The origin of genetic and metabolic systems: Evolutionary structuralinsights

DNA is derived from reverse transcription and its origin is related to reverse transcriptase, DNA polymerase and integrase. The gene structure originated from the evolution of the first RNA polymerase. Thus, an explanation of the origin of the genetic system must also explain the evolution of these enzymes. This paper proposes a polymer structure model, termed the stable complex evolution model, which explains the evolution of enzymes and functional molecules. Enzymes evolved their functions by forming locally tightly packed complexes with specific substrates. A metabolic reaction can therefore be considered to be the result of adaptive evolution in this way when a certain essential molecule is lacking in a cell. The evolution of the primitive genetic and metabolic systems was thus coordinated and synchronized. According to the stable complex model, almost all functional molecules establish binding affinity and specific recognition through complementary interactions, and functional molecules therefore have the nature of being auto-reactive. This is thermodynamically favorable and leads to functional duplication and self-organization. Therefore, it can be speculated that biological systems have a certain tendency to maintain functional stability or are influenced by an inherent selective power. The evolution of dormant bacteria may support this hypothesis, and inherent selectivity can be unified with natural selection at the molecular level.

Molecular Evolution of rbcL in Orthotrichales (Bryophyta): Site Variation, Adaptive Evolution, and Coevolutionary Patterns of Amino Acid Replacements

Molecular evolution of the large subunit of the RuBisCO enzyme is understudied in early diverging land plants. These groups show morphological and eco-physiological adaptations to the uneven and intermittent distribution of water in the terrestrial environment. This might have prompted a continuous fine-tuning of RuBisCO under a selective pressure modifying the species-specific optima for photosynthesis in contrasting microdistributions and environmental niches. To gain a better insight into the molecular evolution of RuBisCO large subunits, the aim of this study was to assess the pattern of evolutionary change in the amino acid residues in a monophyletic group of Bryophyta (Orthotrichaceae). Tests for positive, neutral, or purifying selection at the amino acid level were assessed by comparing rates (ω) of non-synonymous (d_N) and synonymous (d_S) nucleotide substitutions along a Maximum Likelihood phylogenetic tree. Molecular adaptation tests using likelihood ratio tests, reconstruction of ancestral amino acid sites, and intra-protein coevolution analyses were performed. Variable amino acid sites (39) were unevenly distributed across the LSU. The residues are located on rbcL sites that are highly variable in higher plants and close to key regions implying dimer-dimer (L₂L₂), RuBisCO-activase interactions, and conformational functions during catalysis. Ten rbcL sites (32, 33, 91, 230, 247, 251, 255, 424, 449 and 475) have been identified by the Bayesian Empirical Bayes inference to be under positive selection and under adaptive evolution under the M8 model. The pattern of amino acid variation suggests that it is not lineage specific, but rather representative of a case of convergent evolution, suggesting recurrent changes that potentially favor the same amino acid substitutions that are likely optimized the RuBisCO activity.

Analysis of Adaptive Evolution and Coevolution of rbcL Gene in the Genus Hildenbrandia (Rhodophyta)

The adaptive evolution and coevolution of the ribulose-1,5-bisphosphate carboxylase/oxygenase large subunit (rbcL) gene in the genus Hildenbrandia were studied based on phylogenetic tree construction and the physicochemical properties and the secondary structures of protein encoded by rbcL (Rubisco large subunit) were analyzed. The amino acids compositions and grand average of hydropathicity of freshwater H. rivularis and marine H. rubra were similar. Rubisco large subunit of Hildenbrandia was hydrophilic and the secondary structure was primarily composed of α-helixes and β-sheets, revealing the relatively stable structure of this protein. The predicted phosphorylation sites in H. rivularis and H. rubra were 33 and 36, respectively. No positive selection sites were detected in the genus Hildenbrandia, implying that rbcL gene evolved either neutrally or under purifying selection. A total of 41 coevolutionary groups were detected in the Rubisco large subunit of Hildenbrandia and the coevolving sites are in closer proximity in 3-dimensional structure of the protein. Despite the long evolutionary history, rbcL gene in genus Hildenbrandia under different environments is rather conservative.

Coevolutive, evolutive and stochastic information in protein-protein interactions

Here, we investigate the contributions of coevolutive, evolutive and stochastic information in determining protein-protein interactions (PPIs) based on primary sequences of two interacting protein families A and B. Specifically, under the assumption that coevolutive information is imprinted on the interacting amino acids of two proteins in contrast to other (evolutive and stochastic) sources spread over their sequences, we dissect those contributions in terms of compensatory mutations at physically-coupled and uncoupled amino acids of A and B. We find that physically-coupled amino-acids at short range distances store the largest per-contact mutual information content, with a significant fraction of that content resulting from coevolutive sources alone. The information stored in coupled amino acids is shown further to discriminate multi-sequence alignments (MSAs) with the largest expectation fraction of PPI matches – a conclusion that holds against various definitions of intermolecular contacts and binding modes. When compared to the informational content resulting from evolution at long-range interactions, the mutual information in physically-coupled amino-acids is the strongest signal to distinguish PPIs derived from cospeciation and likely, the unique indication in case of molecular coevolution in independent genomes as the evolutive information must vanish for uncorrelated proteins.

