Significant impact of protein dispensability on the instantaneous rate of protein evolution

The neutral theory of molecular evolution predicts that important proteins evolve more slowly than unimportant ones. High-throughput gene-knockout experiments in model organisms have provided information on the dispensability, and therefore importance, of thousands of proteins in a genome. However, previous studies of the correlation between protein dispensability and evolutionary rate were equivocal, and it has been proposed that the observed correlation is due to the covariation with the level of gene expression or is limited to duplicate genes. We here analyzed the gene dispensability data of the yeast Saccharomyces cerevisiae and estimated protein evolutionary rates by comparing S. cerevisiae with nine species of varying degrees of divergence from S. cerevisiae. The correlation between gene dispensability and evolutionary rate, although low, is highly significant, even when the gene expression level is controlled for or when duplicate genes are excluded. Our results thus support the hypothesis of lower evolution rates for more important proteins, a widely used principle in the daily practice of molecular biology. When the evolutionary rate is estimated from closely related species, the ratio between the mean rate of nonessential proteins to that of essential proteins is 1.4. This ratio declines to 1.1 when the evolutionary rate is estimated from distantly related species, suggesting that the importance of a protein may change in evolution, so the dispensability data obtained from a model organism only predicts a short-term rate of protein evolution. A comparison of the fitness contributions of orthologous genes in yeast and nematode supports this conclusion.     

The effects of network neighbours on protein evolution

Interacting proteins may often experience similar selection pressures. Thus, we may expect that neighbouring proteins in biological interaction networks evolve at similar rates. This has been previously shown for protein-protein interaction networks. Similarly, we find correlated rates of evolution of neighbours in networks based on co-expression, metabolism, and synthetic lethal genetic interactions. While the correlations are statistically significant, their magnitude is small, with network effects explaining only between 2% and 7% of the variation. The strongest known predictor of the rate of protein evolution remains expression level. We confirmed the previous observation that similar expression levels of neighbours indeed explain their similar evolution rates in protein-protein networks, and showed that the same is true for metabolic networks. In co-expression and synthetic lethal genetic interaction networks, however, neighbouring genes still show somewhat similar evolutionary rates even after simultaneously controlling for expression level, gene essentiality and gene length. Thus, similar expression levels and related functions (as inferred from co-expression and synthetic lethal interactions) seem to explain correlated evolutionary rates of network neighbours across all currently available types of biological networks.     

Assessing the determinants of evolutionary rates in the presence of noise

Although protein sequences are known to evolve at vastly different rates, little is known about what determines their rate of evolution. However, a recent study using principal component regression (PCR) has concluded that evolutionary rates in yeast are primarily governed by a single determinant related to translation frequency. Here, we demonstrate that noise in biological data can confound PCRs, leading to spurious conclusions. When equalizing noise levels across 7 predictor variables used in previous studies, we find no evidence that protein evolution is dominated by a single determinant. Our results indicate that a variety of factors--including expression level, gene dispensability, and protein-protein interactions--may independently affect evolutionary rates in yeast. More accurate measurements or more sophisticated statistical techniques will be required to determine which one, if any, of these factors dominates protein evolution.     

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 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.     

The impact of gene expression regulation on evolution of extracellular signaling pathways

Extracellular protein interactions are crucial to the development of multicellular organisms because they initiate signaling pathways and enable cellular recognition cues. Despite their importance, extracellular protein interactions are often under-represented in large scale protein interaction data sets because most high throughput assays are not designed to detect low affinity extracellular interactions. Due to the lack of a comprehensive data set, the evolution of extracellular signaling pathways has remained largely a mystery. We investigated this question using a combined data set of physical pairwise interactions between zebrafish extracellular proteins, mainly from the immunoglobulin superfamily and leucine-rich repeat families, and their spatiotemporal expression profiles. We took advantage of known homology between proteins to estimate the relative rates of changes of four parameters after gene duplication, namely extracellular protein interaction, expression pattern, and the divergence of extracellular and intracellular protein sequences. We showed that change in expression profile is a major contributor to the evolution of signaling pathways followed by divergence in intracellular protein sequence, whereas extracellular sequence and interaction profiles were relatively more conserved. Rapidly evolving expression profiles will eventually drive other parameters to diverge more quickly because differentially expressed proteins get exposed to different environments and potential binding partners. This allows homologous extracellular receptors to attain specialized functions and become specific to tissues and/or developmental stages.     

