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PubMed2005

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.

Animals↗

PubMed1987

Absorbent Markov chains as a model for the study of the evolution of proteins.

The formalism of absorbent Markov chains, previously developed by Kemeny & Snell (1960) is used as a model for the study of the evolution of proteins. Within the limits of statistical analysis used, the amino acid substitution frequencies of McLachlan (1972) are explained by the numerical values derived from the model used. In addition, the amino acid composition of proteins is partially explained and the relative mutability of amino acids receives a new interpretation in the light of the above mentioned stochastic model. The results show that some basic aspect of protein evolution can be predicted by a stochastic model and therefore a significant component of protein evolution is driven by a random element.

Amino Acid Sequence↗

PubMed2003

A structural perspective on genome evolution.

Protein translations of over 100 complete genomes are now available. About half of these sequences can be provided with structural annotation, thereby enabling some profound insights into protein and pathway evolution. Whereas the major domain structure families are common to all kingdoms of life, these are combined in different ways in multidomain proteins to give various domain architectures that are specific to kingdoms or individual genomes, and contribute to the diverse phenotypes observed. These data argue for more targets in structural genomics initiatives and particularly for the selection of different domain architectures to gain better insights into protein functions.

Evolution, Molecular↗

PubMed1993

Step-wise mutation of barnase to binase. A procedure for engineering increased stability of proteins and an experimental analysis of the evolution of protein stability.

We have chosen two members of the microbial RNase family, barnase and binase, which have 85% identity (17 substitutions and 1 deletion) and almost identical three-dimensional structure, to study the evolution of protein stability. The 17 residues that differ are scattered throughout the molecule. Each of the 17 differing residues has been mutated independently and the effect on protein stability analysed. Each point mutation has an effect on protein stability that ranges from +1.1 to -1.1 kcal mol-1. These changes in energy are additive. There is no clear correlation between the type of mutation and the effect on protein stability. A multiple mutant having six of the single mutations that increase the stability of barnase is 3.3 kcal mol-1 more stable than wild type and has the same activity. There could be selective pressure to maintain proteins at a certain stability and, consequently, mutations that decrease stability tend to be counterbalanced by stabilizing mutations. Alternatively, there could simply be pressure to maintain stability above a certain level, and any further increases in stability need not be maintained during evolution. These results suggest a simple way to improve the stability of proteins: choose two homologous proteins that have high similarity, mutate individually all of the residues that differ between the two, and combine the mutations that increase the stability in a multiple mutant.

Amino Acid Sequence↗

PubMed2004

Imprint of evolution on protein structures.

We attempt to understand the evolutionary origin of protein folds by simulating their divergent evolution with a three-dimensional lattice model. Starting from an initial seed lattice structure, evolution of model proteins progresses by sequence duplication and subsequent point mutations. A new gene's ability to fold into a stable and unique structure is tested each time through direct kinetic folding simulations. Where possible, the algorithm accepts the new sequence and structure and thus a "new protein structure" is born. During the course of each run, this model evolutionary algorithm provides several thousand new proteins with diverse structures. Analysis of evolved structures shows that later evolved structures are more designable than seed structures as judged by recently developed structural determinant of protein designability, as well as direct estimate of designability for selected structures by thermodynamic sampling of their sequence space. We test the significance of this trend predicted on lattice models on real proteins and show that protein domains that are found in eukaryotic organisms only feature statistically significant higher designability than their prokaryotic counterparts. These results present a fundamental view on protein evolution highlighting the relative roles of structural selection and evolutionary dynamics on genesis of modern proteins.

Algorithms↗

PubMed2004

Evolution of protein superfamilies and bacterial genome size.

We present the structural annotation of 56 different bacterial species based on the assignment of genes to 816 evolutionary superfamilies in the CATH domain structure database. These assignments have enabled us to analyse the recurrence of specific superfamilies within and across the genomes. We have selected the superfamilies that have a very broad representation and therefore appear to be universally distributed in a significant number of bacterial lineages. Occurrence profiles of these universally distributed superfamilies are compared with genome size in order to estimate the correlation between superfamily duplication and the increase in proteome size. This distinguishes between those size-dependent superfamilies where frequency of occurrence is highly correlated with increase in genome size, and size-independent superfamilies where no correlation is observed. Consideration of the size correlation and the ratio between the mean and the standard deviations for all the superfamily profiles allows more detailed subdivisions and classification of superfamilies. For example, within the size-independent superfamilies, we distinguished a group that are distributed evenly amongst all the genomes. Within the size-dependent superfamilies we differentiated two groups: linearly distributed and non-linearly distributed. Functional annotation using the COG database was performed for all superfamilies in each of these groups, and this revealed significant differences amongst the three sets of superfamilies. Evenly distributed, size-independent domains are shown to be involved primarily in protein translation and biosynthesis. For the size-dependent superfamilies, linearly distributed superfamilies are involved mainly in metabolism, and non-linearly distributed superfamily domains are involved principally in gene regulation.

