Identification of coevolving positions by ancestral reconstruction

Coevolution within proteins occurs when changes in one position affect the selective pressure in another position to preserve the protein structure or function. The identification of coevolving positions within proteins remains contentious, with most methods disregarding the phylogenetic information. Here, we present a time-efficient approach for detecting coevolving pairs, which is almost perfect in terms of precision and specificity. It is based on maximum parsimony-based ancestral reconstruction followed by the identification of pairs with a depletion on separate changes when compared to their number of concurrent changes. Our analysis of a previously characterised biological dataset shows that the coevolving pairs that we identified tend to be close in the protein sequence and structure, slightly less solvent exposed and have a higher mutation rate. We also show how the ancestral reconstruction can be used to detect favourable and unfavourable amino acid combinations. Altogether, we demonstrate how this approach is essential for identifying pairs of positions with weak covariation patterns.

nT4X and nT4M: Novel Time Non-reversible Mixture Amino Acid Substitution Models

One of the most important and difficult challenges in the research of molecular evolution is modeling the process of amino acid substitutions. Although single-matrix models, such as the LG model, are popular, their capability to properly capture the heterogeneity of the substitution process across sites is still questioned. Several mixture models with multiple matrices have been introduced and shown to offer advantages over single-matrix models. Current general mixture models assume the reversibility of the evolutionary process, implying that substitution rates between any two amino acids are equal in both forward and backward directions. This assumption is not based on biological properties but rather on computational simplicity. The well-known hypothesis is that more realistic models can yield more accurate evolutionary inferences; therefore, our aim is to estimate more biologically realistic models. To this end, we relax the assumption of reversibility and introduce two new general non-reversible 4-matrix mixture models, called nT4M and nT4X. Using alignments from HSSP and TreeBASE databases as data, our newly estimated models outperformed all single-matrix models and almost all reversible mixture models. Moreover, the new non-reversible mixture models enable us to infer rooted trees.

The Structural Determinants of Intra-Protein Compensatory Substitutions

Compensatory substitutions happen when one mutation is advantageously selected because it restores the loss of fitness induced by a previous deleterious mutation. How frequent such mutations occur in evolution and what is the structural and functional context permitting their emergence remain open questions. We built an atlas of intra-protein compensatory substitutions using a phylogenetic approach and a dataset of 1,630 bacterial protein families for which high-quality sequence alignments and experimentally derived protein structures were available. We identified more than 51,000 positions coevolving by the mean of predicted compensatory mutations. Using the evolutionary and structural properties of the analyzed positions, we demonstrate that compensatory mutations are scarce (typically only a few in the protein history) but widespread (the majority of proteins experienced at least one). Typical coevolving residues are evolving slowly, are located in the protein core outside secondary structure motifs, and are more often in contact than expected by chance, even after accounting for their evolutionary rate and solvent exposure. An exception to this general scheme is residues coevolving for charge compensation, which are evolving faster than noncoevolving sites, in contradiction with predictions from simple coevolutionary models, but similar to stem pairs in RNA. While sites with a significant pattern of coevolution by compensatory mutations are rare, the comparative analysis of hundreds of structures ultimately permits a better understanding of the link between the three-dimensional structure of a protein and its fitness landscape.