Landscape of the hnRNP K protein-protein interactome

The heterogeneous nuclear ribonucleoprotein K is an ancient RNA/DNA-binding protein that is involved in multiple processes that compose gene expression. The pleiotropic action of K protein reflects its ability to interact with different classes of factors, interactions that are regulated by extracellular signals. We used affinity purification and MS to better define the repertoire of K protein partners. We identified a large number of new K protein partners, some typically found in subcellular compartments, such as plasma membrane, where K protein has not previously been seen. Electron microscopy showed K protein in the nucleus, cytoplasm, mitochondria, and in vicinity of plasma membrane. These observations greatly expanded the view of the landscape of K protein-protein interaction and provide new opportunities to explore signal transduction and gene expression in several subcellular compartments.     

Protein microarrays, biosensors, and cell-based methods for secretome-wide extracellular protein-protein interaction mapping

Approximately one quarter of all human genes encode proteins that function in the extracellular space or serve to bridge the extracellular and intracellular environments. Physical associations between these secretome proteins serve to regulate a wide range of biological activities and consequently represent important therapeutic targets. Moreover, some extracellular proteins are targeted by pathogens to allow host access or immune evasion. Despite the importance of extracellular protein-protein interactions, our knowledge in this area has remained sparse. Weak affinities and low abundance have often hindered efforts to identify these interactions using traditional methods such as biochemical purification and cDNA library expression cloning. Moreover, current large-scale protein-protein interaction mapping techniques largely under represent extracellular protein-protein interactions. This review highlights emerging biosensor and protein microarray technology, along with more traditional cell-based techniques, that are compatible with secretome-wide screens for extracellular protein-protein interaction discovery. A combination of these approaches will serve to rapidly expand our knowledge of the extracellular protein-protein interactome.     

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.     

N‐glycoproteins exhibit a positive expression level–evolutionary rate correlation

The different proteins of any proteome evolve at enormously different rates. One of the primary factors influencing rates of protein evolution is expression level, with highly expressed proteins tending to evolve at slow rates. This phenomenon, known as the expression level–evolutionary rate (E–R) anticorrelation, has been attributed to the abundance‐dependent deleterious effects of misfolding or misinteraction. We have recently shown that secreted proteins either lack an E–R anticorrelation or exhibit a significantly reduced E–R anticorrelation. This effect may be due to the strict quality control to which secreted proteins are subject in the endoplasmic reticulum (which is expected to reduce the rate of misfolding and its deleterious effects) or to their extracellular location (expected to reduce the rate of misinteraction and its deleterious effects). Among secreted proteins, N‐glycosylated ones are under particularly strong quality control. Here, we investigate how N‐linked glycosylation affects the E–R anticorrelation. Strikingly, we observe a positive E–R correlation among N‐glycosylated proteins. That is, N‐glycoproteins that are highly expressed evolve at faster rates than lowly expressed N‐glycoproteins, in contrast to what is observed among intracellular proteins.     

Accelerated Evolutionary Rate May Be Responsible for the Emergence of Lineage-Specific Genes in Ascomycota