Databases, Protein↗

PubMed2004

The evolution dynamics of model proteins.

Explicit simulations of protein evolution, where protein chains are described at a molecular, although simplified, level provide important information to understand the similarities found to exist between known proteins. The results of such simulations suggest that a number of evolutionary-related quantities, such as the distribution of sequence similarity for structurally similar proteins, are controlled by evolutionary kinetics and do not reflect an equilibrium state. An important result for phylogeny is that a subset of the residues of each protein evolve on a much larger time scale than the other residues.

Journal Article↗

PubMed2006

[Accumulation of amino acid substitutions promotes irreversible structural changes in the hemagglutinin of human influenza AH3 virus during evolution].

During protein evolution the amino acid substitutions accumulate with time. However, the effect of accumulation of the amino acid substitutions to structural changes has not been estimated well. We will propose that the discordance of amino acid substitution on the HA protein of influenza A virus is useful for the assessment of structural changes during evolution. Discordance value can be obtained from the experimental data of tolerance or intolerance by introducing site directed mutagenesis at the homologous positions of two HA proteins holding the same amino acid residues. The value of discordance correlated to the number of amino acid differences among proteins. In the H3HA discordance rate was calculated to be 0.45% per one amino acid change. Furthermore, discordance of amino acid substitutions suggests that tolerable amino acid substitutions in different order have a probability of promoting irreversible divergence of the HA protein to different subtypes.

Amino Acid Substitution↗

PubMed2002

Lack of self-averaging in neutral evolution of proteins.

We simulate neutral evolution of proteins imposing conservation of the thermodynamic stability of the native state in the framework of an effective model of folding thermodynamics. This procedure generates evolutionary trajectories in sequence space which share two universal features for all of the examined proteins. First, the number of neutral mutations fluctuates broadly from one sequence to another, leading to a non-Poissonian substitution process. Second, the number of neutral mutations displays strong correlations along the trajectory, thus causing the breakdown of self-averaging of the resulting evolutionary substitution process.

Cytochrome c Group↗

PubMed2001

Directed evolution of proteins by exon shuffling.

Evolution of eukaryotes is mediated by sexual recombination of parental genomes. Crossovers occur in random, but homologous, positions at a frequency that depends on DNA length. As exons occupy only 1% of the human genome and introns about 24%, by far most of the crossovers occur between exons, rather than inside. The natural process of creating new combinations of exons by intronic recombination is called exon shuffling. Our group is developing in vitro formats for exon shuffling and applying these to the directed evolution of proteins. Based on the splice frame junctions, nine classes of exons and three classes of introns can be distinguished. Splice frame diagrams of natural genes show how the splice frame rules govern exon shuffling. Here, we review various approaches to constructing libraries of exon-shuffled genes. For example, exon shuffling of human pharmaceutical proteins can generate libraries in which all of the sequences are fully human, without the point mutations that raise concerns about immunogenicity.

Directed Molecular Evolution↗

PubMed2004

Evidence for conservation of architecture and physical properties of Omp85-like proteins throughout evolution.

Omp85-like proteins represent a family of proteins involved in protein translocation, and they are present in all domains of life, except archaea. In eukaryotes, Omp85-like proteins have been demonstrated to form tetrameric pore-forming complexes that interact directly with their substrate proteins. Studies performed with bacterial Omp85-like proteins have demonstrated pore-forming activity but no evidence of multimerization. In this article, we characterize the Haemophilus influenzae HMW1B protein, an Omp85-like protein that has been demonstrated to be critical for secretion of the H. influenzae HMW1 adhesin. Analysis of purified protein by biochemical and electron microscopic techniques revealed that HMW1B forms a tetramer. Examination using liposome-swelling assays demonstrated that HMW1B has pore-forming activity, with a pore size of approximately equal to 2.7 nm. Far-Western blot analysis established that HMW1B interacts with the N terminus of HMW1. These results provide evidence that a bacterial Omp85-like protein forms a tetramer and interacts directly with a substrate protein, suggesting that the architecture and physical properties of Omp85-like proteins have been conserved throughout evolution.

