting regions of proteins differently. Pressures proportional to mRNA level slow evolutionary rates of all structural regions and reduce the variability in rate differences between interfaces and other surfaces. In contrast, the evolutionary rate variation within a domain is much less correlated to protein abundance. Distinct pressures may be associated primarily with the cost (mRNA level) and functional (protein abundance) benefit of protein production. Interfaces of proteins with low mRNA levels may have higher evolutionary flexibility and could constitute the raw material for new functions.     

A single determinant dominates the rate of yeast protein evolution

A gene's rate of sequence evolution is among the most fundamental evolutionary quantities in common use, but what determines evolutionary rates has remained unclear. Here, we carry out the first combined analysis of seven predictors (gene expression level, dispensability, protein abundance, codon adaptation index, gene length, number of protein-protein interactions, and the gene's centrality in the interaction network) previously reported to have independent influences on protein evolutionary rates. Strikingly, our analysis reveals a single dominant variable linked to the number of translation events which explains 40-fold more variation in evolutionary rate than any other, suggesting that protein evolutionary rate has a single major determinant among the seven predictors. The dominant variable explains nearly half the variation in the rate of synonymous and protein evolution. We show that the two most commonly used methods to disentangle the determinants of evolutionary rate, partial correlation analysis and ordinary multivariate regression, produce misleading or spurious results when applied to noisy biological data. We overcome these difficulties by employing principal component regression, a multivariate regression of evolutionary rate against the principal components of the predictor variables. Our results support the hypothesis that translational selection governs the rate of synonymous and protein sequence evolution in yeast.     

Evolution of protein-coding genes in Drosophila

Several contributing factors have been implicated in evolutionary rate heterogeneity among proteins, but their evolutionary mechanisms remain poorly characterized. The recently sequenced 12 Drosophila genomes provide a unique opportunity to shed light on these unresolved issues. Here, we focus on the role of natural selection in shaping evolutionary rates. We use the Drosophila genomic data to distinguish between factors that increase the strength of purifying selection on proteins and factors that affect the amount of positive selection experienced by proteins. We confirm the importance of translational selection in shaping protein evolution in Drosophila and show that factors such as tissue bias in expression, gene essentiality, intron number, and recombination rate also contribute to evolutionary rate variation among proteins.     

Three Independent Determinants of Protein Evolutionary Rate

One of the most widely accepted ideas related to the evolutionary rates of proteins is that functionally important residues or regions evolve slower than other regions, a reasonable outcome of which should be a slower evolutionary rate of the proteins with a higher density of functionally important sites. Oddly, the role of functional importance, mainly measured by essentiality, in determining evolutionary rate has been challenged in recent studies. Several variables other than protein essentiality, such as expression level, gene compactness, protein–protein interactions, etc., have been suggested to affect protein evolutionary rate. In the present review, we try to refine the concept of functional importance of a gene, and consider three factors—functional importance, expression level, and gene compactness, as independent determinants of evolutionary rate of a protein, based not only on their known correlation with evolutionary rate but also on a reasonable mechanistic model. We suggest a framework based on these mechanistic models to correctly interpret the correlations between evolutionary rates and the various variables as well as the interrelationships among the variables.     

Evolution of proteins and gene expression levels are coupled in Drosophila and are independently associated with mRNA abundance, protein length, and number of protein-protein interactions

Organismic evolution requires that variation at distinct hierarchical levels and attributes be coherently integrated, often in the face of disparate environmental and genetic pressures. A central part of the evolutionary analysis of biological systems remains to decipher the causal connections between organism-wide (or genome-wide) attributes (e.g., mRNA abundance, protein length, codon bias, recombination rate, genomic position, mutation rate, etc) as well as their role-together with mutation, selection, and genetic drift-in shaping patterns of evolutionary variation in any of the attributes themselves. Here we combine genome-wide evolutionary analysis of protein and gene expression data to highlight fundamental relationships among genomic attributes and their associations with the evolution of both protein sequences and gene expression levels. Our results show that protein divergence is positively coupled with both gene expression polymorphism and divergence. We show moreover that although the number of protein-protein interactions in Drosophila is negatively associated with protein divergence as well as gene expression polymorphism and divergence, protein-protein interactions cannot account for the observed coupling between regulatory and structural evolution. Furthermore, we show that proteins with higher rates of amino acid substitutions tend to have larger sizes and tend to be expressed at lower mRNA abundances, whereas genes with higher levels of gene expression divergence and polymorphism tend to have shorter sizes and tend to be expressed at higher mRNA abundances. Finally, we show that protein length is negatively associated with both number of protein-protein interactions and mRNA abundance and that interacting proteins in Drosophila show similar amounts of divergence. We suggest that protein sequences and gene expression are subjected to similar evolutionary dynamics, possibly because of similarity in the fitness effect (i.e., strength of stabilizing selection) of disruptions in a gene's protein sequence or its mRNA expression. We conclude that, as more and better data accumulate, understanding the causal connections among biological traits and how they are integrated over time to constrain or promote structural and regulatory evolution may finally become possible.     