The evolutionary origin of “orphan” genes, genes that lack sequence similarity to any known gene, remains a mystery. One suggestion has been that most orphan genes evolve rapidly so that similarity to other genes cannot be traced after a certain evolutionary distance. This can be tested by examining the divergence rates of genes with different degrees of lineage specificity. Here the lineage specificity (LS) of a gene describes the phylogenetic distribution of that gene’s orthologues in related species. Highly lineage-specific genes will be distributed in fewer species in a phylogeny. In this study, we have used the complete genomes of seven ascomycotan fungi and two animals to define several levels of LS, such as Eukaryotes-core, Ascomycota-core, Euascomycetes-specific, Hemiascomycetes-specific, Aspergillus-specific, and Saccharomyces-specific. We compare the rates of gene evolution in groups of higher LS to those in groups with lower LS. Molecular evolutionary analyses indicate an increase in nonsynonymous nucleotide substitution rates in genes with higher LS. Several analyses suggest that LS is correlated with the evolutionary rate of the gene. This correlation is stronger than those of a number of other factors that have been proposed as predictors of a gene’s evolutionary rate, including the expression level of genes, gene essentiality or dispensability, and the number of protein-protein interactions. The accelerated evolutionary rates of genes with higher LS may reflect the influence of selection and adaptive divergence during the emergence of orphan genes. These analyses suggest that accelerated rates of gene evolution may be responsible for the emergence of apparently orphan genes. Electronic Supplementary Material Electronic Supplementary material is available for this article at and accessible for authorised users. [Reviewing Editor: Dr. Martin Kreitman]     

Rate of protein evolution versus fitness effect of gene deletion

Whether nonessential genes evolve faster than essential genes has been a controversial issue. To resolve this issue, we use the data from a nearly complete set of single-gene deletions in the yeast Saccharomyces cerevisiae to assess protein dispensability. Also, instead of the nematode, which was used previously but is only distantly related to S. cerevisiae, we use another yeast, Candida albicans, as a second species to estimate the evolutionary distances between orthologous genes in two species. Our analysis reveals only a weak correlation between protein dispensability and evolutionary rate. More important, the correlation disappears when duplicate genes are removed from the analysis. And surprisingly, the average rate of nonsynonymous substitution is considerably lower than that for single-copy genes in the yeast genome. This observation suggests that structural constraints are more important in determining the rate of evolution of a protein than dispensability because duplicate genes are on average more dispensable than single-copy genes. For duplicate genes, those with only a weak effect or no effect of deletion on fitness evolve on average faster than those with a moderate or strong effect of deletion on fitness, which in turn evolve on average faster than those with a lethal effect of deletion.     

Sex-Specific Functional Specialization and the Evolutionary Rates of Essential Fertility Genes

Genes related to sex and reproduction are known to evolve rapidly, however, the mechanism for rapid evolutionary change is proving to be more complex than a simple relaxation of selective constraint. We compared the divergence between orthologous human and mouse fertility genes according to their degree of dispensability as suggested by mouse knockout mutation phenotypes. The dataset consisted of 161 orthologous genes affecting fertility and 803 orthologous genes affecting viability. We find that essential fertility genes affecting both sexes evolve at a similar rate as essential viability genes, but that within sexes the degree of dispensability is not an important factor affecting the rate of fertility gene evolution. We also find no difference in the evolutionary rates of fertility genes that affect the male versus the female, however, there are a greater number of sterility genes that affect the male. Generally there are a significantly greater number of fertility genes that affect one sex rather than both, suggesting that fertility genes tend toward sex-specific functions, particularly in the male. Our findings support the hypothesis that the rapid evolution of sex- and reproduction-related genes is facilitated through an increased specialization of gene function and that dispensability is not a major factor determining their evolutionary rate. Electronic Supplementary Material Electronic Supplementary material is available for this article at and accessible for authorised users. [Reviewing Editor: Dr. Willie J. Swanson]     

Subcellular localization of interacting proteins by bimolecular fluorescence complementation in planta

Bimolecular fluorescence complementation (BiFC) represents one of the most advanced and powerful tools for studying and visualizing protein-protein interactions in living cells. In this method, putative interacting protein partners are fused to complementary non-fluorescent fragments of an autofluorescent protein, such as the yellow spectral variant of the green fluorescent protein. Interaction of the test proteins may result in reconstruction of fluorescence if the two portions of yellow spectral variant of the green fluorescent protein are brought together in such a way that they can fold properly. BiFC provides an assay for detection of protein-protein interactions, and for the subcellular localization of the interacting protein partners. To facilitate the application of BiFC to plant research, we designed a series of vectors for easy construction of N-terminal and C-terminal fusions of the target protein to the yellow spectral variant of the green fluorescent protein fragments. These vectors carry constitutive expression cassettes with an expanded multi-cloning site. In addition, these vectors facilitate the assembly of BiFC expression cassettes into Agrobacterium multi-gene expression binary plasmids for co-expression of interacting partners and additional autofluorescent proteins that may serve as internal transformation controls and markers of subcellular compartments. We demonstrate the utility of these vectors for the analysis of specific protein-protein interactions in various cellular compartments, including the nucleus, plasmodesmata, and chloroplasts of different plant species and cell types.     