Bacterial Outer Membrane Proteins↗

PubMed2003

Characterization of the Aalpha and Abeta subunit isoforms of protein phosphatase 2A: differences in expression, subunit interaction, and evolution.

Protein phosphatase 2A (PP2A) is very versatile owing to a large number of regulatory subunits and its ability to interact with numerous other proteins. The regulatory A subunit exists as two closely related isoforms designated Aalpha and Abeta. Mutations have been found in both isoforms in a variety of human cancers. Although Aalpha has been intensely studied, little is known about Abeta. We generated Abeta-specific antibodies and determined the cell cycle expression, subcellular distribution, and metabolic stability of Abeta in comparison with Aalpha. Both forms were expressed at constant levels throughout the cell cycle, but Aalpha was expressed at a much higher level than Abeta. Both forms were found predominantly in the cytoplasm, and both had a half-life of approx. 10 h. However, Aalpha and Abeta differed substantially in their expression patterns in normal tissues and in tumour cell lines. Whereas Aalpha was expressed at similarly high levels in all tissues and cell lines, Abeta expression varied greatly. In addition, in vivo studies with epitope-tagged Aalpha and Abeta subunits demonstrated that Abeta is a markedly weaker binder of regulatory B and catalytic C subunits than Aalpha. Construction of phylogenetic trees revealed that the conservation of Aalpha during the evolution of mammals is extraordinarily high in comparison with both Abeta and cytochrome c, suggesting that Aalpha is involved in more protein-protein interactions than Abeta. We also measured the binding of polyoma virus middle tumour antigen and simian virus 40 (SV40) small tumour antigen to Aalpha and Abeta. Whereas both isoforms bound polyoma virus middle tumour antigen equally well, only Aalpha bound SV40 small tumour antigen.

Animals↗

PubMed2006

Exploiting protein structure data to explore the evolution of protein function and biological complexity.

New directions in biology are being driven by the complete sequencing of genomes, which has given us the protein repertoires of diverse organisms from all kingdoms of life. In tandem with this accumulation of sequence data, worldwide structural genomics initiatives, advanced by the development of improved technologies in X-ray crystallography and NMR, are expanding our knowledge of structural families and increasing our fold libraries. Methods for detecting remote sequence similarities have also been made more sensitive and this means that we can map domains from these structural families onto genome sequences to understand how these families are distributed throughout the genomes and reveal how they might influence the functional repertoires and biological complexities of the organisms. We have used robust protocols to assign sequences from completed genomes to domain structures in the CATH database, allowing up to 60% of domain sequences in these genomes, depending on the organism, to be assigned to a domain family of known structure. Analysis of the distribution of these families throughout bacterial genomes identified more than 300 universal families, some of which had expanded significantly in proportion to genome size. These highly expanded families are primarily involved in metabolism and regulation and appear to make major contributions to the functional repertoire and complexity of bacterial organisms. When comparisons are made across all kingdoms of life, we find a smaller set of universal domain families (approx. 140), of which families involved in protein biosynthesis are the largest conserved component. Analysis of the behaviour of other families reveals that some (e.g. those involved in metabolism, regulation) have remained highly innovative during evolution, making it harder to trace their evolutionary ancestry. Structural analyses of metabolic families provide some insights into the mechanisms of functional innovation, which include changes in domain partnerships and significant structural embellishments leading to modulation of active sites and protein interactions.

Algorithms↗

PubMed2003

Sequence optimization for native state stability determines the evolution and folding kinetics of a small protein.

Investigating the relative importance of protein stability, function, and folding kinetics in driving protein evolution has long been hindered by the fact that we can only compare modern natural proteins, the products of the very process we seek to understand, to each other, with no external references or baselines. Through a large-scale all-atom simulation of protein evolution, we have created a large diverse alignment of SH3 domain sequences which have been selected only for native state stability, with no other influencing factors. Although the average pairwise identity between computationally evolved and natural sequences is only 17%, the residue frequency distributions of the computationally evolved sequences are similar to natural SH3 sequences at 86% of the positions in the domain, suggesting that optimization for the native state structure has dominated the evolution of natural SH3 domains. Additionally, the positions which play a consistent role in the transition state of three well-characterized SH3 domains (by phi-value analysis) are structurally optimized for the native state, and vice versa. Indeed, we see a specific and significant correlation between sequence optimization for native state stability and conservation of transition state structure.

