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Jianzhi Zhang - One of the best experts on this subject based on the ideXlab platform.
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measuring the Evolutionary Rate of protein protein interaction
2011Co-Authors: Wenfeng Qian, Edwin Chan, Jianzhi ZhangAbstract:Despite our extensive knowledge about the Rate of protein sequence evolution for thousands of genes in hundreds of species, the corresponding Rate of protein function evolution is virtually unknown, especially at the genomic scale. This lack of knowledge is primarily because of the huge diversity in protein function and the consequent difficulty in gauging and comparing Rates of protein function evolution. Nevertheless, most proteins function through interacting with other proteins, and protein–protein interaction (PPI) can be tested by standard assays. Thus, the Rate of protein function evolution may be measured by the Rate of PPI evolution. Here, we experimentally examine 87 potential interactions between Kluyveromyces waltii proteins, whose one to one orthologs in the related budding yeast Saccharomyces cerevisiae have been reported to interact. Combining our results with available data from other eukaryotes, we estimate that the Evolutionary Rate of protein interaction is (2.6 ± 1.6) × 10−10 per PPI per year, which is three orders of magnitude lower than the Rate of protein sequence evolution measured by the number of amino acid substitutions per protein per year. The extremely slow evolution of protein molecular function may account for the remarkable conservation of life at molecular and cellular levels and allow for studying the mechanistic basis of human disease in much simpler organisms.
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impact of extracellularity on the Evolutionary Rate of mammalian proteins
2010Co-Authors: Benyang Liao, Meng Pin Weng, Jianzhi ZhangAbstract:It is of fundamental importance to understand the determinants of the Rate of protein evolution. Eukaryotic extracellular proteins are known to evolve faster than intracellular proteins. Although this Rate difference appears to be due to the lower essentiality of extracellular proteins than intracellular proteins in yeast, we here show that, in mammals, the impact of extracellularity is independent from the impact of gene essentiality. Our partial correlation analysis indicated that the impact of extracellularity on mammalian protein Evolutionary Rate is also independent from those of tissue-specificity, expression level, gene compactness, and the number of protein–protein interactions and, surprisingly, is the strongest among all the factors we examined. Similar results were also found from principal component regression analysis. Our findings suggest that different rules govern the pace of protein sequence evolution in mammals and yeasts.
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why is the correlation between gene importance and gene Evolutionary Rate so weak
2009Co-Authors: Zhi Wang, Jianzhi ZhangAbstract:One of the few commonly believed principles of molecular evolution is that functionally more important genes (or DNA sequences) evolve more slowly than less important ones. This principle is widely used by molecular biologists in daily practice. However, recent genomic analysis of a diverse array of organisms found only weak, negative correlations between the Evolutionary Rate of a gene and its functional importance, typically measured under a single benign lab condition. A frequently suggested cause of the above finding is that gene importance determined in the lab differs from that in an organism's natural environment. Here, we test this hypothesis in yeast using gene importance values experimentally determined in 418 lab conditions or computationally predicted for 10,000 nutritional conditions. In no single condition or combination of conditions did we find a much stronger negative correlation, which is explainable by our subsequent finding that always-essential (enzyme) genes do not evolve significantly more slowly than sometimes-essential or always-nonessential ones. Furthermore, we verified that functional density, approximated by the fraction of amino acid sites within protein domains, is uncorrelated with gene importance. Thus, neither the lab-nature mismatch nor a potentially biased among-gene distribution of functional density explains the observed weakness of the correlation between gene importance and Evolutionary Rate. We conclude that the weakness is factual, rather than artifactual. In addition to being weakened by population genetic reasons, the correlation is likely to have been further weakened by the presence of multiple nontrivial Rate determinants that are independent from gene importance. These findings notwithstanding, we show that the principle of slower evolution of more important genes does have some predictive power when genes with vastly different Evolutionary Rates are compared, explaining why the principle can be practically useful despite the weakness of the correlation.
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impacts of gene essentiality expression pattern and gene compactness on the Evolutionary Rate of mammalian proteins
2006Co-Authors: Benyang Liao, Nicole M Scott, Jianzhi ZhangAbstract:Understanding the determinants of the Rate of protein sequence evolution is of fundamental importance in Evolutionary biology. Many recent studies have focused on the yeast because of the availability of many genome-wide expressional and functional data. Yeast studies revealed a predominant role of gene expression level and a minor role of gene essentiality in determining the Rate of protein sequence evolution. Whether these rules apply to complex organisms such as mammals is unclear. Here we assemble a list of 1,138 essential and 2,341 nonessential mouse genes based on targeted gene deletion experiments and report a significant impact of gene essentiality on the Rate of mammalian protein evolution. Gene expression level has virtually no effect, although tissue specificity in expression pattern has a strong influence. Unexpectedly, gene compactness, measured by average intron size and untranslated region length, has the greatest influence. Hence, the relative importance of the various factors in determining the Rate of mammalian protein evolution is gene compactness > gene essentiality ≈ tissue specificity > expression level. Our results suggest a considerable variation in Rate determinants between unicellular organisms such as the yeast and multicellular organisms such as mammals.