A minimal yet flexible likelihood framework to assess correlated evolution

An evolutionary process is reflected in the sequence of changes of any trait (e.g. morphological or molecular) through time. Yet, a better understanding of evolution would be procured by characterizing correlated evolution, or when two or more evolutionary processes interact. Previously developed parametric methods often require significant computing time as they rely on the estimation of many parameters. Here we propose a minimal likelihood framework modelling the joint evolution of two traits on a known phylogenetic tree. The type and strength of correlated evolution is characterized by a few parameters tuning mutation rates of each trait and interdependencies between these rates. The framework can be applied to study any discrete trait or character ranging from nucleotide substitution to gain or loss of a biological function. More specifically, it can be used to 1) test for independence between two evolutionary processes, 2) identify the type of interaction between them and 3) estimate parameter values of the most likely model of interaction. In the current implementation, the method takes as input a phylogenetic tree with discrete evolutionary events mapped on its branches. The method then maximizes the likelihood for one or several chosen scenarios. The strengths and limits of the method, as well as its relative power compared to a few other methods, are assessed using both simulations and data from 16S rRNA sequences in a sample of 54 γ-enterobacteria. We show that, even with datasets of fewer than 100 species, the method performs well in parameter estimation and in evolutionary model selection.

Robustness of phylogenetic inference to model misspecification caused by pairwise epistasis

Likelihood-based phylogenetic inference posits a probabilistic model of character state change along branches of a phylogenetic tree. These models typically assume statistical independence of sites in the sequence alignment. This is a restrictive assumption that facilitates computational tractability, but ignores how epistasis, the effect of genetic background on mutational effects, influences the evolution of functional sequences. We consider the effect of using a misspecified site-independent model on the accuracy of Bayesian phylogenetic inference in the setting of pairwise-site epistasis. Previous work has shown that as alignment length increases, tree reconstruction accuracy also increases. Here, we present a simulation study demonstrating that accuracy increases with alignment size even if the additional sites are epistatically coupled. We introduce an alignment-based test statistic that is a diagnostic for pairwise epistasis and can be used in posterior predictive checks.

Two-step mechanism of J-domain action in driving Hsp70 function

J-domain proteins (JDPs), obligatory Hsp70 cochaperones, play critical roles in protein homeostasis. They promote key allosteric transitions that stabilize Hsp70 interaction with substrate polypeptides upon hydrolysis of its bound ATP. Although a recent crystal structure revealed the physical mode of interaction between a J-domain and an Hsp70, the structural and dynamic consequences of J-domain action once bound and how Hsp70s discriminate among its multiple JDP partners remain enigmatic. We combined free energy simulations, biochemical assays and evolutionary analyses to address these issues. Our results indicate that the invariant aspartate of the J-domain perturbs a conserved intramolecular Hsp70 network of contacts that crosses domains. This perturbation leads to destabilization of the domain-domain interface—thereby promoting the allosteric transition that triggers ATP hydrolysis. While this mechanistic step is driven by conserved residues, evolutionarily variable residues are key to initial JDP/Hsp70 recognition—via electrostatic interactions between oppositely charged surfaces. We speculate that these variable residues allow an Hsp70 to discriminate amongst JDP partners, as many of them have coevolved. Together, our data points to a two-step mode of J-domain action, a recognition stage followed by a mechanistic stage.

It is well appreciated that Hsp70-based systems are the most versatile among molecular chaperones—functioning in all cell types and in all subcellular compartments. Via cyclic binding to protein substrates, Hsp70s facilitate their folding, trafficking, degradation and ability to interact with other proteins. Hsp70 function, however, depends on transient interaction with J-domain protein cochaperones that not only deliver substrates, but also activate the structural changes needed for efficient Hsp70 binding to substrate. But how J-domain proteins mechanistically function to drive these changes and how an Hsp70 discriminates among multiple J-domain partners have remained challenging central questions. Here, by using a combination of computational, evolutionary and experimental approaches, we provide evidence for a two-step mechanism. The initial recognition step involves variable residues that allow fine tuning of both the specificity and strength of J-domain protein interaction with Hsp70. The second, that is the mechanistic step, involves conserved residues that act to disrupt a conserved network of intramolecular interactions within Hsp70, thus ensuring robust activation of the structural changes necessary for effective substrate binding. We suggest that our findings are likely applicable to most Hsp70 systems.