Deciphering the cause of evolutionary variance within intrinsically disordered regions in human proteins

Why the intrinsically disordered regions evolve within human proteome has became an interesting question for a decade. Till date, it remains an unsolved yet an intriguing issue to investigate why some of the disordered regions evolve rapidly while the rest are highly conserved across mammalian species. Identifying the key biological factors, responsible for the variation in the conservation rate of different disordered regions within the human proteome, may revisit the above issue. We emphasized that among the other biological features (multifunctionality, gene essentiality, protein connectivity, number of unique domains, gene expression level and expression breadth) considered in our study, the number of unique protein domains acts as a strong determinant that negatively influences the conservation of disordered regions. In this context, we justified that proteins having a fewer types of domains preferably need to conserve their disordered regions to enhance their structural flexibility which in turn will facilitate their molecular interactions. In contrast, the selection pressure acting on the stretches of disordered regions is not so strong in the case of multi-domains proteins. Therefore, we reasoned that the presence of conserved disordered stretches may compensate the functions of multiple domains within a single domain protein. Interestingly, we noticed that the influence of the unique domain number and expression level acts differently on the evolution of disordered regions from that of well-structured ones.     

Interresidue interactions in protein classes

The free energy difference between folded and unfolded state is about the same for most proteins and it is not more than the energy of a few noncovalent interactions. In addition to the numerous noncovalent interactions, some proteins contain one or more disulfide bonds, which, as covalent crosslinks, significantly stabilize their tertiary structure. Correlation between the presence of disulfide bond(s), and the number noncovalent interresidue interactions of various kinds is analyzed here. The number of interactions per residue is almost the same for all protein. Also the number of long-range interactions per residue is the same in all proteins. Proteins with S(SINGLE BOND)S bond(s) (extracellular proteins) have more medium-range and fewer short-range interactions than those without S(SINGLE BOND)S bonds. However, the difference is independent of the number of these covalent crosslinks. We concluded that the different distributions of the various kinds of noncovalent interaction reflect the needs of proteins in the different environments, the extracellular and the intracellular ones, rather than the presence of the disulfide bond(s). We also pointed out that the observed differences in the distributions of short- and medium-range interactions are in good agreement with different secondary structure compositions of extracellular and intracellular proteins. Proteins 27:360–366, 1997. © 1997 Wiley-Liss, Inc.     

The relationships among microRNA regulation, intrinsically disordered regions, and other indicators of protein evolutionary rate

Many indicators of protein evolutionary rate have been proposed, but some of them are interrelated. The purpose of this study is to disentangle their correlations. We assess the strength of each indicator by controlling for the other indicators under study. We find that the number of microRNA (miRNA) types that regulate a gene is the strongest rate indicator (a negative correlation), followed by disorder content (the percentage of disordered regions in a protein, a positive correlation); the strength of disorder content as a rate indicator is substantially increased after controlling for the number of miRNA types. By dividing proteins into lowly and highly intrinsically disordered proteins (L-IDPs and H-IDPs), we find that proteins interacting with more H-IDPs tend to evolve more slowly, which largely explains the previous observation of a negative correlation between the number of protein-protein interactions and evolutionary rate. Moreover, all of the indicators examined here, except for the number of miRNA types, have different strengths in L-IDPs and in H-IDPs. Finally, the number of phosphorylation sites is weakly correlated with the number of miRNA types, and its strength as a rate indicator is substantially reduced when other indicators are considered. Our study reveals the relative strength of each rate indicator and increases our understanding of protein evolution.