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impacts of gene essentiality expression pattern and gene compactness on the Evolutionary Rate of mammalian proteins
2006Co-Authors: Benyang Liao, Nicole M Scott, Jianzhi ZhangAbstract:Understanding the determinants of the Rate of protein sequence evolution is of fundamental importance in Evolutionary biology. Many recent studies have focused on the yeast because of the availability of many genome-wide expressional and functional data. Yeast studies revealed a predominant role of gene expression level and a minor role of gene essentiality in determining the Rate of protein sequence evolution. Whether these rules apply to complex organisms such as mammals is unclear. Here we assemble a list of 1,138 essential and 2,341 nonessential mouse genes based on targeted gene deletion experiments and report a significant impact of gene essentiality on the Rate of mammalian protein evolution. Gene expression level has virtually no effect, although tissue specificity in expression pattern has a strong influence. Unexpectedly, gene compactness, measured by average intron size and untranslated region length, has the greatest influence. Hence, the relative importance of the various factors in determining the Rate of mammalian protein evolution is gene compactness > gene essentiality approximately tissue specificity > expression level. Our results suggest a considerable variation in Rate determinants between unicellular organisms such as the yeast and multicellular organisms such as mammals.
Christoph Adami - One of the best experts on this subject based on the ideXlab platform.
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Evolutionary Rate depends on number of protein protein interactions independently of gene expression level response
2004Co-Authors: Jesse D Bloom, Christoph AdamiAbstract:A response to Fraser HB, Hirsh AE: Evolutionary Rate depends on number of protein-protein interactions independently of gene expression level. BMC Evol Biol 2004, 4:13
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apparent dependence of protein Evolutionary Rate on number of interactions is linked to biases in protein protein interactions data sets
2003Co-Authors: Jesse D Bloom, Christoph AdamiAbstract:Several studies have suggested that proteins that interact with more partners evolve more slowly. The strength and validity of this association has been called into question. Here we investigate how biases in high-throughput protein–protein interaction studies could lead to a spurious correlation. We examined the correlation between Evolutionary Rate and the number of protein–protein interactions for sets of interactions determined by seven different high-throughput methods in Saccharomyces cerevisiae. Some methods have been shown to be biased towards counting more interactions for abundant proteins, a fact that could be important since abundant proteins are known to evolve more slowly. We show that the apparent tendency for interactive proteins to evolve more slowly varies directly with the bias towards counting more interactions for abundant proteins. Interactions studies with no bias show no correlation between Evolutionary Rate and the number of interactions, and the one study biased towards counting fewer interactions for abundant proteins actually suggests that interactive proteins evolve more rapidly. In all cases, controlling for protein abundance significantly decreases the observed correlation between interactions and Evolutionary Rate. Finally, we disprove the hypothesis that small data set size accounts for the failure of some interactions studies to show a correlation between Evolutionary Rate and the number of interactions. The only correlation supported by a careful analysis of the data is between Evolutionary Rate and protein abundance. The reported correlation between Evolutionary Rate and protein–protein interactions cannot be sepaRated from the biases of some protein–protein interactions studies to count more interactions for abundant proteins.
Yu Xia - One of the best experts on this subject based on the ideXlab platform.
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non catalytic binding sites induce weaker long range Evolutionary Rate gradients than catalytic sites in enzymes
2019Co-Authors: Avital Sharirivry, Yu XiaAbstract:Abstract 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.
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non catalytic binding sites induce weaker long range Evolutionary Rate gradients than catalytic sites in enzymes
2019Co-Authors: Avital Sharirivry, Yu XiaAbstract:Abstract 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 was 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 on enzymes and other proteins: catalytic sites, non-catalytic ligand binding sites, allosteric binding sites, and protein-protein interaction sites. We show that catalytic binding 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 on enzymes is nearly identical in magnitude to those induced by ligand binding sites on 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.