Amino Acid Sequence↗

PubMed2000

Co-evolution of proteins with their interaction partners.

The divergent evolution of proteins in cellular signaling pathways requires ligands and their receptors to co-evolve, creating new pathways when a new receptor is activated by a new ligand. However, information about the evolution of binding specificity in ligand-receptor systems is difficult to glean from sequences alone. We have used phosphoglycerate kinase (PGK), an enzyme that forms its active site between its two domains, to develop a standard for measuring the co-evolution of interacting proteins. The N-terminal and C-terminal domains of PGK form the active site at their interface and are covalently linked. Therefore, they must have co-evolved to preserve enzyme function. By building two phylogenetic trees from multiple sequence alignments of each of the two domains of PGK, we have calculated a correlation coefficient for the two trees that quantifies the co-evolution of the two domains. The correlation coefficient for the trees of the two domains of PGK is 0. 79, which establishes an upper bound for the co-evolution of a protein domain with its binding partner. The analysis is extended to ligands and their receptors, using the chemokines as a model. We show that the correlation between the chemokine ligand and receptor trees' distances is 0.57. The chemokine family of protein ligands and their G-protein coupled receptors have co-evolved so that each subgroup of chemokine ligands has a matching subgroup of chemokine receptors. The matching subfamilies of ligands and their receptors create a framework within which the ligands of orphan chemokine receptors can be more easily determined. This approach can be applied to a variety of ligand and receptor systems.

Chemokines↗

PubMed1996

Domain organizations of modular extracellular matrix proteins and their evolution.

Multidomain proteins which are composed of modular units are a rather recent invention of evolution. Domains are defined as autonomously folding regions of a protein, and many of them are similar in sequence and structure, indicating common ancestry. Their modular nature is emphasized by frequent repetitions in identical or in different proteins and by a large number of different combinations with other domains. The extracellular matrix is perhaps the largest biological system composed of modular mosaic proteins, and its astonishing complexity and diversity are based on them. A cluster of minireviews on modular proteins is being published in Matrix Biology. These deal with the evolution of modular proteins, the three-dimensional structure of domains and the ways in which these interact in a multidomain protein. They discuss structure-function relationships in calcium binding domains, collagen helices, alpha-helical coiled-coil domains and C-lectins. The present minireview is focused on some general aspects and serves as an introduction to the cluster.

Animals↗

PubMed2005

Cooperative evolution in protein complexes of yeast from comparative analyses of its interaction network.

A comparative analysis among Saccharomyces cerevisiae and the other four yeasts Candida glabrata, Kluyveromyces lactis, Debaryomyces hansenii, and Yarrowia lipolytica is presented. The broad evolutionary range spanned by the organisms allows to quantitatively demonstrate novel evolutionary effects in protein complexes. The evolution rates within cliques of interlinked proteins are found to bear strong multipoint correlations, witnessing a cooperative coevolution of complex subunits. The coevolution is found to be largely independent of the tendency of the subunits to have similar abundances.

Candida glabrata↗

PubMed2001

Protein dispensability and rate of evolution.

If protein evolution is due in large part to slightly deleterious amino acid substitutions, then the rate of evolution should be greater in proteins that contribute less to individual fitness. The rationale for this prediction is that relatively dispensable proteins should be subject to weaker purifying selection, and should therefore accumulate mildly deleterious substitutions more rapidly. Although this argument was presented over twenty years ago, and is fundamental to many applications of evolutionary theory, the prediction has proved difficult to confirm. In fact, a recent study showed that essential mouse genes do not evolve more slowly than non-essential ones. Thus, although a variety of factors influencing the rate of protein evolution have been supported by extensive sequence analysis, the relationship between protein dispensability and evolutionary rate has remained unconfirmed. Here we use the results from a highly parallel growth assay of single gene deletions in yeast to assess protein dispensability, which we relate to evolutionary rate estimates that are based on comparisons of sequences drawn from twenty-one fully annotated genomes. Our analysis reveals a highly significant relationship between protein dispensability and evolutionary rate, and explains why this relationship is not detectable by categorical comparison of essential versus non-essential proteins. The relationship is highly conserved, so that protein dispensability in yeast is also predictive of evolutionary rate in a nematode worm.

Amino Acid Substitution↗