High-confidence prediction of global interactomes based on genome-wide coevolutionary networks

Predicting protein-protein interaction by the mirrortree method: possibilities and limitations

Molecular co-evolution analysis as a sequence-only based method has been used to predict protein-protein interactions. In co-evolution analysis, Pearson's correlation within the mirrortree method is a well-known way of quantifying the correlation between protein pairs. Here we studied the mirrortree method on both known interacting protein pairs and sets of presumed non-interacting protein pairs, to evaluate the utility of this correlation analysis method for predicting protein-protein interactions within eukaryotes. We varied metrics for computing evolutionary distance and evolutionary span of the species analyzed. We found the differences between co-evolutionary correlation scores of the interacting and non-interacting proteins, normalized for evolutionary span, to be significantly predictive for proteins conserved over a wide range of eukaryotic clades (from mammals to fungi). On the other hand, for narrower ranges of evolutionary span, the predictive power was much weaker.

Calculation of evolutionary correlation between individual genes and full-length genome: a method useful for choosing phylogenetic markers for molecular epidemiology

Individual genes or regions are still commonly used to estimate the phylogenetic relationships among viral isolates. The genomic regions that can faithfully provide assessments consistent with those predicted with full-length genome sequences would be preferable to serve as good candidates of the phylogenetic markers for molecular epidemiological studies of many viruses. Here we employed a statistical method to evaluate the evolutionary relationships between individual viral genes and full-length genomes without tree construction as a way to determine which gene can match the genome well in phylogenetic analyses. This method was performed by calculation of linear correlations between the genetic distance matrices of aligned individual gene sequences and aligned genome sequences. We applied this method to the phylogenetic analyses of porcine circovirus 2 (PCV2), measles virus (MV), hepatitis E virus (HEV) and Japanese encephalitis virus (JEV). Phylogenetic trees were constructed for comparisons and the possible factors affecting the method accuracy were also discussed in the calculations. The results revealed that this method could produce results consistent with those of previous studies about the proper consensus sequences that could be successfully used as phylogenetic markers. And our results also suggested that these evolutionary correlations could provide useful information for identifying genes that could be used effectively to infer the genetic relationships.

Dissection of evolutionary networks to assess their role in the evolution of robustness, function, and diversification

Biological systems are remarkably robust in the face of environmental, mutational, and developmental perturbations. Analyses of molecular networks reveal recurrent features, such as modularity, that have been implicated in robustness and evolvability. Multiple theoretical models account for these features, yet few empirical tests of these models exist. Here I develop a set of broadly applicable methodologies to enable expanded empirical evaluation of model predictions. The methodologies focus on the inference and analysis of networks that depict evolutionary correlations among characters. I apply these methodologies to analyze an evolutionary network at a larger scale of organization among 42 stem anatomical and morphological characters of 52 species in the genus Adenia (Passifloraceae). I evaluate a model predicting that modular evolutionary networks will evolve in response to environmental change. The evolutionary network of Adenia is modular and "small-world," and the three diagnosed modules correspond roughly to functions of transport, storage, and mechanical support. The phylogenetically informed analyses suggest that the storage module is more impacted by environmental change than expected by chance. These results corroborate the hypothesis that modularity reduces the impact of environmental change, but this result requires further empirical evaluation that can be aided by the proposed methods in additional study systems.

Emerging methods in protein co-evolution

Co-evolution is a fundamental component of the theory of evolution and is essential for understanding the relationships between species in complex ecological networks. A wide range of co-evolution-inspired computational methods has been designed to predict molecular interactions, but it is only recently that important advances have been made. Breakthroughs in the handling of phylogenetic information and in disentangling indirect relationships have resulted in an improved capacity to predict interactions between proteins and contacts between different protein residues. Here, we review the main co-evolution-based computational approaches, their theoretical basis, potential applications and foreseeable developments.