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integRated assessment of genomic correlates of protein Evolutionary Rate
2009Co-Authors: Yu Xia, Eric A Franzosa, Mark GersteinAbstract:Rates of evolution differ widely among proteins, but the causes and consequences of such differences remain under debate. With the advent of high-throughput functional genomics, it is now possible to rigorously assess the genomic correlates of protein Evolutionary Rate. However, dissecting the correlations among Evolutionary Rate and these genomic features remains a major challenge. Here, we use an integRated probabilistic modeling approach to study genomic correlates of protein Evolutionary Rate in Saccharomyces cerevisiae. We measure and rank degrees of association between (i) an approximate measure of protein Evolutionary Rate with high genome coverage, and (ii) a diverse list of protein properties (sequence, structural, functional, network, and phenotypic). We observe, among many statistically significant correlations, that slowly evolving proteins tend to be regulated by more transcription factors, deficient in predicted structural disorder, involved in characteristic biological functions (such as translation), biased in amino acid composition, and are generally more abundant, more essential, and enriched for interaction partners. Many of these results are in agreement with recent studies. In addition, we assess information contribution of different subsets of these protein properties in the task of predicting slowly evolving proteins. We employ a logistic regression model on binned data that is able to account for intercorrelation, non-linearity, and heterogeneity within features. Our model considers features both individually and in natural ensembles (“meta-features”) in order to assess joint information contribution and degree of contribution independence. Meta-features based on protein abundance and amino acid composition make strong, partially independent contributions to the task of predicting slowly evolving proteins; other meta-features make additional minor contributions. The combination of all meta-features yields predictions comparable to those based on paired species comparisons, and approaching the predictive limit of optimal lineage-insensitive features. Our integRated assessment framework can be readily extended to other correlational analyses at the genome scale.
Nathan L Clark - One of the best experts on this subject based on the ideXlab platform.
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Evolutionary Rate covariation identifies slc30a9 znt9 as a mitochondrial zinc transporter
2021Co-Authors: Amanda Kowalczyk, Maria Chikina, Omotola Gbadamosi, Kathryn Kolor, Jahree Sosa, Livia Andrzejczuk, Gregory A Gibson, Claudette M St Croix, Elias Aizenman, Nathan L ClarkAbstract:Recent advances in genome sequencing have led to the identification of new ion and metabolite transporters, many of which have not been characterized. Due to the variety of subcellular localizations, cargo and transport mechanisms, such characterization is a daunting task, and predictive approaches focused on the functional context of transporters are very much needed. Here we present a case for identifying a transporter localization using Evolutionary Rate covariation (ERC), a computational approach based on pairwise correlations of amino acid sequence Evolutionary Rates across the mammalian phylogeny. As a case study, we find that poorly characterized transporter SLC30A9 (ZnT9) coevolves with several components of the mitochondrial oxidative phosphorylation chain, suggesting mitochondrial localization. We confirmed this computational finding experimentally using recombinant human SLC30A9. SLC30A9 loss caused zinc mishandling in the mitochondria, suggesting that under normal conditions it acts as a zinc exporter. We therefore propose that ERC can be used to predict the functional context of novel transporters and other poorly characterized proteins.
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Evolutionary Rate covariation identifies slc30a9 znt9 as a mitochondrial zinc transporter
2021Co-Authors: Maria Chikina, Amanda Kowalczyk, Omotola Gbadamosi, Kathryn Kolor, Jahree Sosa, Gregory A Gibson, Claudette M St Croix, Elias Aizenman, Nathan L ClarkAbstract:Abstract Recent advances in genome sequencing have led to the identification of new ion and metabolite transporters, many of which have not been characterized. Due to the variety of subcellular localizations, cargo and transport mechanisms, such characterization is a daunting task, and predictive approaches focused on the functional context of transporters are very much needed. Here we present a case for identifying a transporter localization using Evolutionary Rate covariation (ERC), a computational approach based on pairwise correlations of amino acid sequence Evolutionary Rates across the mammalian phylogeny. As a case study, we find that poorly characterized transporter SLC30A9 (ZnT9) uniquely and prominently coevolves with several components of the mitochondrial oxidative phosphorylation chain, suggesting mitochondrial localization. We confirmed this computational finding experimentally using recombinant human SLC30A9. SLC30A9 loss caused zinc mishandling in the mitochondria, suggesting that under normal conditions it acts as a zinc exporter. We therefore propose that ERC can be used to predict the functional context of novel transporters and other poorly characterized proteins.