Detecting genetic interactions using parallel evolution in experimental populations

Eukaryotic genomes contain thousands of genes organized into complex and interconnected genetic interaction networks. Most of our understanding of how genetic variation affects these networks comes from quantitative-trait loci mapping and from the systematic analysis of double-deletion (or knockdown) mutants, primarily in the yeast Saccharomyces cerevisiae. Evolve and re-sequence experiments are an alternative approach for identifying novel functional variants and genetic interactions, particularly between non-loss-of-function mutations. These experiments leverage natural selection to obtain genotypes with functionally important variants and positive genetic interactions. However, no systematic methods for detecting genetic interactions in these data are yet available. Here, we introduce a computational method based on the idea that variants in genes that interact will co-occur in evolved genotypes more often than expected by chance. We apply this method to a previously published yeast experimental evolution dataset. We find that genetic targets of selection are distributed non-uniformly among evolved genotypes, indicating that genetic interactions had a significant effect on evolutionary trajectories. We identify individual gene pairs with a statistically significant genetic interaction score. The strongest interaction is between genes TRK1 and PHO84, genes that have not been reported to interact in previous systematic studies. Our work demonstrates that leveraging parallelism in experimental evolution is useful for identifying genetic interactions that have escaped detection by other methods. This article is part of the theme issue 'Convergent evolution in the genomics era: new insights and directions'.

Pathogenicity of the H1N1 influenza virus enhanced by functional synergy between the NPV100I and NAD248N pair

By comparing and measuring covariations of viral protein sequences from isolates of the 2009 pH1N1 influenza A virus (IAV), specific substitutions that co-occur in the NP-NA pair were identified. To investigate the effect of these co-occurring substitution pairs, the V100I substitution in NP and the D248N substitution in NA were introduced into laboratory-adapted WSN IAVs. The recombinant WSN with the covarying NP^V100I-NA^D248N pair exhibited enhanced pathogenicity, as characterized by increased viral production, increased death and inflammation of host cells, and high mortality in infected mice. Although direct interactions between the NP^V100I and NA^D248N proteins were not detected, the RNA-binding ability of NP^V100I was increased, which was further strengthened by NA^D248N, in expression-plasmid-transfected cells. Additionally, the NA^D248N protein was frequently recruited within lipid rafts, indirectly affecting the RNA-binding ability of NP as well as viral release. Altogether, our data indicate that the covarying NP^V100I-NA^D248N pair obtained from 2009 pH1N1 IAV sequence information function together to synergistically augment viral assembly and release, which may explain the observed enhanced viral pathogenicity.

Preferential retention of genes from one parental genome after polyploidy illustrates the nature and scope of the genomic conflicts induced by hybridization

Polyploidy is increasingly seen as a driver of both evolutionary innovation and ecological success. One source of polyploid organisms' successes may be their origins in the merging and mixing of genomes from two different species (e.g., allopolyploidy). Using POInT (the Polyploid Orthology Inference Tool), we model the resolution of three allopolyploidy events, one from the bakers' yeast (Saccharomyces cerevisiae), one from the thale cress (Arabidopsis thaliana) and one from grasses including Sorghum bicolor. Analyzing a total of 21 genomes, we assign to every gene a probability for having come from each parental subgenome (i.e., derived from the diploid progenitor species), yielding orthologous segments across all genomes. Our model detects statistically robust evidence for the existence of biased fractionation in all three lineages, whereby genes from one of the two subgenomes were more likely to be lost than those from the other subgenome. We further find that a driver of this pattern of biased losses is the co-retention of genes from the same parental genome that share functional interactions. The pattern of biased fractionation after the Arabidopsis and grass allopolyploid events was surprisingly constant in time, with the same parental genome favored throughout the lineages' history. In strong contrast, the yeast allopolyploid event shows evidence of biased fractionation only immediately after the event, with balanced gene losses more recently. The rapid loss of functionally associated genes from a single subgenome is difficult to reconcile with the action of genetic drift and suggests that selection may favor the removal of specific duplicates. Coupled to the evidence for continuing, functionally-associated biased fractionation after the A. thaliana At-α event, we suggest that, after allopolyploidy, there are functional conflicts between interacting genes encoded in different subgenomes that are ultimately resolved through preferential duplicate loss.

Positively selected amino acid replacements within the RuBisCO enzyme of oak trees are associated with ecological adaptations

Phylogenetic analysis by maximum likelihood (PAML) has become the standard approach to study positive selection at the molecular level, but other methods may provide complementary ways to identify amino acid replacements associated with particular conditions. Here, we compare results of the decision tree (DT) model method with ones of PAML using the key photosynthetic enzyme RuBisCO as a model system to study molecular adaptation to particular ecological conditions in oaks (Quercus). We sequenced the chloroplast rbcL gene encoding RuBisCO large subunit in 158 Quercus species, covering about a third of the global genus diversity. It has been hypothesized that RuBisCO has evolved differentially depending on the environmental conditions and leaf traits governing internal gas diffusion patterns. Here, we show, using PAML, that amino acid replacements at the residue positions 95, 145, 251, 262 and 328 of the RuBisCO large subunit have been the subject of positive selection along particular Quercus lineages associated with the leaf traits and climate characteristics. In parallel, the DT model identified amino acid replacements at sites 95, 219, 262 and 328 being associated with the leaf traits and climate characteristics, exhibiting partial overlap with the results obtained using PAML.

