An evolutionary model motivated by physicochemical properties of amino acids reveals variation among proteins

Exchanges in the 'a' determinant of the hepatitis B virus surface antigen revisited

The hepatitis B virus surface antigen's (HBsAg) 'a' determinant comprises a sequence of amino acid residues located in the major hydrophilic region of the S protein, whose exchanges are closely associated with compromising the antigenicity and immunogenicity of that antigen. The HBsAg is generally present in the bloodstream of individuals with acute or chronic hepatitis B virus (HBV) infection. It is classically known as the HBV infection marker, and is therefore the first marker to be investigated in the laboratory in the clinical hypothesis of infection by this agent. One of the factors that compromises the HBsAg detection in the bloodstream by the assays adopted in serological screening in both clinical contexts is the loss of S protein antigenicity. This can occur due to mutations that emerge in the HBV genome regions that encode the S protein, especially for its immunodominant region - the 'a' determinant. These mutations can induce exchanges of amino acid residues in the S protein's primary structure, altering its tertiary structure and the antigenic conformation, which may not be recognized by anti-HBs antibodies, compromising the infection diagnosis. In addition, these exchanges can render ineffective the anti-HBs antibodies action acquired by vaccination, compromise the effectiveness of the chronically HBV infected patient's treatment, and also the HBsAg immunogenicity, by promoting its retention within the cell. In this review, the residues exchange that alter the S protein's structure is revisited, as well as the mechanisms that lead to the HBsAg antigenicity loss, and the clinical, laboratory and epidemiological consequences of this phenomenon.

Specialization restricts the evolutionary paths available to yeast sugar transporters

Functional innovation at the protein level is a key source of evolutionary novelties. The constraints on functional innovations are likely to be highly specific in different proteins, which are shaped by their unique histories and the extent of global epistasis that arises from their structures and biochemistries. These contextual nuances in the sequence-function relationship have implications both for a basic understanding of the evolutionary process and for engineering proteins with desirable properties. Here, we have investigated the molecular basis of novel function in a model member of an ancient, conserved, and biotechnologically relevant protein family. These Major Facilitator Superfamily sugar porters are a functionally diverse group of proteins that are thought to be highly plastic and evolvable. By dissecting a recent evolutionary innovation in an α-glucoside transporter from the yeast Saccharomyces eubayanus, we show that the ability to transport a novel substrate requires high-order interactions between many protein regions and numerous specific residues proximal to the transport channel. To reconcile the functional diversity of this family with the constrained evolution of this model protein, we generated new, state-of-the-art genome annotations for 332 Saccharomycotina yeast species spanning approximately 400 million years of evolution. By integrating phylogenetic and phenotypic analyses across these species, we show that the model yeast α-glucoside transporters likely evolved from a multifunctional ancestor and became subfunctionalized. The accumulation of additive and epistatic substitutions likely entrenched this subfunction, which made the simultaneous acquisition of multiple interacting substitutions the only reasonably accessible path to novelty.

The Structure of Evolutionary Model Space for Proteins across the Tree of Life

The factors that determine the relative rates of amino acid substitution during protein evolution are complex and known to vary among taxa. We estimated relative exchangeabilities for pairs of amino acids from clades spread across the tree of life and assessed the historical signal in the distances among these clade-specific models. We separately trained these models on collections of arbitrarily selected protein alignments and on ribosomal protein alignments. In both cases, we found a clear separation between the models trained using multiple sequence alignments from bacterial clades and the models trained on archaeal and eukaryotic data. We assessed the predictive power of our novel clade-specific models of sequence evolution by asking whether fit to the models could be used to identify the source of multiple sequence alignments. Model fit was generally able to correctly classify protein alignments at the level of domain (bacterial versus archaeal), but the accuracy of classification at finer scales was much lower. The only exceptions to this were the relatively high classification accuracy for two archaeal lineages: Halobacteriaceae and Thermoprotei. Genomic GC content had a modest impact on relative exchangeabilities despite having a large impact on amino acid frequencies. Relative exchangeabilities involving aromatic residues exhibited the largest differences among models. There were a small number of exchangeabilities that exhibited large differences in comparisons among major clades and between generalized models and ribosomal protein models. Taken as a whole, these results reveal that a small number of relative exchangeabilities are responsible for much of the structure of the "model space" for protein sequence evolution. The clade-specific models we generated may be useful tools for protein phylogenetics, and the structure of evolutionary model space that they revealed has implications for phylogenomic inference across the tree of life.

dN/dS-H, a New Test to Distinguish Different Selection Modes in Protein Evolution and Cancer Evolution