CoevDB: a database of intramolecular coevolution among protein-coding genes of the bony vertebrates

The study of molecular coevolution, due to its potential to identify gene regions under functional or structural constraints, has recently been subject to numerous scientific inquiries. Particular efforts have been conducted to develop methods predicting the presence of coevolution in molecular sequences. Among these methods, a few aim to model the underlying evolutionary process of coevolution, which enable to differentiate the shared history of genes to coevolution and thus improve their accuracy. However, the usage of such methods remains sparse due to their expensive computational cost and the lack of resources alleviating this issue. Here we present CoevDB (http://phylodb.unil.ch/CoevDB), a database containing the result of a large-scale analysis of intramolecular coevolution of 8201 protein-coding genes of bony vertebrates. The web interface of CoevDB gives access to the results to 800 millions of statistical tests corresponding to all the pairs of sites analyzed. Several type of queries enable users to explore the database by either targeting specific genes or by discovering genes having promising estimations of coevolution.

Duplication history and molecular evolution of the rbcS multigene family in angiosperms

Ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO) is considered to be the main enzyme determining the rate of photosynthesis. The small subunit of the protein, encoded by the rbcS gene, has been shown to influence the catalytic efficiency, CO2 specificity, assembly, activity, and stability of RuBisCO. However, the evolution of the rbcS gene remains poorly studied. We inferred the phylogenetic tree of the rbcS gene in angiosperms using the nucleotide sequences and found that it is composed of two lineages that may have existed before the divergence of land plants. Although almost all species sampled carry at least one copy of lineage 1, genes of lineage 2 were lost in most angiosperm species. We found the specific residues that have undergone positive selection during the evolution of the rbcS gene. We detected intensive coevolution between each rbcS gene copy and the rbcL gene encoding the large subunit of RuBisCO. We tested the role played by each rbcS gene copy on the stability of the RuBisCO protein through homology modelling. Our results showed that this evolutionary constraint could limit the level of divergence seen in the rbcS gene, which leads to the similarity among the rbcS gene copies of lineage 1 within species.

Simultaneous Bayesian inference of phylogeny and molecular coevolution

Patterns of molecular coevolution can reveal structural and functional constraints within or among organic molecules. These patterns are better understood when considering the underlying evolutionary process, which enables us to disentangle the signal of the dependent evolution of sites (coevolution) from the effects of shared ancestry of genes. Conversely, disregarding the dependent evolution of sites when studying the history of genes negatively impacts the accuracy of the inferred phylogenetic trees. Although molecular coevolution and phylogenetic history are interdependent, analyses of the two processes are conducted separately, a choice dictated by computational convenience, but at the expense of accuracy. We present a Bayesian method and associated software to infer how many and which sites of an alignment evolve according to an independent or a pairwise dependent evolutionary process, and to simultaneously estimate the phylogenetic relationships among sequences. We validate our method on synthetic datasets and challenge our predictions of coevolution on the 16S rRNA molecule by comparing them with its known molecular structure. Finally, we assess the accuracy of phylogenetic trees inferred under the assumption of independence among sites using synthetic datasets, the 16S rRNA molecule and 10 additional alignments of protein-coding genes of eukaryotes. Our results demonstrate that inferring phylogenetic trees while accounting for dependent site evolution significantly impacts the estimates of the phylogeny and the evolutionary process.

Polymorphic sites preferentially avoid co-evolving residues in MHC class I proteins

Major histocompatibility complex class I (MHC-I) molecules are critical to adaptive immune defence mechanisms in vertebrate species and are encoded by highly polymorphic genes. Polymorphic sites are located close to the ligand-binding groove and entail MHC-I alleles with distinct binding specificities. Some efforts have been made to investigate the relationship between polymorphism and protein stability. However, less is known about the relationship between polymorphism and MHC-I co-evolutionary constraints. Using Direct Coupling Analysis (DCA) we found that co-evolution analysis accurately pinpoints structural contacts, although the protein family is restricted to vertebrates and comprises less than five hundred species, and that the co-evolutionary signal is mainly driven by inter-species changes, and not intra-species polymorphism. Moreover, we show that polymorphic sites in human preferentially avoid co-evolving residues, as well as residues involved in protein stability. These results suggest that sites displaying high polymorphism may have been selected during vertebrates’ evolution to avoid co-evolutionary constraints and thereby maximize their mutability.