Essentiality Is a Strong Determinant of Protein Rates of Evolution during Mutation Accumulation Experiments in Escherichia coli

The Neutral Theory of Molecular Evolution is considered the most powerful theory to understand the evolutionary behavior of proteins. One of the main predictions of this theory is that essential proteins should evolve slower than dispensable ones owing to increased selective constraints. Comparison of genomes of different species, however, has revealed only small differences between the rates of evolution of essential and nonessential proteins. In some analyses, these differences vanish once confounding factors are controlled for, whereas in other cases essentiality seems to have an independent, albeit small, effect. It has been argued that comparing relatively distant genomes may entail a number of limitations. For instance, many of the genes that are dispensable in controlled lab conditions may be essential in some of the conditions faced in nature. Moreover, essentiality can change during evolution, and rates of protein evolution are simultaneously shaped by a variety of factors, whose individual effects are difficult to isolate. Here, we conducted two parallel mutation accumulation experiments in Escherichia coli, during 5,500–5,750 generations, and compared the genomes at different points of the experiments. Our approach (a short-term experiment, under highly controlled conditions) enabled us to overcome many of the limitations of previous studies. We observed that essential proteins evolved substantially slower than nonessential ones during our experiments. Strikingly, rates of protein evolution were only moderately affected by expression level and protein length.

Robust Yet Fragile: Expression Noise, Protein Misfolding, and Gene Dosage in the Evolution of Genomes

The complex manner in which organisms respond to changes in their gene dosage has long fascinated geneticists. Oddly, although the existence of dominance implies that dosage reductions often have mild phenotypes, extra copies of whole chromosomes (aneuploidy) are generally strongly deleterious. Even more paradoxically, an extra copy of the genome is better tolerated than is aneuploidy. We review the resolution of this paradox, highlighting the roles of biochemistry, protein aggregation, and disruption of cellular microstructure in that explanation. Returning to life's curious combination of robustness and sensitivity to dosage changes, we argue that understanding how biological robustness evolved makes these observations less inexplicable. We propose that noise in gene expression and evolutionary strategies for its suppression play a role in generating dosage phenotypes. Finally, we outline an unappreciated mechanism for the preservation of duplicate genes, namely preservation to limit expression noise, arguing that it is particularly relevant in polyploid organisms.

Patterns of Gene Conversion in Duplicated Yeast Histones Suggest Strong Selection on a Coadapted Macromolecular Complex

We find evidence for interlocus gene conversion in five duplicated histone genes from six yeast species. The sequences of these duplicated genes, surviving from the ancient genome duplication, show phylogenetic patterns inconsistent with the well-resolved orthology relationships inferred from a likelihood model of gene loss after the genome duplication. Instead, these paralogous genes are more closely related to each other than any is to its nearest ortholog. In addition to simulations supporting gene conversion, we also present evidence for elevated rates of radical amino acid substitutions along the branches implicated in the conversion events. As these patterns are similar to those seen in ribosomal proteins that have undergone gene conversion, we speculate that in cases where duplicated genes code for proteins that are a part of tightly interacting complexes, selection may favor the fixation of gene conversion events in order to maintain high protein identities between duplicated copies.

Analysis of human rotaviruses from a single location over an 18-year time span suggests that protein coadaption influences gene constellations

Rotaviruses (RVs) are 11-segmented, double-stranded RNA viruses that cause severe gastroenteritis in children. In addition to an error-prone genome replication mechanism, RVs can increase their genetic diversity by reassorting genes during host coinfection. Such exchanges allow RVs to acquire advantageous genes and adapt in the face of selective pressures. However, reassortment may also impose fitness costs if it unlinks genes/proteins that have accumulated compensatory, coadaptive mutations and that operate best when kept together. To better understand human RV evolutionary dynamics, we analyzed the genome sequences of 135 strains (genotype G1/G3/G4-P[8]-I1-C1-R1-A1-N1-T1-E1-H1) that were collected at a single location in Washington, DC, during the years 1974 to 1991. Intragenotypic phylogenetic trees were constructed for each viral gene using the nucleotide sequences, thereby defining novel allele level gene constellations (GCs) and illuminating putative reassortment events. The results showed that RVs with distinct GCs cocirculated during the vast majority of the collection years and that some of these GCs persisted in the community unchanged by reassortment. To investigate the influence of protein coadaptation on GC maintenance, we performed a mutual information-based analysis of the concatenated amino acid sequences and identified an extensive covariance network. Unexpectedly, amino acid covariation was highest between VP4 and VP2, which are structural components of the RV virion that are not thought to directly interact. These results suggest that GCs may be influenced by the selective constraints placed on functionally coadapted, albeit noninteracting, viral proteins. This work raises important questions about mutation-reassortment interplay and its impact on human RV evolution.

IMPORTANCE

Rotaviruses are devastating human pathogens that cause severe diarrhea and kill >450,000 children each year. The virus can evolve by accumulating mutations and by acquiring new genes from other strains via a process called reassortment. However, little is known about the relationship between mutation accumulation and gene reassortment for rotaviruses and how it impacts viral evolution. In this study, we analyzed the genome sequences of human strains found in clinical fecal specimens that were collected at a single hospital over an 18-year time span. We found that many rotaviruses did not reassort their genes but instead maintained them as specific sets (i.e., constellations). By analyzing the encoded proteins, we discovered concurrent amino acid changes among them, which suggests that they are functionally coadapted to operate best when kept together. This study increases our understanding of how rotaviruses evolve over time in the human population.