Protein biophysics explains why highly abundant proteins evolve slowly

The consistent observation across all kingdoms of life that highly abundant proteins evolve slowly demonstrates that cellular abundance is a key determinant of protein evolutionary rate. However, other empirical findings, such as the broad distribution of evolutionary rates, suggest that additional variables determine the rate of protein evolution. Here, we report that under the global selection against the cytotoxic effects of misfolded proteins, folding stability (ΔG), simultaneous with abundance, is a causal variable of evolutionary rate. Using both theoretical analysis and multiscale simulations, we demonstrate that the anticorrelation between the premutation ΔG and the arising mutational effect (ΔΔG), purely biophysical in origin, is a necessary requirement for abundance-evolutionary rate covariation. Additionally, we predict and demonstrate in bacteria that the strength of abundance-evolutionary rate correlation depends on the divergence time separating reference genomes. Altogether, these results highlight the intrinsic role of protein biophysics in the emerging universal patterns of molecular evolution.     

Structural Determinants of Yeast Protein-Protein Interaction Interface Evolution at the Residue Level

Interfaces of contact between proteins play important roles in determining the proper structure and function of protein–protein interactions (PPIs). Therefore, to fully understand PPIs, we need to better understand the evolutionary design principles of PPI interfaces. Previous studies have uncovered that interfacial sites are more evolutionarily conserved than other surface protein sites. Yet, little is known about the nature and relative importance of evolutionary constraints in PPI interfaces. Here, we explore constraints imposed by the structure of the microenvironment surrounding interfacial residues on residue evolutionary rate using a large dataset of over 700 structural models of baker’s yeast PPIs. We find that interfacial residues are, on average, systematically more conserved than all other residues with a similar degree of total burial as measured by relative solvent accessibility (RSA). Besides, we find that RSA of the residue when the PPI is formed is a better predictor of interfacial residue evolutionary rate than RSA in the monomer state. Furthermore, we investigate four structure-based measures of residue interfacial involvement, including change in RSA upon binding (ΔRSA), number of residue-residue contacts across the interface, and distance from the center or the periphery of the interface. Integrated modeling for evolutionary rate prediction in interfaces shows that ΔRSA plays a dominant role among the four measures of interfacial involvement, with minor, but independent contributions from other measures. These results yield insight into the evolutionary design of interfaces, improving our understanding of the role that structure plays in the molecular evolution of PPIs at the residue level.     

BIRTH‐ORDER DIFFERENCES CAN DRIVE NATURAL SELECTION ON AGING

Senescence—the deterioration of survival and reproductive capacity with increasing age—is generally held to be an evolutionary consequence of the declining strength of natural selection with increasing age. The diversity in rates of aging observed in nature suggests that the rate at which age‐specific selection weakens is determined by species‐specific ecological factors. We propose that, in iteroparous species, relationships between parental age, offspring birth order, and environment may affect selection on senescence. Later‐born siblings have, on average, older parents than do first borns. Offspring born to older parents may experience different environments in terms of family support or inherited resources, factors often mediated by competition from siblings. Thus, age‐specific selection on parents may change if the environment produces birth‐order related gradients in reproductive success. We use an age‐and‐stage structured population model to investigate the impact of sibling environmental inequality on the expected evolution of senescence. We show that accelerated senescence evolves when later‐born siblings are likely to experience an environment detrimental to lifetime reproduction. In general, sibling inequality is likely to be of particular importance for the evolution of senescence in species such as humans, where family interactions and resource inheritance have important roles in determining lifetime reproduction.