Ancient whale rhodopsin reconstructs dim-light vision over a major evolutionary transition: Implications for ancestral diving behavior

Dengue is a mosquito-borne viral disease of which incidence has rapidly increased in the last few years. Despite the recent development of a licensed dengue vaccine, safer and more efficacious dengue vaccine still needs to be developed. Dengue virus has four antigenically and genetically distinct serotypes. Ancestral sequence reconstruction (ASR) and consensus sequence (CS) might be able to overcome antigenic distinction between those four serotypes. Envelope (E) protein is responsible for a wide range of dengue virus biological activities. Domain III of the E protein (EDIII) plays a role in receptor binding for viral entry and inducing protective immunity against the dengue virus. We utilised bioinformatics software to computationally design ancestral and consensus sequences of Asian dengue E protein. E protein sequences of 987 DENV strains and 5 outgroups were retrieved from GenBank. We constructed ancestral and consensus sequences for each serotype. For ASR, ancestral sequences were gradually designed to construct ancestral sequence for all serotypes using MEGA X. For CS, all four consensus sequences were directly used to construct consensus sequence for all serotypes using UGENE 1.32. Phylogenetic tree consisting existing dengue sequences as well as ancestral and consensus sequences were visualised using FigTree 1.4.4. All ancestral and consensus sequences were analysed for conserved motifs, especially in domain III region. ASR sequences were closer to the centre of phylogenetic tree branches while consensus sequences were located among natural isolates. Further CD4 T cell immunogenicity prediction on domain III (EDIII) showed that both ASR and consensus EDIII have the two-highest combined immunogenicity scores. These sequences are potential for further in vitro and in vivo studies as dengue vaccine candidate. Keywords: ancestral sequence, consensus, dengue, envelope protein, vaccine

BackgroundModern-day proteins were selected during long evolutionary history as descendants of ancient life forms. In silico reconstruction of such ancestral protein sequences facilitates our understanding of evolutionary processes, protein classification and biological function. Additionally, reconstructed ancestral protein sequences could serve to fill in sequence space thus aiding remote homology inference.ResultsWe developed ANCESCON, a package for distance-based phylogenetic inference and reconstruction of ancestral protein sequences that takes into account the observed variation of evolutionary rates between positions that more precisely describes the evolution of protein families. To improve the accuracy of evolutionary distance estimation and ancestral sequence reconstruction, two approaches are proposed to estimate position-specific evolutionary rates. Comparisons show that at large evolutionary distances our method gives more accurate ancestral sequence reconstruction than PAML, PHYLIP and PAUP*. We apply the reconstructed ancestral sequences to homology inference and functional site prediction. We show that the usage of hypothetical ancestors together with the present day sequences improves profile-based sequence similarity searches; and that ancestral sequence reconstruction methods can be used to predict positions with functional specificity.ConclusionsAs a computational tool to reconstruct ancestral protein sequences from a given multiple sequence alignment, ANCESCON shows high accuracy in tests and helps detection of remote homologs and prediction of functional sites. ANCESCON is freely available for non-commercial use. Pre-compiled versions for several platforms can be downloaded from .

Background: It is important to design a malaria vaccine targeting all human malaria parasites as well as non-human primate parasites to eradicate malaria and prevent zoonotic malaria. Apical membrane antigen 1 (AMA1) protein is shared by human-infecting Plasmodium species. Ancestral sequence reconstruction (ASR) and consensus sequence construction on AMA1 might be able to overcome the antigenic distinction between those species. 
 Objective: We aimed to computationally design the ancestral and consensus sequence of Plasmodium AMA1 protein and analyze the sequences for its putative immunogenicity.
 Methods: We utilized bioinformatics software to computationally design ancestral and consensus sequences of AMA1 protein. AMA1 protein sequences of human-infecting Plasmodium and non-human primate Plasmodium were retrieved from PlasmoDB. ASR was designed using MEGA X while consensus was inferred using UGENE. Phylogenetic tree consisting of existing Plasmodium sequences and the ancestral sequence was constructed using IQTREE webserver and visualized with FigTree.
 Results: Phylogenetic analysis showed that Plasmodium spp. were divided into 2 major groups, P. falciparum (Clade F) and non-falciparum (Clade NF) thus three ancestral and consensus sequences were designed based on each clade and both clades at once. Reconstructed ancestral sequences were located as sister branch for naturally occurring strains. On the contrary, consensus sequences are located within the branch of corresponding naturally occurring strains. Sequence analysis showed the presence of CD8+ T cell epitope in all computationally-designed sequences.
 Conclusion: Ancestral and consensus AMA1 sequences are potential for further studies as a malaria vaccine candidate.

