Quaternary structure constraints on evolutionary sequence divergence

The structurally constrained protein evolution (SCPE) model simulates protein divergence considering protein structure explicitly. The model is based on the observation that protein structure is more conserved during evolution than the sequences encoding for that structure. In the previous work, the SCPE model considered only the tertiary structure. Here we show that the performance of the model is enhanced when the oligomeric structure is taken into account. Our results agree with recent evolutionary studies of oligomeric proteins, which show that conservation of the quaternary structure imposes additional constraints on sequence divergence. The incorporation of protein-protein interactions into protein evolution models may be important in the study of quaternary protein structures and complex protein assemblies.     

Ensembles from Ordered and Disordered Proteins Reveal Similar Structural Constraints during Evolution

The conformations accessible to proteins are determined by the inter-residue interactions between amino acid residues. During evolution, structural constraints that are required for protein function providing biologically relevant information can exist. Here, we studied the proportion of sites evolving under structural constraints in two very different types of ensembles, those coming from ordered and disordered proteins. Using a structurally constrained model of protein evolution, we found that both types of ensembles show comparable, near 40%, number of positions evolving under structural constraints. Among these sites, ~ 68% are in disordered regions and ~ 57% of them show long-range inter-residue contacts. Also, we found that disordered ensembles are redundant in reference to their structurally constrained evolutionary information and could be described on average with ~ 11 conformers. Despite the different complexity of the studied ensembles and proteins, the similar constraints reveal a comparable level of selective pressure to maintain their biological functions. These results highlight the importance of the evolutionary information to recover meaningful biological information to further characterize conformational ensembles.     

The Evolution of Enamel Microstructure: How Important Is Amelogenin?

The shape, size, and orientation of enamel prisms have heretofore been thought to be controlled solely by the shape of the Tomes' process. It is known, however, that amelogenin proteins play an important role in enamel deposition and maturation and it is possible that they contribute independently to enamel structure. Using a phylogenetic framework, we clarify the role of amelogenin proteins in the formation of enamel microstructure. We found a negative association between evolutionary changes in amelogenin protein sequences and enamel complexity: amelogenin evolution slows as enamel complexity increases. This is probably because selective constraints on amelogenin increase as enamel complexity increases. Monotremes, which have lost their adult dentition, have particularly high rates of amelogenin evolution while rodents, which have very complex enamel, have very low rates. There is a positive correlation between the number of different amelogenin proteins in a given species and the complexity of its enamel microstructure. An increased number of amelogenins may be necessary for the formation of multiple enamel types in the same tooth. Alternative splicing of amelogenin exons, which allows multiple protein products to be produced from the same gene, may be a key innovation in the diversification of enamel microstructure.     

On the evolution of structure in aminoacyl-tRNA synthetases.

The aminoacyl-tRNA synthetases are one of the major protein components in the translation machinery. These essential proteins are found in all forms of life and are responsible for charging their cognate tRNAs with the correct amino acid. The evolution of the tRNA synthetases is of fundamental importance with respect to the nature of the biological cell and the transition from an RNA world to the modern world dominated by protein-enzymes. We present a structure-based phylogeny of the aminoacyl-tRNA synthetases. By using structural alignments of all of the aminoacyl-tRNA synthetases of known structure in combination with a new measure of structural homology, we have reconstructed the evolutionary history of these proteins. In order to derive unbiased statistics from the structural alignments, we introduce a multidimensional QR factorization which produces a nonredundant set of structures. Since protein structure is more highly conserved than protein sequence, this study has allowed us to glimpse the evolution of protein structure that predates the root of the universal phylogenetic tree. The extensive sequence-based phylogenetic analysis of the tRNA synthetases (Woese et al., Microbiol. Mol. Biol. Rev. 64:202-236, 2000) has further enabled us to reconstruct the complete evolutionary profile of these proteins and to make connections between major evolutionary events and the resulting changes in protein shape. We also discuss the effect of functional specificity on protein shape over the complex evolutionary course of the tRNA synthetases.     