Multidimensional mutual information methods for the analysis of covariation in multiple sequence alignments

Background

Several methods are available for the detection of covarying positions from a multiple sequence alignment (MSA). If the MSA contains a large number of sequences, information about the proximities between residues derived from covariation maps can be sufficient to predict a protein fold. However, in many cases the structure is already known, and information on the covarying positions can be valuable to understand the protein mechanism and dynamic properties.

Results

In this study we have sought to determine whether a multivariate (multidimensional) extension of traditional mutual information (MI) can be an additional tool to study covariation. The performance of two multidimensional MI (mdMI) methods, designed to remove the effect of ternary/quaternary interdependencies, was tested with a set of 9 MSAs each containing <400 sequences, and was shown to be comparable to that of the newest methods based on maximum entropy/pseudolikelyhood statistical models of protein sequences. However, while all the methods tested detected a similar number of covarying pairs among the residues separated by < 8 Å in the reference X-ray structures, there was on average less than 65% overlap between the top scoring pairs detected by methods that are based on different principles.

Conclusions

Given the large variety of structure and evolutionary history of different proteins it is possible that a single best method to detect covariation in all proteins does not exist, and that for each protein family the best information can be derived by merging/comparing results obtained with different methods. This approach may be particularly valuable in those cases in which the size of the MSA is small or the quality of the alignment is low, leading to significant differences in the pairs detected by different methods.

Quantum coupled mutation finder: predicting functionally or structurally important sites in proteins using quantum Jensen-Shannon divergence and CUDA programming

Background

The identification of functionally or structurally important non-conserved residue sites in protein MSAs is an important challenge for understanding the structural basis and molecular mechanism of protein functions. Despite the rich literature on compensatory mutations as well as sequence conservation analysis for the detection of those important residues, previous methods often rely on classical information-theoretic measures. However, these measures usually do not take into account dis/similarities of amino acids which are likely to be crucial for those residues. In this study, we present a new method, the Quantum Coupled Mutation Finder (QCMF) that incorporates significant dis/similar amino acid pair signals in the prediction of functionally or structurally important sites.

Results

The result of this study is twofold. First, using the essential sites of two human proteins, namely epidermal growth factor receptor (EGFR) and glucokinase (GCK), we tested the QCMF-method. The QCMF includes two metrics based on quantum Jensen-Shannon divergence to measure both sequence conservation and compensatory mutations. We found that the QCMF reaches an improved performance in identifying essential sites from MSAs of both proteins with a significantly higher Matthews correlation coefficient (MCC) value in comparison to previous methods. Second, using a data set of 153 proteins, we made a pairwise comparison between QCMF and three conventional methods. This comparison study strongly suggests that QCMF complements the conventional methods for the identification of correlated mutations in MSAs.

Conclusions

QCMF utilizes the notion of entanglement, which is a major resource of quantum information, to model significant dissimilar and similar amino acid pair signals in the detection of functionally or structurally important sites. Our results suggest that on the one hand QCMF significantly outperforms the previous method, which mainly focuses on dissimilar amino acid signals, to detect essential sites in proteins. On the other hand, it is complementary to the existing methods for the identification of correlated mutations. The method of QCMF is computationally intensive. To ensure a feasible computation time of the QCMF’s algorithm, we leveraged Compute Unified Device Architecture (CUDA).

The QCMF server is freely accessible at http://qcmf.informatik.uni-goettingen.de/.

The functional importance of co-evolving residues in proteins

Computational approaches for detecting co-evolution in proteins allow for the identification of protein-protein interaction networks in different organisms and the assignment of function to under-explored proteins. The detection of co-variation of amino acids within or between proteins, moreover, allows for the discovery of residue-residue contacts and highlights functional residues that can affect the binding affinity, catalytic activity, or substrate specificity of a protein. To explore the functional impact of co-evolutionary changes in proteins, a combined experimental and computational approach must be recruited. Here, we review recent studies that apply computational and experimental tools to obtain novel insight into the structure, function, and evolution of proteins. Specifically, we describe the application of co-evolutionary analysis for predicting high-resolution three-dimensional structures of proteins. In addition, we describe computational approaches followed by experimental analysis for identifying specificity-determining residues in proteins. Finally, we discuss studies addressing the importance of such residues in terms of the functional divergence of proteins, allowing proteins to evolve new functions while avoiding crosstalk with existing cellular pathways or forming reproductive barriers and hence promoting speciation.

Bi-factor analysis based on noise-reduction (BIFANR): a new algorithm for detecting coevolving amino acid sites in proteins

Previous statistical analyses have shown that amino acid sites in a protein evolve in a correlated way instead of independently. Even though located distantly in the linear sequence, the coevolved amino acids could be spatially adjacent in the tertiary structure, and constitute specific protein sectors. Moreover, these protein sectors are independent of one another in structure, function, and even evolution. Thus, systematic studies on protein sectors inside a protein will contribute to the clarification of protein function. In this paper, we propose a new algorithm BIFANR (Bi-factor Analysis Based on Noise-reduction) for detecting protein sectors in amino acid sequences. After applying BIFANR on S1A family and PDZ family, we carried out internal correlation test, statistical independence test, evolutionary rate analysis, evolutionary independence analysis, and function analysis to assess the prediction. The results showed that the amino acids in certain predicted protein sector are closely correlated in structure, function, and evolution, while protein sectors are nearly statistically independent. The results also indicated that the protein sectors have distinct evolutionary directions. In addition, compared with other algorithms, BIFANR has higher accuracy and robustness under the influence of noise sites.

