Pandit: a database of protein and associated nucleotide domains with inferred trees

MOTIVATION: A large, high-quality database of homologous sequence alignments with good estimates of their corresponding phylogenetic trees will be a valuable resource to those studying phylogenetics. It will allow researchers to compare current and new models of sequence evolution across a large variety of sequences. The large quantity of data may provide inspiration for new models and methodology to study sequence evolution and may allow general statements about the relative effect of different molecular processes on evolution. RESULTS: The Pandit 7.6 database contains 4341 families of sequences derived from the seed alignments of the Pfam database of amino acid alignments of families of homologous protein domains (Bateman et al., 2002). Each family in Pandit includes an alignment of amino acid sequences that matches the corresponding Pfam family seed alignment, an alignment of DNA sequences that contain the coding sequence of the Pfam alignment when they can be recovered (overall, 82.9% of sequences taken from Pfam) and the alignment of amino acid sequences restricted to only those sequences for which a DNA sequence could be recovered. Each of the alignments has an estimate of the phylogenetic tree associated with it. The tree topologies were obtained using the neighbor joining method based on maximum likelihood estimates of the evolutionary distances, with branch lengths then calculated using a standard maximum likelihood approach.     

Epistasis Creates Invariant Sites and Modulates the Rate of Molecular Evolution

Invariant sites are a common feature of amino acid sequence evolution. The presence of invariant sites is frequently attributed to the need to preserve function through site-specific conservation of amino acid residues. Amino acid substitution models without a provision for invariant sites often fit the data significantly worse than those that allow for an excess of invariant sites beyond those predicted by models that only incorporate rate variation among sites (e.g., a Gamma distribution). An alternative is epistasis between sites to preserve residue interactions that can create invariant sites. Through computer-simulated sequence evolution, we evaluated the relative effects of site-specific preferences and site-site couplings in the generation of invariant sites and the modulation of the rate of molecular evolution. In an analysis of ten major families of protein domains with diverse sequence and functional properties, we find that the negative selection imposed by epistasis creates many more invariant sites than site-specific residue preferences alone. Further, epistasis plays an increasingly larger role in creating invariant sites over longer evolutionary periods. Epistasis also dictates rates of domain evolution over time by exerting significant additional purifying selection to preserve site couplings. These patterns illuminate the mechanistic role of epistasis in the processes underlying observed site invariance and evolutionary rates.     

NIFAS: visual analysis of domain evolution in proteins

MOTIVATION: Multi-domain proteins have evolved by insertions or deletions of distinct protein domains. Tracing the history of a certain domain combination can be important for functional annotation of multi-domain proteins, and for understanding the function of individual domains. In order to analyze the evolutionary history of the domains in modular proteins it is desirable to inspect a phylogenetic tree based on sequence divergence with the modular architecture of the sequences superimposed on the tree. RESULT: A Java applet, NIFAS, that integrates graphical domain schematics for each sequence in an evolutionary tree was developed. NIFAS retrieves domain information from the Pfam database and uses CLUSTAL W to calculate a tree for a given Pfam domain. The tree can be displayed with symbolic bootstrap values, and to allow the user to focus on a part of the tree, the layout can be altered by swapping nodes, changing the outgroup, and showing/collapsing subtrees. NIFAS is integrated with the Pfam database and is accessible over the internet (http://www.cgr.ki.se/Pfam). As an example, we use NIFAS to analyze the evolution of domains in Protein Kinases C.     

indel-Seq-Gen: a new protein family simulator incorporating domains, motifs, and indels