On the Evolution of Structure in Aminoacyl-tRNA Synthetases

The aminoacyl-tRNA synthetases are one of the major protein components in the translation machinery. These essential proteins are found in all forms of life and are responsible for charging their cognate tRNAs with the correct amino acid. The evolution of the tRNA synthetases is of fundamental importance with respect to the nature of the biological cell and the transition from an RNA world to the modern world dominated by protein-enzymes. We present a structure-based phylogeny of the aminoacyl-tRNA synthetases. By using structural alignments of all of the aminoacyl-tRNA synthetases of known structure in combination with a new measure of structural homology, we have reconstructed the evolutionary history of these proteins. In order to derive unbiased statistics from the structural alignments, we introduce a multidimensional QR factorization which produces a nonredundant set of structures. Since protein structure is more highly conserved than protein sequence, this study has allowed us to glimpse the evolution of protein structure that predates the root of the universal phylogenetic tree. The extensive sequence-based phylogenetic analysis of the tRNA synthetases (Woese et al., Microbiol. Mol. Biol. Rev. 64:202-236, 2000) has further enabled us to reconstruct the complete evolutionary profile of these proteins and to make connections between major evolutionary events and the resulting changes in protein shape. We also discuss the effect of functional specificity on protein shape over the complex evolutionary course of the tRNA synthetases.     

METABOLIC FLUX IS A DETERMINANT OF THE EVOLUTIONARY RATES OF ENZYME‐ENCODING GENES

Relationships between evolutionary rates and gene properties on a genomic, functional, pathway, or system level are being explored to unravel the principles of the evolutionary process. In particular, functional network properties have been analyzed to recognize the constraints they may impose on the evolutionary fate of genes. Here we took as a case study the core metabolic network in human erythrocytes and we analyzed the relationship between the evolutionary rates of its genes and the metabolic flux distribution throughout it. We found that metabolic flux correlates with the ratio of nonsynonymous to synonymous substitution rates. Genes encoding enzymes that carry high fluxes have been more constrained in their evolution, while purifying selection is more relaxed in genes encoding enzymes carrying low metabolic fluxes. These results demonstrate the importance of considering the dynamical functioning of gene networks when assessing the action of selection on system‐level properties.     

A thermodynamic model of protein structure evolution explains empirical amino acid substitution matrices

Proteins evolve under a myriad of biophysical selection pressures that collectively control the patterns of amino acid substitutions. These evolutionary pressures are sufficiently consistent over time and across protein families to produce substitution patterns, summarized in global amino acid substitution matrices such as BLOSUM, JTT, WAG, and LG, which can be used to successfully detect homologs, infer phylogenies, and reconstruct ancestral sequences. Although the factors that govern the variation of amino acid substitution rates have received much attention, the influence of thermodynamic stability constraints remains unresolved. Here we develop a simple model to calculate amino acid substitution matrices from evolutionary dynamics controlled by a fitness function that reports on the thermodynamic effects of amino acid mutations in protein structures. This hybrid biophysical and evolutionary model accounts for nucleotide transition/transversion rate bias, multi‐nucleotide codon changes, the number of codons per amino acid, and thermodynamic protein stability. We find that our theoretical model accurately recapitulates the complex yet universal pattern observed in common global amino acid substitution matrices used in phylogenetics. These results suggest that selection for thermodynamically stable proteins, coupled with nucleotide mutation bias filtered by the structure of the genetic code, is the primary driver behind the global amino acid substitution patterns observed in proteins throughout the tree of life.     

EVOLUTIONARILY STABLE SELFING RATES OF HERMAPHRODITIC PLANTS IN COMPETING AND DELAYED SELFING MODES WITH ALLOCATION TO ATTRACTIVE STRUCTURES

I present a resource-allocation model to analyze how patterns of allocation to reproductive structures influence the evolution of selfing rates in hermaphrodites subject to competing and delayed forms of self-fertilization. The evolutionarily stable state does not depend on the mode of pollination. In contrast to previous models in which the number and the size of flowers were not considered, intermediate selfing is not evolutionarily stable with linear constraints on flower number and size. In contrast, intermediate selfing can be evolutionarily stable with nonlinear constraints on flower number and size. Optimal allocations to attractive structures increase and selfing rates decrease in the presence of inbreeding depression. In particular, stable intermediate levels of selfing may be favored when flower number is strongly constrained. Thus, nonlinear constraints on flower number and size could favor the evolution of intermediate selfing in either the delayed or the competing modes of selfing. Outcrossing is not favored in the absence of inbreeding depression, a result inconsistent with Holsinger's results in which allocation to attractive structures was not considered.     