Structural basis of a rationally rewired protein-protein interface critical to bacterial signaling

Two-component signal transduction systems typically involve a sensor histidine kinase that specifically phosphorylates a single, cognate response regulator. This protein-protein interaction relies on molecular recognition via a small set of residues in each protein. To better understand how these residues determine the specificity of kinase-substrate interactions, we rationally rewired the interaction interface of a Thermotoga maritima two-component system, HK853-RR468, to match that found in a different two-component system, Escherichia coli PhoR-PhoB. The rewired proteins interacted robustly with each other, but no longer interacted with the parent proteins. Analysis of the crystal structures of the wild-type and mutant protein complexes and a systematic mutagenesis study reveal how individual mutations contribute to the rewiring of interaction specificity. Our approach and conclusions have implications for studies of other protein-protein interactions and protein evolution and for the design of novel protein interfaces.

Engineering the acyltransferase substrate specificity of assembly line polyketide synthases

Polyketide natural products act as a broad range of therapeutics, including antibiotics, immunosuppressants and anti-cancer agents. This therapeutic diversity stems from the structural diversity of these small molecules, many of which are produced in an assembly line manner by modular polyketide synthases. The acyltransferase (AT) domains of these megasynthases are responsible for selection and incorporation of simple monomeric building blocks, and are thus responsible for a large amount of the resulting polyketide structural diversity. The substrate specificity of these domains is often targeted for engineering in the generation of novel, therapeutically active natural products. This review outlines recent developments that can be used in the successful engineering of these domains, including AT sequence and structural data, mechanistic insights and the production of a diverse pool of extender units. It also provides an overview of previous AT domain engineering attempts, and concludes with proposed engineering approaches that take advantage of current knowledge. These approaches may lead to successful production of biologically active 'unnatural' natural products.

Evolutionary rate and duplicability in the Arabidopsis thaliana protein-protein interaction network

Genes show a bewildering variation in their patterns of molecular evolution, as a result of the action of different levels and types of selective forces. The factors underlying this variation are, however, still poorly understood. In the last decade, the position of proteins in the protein-protein interaction network has been put forward as a determinant factor of the evolutionary rate and duplicability of their encoding genes. This conclusion, however, has been based on the analysis of the limited number of microbes and animals for which interactome-level data are available (essentially, Escherichia coli, yeast, worm, fly, and humans). Here, we study, for the first time, the relationship between the position of proteins in the high-density interactome of a plant (Arabidopsis thaliana) and the patterns of molecular evolution of their encoding genes. We found that genes whose encoded products act at the center of the network are more evolutionarily constrained than those acting at the network periphery. This trend remains significant when potential confounding factors (gene expression level and breadth, duplicability, function, and length of the encoded products) are controlled for. Even though the correlation between centrality measures and rates of evolution is generally weak, for some functional categories, it is comparable in strength to (or even stronger than) the correlation between evolutionary rates and expression levels or breadths. In addition, genes encoding interacting proteins in the network evolve at relatively similar rates. Finally, Arabidopsis proteins encoded by duplicated genes are more highly connected than those encoded by singleton genes. This observation is in agreement with the patterns observed in humans, but in contrast with those observed in E. coli, yeast, worm, and fly (whose duplicated genes tend to act at the periphery of the network), implying that the relationship between duplicability and centrality inverted at least twice during eukaryote evolution. Taken together, these results indicate that the structure of the A. thaliana network constrains the evolution of its components at multiple levels.

Improving the thermostability of alpha-amylase by combinatorial coevolving-site saturation mutagenesis

Background

The generation of focused mutant libraries at hotspot residues is an important strategy in directed protein evolution. Existing methods, such as combinatorial active site testing and residual coupling analysis, depend primarily on the evolutionary conserved information to find the hotspot residues. Hardly any attention has been paid to another important functional and structural determinants, the functionally correlated variation information--coevolution.

Results

In this paper, we suggest a new method, named combinatorial coevolving-site saturation mutagenesis (CCSM), in which the functionally correlated variation sites of proteins are chosen as the hotspot sites to construct focused mutant libraries. The CCSM approach was used to improve the thermal stability of α-amylase from Bacillus subtilis CN7 (Amy7C). The results indicate that the CCSM can identify novel beneficial mutation sites, and enhance the thermal stability of wild-type Amy7C by 8°C ( T5030), which could not be achieved with the ordinarily rational introduction of single or a double point mutation.

Conclusions

Our method is able to produce more thermostable mutant α-amylases with novel beneficial mutations at new sites. It is also verified that the coevolving sites can be used as the hotspots to construct focused mutant libraries in protein engineering. This study throws new light on the active researches of the molecular coevolution.