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Evolutionary Rate covariation analysis of e cadherin identifies raskol as a regulator of cell adhesion and actin dynamics in drosophila
2019Co-Authors: Qanber Raza, Yang Hong, Jae Young Choi, Roisin M Odowd, Simon C Watkins, Maria Chikina, Nathan L Clark, Adam V. KwiatkowskiAbstract:The adherens junction couples the actin cytoskeletons of neighboring cells to provide the foundation for multicellular organization. The core of the adherens junction is the cadherin-catenin complex that arose early in the evolution of multicellularity to link actin to intercellular adhesions. Over time, Evolutionary pressures have shaped the signaling and mechanical functions of the adherens junction to meet specific developmental and physiological demands. Evolutionary Rate covariation (ERC) identifies proteins with correlated fluctuations in Evolutionary Rate that can reflect shared selective pressures and functions. Here we use ERC to identify proteins with Evolutionary histories similar to the Drosophila E-cadherin (DE-cad) ortholog. Core adherens junction components α-catenin and p120-catenin displayed positive ERC correlations with DE-cad, indicating that they evolved under similar selective pressures during evolution between Drosophila species. Further analysis of the DE-cad ERC profile revealed a collection of proteins not previously associated with DE-cad function or cadherin-mediated adhesion. We then analyzed the function of a subset of ERC-identified candidates by RNAi during border cell (BC) migration and identified novel genes that function to regulate DE-cad. Among these, we found that the gene CG42684, which encodes a putative GTPase activating protein (GAP), regulates BC migration and adhesion. We named CG42684 raskol (“to split” in Russian) and show that it regulates DE-cad levels and actin protrusions in BCs. We propose that Raskol functions with DE-cad to restrict Ras/Rho signaling and help guide BC migration. Our results demonstRate that a coordinated selective pressure has shaped the adherens junction and this can be leveraged to identify novel components of the complexes and signaling pathways that regulate cadherin-mediated adhesion.
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Evolutionary Rate covariation analysis of e cadherin identifies raskol as regulator of cell adhesion and actin dynamics in drosophila
2018Co-Authors: Qanber Raza, Yang Hong, Jae Young Choi, Roisin M Odowd, Simon C Watkins, Nathan L Clark, Adam V. KwiatkowskiAbstract:The adherens junction couples the actin cytoskeletons of neighboring cells to provide the foundation for multicellular organization. The core of the adherens junction is the cadherin-catenin complex that arose early in the evolution of multicellularity to link cortical actin to intercellular adhesions. Over time, Evolutionary pressures have shaped the signaling and mechanical functions of the adherens junction to meet specific developmental and physiological demands. Evolutionary Rate covariation (ERC) identifies genes with correlated fluctuations in Evolutionary Rate that can reflect shared selective pressures and functions. Here we use ERC to identify genes with Evolutionary histories similar to shotgun (shg), which encodes the Drosophila E-cadherin (DE-Cad) ortholog. Core adherens junction components ?-catenin and p120-catenin displayed strong ERC correlations with shg, indicating that they evolved under similar selective pressures during evolution between Drosophila species. Further analysis of the shg ERC profile revealed a collection of genes not previously associated with shg function or cadherin-mediated adhesion. We then analyzed the function of a subset of ERC-identified candidate genes by RNAi during border cell (BC) migration and identified novel genes that function to regulate DE-Cad. Among these, we found that the gene CG42684, which encodes a putative GTPase activating protein (GAP), regulates BC migration and adhesion. We named CG42684 raskol (“to split” in Russian) and show that it regulates DE-Cad levels and actin protrusions in BCs. We propose that Raskol functions with DE-Cad to restrict Ras/Rho signaling and help guide BC migration. Our results demonstRate that a coordinated selective pressure has shaped the adherens junction and this can be leveraged to identify novel components of the complexes and signaling pathways that regulate cadherin-mediated adhesion.
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Evolutionary Rate covariation identifies new members of a protein network required for drosophila melanogaster female post mating responses
2014Co-Authors: Geoffrey D Findlay, Nathan L Clark, Charles F Aquadro, Jessica L Sitnik, Wenke Wang, Mariana F WolfnerAbstract:Seminal fluid proteins transferred from males to females during copulation are required for full fertility and can exert dramatic effects on female physiology and behavior. In Drosophila melanogaster, the seminal protein sex peptide (SP) affects mated females by increasing egg production and decreasing receptivity to courtship. These behavioral changes persist for several days because SP binds to sperm that are stored in the female. SP is then gradually released, allowing it to interact with its female-expressed receptor. The binding of SP to sperm requires five additional seminal proteins, which act together in a network. Hundreds of uncharacterized male and female proteins have been identified in this species, but individually screening each protein for network function would present a logistical challenge. To prioritize the screening of these proteins for involvement in the SP network, we used a comparative genomic method to identify candidate proteins whose Evolutionary Rates across the Drosophila phylogeny co-vary with those of the SP network proteins. Subsequent functional testing of 18 co-varying candidates by RNA interference identified three male seminal proteins and three female reproductive tract proteins that are each required for the long-term persistence of SP responses in females. Molecular genetic analysis showed the three new male proteins are required for the transfer of other network proteins to females and for SP to become bound to sperm that are stored in mated females. The three female proteins, in contrast, act downstream of SP binding and sperm storage. These findings expand the number of seminal proteins required for SP's actions in the female and show that multiple female proteins are necessary for the SP response. Furthermore, our functional analyses demonstRate that Evolutionary Rate covariation is a valuable predictive tool for identifying candidate members of interacting protein networks.