Modularity in the evolution of yeast protein interaction network

Protein interaction networks are known to exhibit remarkable structures: scale-free and small-world and modular structures. To explain the evolutionary processes of protein interaction networks possessing scale-free and small-world structures, preferential attachment and duplication-divergence models have been proposed as mathematical models. Protein interaction networks are also known to exhibit another remarkable structural characteristic, modular structure. How the protein interaction networks became to exhibit modularity in their evolution? Here, we propose a hypothesis of modularity in the evolution of yeast protein interaction network based on molecular evolutionary evidence. We assigned yeast proteins into six evolutionary ages by constructing a phylogenetic profile. We found that all the almost half of hub proteins are evolutionarily new. Examining the evolutionary processes of protein complexes, functional modules and topological modules, we also found that member proteins of these modules tend to appear in one or two evolutionary ages. Moreover, proteins in protein complexes and topological modules show significantly low evolutionary rates than those not in these modules. Our results suggest a hypothesis of modularity in the evolution of yeast protein interaction network as systems evolution.     

Macroevolutionary insights into sedges (Carex: Cyperaceae): The effects of rapid chromosome number evolution on lineage diversification

Changes in holocentric chromosome number due to fission and fusion have direct and immediate effects on genome structure and recombination rates. These, in turn, may influence ecology and evolutionary trajectories profoundly. Sedges of the genus Carex (Cyperaceae) comprise ca. 2000 species with holocentric chromosomes. The genus exhibits a phenomenal range in the chromosome number (2n = 10 − 132) with almost not polyploidy. In this study, we integrated the most comprehensive cytogenetic and phylogenetic data for sedges with associated climatic and morphological data to investigate the hypothesis that high recombination rates are selected when evolutionary innovation is required, using chromosome number evolution as a proxy for recombination rate. We evaluated Ornstein–Uhlenbeck models to infer shifts in chromosome number equilibrium and selective regime. We also tested the relationship between chromosome number and diversification rates. Our analyses demonstrate significant correlations between morphology and climatic niche and chromosome number in Carex. Nevertheless, the amount of chromosomal variation that we are able to explain is very small. We recognized a large number of shifts in mean chromosome number, but a significantly lower number in climatic niche and morphology. We also detected a peak in diversification rates near intermediate recombination rates. In combination, these analyses point toward the importance of chromosome evolution to the evolutionary history of Carex. Our work suggests that the effect of chromosome evolution on recombination rates, not just on reproductive isolation, may be central to the evolutionary history of sedges.     

Hominin Obstetrics and the Evolution of Constraints

Birth is significantly more complicated and dangerous in modern humans than in other great apes. This disparity is often hypothesized to be the result of evolutionary constraints on obstetric dimensions related to bipedalism and/or thermoregulation in later hominins. Previous attempts to test such hypotheses have used biomechanical methods and results have been mixed. But evolutionary constraints, restrictions or limitations on the course or outcome of evolution, are the result of an interaction between selective pressures and genetic constraints—the latter revealed in patterns of integration. Integration between traits can result in directional or stabilizing selection on one trait leading to correlated responses in other traits, which can bias and constrain evolutionary trajectories. Therefore, trait evolution may be constrained for reasons separate from those that can be estimated using biomechanical models, and to study evolutionary constraints it is necessary to understand the role genetic constraints play in morphological change. The results presented here show that genetic constraints can significantly reduce the evolutionary potential of the birth canal to evolve in humans, apes, and likely earlier hominins, but also point to an overall reduction in the level of constraints during hominin evolution. These findings suggest that divergent selection pressures for obstetric requirements and other pelvic functions in hominins reduced levels of genetic constraint on birth canal evolution, likely lowering the amount of time needed for evolutionary change, and permitting morphological evolution along a trajectory that might have previously been difficult or impossible to traverse.     