Reconstructing the evolutionary history of protein sequences will provide a better understanding of divergence mechanisms of protein superfamilies and their functions. Long-term protein evolution often includes dynamic changes such as insertion, deletion, and domain shuffling. Such dynamic changes make reconstructing protein sequence evolution difficult and affect the accuracy of molecular evolutionary methods, such as multiple alignments and phylogenetic methods. Unfortunately, currently available simulation methods are not sufficiently flexible and do not allow biologically realistic dynamic protein sequence evolution. We introduce a new method, indel-Seq-Gen (iSG), that can simulate realistic evolutionary processes of protein sequences with insertions and deletions (indels). Unlike other simulation methods, iSG allows the user to simulate multiple subsequences according to different evolutionary parameters, which is necessary for generating realistic protein families with multiple domains. iSG tracks all evolutionary events including indels and outputs the "true" multiple alignment of the simulated sequences. iSG can also generate a larger sequence space by allowing the use of multiple related root sequences. With all these functions, iSG can be used to test the accuracy of, for example, multiple alignment methods, phylogenetic methods, evolutionary hypotheses, ancestral protein reconstruction methods, and protein family classification methods. We empirically evaluated the performance of iSG against currently available methods by simulating the evolution of the G protein-coupled receptor and lipocalin protein families. We examined their true multiple alignments, reconstruction of the transmembrane regions and beta-strands, and the results of similarity search against a protein database using the simulated sequences. We also presented an example of using iSG for examining how phylogenetic reconstruction is affected by high indel rates.     

Partial protein domains: evolutionary insights and bioinformatics challenges

Protein domains are generally thought to correspond to units of evolution. New research raises questions about how such domains are defined with bioinformatics tools and sheds light on how evolution has enabled partial domains to be viable.With the rapid expansion in the number of determined protein sequences - over 92 million in UniProt in March 2015 - an ever-increasing number of biologists are using bioinformatics tools for annotation of these sequences. One widely used strategy is to identify occurrences of Pfam families within the sequence of interest [1]. A Pfam family is a multiple sequence alignment of the occurrences of a particular domain both in different species and in different regions of the same protein. The concept underpinning Pfam is that proteins typically comprise one or more domains (regions), each of which is an evolutionary unit that generally has a well-defined biological function. A significant sequence similarity between a query protein and a Pfam family provides the basis for annotations. Two recent articles [2,3] in Genome Biology evaluate the implications of having the query sequence only matching part of a Pfam family, which is an intriguing finding, given that a Pfam family is considered to be an evolutionary unit.     

Architectures of the unique domains associated with the DEAD-box helicase motif

Helicases are motor proteins of biological system, which catalyze the opening of energetically stable duplex nucleic acids in an ATP-dependent manner and thereby are involved in almost all aspects of nucleic acid metabolism including cell cycle progression. They contain several conserved domains including the DEAD-box and also several unique domains associated with these. The Pfam database (http://pfam.janelia.org/) is a large collection of protein families, each represented by multiple sequence alignments and hidden Markov models (HMMs). A diverse range of proteins are found in nature, and the functional specificity to each protein, to a greater extent, is imparted by its domain architecture. To this extent, a DEAD-box ATP-dependent RNA helicase (LOC_Os01g36890; Genomic sequence length: 6284 nucleotides; CDS length: 1299 nucleotides; Protein length: 432 amino acids) was studied. The protein sequence was imported for domain search on Pfam. This particular Pfam entry after covering a large proportion of the sequences in the underlying database has generated a more comprehensive coverage across a wide range of phyla of the known domains that are associated with the typical DEAD-box helicase motif. A total of 362 domain architectures were recollected from the Pfam database for the Family: DEAD (PF00270). We have therefore systematically analyzed the domains closely associated with DEAD-motif, which occur in a variety of proteins and can provide insights into their function.     

Basing population genetic inferences and models of molecular evolution upon desired stationary distributions of DNA or protein sequences

Models of molecular evolution tend to be overly simplistic caricatures of biology that are prone to assigning high probabilities to biologically implausible DNA or protein sequences. Here, we explore how to construct time-reversible evolutionary models that yield stationary distributions of sequences that match given target distributions. By adopting comparatively realistic target distributions,evolutionary models can be improved. Instead of focusing on estimating parameters, we concentrate on the population genetic implications of these models. Specifically, we obtain estimates of the product of effective population size and relative fitness difference of alleles. The approach is illustrated with two applications to protein-coding DNA. In the first, a codon-based evolutionary model yields a stationary distribution of sequences, which, when the sequences are translated,matches a variable-length Markov model trained on human proteins. In the second, we introduce an insertion-deletion model that describes selectively neutral evolutionary changes to DNA. We then show how to modify the neutral model so that its stationary distribution at the amino acid level can match a profile hidden Markov model, such as the one associated with the Pfam database.     

Phosphoprotein and nucleocapsid protein evolution of vesicular stomatitis virus New Jersey.