The role of insulin C-peptide in the coevolution analyses of the insulin signaling pathway: a hint for its functions

As the linker between the A chain and B chain of proinsulin, C-peptide displays high variability in length and amino acid composition, and has been considered as an inert byproduct of insulin synthesis and processing for many years. Recent studies have suggested that C-peptide can act as a bioactive hormone, exerting various biological effects on the pathophysiology and treatment of diabetes. In this study, we analyzed the coevolution of insulin molecules among vertebrates, aiming at exploring the evolutionary characteristics of insulin molecule, especially the C-peptide. We also calculated the correlations of evolutionary rates between the insulin and the insulin receptor (IR) sequences as well as the domain-domain pairs of the ligand and receptor by the mirrortree method. The results revealed distinctive features of C-peptide in insulin intramolecular coevolution and correlated residue substitutions, which partly supported the idea that C-peptide can act as a bioactive hormone, with significant sequence features, as well as a linker assisting the formation of mature insulin during synthesis. Interestingly, the evolution of C-peptide exerted the highest correlation with that of the insulin receptor and its ligand binding domain (LBD), implying a potential relationship with the insulin signaling pathway.

The origins of the evolutionary signal used to predict protein-protein interactions

Background

The correlation of genetic distances between pairs of protein sequence alignments has been used to infer protein-protein interactions. It has been suggested that these correlations are based on the signal of co-evolution between interacting proteins. However, although mutations in different proteins associated with maintaining an interaction clearly occur (particularly in binding interfaces and neighbourhoods), many other factors contribute to correlated rates of sequence evolution. Proteins in the same genome are usually linked by shared evolutionary history and so it would be expected that there would be topological similarities in their phylogenetic trees, whether they are interacting or not. For this reason the underlying species tree is often corrected for. Moreover processes such as expression level, are known to effect evolutionary rates. However, it has been argued that the correlated rates of evolution used to predict protein interaction explicitly includes shared evolutionary history; here we test this hypothesis.

Results

In order to identify the evolutionary mechanisms giving rise to the correlations between interaction proteins, we use phylogenetic methods to distinguish similarities in tree topologies from similarities in genetic distances. We use a range of datasets of interacting and non-interacting proteins from Saccharomyces cerevisiae. We find that the signal of correlated evolution between interacting proteins is predominantly a result of shared evolutionary rates, rather than similarities in tree topology, independent of evolutionary divergence.

Conclusions

Since interacting proteins do not have tree topologies that are more similar than the control group of non-interacting proteins, it is likely that coevolution does not contribute much to, if any, of the observed correlations.

Evolutionary rate covariation in meiotic proteins results from fluctuating evolutionary pressure in yeasts and mammals

Evolutionary rates of functionally related proteins tend to change in parallel over evolutionary time. Such evolutionary rate covariation (ERC) is a sequence-based signature of coevolution and a potentially useful signature to infer functional relationships between proteins. One major hypothesis to explain ERC is that fluctuations in evolutionary pressure acting on entire pathways cause parallel rate changes for functionally related proteins. To explore this hypothesis we analyzed ERC within DNA mismatch repair (MMR) and meiosis proteins over phylogenies of 18 yeast species and 22 mammalian species. We identified a strong signature of ERC between eight yeast proteins involved in meiotic crossing over, which seems to have resulted from relaxation of constraint specifically in Candida glabrata. These and other meiotic proteins in C. glabrata showed marked rate acceleration, likely due to its apparently clonal reproductive strategy and the resulting infrequent use of meiotic proteins. This correlation between change of reproductive mode and change in constraint supports an evolutionary pressure origin for ERC. Moreover, we present evidence for similar relaxations of constraint in additional pathogenic yeast species. Mammalian MMR and meiosis proteins also showed statistically significant ERC; however, there was not strong ERC between crossover proteins, as observed in yeasts. Rather, mammals exhibited ERC in different pathways, such as piRNA-mediated defense against transposable elements. Overall, if fluctuation in evolutionary pressure is responsible for ERC, it could reveal functional relationships within entire protein pathways, regardless of whether they physically interact or not, so long as there was variation in constraint on that pathway.