Origins of globular structure in proteins

Since natural proteins are the products of a long evolutionary process, the structural properties of present-day proteins should depend not only on physico-chemical constraints, but also on evolutionary constraints. Here we propose a model for protein evolution, in which membranes play a key role as a scaffold for supporting the gradual evolution from flexible polypeptides to well-folded proteins. We suggest that the folding process of present-day globular proteins is a relic of this putative evolutionary process. To test the hypothesis that membranes once acted as a cradle for the folding of globular proteins, extensive research on membrane proteins and the interactions of globular proteins with membranes will be required.     

Structure and age jointly influence rates of protein evolution

What factors determine a protein's rate of evolution are actively debated. Especially unclear is the relative role of intrinsic factors of present-day proteins versus historical factors such as protein age. Here we study the interplay of structural properties and evolutionary age, as determinants of protein evolutionary rate. We use a large set of one-to-one orthologs between human and mouse proteins, with mapped PDB structures. We report that previously observed structural correlations also hold within each age group - including relationships between solvent accessibility, designabililty, and evolutionary rates. However, age also plays a crucial role: age modulates the relationship between solvent accessibility and rate. Additionally, younger proteins, despite being less designable, tend to evolve faster than older proteins. We show that previously reported relationships between age and rate cannot be explained by structural biases among age groups. Finally, we introduce a knowledge-based potential function to study the stability of proteins through large-scale computation. We find that older proteins are more stable for their native structure, and more robust to mutations, than younger ones. Our results underscore that several determinants, both intrinsic and historical, can interact to determine rates of protein evolution.     

MEASURING SELECTION AND CONSTRAINT IN THE EVOLUTION OF GROWTH

We present a quantitative genetic model for the evolution of growth trajectories that makes no assumptions about the shapes of growth trajectories that are possible. Evolution of a population's mean growth trajectory is governed by the selection gradient function and the additive genetic covariance function. The selection gradient function is determined by the impact of changes in size on the birth and death rates at different ages, and can be estimated for natural populations. The additive genetic covariance function can also be estimated empirically, as we demonstrate with four vertebrate populations. Using the genetic data from mice, a computer simulation shows that evolution of a growth trajectory can be constrained by the absence of genetic variation for certain changes in the trajectory's shape. These constraints can be visualized with an analysis of the covariance function. Results from four vertebrate populations show that while each has substantial genetic variation for some evolutionary changes in its growth trajectory, most types of changes have little or no variation available. This suggests that constraints may often play an important role in the evolution of growth.     

Protein 3D structure computed from evolutionary sequence variation

The evolutionary trajectory of a protein through sequence space is constrained by its function. Collections of sequence homologs record the outcomes of millions of evolutionary experiments in which the protein evolves according to these constraints. Deciphering the evolutionary record held in these sequences and exploiting it for predictive and engineering purposes presents a formidable challenge. The potential benefit of solving this challenge is amplified by the advent of inexpensive high-throughput genomic sequencing.In this paper we ask whether we can infer evolutionary constraints from a set of sequence homologs of a protein. The challenge is to distinguish true co-evolution couplings from the noisy set of observed correlations. We address this challenge using a maximum entropy model of the protein sequence, constrained by the statistics of the multiple sequence alignment, to infer residue pair couplings. Surprisingly, we find that the strength of these inferred couplings is an excellent predictor of residue-residue proximity in folded structures. Indeed, the top-scoring residue couplings are sufficiently accurate and well-distributed to define the 3D protein fold with remarkable accuracy.We quantify this observation by computing, from sequence alone, all-atom 3D structures of fifteen test proteins from different fold classes, ranging in size from 50 to 260 residues., including a G-protein coupled receptor. These blinded inferences are de novo, i.e., they do not use homology modeling or sequence-similar fragments from known structures. The co-evolution signals provide sufficient information to determine accurate 3D protein structure to 2.7–4.8 Å C_α-RMSD error relative to the observed structure, over at least two-thirds of the protein (method called EVfold, details at http://EVfold.org). This discovery provides insight into essential interactions constraining protein evolution and will facilitate a comprehensive survey of the universe of protein structures, new strategies in protein and drug design, and the identification of functional genetic variants in normal and disease genomes.     