The entire phosphoprotein (P) and nucleocapsid (N) protein gene sequences and deduced amino acid sequences for 18 selected vesicular stomatitis virus isolates representative of the natural genetic diversity within the New Jersey serotype are reported. Phylogenetic analysis of the data using maximum parsimony allowed construction of evolutionary trees for the individual genes and the combined N, P, and glycoprotein (G) genes of these viruses. Virtually identical rates of nucleotide substitutions were found for each gene, indicating that evolution of these genes occurs at essentially the same rate. Although up to 19 and 17% sequence differences were evident in the P and N genes, respectively, no variation in gene length or evidence of recombinational rearrangements was found. However, striking evolutionary differences were observed among the amino acid sequences of vesicular stomatitis virus New Jersey N, P, and G proteins. The N protein amino acid sequence was the most highly conserved among the different isolates, indicating strong functional and structural constraints. Conversely, the P protein amino acid sequences were highly variable, indicating considerably fewer constraints or greater evolutionary pressure on the P protein. Much of the remarkable amino acid variability of the P protein resided in a hypervariable domain located between amino acids 153 and 205. The variability within this region would be consistent with it playing a structural role as a spacer to maintain correct conformational presentation of the separate active domains of this multifunctional protein. In marked contrast, the adjacent domain I of the P protein (previously thought to be under little evolutionary constraint) contained a highly conserved region. The colocalization of a short, potentially functional overlapping open reading frame to this region may explain this apparent anomaly.     

An Empirical Analysis of mt 16S rRNA Covarion-Like Evolution in Insects: Site-Specific Rate Variation Is Clustered and Frequently Detected

The structural and functional analysis of rRNA molecules has attracted considerable scientific interest. Empirical studies have demonstrated that sequence variation is not directly translated into modifications of rRNA secondary structure. Obviously, the maintenance of secondary structure and sequence variation are in part governed by different selection regimes. The nature of those selection regimes still remains quite elusive. The analysis of individual bacterial models cannot adequately explore this topic. Therefore, we used primary sequence data and secondary structures of a mitochondrial 16S rRNA fragment of 558 insect species from 15 monophyletic groups to study patterns of sequence variation, and variation of secondary structure. Using simulation studies to establish significance levels of change, we found that despite conservation of secondary structure, the location of sequence variation within the conserved rRNA structure changes significantly between groups of insects. Despite our conservative estimation procedure we found significant site-specific rate changes at 56 sites out of 184. Additionally, site-specific rate variation is somewhat clustered in certain helices. Both results confirm what has been predicted from an application of non-stationary maximum likelihood models to rRNA sequences. Clearly, constraints on sequence variation evolve and leave footprints in the form of evolutionary plasticity in rRNA sequences. Here, we show that a better understanding of the evolution of rRNA sequences can be obtained by integrating both phylogenetic and structural information.     

TIAMMAt: Leveraging Biodiversity to Revise Protein Domain Models,Evidence from Innate Immunity

Sequence annotation is fundamental for studying the evolution of protein families, particularly when working with nonmodel species. Given the rapid, ever-increasing number of species receiving high-quality genome sequencing, accurate domain modeling that is representative of species diversity is crucial for understanding protein family sequence evolution and their inferred function(s). Here, we describe a bioinformatic tool called Taxon-Informed Adjustment of Markov Model Attributes (TIAMMAt) which revises domain profile hidden Markov models (HMMs) by incorporating homologous domain sequences from underrepresented and nonmodel species. Using innate immunity pathways as a case study, we show that revising profile HMM parameters to directly account for variation in homologs among underrepresented species provides valuable insight into the evolution of protein families. Following adjustment by TIAMMAt, domain profile HMMs exhibit changes in their per-site amino acid state emission probabilities and insertion/deletion probabilities while maintaining the overall structure of the consensus sequence. Our results show that domain revision can heavily impact evolutionary interpretations for some families (i.e., NLR’s NACHT domain), whereas impact on other domains (e.g., rel homology domain and interferon regulatory factor domains) is minimal due to high levels of sequence conservation across the sampled phylogenetic depth (i.e., Metazoa). Importantly, TIAMMAt revises target domain models to reflect homologous sequence variation using the taxonomic distribution under consideration by the user. TIAMMAt’s flexibility to revise any subset of the Pfam database using a user-defined taxonomic pool will make it a valuable tool for future protein evolution studies, particularly when incorporating (or focusing) on nonmodel species.     