Increased genome sampling reveals a dynamic relationship between gene duplicability and the structure of the primate protein-protein interaction network

Although gene duplications occur at a higher rate, only a small fraction of these are retained. The position of a gene's encoded product in the protein-protein interaction network has recently emerged as a determining factor of gene duplicability. However, the direction of the relationship between network centrality and duplicability is not universal: In Escherichia coli, yeast, fly, and worm, duplicated genes more often act at the periphery of the network, whereas in humans, such genes tend to occupy the most central positions. Herein, we have inferred duplication events that took place in the different branches of the primate phylogeny. In agreement with previous observations, we found that duplications generally affected the most central network genes, which is presumably the process that has most influenced the trend in humans. However, the opposite trend--that is, duplication being more common in genes whose encoded products are peripheral in the network--is observed for three recent branches, including, quite counterintuitively, the external branch leading to humans. This indicates a shift in the relationship between centrality and duplicability during primate evolution. Furthermore, we found that genes encoding interacting proteins exhibit phylogenetic tree topologies that are more similar than expected for random pairs and that genes duplicated in a given branch of the phylogeny tend to interact with those that duplicated in the same lineage. These results indicate that duplication of a gene increases the likelihood of duplication of its interacting partners. Our observations indicate that the structure of the primate protein-protein interaction network affects gene duplicability in previously unrecognized ways.

Integrated analysis of residue coevolution and protein structure in ABC transporters

Intraprotein side chain contacts can couple the evolutionary process of amino acid substitution at one position to that at another. This coupling, known as residue coevolution, may vary in strength. Conserved contacts thus not only define 3-dimensional protein structure, but also indicate which residue-residue interactions are crucial to a protein's function. Therefore, prediction of strongly coevolving residue-pairs helps clarify molecular mechanisms underlying function. Previously, various coevolution detectors have been employed separately to predict these pairs purely from multiple sequence alignments, while disregarding available structural information. This study introduces an integrative framework that improves the accuracy of such predictions, relative to previous approaches, by combining multiple coevolution detectors and incorporating structural contact information. This framework is applied to the ABC-B and ABC-C transporter families, which include the drug exporter P-glycoprotein involved in multidrug resistance of cancer cells, as well as the CFTR chloride channel linked to cystic fibrosis disease. The predicted coevolving pairs are further analyzed based on conformational changes inferred from outward- and inward-facing transporter structures. The analysis suggests that some pairs coevolved to directly regulate conformational changes of the alternating-access transport mechanism, while others to stabilize rigid-body-like components of the protein structure. Moreover, some identified pairs correspond to residues previously implicated in cystic fibrosis.

Phylogenetic and coevolutionary analysis of the β-barrel protein family comprised of mitochondrial porin (VDAC) and Tom40

Beta-barrel proteins are the main transit points across the mitochondrial outer membrane. Mitochondrial porin, the voltage-dependent, anion-selective channel (VDAC), is responsible for the passage of small molecules between the mitochondrion and the cytosol. Through interactions with other mitochondrial and cellular proteins, it is involved in regulating organellar and cellular metabolism and likely contributes to mitochondrial structure. Tom40 is part of the translocase of the outer membrane, and acts as the channel for passage of preproteins during their import into the organelle. These proteins appear to share a common evolutionary origin and structure. In the current study, the evolutionary relationships between and within both proteins were investigated through phylogenetic analysis. The two groups have a common origin and have followed independent, complex evolutionary pathways, leading to the generation of paralogues in animals and plants. Structures of diverse representatives were modeled, revealing common themes rather than sites of high identity in both groups. Within each group, intramolecular coevolution was assessed, revealing a new set of sites potentially involved in structure-function relationships in these molecules. A weak link between Tom40 and proteins related to the mitochondrial distribution and morphology protein, Mdm10, was identified. This article is part of a Special Issue entitled: VDAC structure, function, and regulation of mitochondrial metabolism.

Molecular evolution of rbcL in three gymnosperm families: identifying adaptive and coevolutionary patterns

Background

The chloroplast-localized ribulose-1, 5-biphosphate carboxylase/oxygenase (Rubisco), the primary enzyme responsible for autotrophy, is instrumental in the continual adaptation of plants to variations in the concentrations of CO₂. The large subunit (LSU) of Rubisco is encoded by the chloroplast rbcL gene. Although adaptive processes have been previously identified at this gene, characterizing the relationships between the mutational dynamics at the protein level may yield clues on the biological meaning of such adaptive processes. The role of such coevolutionary dynamics in the continual fine-tuning of RbcL remains obscure.

Results

We used the timescale and phylogenetic analyses to investigate and search for processes of adaptive evolution in rbcL gene in three gymnosperm families, namely Podocarpaceae, Taxaceae and Cephalotaxaceae. To understand the relationships between regions identified as having evolved under adaptive evolution, we performed coevolutionary analyses using the software CAPS. Importantly, adaptive processes were identified at amino acid sites located on the contact regions among the Rubisco subunits and on the interface between Rubisco and its activase. Adaptive amino acid replacements at these regions may have optimized the holoenzyme activity. This hypothesis was pinpointed by evidence originated from our analysis of coevolution that supported the correlated evolution between Rubisco and its activase. Interestingly, the correlated adaptive processes between both these proteins have paralleled the geological variation history of the concentration of atmospheric CO₂.

Conclusions

The gene rbcL has experienced bursts of adaptations in response to the changing concentration of CO₂ in the atmosphere. These adaptations have emerged as a result of a continuous dynamic of mutations, many of which may have involved innovation of functional Rubisco features. Analysis of the protein structure and the functional implications of such mutations put forward the conclusion that this evolutionary scenario has been possible through a complex interplay between adaptive mutations, often structurally destabilizing, and compensatory mutations. Our results unearth patterns of evolution that have likely optimized the Rubisco activity and uncover mutational dynamics useful in the molecular engineering of enzymatic activities.