Evolution is marked by well-defined events involving profound innovations that are known as 'major evolutionary transitions'. They involve the integration of autonomous elements into a new, higher-level organization whereby the former isolated units interact in novel ways, losing their original autonomy. All major transitions, which include the origin of life, cells, multicellular systems, societies or language (among other examples), took place millions of years ago. Are these transitions unique, rare events? Have they instead universal traits that make them almost inevitable when the right pieces are in place? Are there general laws of evolutionary innovation? In order to approach this problem under a novel perspective, we argue that a parallel class of evolutionary transitions can be explored involving the use of artificial evolutionary experiments where alternative paths to innovation can be explored. These 'synthetic' transitions include, for example, the artificial evolution of multicellular systems or the emergence of language in evolved communicating robots. These alternative scenarios could help us to understand the underlying laws that predate the rise of major innovations and the possibility for general laws of evolved complexity. Several key examples and theoretical approaches are summarized and future challenges are outlined.This article is part of the themed issue 'The major synthetic evolutionary transitions'.

Modern phylogenetic methods allow inference of ancestral molecular sequences given an alignment and phylogeny relating present-day sequences. This provides insight into the evolutionary history of molecules, helping to understand gene function and to study biological processes such as adaptation and convergent evolution across a variety of applications. Here, we propose a dynamic programming algorithm for fast joint likelihood-based reconstruction of ancestral sequences under the Poisson Indel Process (PIP). Unlike previous approaches, our method, named ARPIP, enables the reconstruction with insertions and deletions based on an explicit indel model. Consequently, inferred indel events have an explicit biological interpretation. Likelihood computation is achieved in linear time with respect to the number of sequences. Our method consists of two steps, namely finding the most probable indel points and reconstructing ancestral sequences. First, we find the most likely indel points and prune the phylogeny to reflect the insertion and deletion events per site. Second, we infer the ancestral states on the pruned subtree in a manner similar to FastML. We applied ARPIP (Ancestral Reconstruction under PIP) on simulated data sets and on real data from the Betacoronavirus genus. ARPIP reconstructs both the indel events and substitutions with a high degree of accuracy. Our method fares well when compared to established state-of-the-art methods such as FastML and PAML. Moreover, the method can be extended to explore both optimal and suboptimal reconstructions, include rate heterogeneity through time and more. We believe it will expand the range of novel applications of ancestral sequence reconstruction. [Ancestral sequences; dynamic programming; evolutionary stochastic process; indel; joint ancestral sequence reconstruction; maximum likelihood; Poisson Indel Process; phylogeny; SARS-CoV.].

Reconstruction of ancestral DNA and amino acid sequences is an important means of inferring information about past evolutionary events. Such reconstructions suggest changes in molecular function and evolutionary processes over the course of evolution and are used to infer adaptation and convergence. Maximum likelihood (ML) is generally thought to provide relatively accurate reconstructed sequences compared to parsimony, but both methods lead to the inference of multiple directional changes in nucleotide frequencies in primate mitochondrial DNA (mtDNA). To better understand this surprising result, as well as to better understand how parsimony and ML differ, we constructed a series of computationally simple "conditional pathway" methods that differed in the number of substitutions allowed per site along each branch, and we also evaluated the entire Bayesian posterior frequency distribution of reconstructed ancestral states. We analyzed primate mitochondrial cytochrome b (Cyt-b) and cytochrome oxidase subunit I (COI) genes and found that ML reconstructs ancestral frequencies that are often more different from tip sequences than are parsimony reconstructions. In contrast, frequency reconstructions based on the posterior ensemble more closely resemble extant nucleotide frequencies. Simulations indicate that these differences in ancestral sequence inference are probably due to deterministic bias caused by high uncertainty in the optimization-based ancestral reconstruction methods (parsimony, ML, Bayesian maximum a posteriori). In contrast, ancestral nucleotide frequencies based on an average of the Bayesian set of credible ancestral sequences are much less biased. The methods involving simpler conditional pathway calculations have slightly reduced likelihood values compared to full likelihood calculations, but they can provide fairly unbiased nucleotide reconstructions and may be useful in more complex phylogenetic analyses than considered here due to their speed and flexibility. To determine whether biased reconstructions using optimization methods might affect inferences of functional properties, ancestral primate mitochondrial tRNA sequences were inferred and helix-forming propensities for conserved pairs were evaluated in silico. For ambiguously reconstructed nucleotides at sites with high base composition variability, ancestral tRNA sequences from Bayesian analyses were more compatible with canonical base pairing than were those inferred by other methods. Thus, nucleotide bias in reconstructed sequences apparently can lead to serious bias and inaccuracies in functional predictions.