Protein co-evolution: how do we combine bioinformatics and experimental approaches?

Molecular co-evolution is manifested by compensatory changes in proteins designed to enable adaptation to their natural environment. In recent years, bioinformatics approaches allowed for the detection of co-evolution at the level of the whole protein or of specific residues. Such efforts enabled prediction of protein-protein interactions, functional assignments of proteins and the identification of interacting residues, thereby providing information on protein structure. Still, despite such advances, relatively little is known regarding the functional implications of sequence divergence resulting from protein co-evolution. While bioinformatics approaches usually analyze thousands of proteins to obtain a broad view of protein co-evolution, experimental evaluation of protein co-evolution serves to study only individual proteins. In this review, we describe recent advances in bioinformatics and experimental efforts aimed at examining protein co-evolution. Accordingly, we discuss possible modes of crosstalk between the bioinformatics and experimental approaches to facilitate the identification of co-evolutionary signals in proteins and to understand their implications for the structure and function of proteins.

Efficient prediction of co-complexed proteins based on coevolution

The prediction of the network of protein-protein interactions (PPI) of an organism is crucial for the understanding of biological processes and for the development of new drugs. Machine learning methods have been successfully applied to the prediction of PPI in yeast by the integration of multiple direct and indirect biological data sources. However, experimental data are not available for most organisms. We propose here an ensemble machine learning approach for the prediction of PPI that depends solely on features independent from experimental data. We developed new estimators of the coevolution between proteins and combined them in an ensemble learning procedure.

We applied this method to a dataset of known co-complexed proteins in Escherichia coli and compared it to previously published methods. We show that our method allows prediction of PPI with an unprecedented precision of 95.5% for the first 200 sorted pairs of proteins compared to 28.5% on the same dataset with the previous best method.

A close inspection of the best predicted pairs allowed us to detect new or recently discovered interactions between chemotactic components, the flagellar apparatus and RNA polymerase complexes in E. coli.

Incorporating information on predicted solvent accessibility to the co-evolution-based study of protein interactions

A widespread family of methods for studying and predicting protein interactions using sequence information is based on co-evolution, quantified as similarity of phylogenetic trees. Part of the co-evolution observed between interacting proteins could be due to co-adaptation caused by inter-protein contacts. In this case, the co-evolution is expected to be more evident when evaluated on the surface of the proteins or the internal layers close to it. In this work we study the effect of incorporating information on predicted solvent accessibility to three methods for predicting protein interactions based on similarity of phylogenetic trees. We evaluate the performance of these methods in predicting different types of protein associations when trees based on positions with different characteristics of predicted accessibility are used as input. We found that predicted accessibility improves the results of two recent versions of the mirrortree methodology in predicting direct binary physical interactions, while it neither improves these methods, nor the original mirrortree method, in predicting other types of interactions. That improvement comes at no cost in terms of applicability since accessibility can be predicted for any sequence. We also found that predictions of protein-protein interactions are improved when multiple sequence alignments with a richer representation of sequences (including paralogs) are incorporated in the accessibility prediction.

Uncovering the co-evolutionary network among prokaryotic genes

MOTIVATION

Correlated events of gains and losses enable inference of co-evolution relations. The reconstruction of the co-evolutionary interactions network in prokaryotic species may elucidate functional associations among genes.