Claus O Wilke - One of the best experts on this subject based on the ideXlab platform.
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causes of Evolutionary Rate variation among protein sites
2016Co-Authors: Julian Echave, Stephanie J Spielman, Claus O WilkeAbstract:It has long been recognized that certain sites within a protein, such as sites in the protein core or catalytic residues in enzymes, are evolutionarily more conserved than other sites. However, our understanding of Rate variation among sites remains surprisingly limited. Recent progress to address this includes the development of a wide array of reliable methods to estimate site-specific substitution Rates from sequence alignments. In addition, several molecular traits have been identified that correlate with site-specific mutation Rates, and novel mechanistic biophysical models have been proposed to explain the observed correlations. Nonetheless, current models explain, at best, approximately 60% of the observed variance, highlighting the limitations of current methods and models and the need for new research directions.
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cross species comparison of site specific Evolutionary Rate variation in influenza haemagglutinin
2013Co-Authors: Austin G Meyer, Eric T Dawson, Claus O WilkeAbstract:We investigate the causes of site-specific Evolutionary-Rate variation in influenza haemagglutinin (HA) between human and avian influenza, for subtypes H1, H3, and H5. By calculating the Evolutionary-Rate ratio, ω = dN/dS as a function of a residue's solvent accessibility in the three-dimensional protein structure, we show that solvent accessibility has a significant but relatively modest effect on site-specific Rate variation. By comparing Rates within HA subtypes among host species, we derive an upper limit to the amount of variation that can be explained by structural constraints of any kind. Protein structure explains only 20-40% of the variation in ω. Finally, by comparing ω at sites near the sialic-acid-binding region to ω at other sites, we show that ω near the sialic-acid-binding region is significantly elevated in both human and avian influenza, with the exception of avian H5. We conclude that protein structure, HA subtype, and host biology all impose distinct selection pressures on sites in influenza HA.
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the relationship between relative solvent accessibility and Evolutionary Rate in protein evolution
2011Co-Authors: Duncan C Ramsey, Tong Zhou, Michael P Scherrer, Claus O WilkeAbstract:Recent work with Saccharomyces cerevisiae shows a linear relationship between the Evolutionary Rate of sites and the relative solvent accessibility (RSA) of the corresponding residues in the folded protein. Here, we aim to develop a mathematical model that can reproduce this linear relationship. We first demonstRate that two models that both seem reasonable choices (a simple model in which selection strength correlates with RSA and a more complex model based on RSA-dependent amino acid distributions) fail to reproduce the observed relationship. We then develop a model on the basis of observed site-specific amino acid distributions and show that this model behaves appropriately. We conclude that Evolutionary Rates are directly linked to the distribution of amino acids at individual sites. Because of this link, any future insight into the biophysical mechanisms that determine amino acid distributions will improve our understanding of Evolutionary Rates.
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contact density affects protein Evolutionary Rate from bacteria to animals
2008Co-Authors: Tong Zhou, Allan D Drummond, Claus O WilkeAbstract:The density of contacts or the fraction of buried sites in a protein structure is thought to be related to a protein's designability, and genes encoding more designable proteins should evolve faster than other genes. Several recent studies have tested this hypothesis but have found conflicting results. Here, we investigate how a gene's Evolutionary Rate is affected by its protein's contact density, considering the four species Escherichia coli, Saccharomyces cerevisiae, Drosophila melanogaster, and Homo sapiens. We find for all four species that contact density correlates positively with Evolutionary Rate, and that these correlations do not seem to be confounded by gene expression level. The strength of this signal, however, varies widely among species. We also study the effect of contact density on domain evolution in multidomain proteins and find that a domain's contact density influences the domain's Evolutionary Rate. Within the same protein, a domain with higher contact density tends to evolve faster than a domain with lower contact density. Our study provides evidence that contact density can increase Evolutionary Rates, and that it acts similarly on the level of entire proteins and of individual protein domains.