Amino acid co-evolution represents cases of simultaneous substitution of amino acids at distinct positions in protein sequences. In the MHC-I protein family, such co-evolution could result from either amino acid changes across species or changes within species due to the high polymorphism of MHC-I molecules. Here we show that signals captured by global methods such as Direct Coupling Analysis (DCA) to estimate co-evolution primarily result from changes across species. Moreover, our results indicate that polymorphic sites in MHC-I molecules tend to be decoupled from co-evolving ones. This could suggest that they have been selected to maximize their mutability, which is known to be functionally important to entail MHC-I molecules with a wide repertoire of binding specificities for antigen presentation.

Selection on the Major Color Gene Melanocortin-1-Receptor Shaped the Evolution of the Melanocortin System Genes

Modular genetic systems and networks have complex evolutionary histories shaped by selection acting on single genes as well as on their integrated function within the network. However, uncovering molecular coevolution requires the detection of coevolving sites in sequences. Detailed knowledge of the functions of each gene in the system is also necessary to identify the selective agents driving coevolution. Using recently developed computational tools, we investigated the effect of positive selection on the coevolution of ten major genes in the melanocortin system, responsible for multiple physiological functions and human diseases. Substitutions driven by positive selection at the melanocortin-1-receptor (MC1R) induced more coevolutionary changes on the system than positive selection on other genes in the system. Contrarily, selection on the highly pleiotropic POMC gene, which orchestrates the activation of the different melanocortin receptors, had the lowest coevolutionary influence. MC1R and possibly its main function, melanin pigmentation, seems to have influenced the evolution of the melanocortin system more than functions regulated by MC2-5Rs such as energy homeostasis, glucocorticoid-dependent stress and anti-inflammatory responses. Although replication in other regulatory systems is needed, this suggests that single functional aspects of a genetic network or system can be of higher importance than others in shaping coevolution among the genes that integrate it.

Accelerating Bayesian inference for evolutionary biology models

Motivation

Bayesian inference is widely used nowadays and relies largely on Markov chain Monte Carlo (MCMC) methods. Evolutionary biology has greatly benefited from the developments of MCMC methods, but the design of more complex and realistic models and the ever growing availability of novel data is pushing the limits of the current use of these methods.

Results

We present a parallel Metropolis-Hastings (M-H) framework built with a novel combination of enhancements aimed towards parameter-rich and complex models. We show on a parameter-rich macroevolutionary model increases of the sampling speed up to 35 times with 32 processors when compared to a sequential M-H process. More importantly, our framework achieves up to a twentyfold faster convergence to estimate the posterior probability of phylogenetic trees using 32 processors when compared to the well-known software MrBayes for Bayesian inference of phylogenetic trees.

Availability and Implementation

https://bitbucket.org/XavMeyer/hogan

Supplementary information

Supplementary data are available at Bioinformatics online.

State aggregation for fast likelihood computations in molecular evolution

Motivation

Codon models are widely used to identify the signature of selection at the molecular level and to test for changes in selective pressure during the evolution of genes encoding proteins. The large size of the state space of the Markov processes used to model codon evolution makes it difficult to use these models with large biological datasets. We propose here to use state aggregation to reduce the state space of codon models and, thus, improve the computational performance of likelihood estimation on these models.

Results

We show that this heuristic speeds up the computations of the M0 and branch-site models up to 6.8 times. We also show through simulations that state aggregation does not introduce a detectable bias. We analyzed a real dataset and show that aggregation provides highly correlated predictions compared to the full likelihood computations. Finally, state aggregation is a very general approach and can be applied to any continuous-time Markov process-based model with large state space, such as amino acid and coevolution models. We therefore discuss different ways to apply state aggregation to Markov models used in phylogenetics.