RESULTS

We developed a novel probabilistic methodology for the detection of co-evolutionary interactions between pairs of genes. Using this method we inferred the co-evolutionary network among 4593 Clusters of Orthologous Genes (COGs). The number of co-evolutionary interactions substantially differed among COGs. Over 40% were found to co-evolve with at least one partner. We partitioned the network of co-evolutionary relations into clusters and uncovered multiple modular assemblies of genes with clearly defined functions. Finally, we measured the extent to which co-evolutionary relations coincide with other cellular relations such as genomic proximity, gene fusion propensity, co-expression, protein-protein interactions and metabolic connections. Our results show that co-evolutionary relations only partially overlap with these other types of networks. Our results suggest that the inferred co-evolutionary network in prokaryotes is highly informative towards revealing functional relations among genes, often showing signals that cannot be extracted from other network types.

AVAILABILITY AND IMPLEMENTATION

Available under GPL license as open source.

CONTACT

talp@post.tau.ac.il.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Coevolution reveals a network of human proteins originating with multicellularity

Protein interaction networks play central roles in biological systems, from simple metabolic pathways through complex programs permitting the development of organisms. Multicellularity could only have arisen from a careful orchestration of cellular and molecular roles and responsibilities, all properly controlled and regulated. Disease reflects a breakdown of this organismal homeostasis. To better understand the evolution of interactions whose dysfunction may be contributing factors to disease, we derived the human protein coevolution network using our MatrixMatchMaker algorithm and using the Orthologous MAtrix project (OMA) database as a source for protein orthologs from 103 eukaryotic genomes. We annotated the coevolution network using protein–protein interaction data, many functional data sources, and we explored the evolutionary rates and dates of emergence of the proteins in our data set. Strikingly, clustering based only on the topology of the coevolution network partitions it into two subnetworks, one generally representing ancient eukaryotic functions and the other functions more recently acquired during animal evolution. That latter subnetwork is enriched for proteins with roles in cell–cell communication, the control of cell division, and related multicellular functions. Further annotation using data from genetic disease databases and cancer genome sequences strongly implicates these proteins in both ciliopathies and cancer. The enrichment for such disease markers in the animal network suggests a functional link between these coevolving proteins. Genetic validation corroborates the recruitment of ancient cilia in the evolution of multicellularity.

Coevolution in RNA molecules driven by selective constraints: evidence from 5S rRNA

Understanding intra-molecular coevolution helps to elucidate various structural and functional constraints acting on molecules and might have practical applications in predicting molecular structure and interactions. In this study, we used 5S rRNA as a template to investigate how selective constraints have shaped the RNA evolution. We have observed the nonrandom occurrence of paired differences along the phylogenetic trees, the high rate of compensatory evolution, and the high TIR scores (the ratio of the numbers of terminal to intermediate states), all of which indicate that significant positive selection has driven the evolution of 5S rRNA. We found three mechanisms of compensatory evolution: Watson-Crick interaction (the primary one), complex interactions between multiple sites within a stem, and interplay of stems and loops. Coevolutionary interactions between sites were observed to be highly dependent on the structural and functional environment in which they occurred. Coevolution occurred mostly in those sites closest to loops or bulges within structurally or functionally important helices, which may be under weaker selective constraints than other stem positions. Breaking these pairs would directly increase the size of the adjoining loop or bulge, causing a partial or total structural rearrangement. In conclusion, our results indicate that sequence coevolution is a direct result of maintaining optimal structural and functional integrity.

A three-dimensional topology of complex I inferred from evolutionary correlations

Background

The quaternary structure of eukaryotic NADH:ubiquinone oxidoreductase (complex I), the largest complex of the oxidative phosphorylation, is still mostly unresolved. Furthermore, it is unknown where transiently bound assembly factors interact with complex I. We therefore asked whether the evolution of complex I contains information about its 3D topology and the binding positions of its assembly factors. We approached these questions by correlating the evolutionary rates of eukaryotic complex I subunits using the mirror-tree method and mapping the results into a 3D representation by multidimensional scaling.