Reviewers

This article was reviewed by Prof. Christian Blouin (nominated by Dr W Ford Doolittle), Dr Endre Barta (nominated by Dr Sandor Pongor), and Dr Nicolas Galtier.

The contribution of coevolving residues to the stability of KDO8P synthase

Background

The evolutionary tree of 3-deoxy-D-manno-octulosonate 8-phosphate (KDO8P) synthase (KDO8PS), a bacterial enzyme that catalyzes a key step in the biosynthesis of bacterial endotoxin, is evenly divided between metal and non-metal forms, both having similar structures, but diverging in various degrees in amino acid sequence. Mutagenesis, crystallographic and computational studies have established that only a few residues determine whether or not KDO8PS requires a metal for function. The remaining divergence in the amino acid sequence of KDO8PSs is apparently unrelated to the underlying catalytic mechanism.

Methodology/Principal Findings

The multiple alignment of all known KDO8PS sequences reveals that several residue pairs coevolved, an indication of their possible linkage to a structural constraint. In this study we investigated by computational means the contribution of coevolving residues to the stability of KDO8PS. We found that about 1/4 of all strongly coevolving pairs probably originated from cycles of mutation (decreasing stability) and suppression (restoring it), while the remaining pairs are best explained by a succession of neutral or nearly neutral covarions.

Conclusions/Significance

Both sequence conservation and coevolution are involved in the preservation of the core structure of KDO8PS, but the contribution of coevolving residues is, in proportion, smaller. This is because small stability gains or losses associated with selection of certain residues in some regions of the stability landscape of KDO8PS are easily offset by a large number of possible changes in other regions. While this effect increases the tolerance of KDO8PS to deleterious mutations, it also decreases the probability that specific pairs of residues could have a strong contribution to the thermodynamic stability of the protein.

Prevalence of epistasis in the evolution of influenza A surface proteins

The surface proteins of human influenza A viruses experience positive selection to escape both human immunity and, more recently, antiviral drug treatments. In bacteria and viruses, immune-escape and drug-resistant phenotypes often appear through a combination of several mutations that have epistatic effects on pathogen fitness. However, the extent and structure of epistasis in influenza viral proteins have not been systematically investigated. Here, we develop a novel statistical method to detect positive epistasis between pairs of sites in a protein, based on the observed temporal patterns of sequence evolution. The method rests on the simple idea that a substitution at one site should rapidly follow a substitution at another site if the sites are positively epistatic. We apply this method to the surface proteins hemagglutinin and neuraminidase of influenza A virus subtypes H3N2 and H1N1. Compared to a non-epistatic null distribution, we detect substantial amounts of epistasis and determine the identities of putatively epistatic pairs of sites. In particular, using sequence data alone, our method identifies epistatic interactions between specific sites in neuraminidase that have recently been demonstrated, in vitro, to confer resistance to the drug oseltamivir; these epistatic interactions are responsible for widespread drug resistance among H1N1 viruses circulating today. This experimental validation demonstrates the predictive power of our method to identify epistatic sites of importance for viral adaptation and public health. We conclude that epistasis plays a large role in shaping the molecular evolution of influenza viruses. In particular, sites with , which would normally not be identified as positively selected, can facilitate viral adaptation through epistatic interactions with their partner sites. The knowledge of specific interactions among sites in influenza proteins may help us to predict the course of antigenic evolution and, consequently, to select more appropriate vaccines and drugs.

Epistasis describes non-additive interactions among genetic sites: the consequence of a mutation at one site may depend on the status of the genome at other sites. In an extreme case, a mutation may have no effect if it arises on one genetic background, but a strong effect on another background. Epistatic mutations in viruses and bacteria that live under severe conditions, such as antibiotic treatments or immune pressure, often allow pathogens to develop drug resistance or escape the immune system. In this paper we develop a new phylogenetic method for detecting epistasis, and we apply this method to the surface proteins of the influenza A virus, which are important targets of the immune system and drug treatments. The authors identify and characterize hundreds of epistatic mutations in these proteins. Among those identified, we find the specific epistatic mutations that were recently shown, experimentally, to confer resistance to the drug Tamiflu. The results of this study may help to predict the course of influenza's antigenic evolution and to select more appropriate vaccines and drugs.

Regional covariation and its application for predicting protein contact patches

Correlated mutation analysis (CMA) is an effective approach for predicting functional and structural residue interactions from multiple sequence alignments (MSAs) of proteins. As nearby residues may also play a role in a given functional interaction, we were interested in seeing whether covarying sites were clustered, and whether this could be used to enhance the predictive power of CMA. A large-scale search for coevolving regions within protein domains revealed that if two sites in a MSA covary, then neighboring sites in the alignment also typically covary, resulting in clusters of covarying residues. The program PatchD(http://www.uhnres.utoronto.ca/labs/tillier/) was developed to measure the covariation between disconnected sequence clusters to reveal patch covariation. Patches that exhibit strong covariation identify multiple residues that are generally nearby in the protein structure, suggesting that the detection of covarying patches can be used in conjunction with traditional CMA approaches to reveal functional interaction partners.