One of the most popular measures in the analysis of protein sequence evolution is the ratio of nonsynonymous distance (d_N) to synonymous distance (d_S). Under the assumption that synonymous substitutions in the coding region are selectively neutral, the d_N/d_S ratio can be used to statistically detect the adaptive evolution (or purifying selection) if d_N/d_S > 1 (or d_N/d_S < 1) significantly. However, due to strong structural constraints and/or variable functional constraints imposed on amino acid sites, most encoding genes in most species have demonstrated d_N/d_S < 1. Consequently, the statistical power for testing d_N/d_S = 1 may be insufficient to distinguish between different selection modes. In this paper, we propose a more powerful test, called d_N/d_S-H, in which a new parameter H, a relative measure of rate variation among sites, was introduced. Given the condition of strong purifying selections at some sites, the d_N/d_S-H model predicts d_N/d_S = 1-H for neutral evolution, d_N/d_S < 1-H for nearly neutral selection, and d_N/d_S > 1-H for adaptive evolution. The potential of this new method for resolving the neutral-adaptive debates is illustrated by the protein sequence evolution in vertebrates, Drosophila and yeasts, as well as somatic cancer evolution (specialized as the C_N/C_S-H test).

Changes in Neuropeptide Prohormone Genes among Cetartiodactyla Livestock and Wild Species Associated with Evolution and Domestication

The impact of evolution and domestication processes on the sequences of neuropeptide prohormone genes that participate in cell–cell signaling influences multiple biological process that involve neuropeptide signaling. This information is important to understand the physiological differences between Cetartiodactyla domesticated species such as cow, pig, and llama and wild species such as hippopotamus, giraffes, and whales. Systematic analysis of changes associated with evolutionary and domestication forces in neuropeptide prohormone protein sequences that are processed into neuropeptides was undertaken. The genomes from 118 Cetartiodactyla genomes representing 22 families were mined for 98 neuropeptide prohormone genes. Compared to other Cetartiodactyla suborders, Ruminantia preserved PYY2 and lost RLN1. Changes in GNRH2, IAPP, INSL6, POMC, PRLH, and TAC4 protein sequences could result in the loss of some bioactive neuropeptides in some families. An evolutionary model suggested that most neuropeptide prohormone genes disfavor sequence changes that incorporate large and hydrophobic amino acids. A compelling finding was that differences between domestic and wild species are associated with the molecular system underlying ‘fight or flight’ responses. Overall, the results demonstrate the importance of simultaneously comparing the neuropeptide prohormone gene complement from close and distant-related species. These findings broaden the foundation for empirical studies about the function of the neuropeptidome associated with health, behavior, and food production.

The Roles of Protein Structure, Taxon Sampling, and Model Complexity in Phylogenomics: A Case Study Focused on Early Animal Divergences

Despite the long history of using protein sequences to infer the tree of life, the potential for different parts of protein structures to retain historical signal remains unclear. We propose that it might be possible to improve analyses of phylogenomic datasets by incorporating information about protein structure. We test this idea using the position of the root of Metazoa (animals) as a model system. We examined the distribution of “strongly decisive” sites (alignment positions that support a specific tree topology) in a dataset comprising >1500 proteins and almost 100 taxa. The proportion of each class of strongly decisive sites in different structural environments was very sensitive to the model used to analyze the data when a limited number of taxa were used but they were stable when taxa were added. As long as enough taxa were analyzed, sites in all structural environments supported the same topology regardless of whether standard tree searches or decisive sites were used to select the optimal tree. However, the use of decisive sites revealed a difference between the support for minority topologies for sites in different structural environments: buried sites and sites in sheet and coil environments exhibited equal support for the minority topologies, whereas solvent-exposed and helix sites had unequal numbers of sites, supporting the minority topologies. This suggests that the relatively slowly evolving buried, sheet, and coil sites are giving an accurate picture of the true species tree and the amount of conflict among gene trees. Taken as a whole, this study indicates that phylogenetic analyses using sites in different structural environments can yield different topologies for the deepest branches in the animal tree of life and that analyzing larger numbers of taxa eliminates this conflict. More broadly, our results highlight the desirability of incorporating information about protein structure into phylogenomic analyses.

DeepCLA: A Hybrid Deep Learning Approach for the Identification of Clathrin

Clathrin is a highly evolutionarily conserved protein, which can affect membrane cleavage and membrane release of vesicles. The absence of clathrin in the cellular system affects a variety of human diseases. Effective recognition of clathrin plays an important role in the development of drugs to treat related diseases. In recent years, deep learning has been widely applied in the field of bioinformatics because of its high efficiency and accuracy. In this study, we propose a deep learning framework, DeepCLA, which combines two different network structures, including a convolutional neural network and a bidirectional long short-term memory network to identify clathrin. The investigation of different deep network architectures demonstrates that the prediction performance of a hybrid depth network model is better than that of a single depth network. On the independent test dataset, DeepCLA outperforms the state-of-the-art methods. It suggests that DeepCLA is an effective approach for clathrin prediction and can provide more instructive guidance for further experimental investigation of clathrin. Moreover, the source code and training data of DeepCLA are provided at https://github.com/ZhangZhang89/DeepCLA.