Results

More than 60% of the evolutionary correlation among the conserved seven subunits of the complex I matrix arm can be explained by the physical distance between the subunits. The three-dimensional evolutionary model of the eukaryotic conserved matrix arm has a striking similarity to the matrix arm quaternary structure in the bacterium Thermus thermophilus (rmsd=19 Å) and supports the previous finding that in eukaryotes the N-module is turned relative to the Q-module when compared to bacteria. By contrast, the evolutionary rates contained little information about the structure of the membrane arm. A large evolutionary model of 45 subunits and assembly factors allows to predict subunit positions and interactions (rmsd = 52.6 Å). The model supports an interaction of NDUFAF3, C8orf38 and C2orf56 during the assembly of the proximal matrix arm and the membrane arm. The model further suggests a tight relationship between the assembly factor NUBPL and NDUFA2, which both have been linked to iron-sulfur cluster assembly, as well as between NDUFA12 and its paralog, the assembly factor NDUFAF2.

Conclusions

The physical distance between subunits of complex I is a major correlate of the rate of protein evolution in the complex I matrix arm and is sufficient to infer parts of the complex’s structure with high accuracy. The resulting evolutionary model predicts the positions of a number of subunits and assembly factors.

Effect of reference genome selection on the performance of computational methods for genome-wide protein-protein interaction prediction

Background

Recent progress in computational methods for predicting physical and functional protein-protein interactions has provided new insights into the complexity of biological processes. Most of these methods assume that functionally interacting proteins are likely to have a shared evolutionary history. This history can be traced out for the protein pairs of a query genome by correlating different evolutionary aspects of their homologs in multiple genomes known as the reference genomes. These methods include phylogenetic profiling, gene neighborhood and co-occurrence of the orthologous protein coding genes in the same cluster or operon. These are collectively known as genomic context methods. On the other hand a method called mirrortree is based on the similarity of phylogenetic trees between two interacting proteins. Comprehensive performance analyses of these methods have been frequently reported in literature. However, very few studies provide insight into the effect of reference genome selection on detection of meaningful protein interactions.

Methods

We analyzed the performance of four methods and their variants to understand the effect of reference genome selection on prediction efficacy. We used six sets of reference genomes, sampled in accordance with phylogenetic diversity and relationship between organisms from 565 bacteria. We used Escherichia coli as a model organism and the gold standard datasets of interacting proteins reported in DIP, EcoCyc and KEGG databases to compare the performance of the prediction methods.

Conclusions

Higher performance for predicting protein-protein interactions was achievable even with 100–150 bacterial genomes out of 565 genomes. Inclusion of archaeal genomes in the reference genome set improves performance. We find that in order to obtain a good performance, it is better to sample few genomes of related genera of prokaryotes from the large number of available genomes. Moreover, such a sampling allows for selecting 50–100 genomes for comparable accuracy of predictions when computational resources are limited.

Ecological interactions drive evolutionary loss of traits

Loss of traits can dramatically alter the fate of species. Evidence is rapidly accumulating that the prevalence of trait loss is grossly underestimated. New findings demonstrate that traits can be lost without affecting the external phenotype, provided the lost function is compensated for by species interactions. This is important because trait loss can tighten the ecological relationship between partners, affecting the maintenance of species interactions. Here, we develop a new perspective on so-called `compensated trait loss' and how this type of trait loss may affect the evolutionary dynamics between interacting organisms. We argue that: (1) the frequency of compensated trait loss is currently underestimated because it can go unnoticed as long as ecological interactions are maintained; (2) by analysing known cases of trait loss, specific factors promoting compensated trait loss can be identified and (3) genomic sequencing is a key way forwards in detecting compensated trait loss. We present a comprehensive literature survey showing that compensated trait loss is taxonomically widespread, can involve essential traits, and often occurs as replicated evolutionary events. Despite its hidden nature, compensated trait loss is important in directing evolutionary dynamics of ecological relationships and has the potential to change facultative ecological interactions into obligatory ones.

