Reconstruction of ancestral protein sequences and its applications

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.

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.].

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.

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

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.

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.

The reconstruction of genetic material of ancestral organisms constitutes a powerful application of evolutionary biology. A fundamental step in this inference is the ancestral sequence reconstruction (ASR), which can be performed with diverse methodologies implemented in computer frameworks. However, most of these methodologies ignore evolutionary properties frequently observed in microbes, such as genetic recombination and complex selection processes, that can bias the traditional ASR. From a practical perspective, here I review methodologies for the reconstruction of ancestral DNA and protein sequences, with particular focus on microbes, and including biases, recommendations, and software implementations. I conclude that microbial ASR is a complex analysis that should be carefully performed and that there is a need for methods to infer more realistic ancestral microbial sequences.

BackgroundMultiple genome alignment is an important problem in bioinformatics. An important subproblem used by many multiple alignment approaches is that of aligning two multiple alignments. Many popular alignment algorithms for DNA use the sum-of-pairs heuristic, where the score of a multiple alignment is the sum of its induced pairwise alignment scores. However, the biological meaning of the sum-of-pairs of pairs heuristic is not obvious. Additionally, many algorithms based on the sum-of-pairs heuristic are complicated and slow, compared to pairwise alignment algorithms.An alternative approach to aligning alignments is to first infer ancestral sequences for each alignment, and then align the two ancestral sequences. In addition to being fast, this method has a clear biological basis that takes into account the evolution implied by an underlying phylogenetic tree.In this study we explore the accuracy of aligning alignments by ancestral sequence alignment. We examine the use of both maximum likelihood and parsimony to infer ancestral sequences. Additionally, we investigate the effect on accuracy of allowing ambiguity in our ancestral sequences.ResultsWe use synthetic sequence data that we generate by simulating evolution on a phylogenetic tree. We use two different types of phylogenetic trees: trees with a period of rapid growth followed by a period of slow growth, and trees with a period of slow growth followed by a period of rapid growth.We examine the alignment accuracy of four ancestral sequence reconstruction and alignment methods: parsimony, maximum likelihood, ambiguous parsimony, and ambiguous maximum likelihood. Additionally, we compare against the alignment accuracy of two sum-of-pairs algorithms: ClustalW and the heuristic of Ma, Zhang, and Wang.ConclusionWe find that allowing ambiguity in ancestral sequences does not lead to better multiple alignments. Regardless of whether we use parsimony or maximum likelihood, the success of aligning ancestral sequences containing ambiguity is very sensitive to the choice of gap open cost. Surprisingly, we find that using maximum likelihood to infer ancestral sequences results in less accurate alignments than when using parsimony to infer ancestral sequences. Finally, we find that the sum-of-pairs methods produce better alignments than all of the ancestral alignment methods.

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.].

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.

Evolutionary relationships of protein families can be characterized either by networks or by trees. Whereas trees allow for hierarchical grouping and reconstruction of the most likely ancestral sequences, networks lack a time axis but allow for thresholds of pairwise sequence identity to be chosen and, therefore, the clustering of family members with presumably more similar functions. Here, we use the large family of arylsulfatases and phosphonate monoester hydrolases to investigate similarities, strengths and weaknesses in tree and network representations. For varying thresholds of pairwise sequence identity, values of betweenness centrality and clustering coefficients were derived for nodes of the reconstructed ancestors to measure the propensity to act as a bridge in a network. Based on these properties, ancestral protein sequences emerge as bridges in protein sequence networks. Interestingly, many ancestral protein sequences appear close to extant sequences. Therefore, reconstructed ancestor sequences might also be interpreted as yet-to-be-identified homologues. The concept of ancestor reconstruction is compared to consensus sequences, too. It was found that hub sequences in a network, e.g. reconstructed ancestral sequences that are connected to many neighbouring sequences, share closer similarity with derived consensus sequences. Therefore, some reconstructed ancestor sequences can also be interpreted as consensus sequences.

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.

Ancestral sequence reconstruction (ASR) is an important tool to understand how protein structure and function changed over the course of evolution. It essentially relies on models of sequence evolution that can quantitatively describe changes in a sequence over time. Such models usually consider that sequence positions evolve independently from each other and neglect epistasis: the context-dependence of the effect of mutations. On the other hand, the last years have seen major developments in the field of generative protein models, which learn constraints associated with structure and function from large ensembles of evolutionarily related proteins. Here, we show that it is possible to extend a specific type of generative model to describe the evolution of sequences in time while taking epistasis into account. We apply the developed technique to the problem of ASR: given a protein family and its evolutionary tree, we try to infer the sequences of extinct ancestors. Using both simulations and data coming from experimental evolution we show that our method outperforms state-of-the-art ones. Moreover, it allows for sampling a greater diversity of potential ancestors, allowing for a less biased characterization of ancestral sequences.

Ancestral sequence reconstruction is essential to a variety of evolutionary studies. Here, we present the FastML web server, a user-friendly tool for the reconstruction of ancestral sequences. FastML implements various novel features that differentiate it from existing tools: (i) FastML uses an indel-coding method, in which each gap, possibly spanning multiples sites, is coded as binary data. FastML then reconstructs ancestral indel states assuming a continuous time Markov process. FastML provides the most likely ancestral sequences, integrating both indels and characters; (ii) FastML accounts for uncertainty in ancestral states: it provides not only the posterior probabilities for each character and indel at each sequence position, but also a sample of ancestral sequences from this posterior distribution, and a list of the k-most likely ancestral sequences; (iii) FastML implements a large array of evolutionary models, which makes it generic and applicable for nucleotide, protein and codon sequences; and (iv) a graphical representation of the results is provided, including, for example, a graphical logo of the inferred ancestral sequences. The utility of FastML is demonstrated by reconstructing ancestral sequences of the Env protein from various HIV-1 subtypes. FastML is freely available for all academic users and is available online at http://fastml.tau.ac.il/.

The phylogenetic inference of ancestral protein sequences is a powerful technique for the study of molecular evolution, but any conclusions drawn from such studies are only as good as the accuracy of the reconstruction method. Every inference method leads to errors in the ancestral protein sequence, resulting in potentially misleading estimates of the ancestral protein's properties. To assess the accuracy of ancestral protein reconstruction methods, we performed computational population evolution simulations featuring near-neutral evolution under purifying selection, speciation, and divergence using an off-lattice protein model where fitness depends on the ability to be stable in a specified target structure. We were thus able to compare the thermodynamic properties of the true ancestral sequences with the properties of “ancestral sequences” inferred by maximum parsimony, maximum likelihood, and Bayesian methods. Surprisingly, we found that methods such as maximum parsimony and maximum likelihood that reconstruct a “best guess” amino acid at each position overestimate thermostability, while a Bayesian method that sometimes chooses less-probable residues from the posterior probability distribution does not. Maximum likelihood and maximum parsimony apparently tend to eliminate variants at a position that are slightly detrimental to structural stability simply because such detrimental variants are less frequent. Other properties of ancestral proteins might be similarly overestimated. This suggests that ancestral reconstruction studies require greater care to come to credible conclusions regarding functional evolution. Inferred functional patterns that mimic reconstruction bias should be reevaluated.