Dichromatic color vision is mediated by two cone visual pigments in many eutherian mammals. After reentry into the sea, early cetaceans lost their violet-sensitive visual pigment (short wavelength-sensitive 1) independently in the baleen and toothed whale ancestors and thus obtained only monochromatic cone vision. Subsequently, losses of the middle/long wavelength-sensitive (M/LWS) pigment have also been reported in multiple whale lineages, leading to rhodopsin (RH1)-mediated rod monochromatic vision. To further elucidate the phenotypic evolution of whale visual pigments, we assessed the spectral tuning of both M/LWS and RH1 from representative cetacean taxa. Interestingly, although the coding sequences for M/LWS are intact in both the pygmy right whale and the Baird's beaked whale, no spectral sensitivity was detected in vitro. Pseudogenization of other cone vision-related genes is observed in the pygmy right whale, suggesting a loss of cone-mediated vision. After ancestral sequence reconstructions, ancient M/LWS pigments from cetacean ancestors were resurrected and functionally measured. Spectral tuning of M/LWS from the baleen whale ancestor shows that it is green sensitive, with a 40-nm shift in sensitivity to a shorter wavelength. For the ancestor of sperm whales, although no spectral sensitivity could be recorded for its M/LWS pigment, a substantial sensitivity shift (20 to 30 nm) to a shorter wavelength may have also occurred before its functional inactivation. The parallel phenotypic evolution of M/LWS to shorter wavelength sensitivity might be visual adaptations in whales allowing more frequent deep-sea activities, although additional ecological differentiations may have led to their subsequent losses.

Accurate reconstruction of ancestral states is a critical evolutionary analysis when studying ancient proteins and comparing biochemical properties between parental or extinct species and their extant relatives. It relies on multiple sequence alignment (MSA) which may introduce biases, and it remains unknown how MSA methodological approaches impact ancestral sequence reconstruction (ASR). Here, we investigate how MSA methodology modulates ASR using a simulation study of various evolutionary scenarios. We evaluate the accuracy of ancestral protein sequence reconstruction for simulated data and compare reconstruction outcomes using different alignment methods. Our results reveal biases introduced not only by aligner algorithms and assumptions, but also tree topology and the rate of insertions and deletions. Under many conditions we find no substantial differences between the MSAs. However, increasing the difficulty for the aligners can significantly impact ASR. The MAFFT consistency aligners and PRANK variants exhibit the best performance, whereas FSA displays limited performance. We also discover a bias towards reconstructed sequences longer than the true ancestors, deriving from a preference for inferring insertions, in almost all MSA methodological approaches. In addition, we find measures of MSA quality generally correlate highly with reconstruction accuracy. Thus, we show MSA methodological differences can affect the quality of reconstructions and propose MSA methods should be selected with care to accurately determine ancestral states with confidence.

Ancestral Sequence Reconstruction of Von Willebrand Factor Reveals Highly Conserved Structure/Function

Ancestral protein sequence reconstruction is a powerful technique for explicitly testing hypotheses about the evolution of molecular function, allowing researchers to meticulously dissect how historical changes in protein sequence impacted functional repertoire by altering the protein's 3D structure. These techniques have provided concrete, experimentally validated insights into ancient evolutionary processes and help illuminate the complex relationship between protein sequence, structure, and function. Inferring the protein family phylogenies on which ancestral sequence reconstruction depends and reconstructing the sequences, themselves, are amenable to high-throughput computational analysis. However, determining the structures of ancestral-reconstructed proteins and characterizing their functions typically rely on time-consuming and expensive laboratory analyses, limiting most current studies to examining a relatively small number of specific hypotheses. For this reason, we have little detailed, unbiased information about how molecular function evolves across large protein family phylogenies. Here we describe a generalized protocol that integrates ancestral sequence reconstruction with structural homology modeling and structure-based molecular affinity prediction to characterize historical changes in protein function across families with thousands of individual sequences. We highlight key steps in the analysis protocol requiring particularly careful attention to avoid introducing potential errors as well as steps for which computationally efficient subroutines can be substituted for more intensive approaches, allowing researchers to scale the analysis up or down, depending on available resources and requirements for reproducibility and scientific rigor. In our view, this approach provides a compelling compliment to more laboratory-intensive procedures, generating important contextual information that can help guide detailed experiments.

Intriguing work has been carried out in order to decipher the genetic codes of today’s existing species. However, little is known about the genetic makeup of species that existed long ago. Exciting possibilities have recently been raised in the field of computational analysis (1), proposing that reconstruction of ancestral DNA sequences can be performed if the DNA sequences of the existing species are known. Being able to perform such reconstructions would simplify the study of the evolution of these species, and uncover many mysteries regarding life that once existed on this planet.
 In order to perform reconstructions of unknown ancestral DNA sequences, many different types of problems must be solved, all of which can be approached computationally. Examples of such problems include building a phylogenetic tree of the evolutionary line in question, determining a multiple alignment of the existing species being analyzed, or working out the actual identity of the nucleotides within the ancestral sequence. The problem presented in this paper considers the level of modification within the ancestral sequence.