Networks of ProteinProtein Interactions: From Uncertainty to Molecular Details

Proteins are the bricks and mortar of cells. The work of proteins is structural and functional, as they are the principal element of the organization of the cell architecture, but they also play a relevant role in its metabolism and regulation. To perform all these functions, proteins need to interact with each other and with other bio-molecules, either to form complexes or to recognize precise targets of their action. For instance, a particular transcription factor may activate one gene or another depending on its interactions with other proteins and not only with DNA. Hence, the ability of a protein to interact with other bio-molecules, and the partners they have at each particular time and location can be crucial to characterize the role of a protein. Proteins rarely act alone; they rather constitute a mingled network of physical interactions or other types of relationships (such as metabolic and regulatory) or signaling cascades. In this context, understanding the function of a protein implies to recognize the members of its neighborhood and to grasp how they associate, both at the systemic and atomic level. The network of physical interactions between the proteins of a system, cell or organism, is defined as the interactome. The purpose of this review is to deepen the description of interactomes at different levels of detail: from the molecular structure of complexes to the global topology of the network of interactions. The approaches and techniques applied experimentally and computationally to attain each level are depicted. The limits of each technique and its integration into a model network, the challenges and actual problems of completeness of an interactome, and the reliability of the interactions are reviewed and summarized. Finally, the application of the current knowledge of protein-protein interactions on modern network medicine and protein function annotation is also explored.

Evolutionary rate covariation reveals shared functionality and coexpression of genes

Evolutionary rate covariation (ERC) is a phylogenetic signature that reflects the covariation of a pair of proteins over evolutionary time. ERC is typically elevated between interacting proteins and so is a promising signature to characterize molecular and functional interactions across the genome. ERC is often assumed to result from compensatory changes at interaction interfaces (i.e., intermolecular coevolution); however, its origin is still unclear and is likely to be complex. Here, we determine the biological factors responsible for ERC in a proteome-wide data set of 4459 proteins in 18 budding yeast species. We show that direct physical interaction is not required to produce ERC, because we observe strong correlations between noninteracting but cofunctional enzymes. We also demonstrate that ERC is uniformly distributed along the protein primary sequence, suggesting that intermolecular coevolution is not generally responsible for ERC between physically interacting proteins. Using multivariate analysis, we show that a pair of proteins is likely to exhibit ERC if they share a biological function or if their expression levels coevolve between species. Thus, ERC indicates shared function and coexpression of protein pairs and not necessarily coevolution between sites, as has been assumed in previous studies. This full interpretation of ERC now provides us with a powerful tool to assign uncharacterized proteins to functional groups and to determine the interconnectedness between entire genetic pathways.

How advancement in biological network analysis methods empowers proteomics

Proteomics provides important information--that may not be inferable from indirect sources such as RNA or DNA--on key players in biological systems or disease states. However, it suffers from coverage and consistency problems. The advent of network-based analysis methods can help in overcoming these problems but requires careful application and interpretation. This review considers briefly current trends in proteomics technologies and understanding the causes of critical issues that need to be addressed--i.e., incomplete data coverage and inter-sample inconsistency. On the coverage issue, we argue that holistic analysis based on biological networks provides a suitable background on which more robust models and interpretations can be built upon; and we introduce some recently developed approaches. On consistency, group-based approaches based on identified clusters, as well as on properly integrated pathway databases, are particularly useful. Despite that protein interactions and pathway networks are still largely incomplete, given proper quality checks, applications and reasonably sized data sets, they yield valuable insights that greatly complement data generated from quantitative proteomics.

Co-evolution is incompatible with the Markov assumption in phylogenetics

Markov models are extensively used in the analysis of molecular evolution. A recent line of research suggests that pairs of proteins with functional and physical interactions co-evolve with each other. Here, by analyzing hundreds of orthologous sets of three fungi and their co-evolutionary relations, we demonstrate that co-evolutionary assumption may violate the Markov assumption. Our results encourage developing alternative probabilistic models for the cases of extreme co-evolution.