Functionally compensating coevolving positions are neither homoplasic nor conserved in clades

We demonstrated that a pair of positions in phosphoglycerate kinase that score highly by three nonparametric covariation measures are important for function even though the positions can be occupied by aliphatic, aromatic, or charged residues. Examination of these pairs suggested that the majority of the covariation scores could be explained by within-clade conservation. However, an analysis of diversity showed that the conservation within clades of covarying pairs was indistinguishable from pairs of positions that do not covary, thus ruling out both clade conservation and extensive homoplasy as means to identify covarying positions. Mutagenesis showed that the residues in the covarying pair were epistatic, with the type of epistasis being dependent on the initial pair. The results show that nonconserved covarying positions that affect protein function can be identified with high precision.

Identifying coevolutionary patterns in human leukocyte antigen (HLA) molecules

The antigenic peptide, major histocompatibility complex molecule (MHC; also called human leukocyte antigen, HLA), coreceptor CD8, or CD4 and T-cell receptor (TCR) function as a complex to initiate effectors' mechanisms of the immune system. The tight functional and physical interaction among these molecules may have involved strong coevolution links among domains within and between proteins. Despite the importance of unraveling such dependencies to understand the arms race of host-pathogen interaction, no previous studies have aimed at achieving such an objective. Here, we perform an exhaustive coevolution analysis and show that indeed such dependencies are strongly shaping the evolution and probably the function of these molecules. We identify intramolecular coevolution in HLA class I and II at domains important for their immune activity. Most of the amino acid sites identified to be coevolving in HLAI have been also detected to undergo positive Darwinian selection highlighting therefore their adaptive value. We also identify coevolution among antigen-binding pockets (P1-P9) and among these and TCR-binding sites. Conversely to HLAI, coevolution is weaker in HLAII. Our results support that such coevolutionary patterns are due to selective pressures of host-pathogen coevolution and cooperative binding of TCRs, antigenic peptides, and CD8/CD4 to HLAI and HLAII.

Evidence for an episodic model of protein sequence evolution

The evolution of protein function appears to involve alternating periods of conservative evolution and of relatively rapid change. Evidence for such episodic evolution, consistent with some theoretical expectations, comes from the application of increasingly sophisticated models of evolution to large sequence datasets. We present here some of the recent methods to detect functional shifts, using amino acid or codon models. Both provide evidence for punctual shifts in patterns of amino acid conservation, including the fixation of key changes by positive selection. Although a link to gene duplication, a presumed source of functional changes, has been difficult to establish, this episodic model appears to apply to a wide variety of proteins and organisms.

A new method for revealing correlated mutations under the structural and functional constraints in proteins

MOTIVATION

Diverse studies have shown that correlated mutation (CM) is an important molecular evolutionary process alongside conservation. However, attempts to find the residue pairs that co-evolve under the structural and/or functional constraints are complicated by the fact that a large portion of covariance signals found in multiple sequence alignments arise from correlations due to common ancestry and stochastic noise.

RESULTS

Assuming that the background noise can be estimated from the coevolutionary relationships among residues, we propose a new measure for background noise called the normalized coevolutionary pattern similarity (NCPS) score. By subtracting NCPS scores from raw CM scores and combining the results with an entropy factor, we show that these new scores effectively reduce the background noise. To test the effectiveness of this method in detecting residue pairs coevolving under the structural constraints, two independent test sets were performed, showing that this new method performs better than the most accurate method currently available. In addition, we also applied our method to double mutant cycle experiments and protein-protein interactions. Although more rigorous tests are required, we obtained promising results that our method tended to explain those data better than other methods. These results suggest that the new noise-reduced CM scores developed in this study can be a valuable tool for the study of correlated mutations under the structural and/or functional constraints in proteins.

AVAILABILITY

http://pbil.kaist.ac.kr

Phylogenetic dependency networks: inferring patterns of CTL escape and codon covariation in HIV-1 Gag

HIV avoids elimination by cytotoxic T-lymphocytes (CTLs) through the evolution of escape mutations. Although there is mounting evidence that these escape pathways are broadly consistent among individuals with similar human leukocyte antigen (HLA) class I alleles, previous population-based studies have been limited by the inability to simultaneously account for HIV codon covariation, linkage disequilibrium among HLA alleles, and the confounding effects of HIV phylogeny when attempting to identify HLA-associated viral evolution. We have developed a statistical model of evolution, called a phylogenetic dependency network, that accounts for these three sources of confounding and identifies the primary sources of selection pressure acting on each HIV codon. Using synthetic data, we demonstrate the utility of this approach for identifying sites of HLA-mediated selection pressure and codon evolution as well as the deleterious effects of failing to account for all three sources of confounding. We then apply our approach to a large, clinically-derived dataset of Gag p17 and p24 sequences from a multicenter cohort of 1144 HIV-infected individuals from British Columbia, Canada (predominantly HIV-1 clade B) and Durban, South Africa (predominantly HIV-1 clade C). The resulting phylogenetic dependency network is dense, containing 149 associations between HLA alleles and HIV codons and 1386 associations among HIV codons. These associations include the complete reconstruction of several recently defined escape and compensatory mutation pathways and agree with emerging data on patterns of epitope targeting. The phylogenetic dependency network adds to the growing body of literature suggesting that sites of escape, order of escape, and compensatory mutations are largely consistent even across different clades, although we also identify several differences between clades. As recent case studies have demonstrated, understanding both the complexity and the consistency of immune escape has important implications for CTL-based vaccine design. Phylogenetic dependency networks represent a major step toward systematically expanding our understanding of CTL escape to diverse populations and whole viral genes.