New protein-coding genes can evolve from previously noncoding genomic regions through a process known as de novo gene emergence. Evidence suggests that this process has likely occurred throughout evolution and across the tree of life. Yet, confidently identifying de novo emerged genes remains challenging. Ancestral sequence reconstruction is a promising approach for inferring whether a gene has emerged de novo or not, as it allows us to inspect whether a given genomic locus ancestrally harbored protein-coding capacity. However, the use of ancestral sequence reconstruction in the context of de novo emergence is still in its infancy and its capabilities, limitations, and overall potential are largely unknown. Notably, it is difficult to formally evaluate the protein-coding capacity of ancestral sequences, particularly when new gene candidates are short. How well-suited is ancestral sequence reconstruction as a tool for the detection and study of de novo genes? Here, we address this question by designing an ancestral sequence reconstruction workflow incorporating different tools and sets of parameters and by introducing a formal criterion that allows to estimate, within a desired level of confidence, when protein-coding capacity originated at a particular locus. Applying this workflow on ∼2,600 short, annotated budding yeast genes (<1,000 nucleotides), we found that ancestral sequence reconstruction robustly predicts an ancient origin for the most widely conserved genes, which constitute “easy” cases. For less robust cases, we calculated a randomization-based empirical P-value estimating whether the observed conservation between the extant and ancestral reading frame could be attributed to chance. This formal criterion allowed us to pinpoint a branch of origin for most of the less robust cases, identifying 49 genes that can unequivocally be considered de novo originated since the split of the Saccharomyces genus, including 37 Saccharomyces cerevisiae-specific genes. We find that for the remaining equivocal cases we cannot rule out different evolutionary scenarios including rapid evolution, multiple gene losses, or a recent de novo origin. Overall, our findings suggest that ancestral sequence reconstruction is a valuable tool to study de novo gene emergence but should be applied with caution and awareness of its limitations.

The computational reconstruction of ancestral proteins provides information on past biological events and has practical implications for biomedicine and biotechnology. Currently available tools for ancestral sequence reconstruction (ASR) are often based on empirical amino acid substitution models that assume that all sites evolve at the same rate and under the same process. However, this assumption is frequently violated because protein evolution is highly heterogeneous due to different selective constraints among sites. Here, we present ProtASR, a new evolutionary framework to infer ancestral protein sequences accounting for selection on protein stability. First, ProtASR generates site-specific substitution matrices through the structurally constrained mean-field (MF) substitution model, which considers both unfolding and misfolding stability. We previously showed that MF models outperform empirical amino acid substitution models, as well as other structurally constrained substitution models, both in terms of likelihood and correctly inferring amino acid distributions across sites. In the second step, ProtASR adapts a well-established maximum-likelihood (ML) ASR procedure to infer ancestral proteins under MF models. A known bias of ML ASR methods is that they tend to overestimate the stability of ancestral proteins by underestimating the frequency of deleterious mutations. We compared ProtASR under MF to two empirical substitution models (JTT and CAT), reconstructing the ancestral sequences of simulated proteins. ProtASR yields reconstructed proteins with less biased stabilities, which are significantly closer to those of the simulated proteins. Analysis of extant protein families suggests that folding stability evolves through time across protein families, potentially reflecting neutral fluctuation. Some families exhibit a more constant protein folding stability, while others are more variable. ProtASR is freely available from https://github.com/miguelarenas/protasr and includes detailed documentation and ready-to-use examples. It runs in seconds/minutes depending on protein length and alignment size. [Ancestral sequence reconstruction; folding stability; molecular adaptation; phylogenetics; protein evolution; protein structure.].

While a variety of methods exist to reconstruct ancestral sequences, all of them assume that a single phylogeny underlies all the positions in the alignment and therefore that recombination has not taken place. Using computer simulations we show that recombination can severely bias ancestral sequence reconstruction (ASR), and quantify this effect. If recombination is ignored, the ancestral sequences recovered can be quite distinct from the grand most recent common ancestor (GMRCA) of the sample and better resemble the concatenate of partial most recent common ancestors (MRCAs) at each recombination fragment. When independent phylogenetic trees are assumed for the different recombinant segments, the estimation of the fragment MRCAs improves significantly. Importantly, we show that recombination can change the biological predictions derived from ASRs carried out with real data. Given that recombination is widespread on nuclear genes and in particular in RNA viruses and some bacteria, the reconstruction of ancestral sequences in these cases should consider the potential impact of recombination and ideally be carried out using approaches that accommodate recombination.

Infrasonic and Ultrasonic Hearing Evolved after the Emergence of Modern Whales