Availability and Implementation

The heuristic is implemented in the godon package (https://bitbucket.org/Davydov/godon) and in a version of FastCodeML (https://gitlab.isb-sib.ch/phylo/fastcodeml).

Contact

nicolas.salamin@unil.ch

Supplementary Information

Supplementary data are available at Bioinformatics online.

Widespread Historical Contingency in Influenza Viruses

In systems biology and genomics, epistasis characterizes the impact that a substitution at a particular location in a genome can have on a substitution at another location. This phenomenon is often implicated in the evolution of drug resistance or to explain why particular "disease-causing" mutations do not have the same outcome in all individuals. Hence, uncovering these mutations and their locations in a genome is a central question in biology. However, epistasis is notoriously difficult to uncover, especially in fast-evolving organisms. Here, we present a novel statistical approach that replies on a model developed in ecology and that we adapt to analyze genetic data in fast-evolving systems such as the influenza A virus. We validate the approach using a two-pronged strategy: extensive simulations demonstrate a low-to-moderate sensitivity with excellent specificity and precision, while analyses of experimentally validated data recover known interactions, including in a eukaryotic system. We further evaluate the ability of our approach to detect correlated evolution during antigenic shifts or at the emergence of drug resistance. We show that in all cases, correlated evolution is prevalent in influenza A viruses, involving many pairs of sites linked together in chains; a hallmark of historical contingency. Strikingly, interacting sites are separated by large physical distances, which entails either long-range conformational changes or functional tradeoffs, for which we find support with the emergence of drug resistance. Our work paves a new way for the unbiased detection of epistasis in a wide range of organisms by performing whole-genome scans.

Coev-web: a web platform designed to simulate and evaluate coevolving positions along a phylogenetic tree

BACKGROUND

Available methods to simulate nucleotide or amino acid data typically use Markov models to simulate each position independently. These approaches are not appropriate to assess the performance of combinatorial and probabilistic methods that look for coevolving positions in nucleotide or amino acid sequences.

RESULTS

We have developed a web-based platform that gives a user-friendly access to two phylogenetic-based methods implementing the Coev model: the evaluation of coevolving scores and the simulation of coevolving positions. We have also extended the capabilities of the Coev model to allow for the generalization of the alphabet used in the Markov model, which can now analyse both nucleotide and amino acid data sets. The simulation of coevolving positions is novel and builds upon the developments of the Coev model. It allows user to simulate pairs of dependent nucleotide or amino acid positions.

CONCLUSIONS

The main focus of our paper is the new simulation method we present for coevolving positions. The implementation of this method is embedded within the web platform Coev-web that is freely accessible at http://coev.vital-it.ch/, and was tested in most modern web browsers.

A generalized mechanistic codon model

Models of codon evolution have attracted particular interest because of their unique capabilities to detect selection forces and their high fit when applied to sequence evolution. We described here a novel approach for modeling codon evolution, which is based on Kronecker product of matrices. The 61 × 61 codon substitution rate matrix is created using Kronecker product of three 4 × 4 nucleotide substitution matrices, the equilibrium frequency of codons, and the selection rate parameter. The entities of the nucleotide substitution matrices and selection rate are considered as parameters of the model, which are optimized by maximum likelihood. Our fully mechanistic model allows the instantaneous substitution matrix between codons to be fully estimated with only 19 parameters instead of 3,721, by using the biological interdependence existing between positions within codons. We illustrate the properties of our models using computer simulations and assessed its relevance by comparing the AICc measures of our model and other models of codon evolution on simulations and a large range of empirical data sets. We show that our model fits most biological data better compared with the current codon models. Furthermore, the parameters in our model can be interpreted in a similar way as the exchangeability rates found in empirical codon models.