Efficient algorithms for reconstructing gene content by co-evolution

BACKGROUND

In a previous study we demonstrated that co-evolutionary information can be utilized for improving the accuracy of ancestral gene content reconstruction. To this end, we defined a new computational problem, the Ancestral Co-Evolutionary (ACE) problem, and developed algorithms for solving it.

RESULTS

In the current paper we generalize our previous study in various ways. First, we describe new efficient computational approaches for solving the ACE problem. The new approaches are based on reductions to classical methods such as linear programming relaxation, quadratic programming, and min-cut. Second, we report new computational hardness results related to the ACE, including practical cases where it can be solved in polynomial time.Third, we generalize the ACE problem and demonstrate how our approach can be used for inferring parts of the genomes of non-ancestral organisms. To this end, we describe a heuristic for finding the portion of the genome ('dominant set') that can be used to reconstruct the rest of the genome with the lowest error rate. This heuristic utilizes both evolutionary information and co-evolutionary information.We implemented these algorithms on a large input of the ACE problem (95 unicellular organisms, 4,873 protein families, and 10, 576 of co-evolutionary relations), demonstrating that some of these algorithms can outperform the algorithm used in our previous study. In addition, we show that based on our approach a 'dominant set' cab be used reconstruct a major fraction of a genome (up to 79%) with relatively low error-rate (e.g. 0.11). We find that the 'dominant set' tends to include metabolic and regulatory genes, with high evolutionary rate, and low protein abundance and number of protein-protein interactions.

CONCLUSIONS

The ACE problem can be efficiently extended for inferring the genomes of organisms that exist today. In addition, it may be solved in polynomial time in many practical cases. Metabolic and regulatory genes were found to be the most important groups of genes necessary for reconstructing gene content of an organism based on other related genomes.

Selection of organisms for the co-evolution-based study of protein interactions

BACKGROUND

The prediction and study of protein interactions and functional relationships based on similarity of phylogenetic trees, exemplified by the mirrortree and related methodologies, is being widely used. Although dependence between the performance of these methods and the set of organisms used to build the trees was suspected, so far nobody assessed it in an exhaustive way, and, in general, previous works used as many organisms as possible. In this work we asses the effect of using different sets of organism (chosen according with various phylogenetic criteria) on the performance of this methodology in detecting protein interactions of different nature.

RESULTS

We show that the performance of three mirrortree-related methodologies depends on the set of organisms used for building the trees, and it is not always directly related to the number of organisms in a simple way. Certain subsets of organisms seem to be more suitable for the predictions of certain types of interactions. This relationship between type of interaction and optimal set of organism for detecting them makes sense in the light of the phylogenetic distribution of the organisms and the nature of the interactions.

CONCLUSIONS

In order to obtain an optimal performance when predicting protein interactions, it is recommended to use different sets of organisms depending on the available computational resources and data, as well as the type of interactions of interest.

Towards the prediction of protein interaction partners using physical docking

Prediction of physical protein-protein interactions represents a key challenge in computational systems biology. This study provides a proof-of-principle that high-throughput in silico protein docking results can be used to predict interaction partners.

Deciphering the whole network of protein interactions for a given proteome (‘interactome') is the goal of many experimental and computational efforts in Systems Biology. Separately the prediction of the structure of protein complexes by docking methods is a well-established scientific area. To date, docking programs have not been used to predict interaction partners. We provide a proof of principle for such an approach. Using a set of protein complexes representing known interactors in their unbound form, we show that a standard docking program can distinguish the true interactors from a background of 922 non-redundant potential interactors. We additionally show that true interactions can be distinguished from non-likely interacting proteins within the same structural family. Our approach may be put in the context of the proposed ‘funnel-energy model'; the docking algorithm may not find the native complex, but it distinguishes binding partners because of the higher probability of favourable models compared with a collection of non-binders. The potential exists to develop this proof of principle into new approaches for predicting interaction partners and reconstructing biological networks.