Protein evolution with dependence among codons due to tertiary structure

Markovian models of protein evolution that relax the assumption of independent change among codons are considered. With this comparatively realistic framework, an evolutionary rate at a site can depend both on the state of the site and on the states of surrounding sites. By allowing a relatively general dependence structure among sites, models of evolution can reflect attributes of tertiary structure. To quantify the impact of protein structure on protein evolution, we analyze protein-coding DNA sequence pairs with an evolutionary model that incorporates effects of solvent accessibility and pairwise interactions among amino acid residues. By explicitly considering the relationship between nonsynonymous substitution rates and protein structure, this approach can lead to refined detection and characterization of positive selection. Analyses of simulated sequence pairs indicate that parameters in this evolutionary model can be well estimated. Analyses of lysozyme c and annexin V sequence pairs yield the biologically reasonable result that amino acid replacement rates are higher when the replacements lead to energetically favorable proteins than when they destabilize the proteins. Although the focus here is evolutionary dependence among codons that is associated with protein structure, the statistical approach is quite general and could be applied to diverse cases of evolutionary dependence where surrogates for sequence fitness can be measured or modeled.     

Heterotachy, an important process of protein evolution.

Because of functional constraints, substitution rates vary among the positions of a protein but are usually assumed to be constant at a given site during evolution. The distribution of the rates across the sequence positions generally fits a Gamma distribution. Models of sequence evolution were accordingly designed and led to improved phylogenetic reconstruction. However, it has been convincingly demonstrated that the evolutionary rate of a given position is not always constant throughout time. We called such within-site rate variations heterotachy (for "different speed" in Greek). Yet, heterotachy was found among homologous sequences of distantly related organisms, often with different functions. In such cases, the functional constraints are likely different, which would explain the different distribution of variable sites. To evaluate the importance of heterotachy, we focused on amino acid sequences of mitochondrial cytochrome b, for which the function is likely the same in all vertebrates. Using 2,038 sequences, we demonstrate that 95% of the variable positions are heterotachous, i.e., underwent dramatic variations of substitution rate among vertebrate lineages. Heterotachy even occurs at small evolutionary scale, and in these cases it is very unlikely to be related to functional changes. Since a large number of sequences are required to efficiently detect heterotachy, the extent of this phenomenon could not be estimated for all proteins yet. It could be as large as for cytochrome b, since this protein is not a peculiar case. The observations made here open several new avenues of research, such as the understanding of the evolution of functional constraints or the improvement of phylogenetic reconstruction methods.     

Pfam 10 years on: 10,000 families and still growing

Classifications of proteins into groups of related sequences are in some respects like a periodic table for biology, allowing us to understand the underlying molecular biology of any organism. Pfam is a large collection of protein domains and families. Its scientific goal is to provide a complete and accurate classification of protein families and domains. The next release of the database will contain over 10,000 entries, which leads us to reflect on how far we are from completing this work. Currently Pfam matches 72% of known protein sequences, but for proteins with known structure Pfam matches 95%, which we believe represents the likely upper bound. Based on our analysis a further 28,000 families would be required to achieve this level of coverage for the current sequence database. We also show that as more sequences are added to the sequence databases the fraction of sequences that Pfam matches is reduced, suggesting that continued addition of new families is essential to maintain its relevance.     

Comparison of site-specific rate-inference methods for protein sequences: empirical Bayesian methods are superior

The degree to which an amino acid site is free to vary is strongly dependent on its structural and functional importance. An amino acid that plays an essential role is unlikely to change over evolutionary time. Hence, the evolutionary rate at an amino acid site is indicative of how conserved this site is and, in turn, allows evaluation of its importance in maintaining the structure/function of the protein. When using probabilistic methods for site-specific rate inference, few alternatives are possible. In this study we use simulations to compare the maximum-likelihood and Bayesian paradigms. We study the dependence of inference accuracy on such parameters as number of sequences, branch lengths, the shape of the rate distribution, and sequence length. We also study the possibility of simultaneously estimating branch lengths and site-specific rates. Our results show that a Bayesian approach is superior to maximum-likelihood under a wide range of conditions, indicating that the prior that is incorporated into the Bayesian computation significantly improves performance. We show that when branch lengths are unknown, it is better first to estimate branch lengths and then to estimate site-specific rates. This procedure was found to be superior to estimating both the branch lengths and site-specific rates simultaneously. Finally, we illustrate the difference between maximum-likelihood and Bayesian methods when analyzing site-conservation for the apoptosis regulator protein Bcl-x(L).     