A complex-centric view of protein network evolution

The recent availability of protein–protein interaction networks for several species makes it possible to study protein complexes in an evolutionary context. In this article, we present a novel network-based framework for reconstructing the evolutionary history of protein complexes. Our analysis is based on generalizing evolutionary measures for single proteins to the level of whole subnetworks, comprehensively considering a broad set of computationally derived complexes and accounting for both sequence and interaction changes. Specifically, we compute sets of orthologous complexes across species, and use these to derive evolutionary rate and age measures for protein complexes. We observe significant correlations between the evolutionary properties of a complex and those of its member proteins, suggesting that protein complexes form early in evolution and evolve as coherent units. Additionally, our approach enables us to directly quantify the extent to which gene duplication has played a role in the evolution of complexes. We find that about one quarter of the sets of orthologous complexes have originated from evolutionary cores of homodimers that underwent duplication and divergence, testifying to the important role of gene duplication in protein complex evolution.     

Highly constrained proteins contain an unexpectedly large number of amino acid tandem repeats

Single-amino-acid tandem repeats are very common in mammalian proteins but their function and evolution are still poorly understood. Here we investigate how the variability and prevalence of amino acid repeats are related to the evolutionary constraints operating on the proteins. We find a significant positive correlation between repeat size difference and protein nonsynonymous substitution rate in human and mouse orthologous genes. This association is observed for all the common amino acid repeat types and indicates that rapid diversification of repeat structures, involving both trinucleotide slippage and nucleotide substitutions, preferentially occurs in proteins subject to low selective constraints. However, strikingly, we also observe a significant negative correlation between the number of repeats in a protein and the gene nonsynonymous substitution rate, particularly for glutamine, glycine, and alanine repeats. This implies that proteins subject to strong selective constraints tend to contain an unexpectedly high number of repeats, which tend to be well conserved between the two species. This is consistent with a role for selection in the maintenance of a significant number of repeats. Analysis of the codon structure of the sequences encoding the repeats shows that codon purity is associated with high repeat size interspecific variability. Interestingly, polyalanine and polyglutamine repeats associated with disease show very distinctive features regarding the degree of repeat conservation and the protein sequence selective constraints.     

Expression Differentiation Is Constrained to Low-Expression Proteins over Ecological Timescales

Protein expression level is one of the strongest predictors of protein sequence evolutionary rate, with high-expression protein sequences evolving at slower rates than low-expression protein sequences largely because of constraints on protein folding and function. Expression evolutionary rates also have been shown to be negatively correlated with expression level across human and mouse orthologs over relatively long divergence times (i.e., ∼100 million years). Long-term evolutionary patterns, however, often cannot be extrapolated to microevolutionary processes (and vice versa), and whether this relationship holds for traits evolving under directional selection within a single species over ecological timescales (i.e., <5000 years) is unknown and not necessarily expected. Expression is a metabolically costly process, and the expression level of a particular protein is predicted to be a tradeoff between the benefit of its function and the costs of its expression. Selection should drive the expression level of all proteins close to values that maximize fitness, particularly for high-expression proteins because of the increased energetic cost of production. Therefore, stabilizing selection may reduce the amount of standing expression variation for high-expression proteins, and in combination with physiological constraints that may place an upper bound on the range of beneficial expression variation, these constraints could severely limit the availability of beneficial expression variants. To determine whether rapid-expression evolution was restricted to low-expression proteins owing to these constraints on highly expressed proteins over ecological timescales, we compared venom protein expression levels across mainland and island populations for three species of pit vipers. We detected significant differentiation in protein expression levels in two of the three species and found that rapid-expression differentiation was restricted to low-expression proteins. Our results suggest that various constraints on high-expression proteins reduce the availability of beneficial expression variants relative to low-expression proteins, enabling low-expression proteins to evolve and potentially lead to more rapid adaptation.