Enrichment in conservative amino acid changes among fixed and standing missense variations in slowly evolving proteins

The process of molecular evolution has many elements that are not yet fully understood. Evolutionary rates are known to vary among protein coding and noncoding DNAs, and most of the observed changes in amino acid or nucleotide sequences are assumed to be non-adaptive by the neutral theory of molecular evolution. However, it remains unclear whether fixed and standing missense changes in slowly evolving proteins are more or less neutral compared to those in fast evolving genes. Here, based on the evolutionary rates as inferred from identity scores between orthologs in human and Rhesus Macaques (Macaca mulatta), we found that the fraction of conservative substitutions between species was significantly higher in their slowly evolving proteins. Similar results were obtained by using four different methods of scoring conservative substitutions, including three that remove the impact of substitution probability, where conservative changes require fewer mutations. We also examined the single nucleotide polymorphisms (SNPs) by using the 1000 Genomes Project data and found that missense SNPs in slowly evolving proteins also had a higher fraction of conservative changes, especially for common SNPs, consistent with more non-conservative substitutions and hence stronger natural selection for SNPs, particularly rare ones, in fast evolving proteins. These results suggest that fixed and standing missense variants in slowly evolving proteins are more likely to be neutral.

Phylogenetic Analyses of Sites in Different Protein Structural Environments Result in Distinct Placements of the Metazoan Root

Phylogenomics, the use of large datasets to examine phylogeny, has revolutionized the study of evolutionary relationships. However, genome-scale data have not been able to resolve all relationships in the tree of life; this could reflect, at least in part, the poor-fit of the models used to analyze heterogeneous datasets. Some of the heterogeneity may reflect the different patterns of selection on proteins based on their structures. To test that hypothesis, we developed a pipeline to divide phylogenomic protein datasets into subsets based on secondary structure and relative solvent accessibility. We then tested whether amino acids in different structural environments had distinct signals for the topology of the deepest branches in the metazoan tree. We focused on a dataset that appeared to have a mixture of signals and we found that the most striking difference in phylogenetic signal reflected relative solvent accessibility. Analyses of exposed sites (residues located on the surface of proteins) yielded a tree that placed ctenophores sister to all other animals whereas sites buried inside proteins yielded a tree with a sponge+ctenophore clade. These differences in phylogenetic signal were not ameliorated when we conducted analyses using a set of maximum-likelihood profile mixture models. These models are very similar to the Bayesian CAT model, which has been used in many analyses of deep metazoan phylogeny. In contrast, analyses conducted after recoding amino acids to limit the impact of deviations from compositional stationarity increased the congruence in the estimates of phylogeny for exposed and buried sites; after recoding amino acid trees estimated using the exposed and buried site both supported placement of ctenophores sister to all other animals. Although the central conclusion of our analyses is that sites in different structural environments yield distinct trees when analyzed using models of protein evolution, our amino acid recoding analyses also have implications for metazoan evolution. Specifically, our results add to the evidence that ctenophores are the sister group of all other animals and they further suggest that the placozoa+cnidaria clade found in some other studies deserves more attention. Taken as a whole, these results provide striking evidence that it is necessary to achieve a better understanding of the constraints due to protein structure to improve phylogenetic estimation.

Molecular Evolution in Small Steps under Prevailing Negative Selection: A Nearly Universal Rule of Codon Substitution

The widely accepted view that evolution proceeds in small steps is based on two premises: 1) negative selection acts strongly against large differences and 2) positive selection favors small-step changes. The two premises are not biologically connected and should be evaluated separately. We now extend a previous approach to studying codon evolution in the entire genome. Codon substitution rate is a function of the physicochemical distance between amino acids (AAs), equated with the step size of evolution. Between nine pairs of closely related species of plants, invertebrates, and vertebrates, the evolutionary rate is strongly and negatively correlated with a set of AA distances (Δ_U, scaled to [0, 1]). Δ_U, a composite measure of evolutionary rates across diverse taxa, is influenced by almost all of the 48 physicochemical properties used here. The new analyses reveal a crucial trend hidden from previous studies: Δ_U is strongly correlated with the evolutionary rate (R² > 0.8) only when the genes are predominantly under negative selection. Because most genes in most taxa are strongly constrained by negative selection, Δ_U has indeed appeared to be a nearly universal measure of codon evolution. In conclusion, molecular evolution at the codon level generally takes small steps due to the prevailing negative selection. Whether positive selection may, or may not, follow the small-step rule is addressed in a companion study.