What's in a likelihood? Simple models of protein evolution and the contribution of structurally viable reconstructions to the likelihood

Most phylogenetic models of protein evolution assume that sites are independent and identically distributed. Interactions between sites are ignored, and the likelihood can be conveniently calculated as the product of the individual site likelihoods. The calculation considers all possible transition paths (also called substitution histories or mappings) that are consistent with the observed states at the terminals, and the probability density of any particular reconstruction depends on the substitution model. The likelihood is the integral of the probability density of each substitution history taken over all possible histories that are consistent with the observed data. We investigated the extent to which transition paths that are incompatible with a protein's three-dimensional structure contribute to the likelihood. Several empirical amino acid models were tested for sequence pairs of different degrees of divergence. When simulating substitutional histories starting from a real sequence, the structural integrity of the simulated sequences quickly disintegrated. This result indicates that simple models are clearly unable to capture the constraints on sequence evolution. However, when we sampled transition paths between real sequences from the posterior probability distribution according to these same models, we found that the sampled histories were largely consistent with the tertiary structure. This suggests that simple empirical substitution models may be adequate for interpolating changes between observed sequences during phylogenetic inference despite the fact that the models cannot predict the effects of structural constraints from first principles. This study is significant because it provides a quantitative assessment of the biological realism of substitution models from the perspective of protein structure, and it provides insight on the prospects for improving models of protein sequence evolution.     

Reducing the false positive rate in the non-parametric analysis of molecular coevolution

Background  

The strength of selective constraints operating on amino acid sites of proteins has a multifactorial nature. In fact, amino acid sites within proteins coevolve due to their functional and/or structural relationships. Different methods have been developed that attempt to account for the evolutionary dependencies between amino acid sites. Researchers have invested a significant effort to increase the sensitivity of such methods. However, the difficulty in disentangling functional co-dependencies from historical covariation has fuelled the scepticism over their power to detect biologically meaningful results. In addition, the biological parameters connecting linear sequence evolution to structure evolution remain elusive. For these reasons, most of the evolutionary studies aimed at identifying functional dependencies among protein domains have focused on the structural properties of proteins rather than on the information extracted from linear multiple sequence alignments (MSA). Non-parametric methods to detect coevolution have been reported to be especially susceptible to produce false positive results based on the properties of MSAs. However, no formal statistical analysis has been performed to definitively test the differential effects of these properties on the sensitivity of such methods.     

Performance of likelihood ratio tests of evolutionary hypotheses under inadequate substitution models.

In recent years, likelihood ratio tests (LRTs) based on DNA and protein sequence data have been proposed for testing various evolutionary hypotheses. Because conducting an LRT requires an evolutionary model of nucleotide or amino acid substitution, which is almost always unknown, it becomes important to investigate the robustness of LRTs to violations of assumptions of these evolutionary models. Computer simulation was used to examine performance of LRTs of the molecular clock, transition/transversion bias, and among-site rate variation under different substitution models. The results showed that when correct models are used, LRTs perform quite well even when the DNA sequences are as short as 300 nt. However, LRTs were found to be biased under incorrect models. The extent of bias varies considerably, depending on the hypotheses tested, the substitution models assumed, and the lengths of the sequences used, among other things. A preliminary simulation study also suggests that LRTs based on parametric bootstrapping may be more sensitive to substitution models than are standard LRTs. When an assumed substitution model is grossly wrong and a more realistic model is available, LRTs can often reject the wrong model; thus, the performance of LRTs may be improved by using a more appropriate model. On the other hand, many factors of molecular evolution have not been considered in any substitution models so far built, and the possibility of an influence of this negligence on LRTs is often overlooked. The dependence of LRTs on substitution models calls for caution in interpreting test results and highlights the importance of clarifying the substitution patterns of genes and proteins and building more realistic models.