Analysis of coevolving gene families using mutually exclusive orthologous modules

Coevolutionary networks can encapsulate information about the dynamics of presence and absence of gene families in organisms. Analysis of such networks should reveal fundamental principles underlying the evolution of cellular systems and the functionality of sets of genes. In this study, we describe a new approach for analyzing coevolutionary networks. Our method detects Mutually Exclusive Orthologous Modules (MEOMs). A MEOM is composed of two sets of gene families, each including gene families that tend to appear in the same organisms, such that the two sets tend to mutually exclude each other (if one set appears in a certain organism the second set does not). Thus, a MEOM reflects the evolutionary replacement of one set of genes by another due to reasons such as lineage/environmental specificity, incompatibility, or functional redundancy. We use our method to analyze a coevolutionary network that is based on 383 microorganisms from the three domains of life. As we demonstrate, our method is useful for detecting meaningful evolutionary clades of organisms as well as sets of proteins that interact with each other. Among our results, we report that: 1) MEOMs tend to include gene families whose cellular functions involve transport, energy production, metabolism, and translation, suggesting that changes in the metabolic environments that require adaptation to new sources of energy are central triggers of complex/pathway replacement in evolution. 2) Many MEOMs are related to outer membrane proteins, such proteins are involved in interaction with the environment and could thus be replaced as a result of adaptation. 3) MEOMs tend to separate organisms with large phylogenetic distance but they also separate organisms that live in different ecological niches. 4) Strikingly, although many MEOMs can be identified, there are much fewer cases where the two cliques in the MEOM completely mutually exclude each other, demonstrating the flexibility of protein evolution. 5) CO dehydrogenase and thymidylate synthase and the glycine cleavage genes mutually exclude each other in archaea; this may represent an alternative route for generation of methyl donors for thymidine synthesis.

Conserved and variable correlated mutations in the plant MADS protein network

BACKGROUND

Plant MADS domain proteins are involved in a variety of developmental processes for which their ability to form various interactions is a key requisite. However, not much is known about the structure of these proteins or their complexes, whereas such knowledge would be valuable for a better understanding of their function. Here, we analyze those proteins and the complexes they form using a correlated mutation approach in combination with available structural, bioinformatics and experimental data.

RESULTS

Correlated mutations are affected by several types of noise, which is difficult to disentangle from the real signal. In our analysis of the MADS domain proteins, we apply for the first time a correlated mutation analysis to a family of interacting proteins. This provides a unique way to investigate the amount of signal that is present in correlated mutations because it allows direct comparison of mutations in various family members and assessing their conservation. We show that correlated mutations in general are conserved within the various family members, and if not, the variability at the respective positions is less in the proteins in which the correlated mutation does not occur. Also, intermolecular correlated mutation signals for interacting pairs of proteins display clear overlap with other bioinformatics data, which is not the case for non-interacting protein pairs, an observation which validates the intermolecular correlated mutations. Having validated the correlated mutation results, we apply them to infer the structural organization of the MADS domain proteins.

CONCLUSION

Our analysis enables understanding of the structural organization of the MADS domain proteins, including support for predicted helices based on correlated mutation patterns, and evidence for a specific interaction site in those proteins.

Reconstructing ancestral genomic sequences by co-evolution: formal definitions, computational issues, and biological examples

The inference of ancestral genomes is a fundamental problem in molecular evolution. Due to the statistical nature of this problem, the most likely or the most parsimonious ancestral genomes usually include considerable error rates. In general, these errors cannot be abolished by utilizing more exhaustive computational approaches, by using longer genomic sequences, or by analyzing more taxa. In recent studies, we showed that co-evolution is an important force that can be used for significantly improving the inference of ancestral genome content. In this work we formally define a computational problem for the inference of ancestral genome content by co-evolution. We show that this problem is NP-hard and hard to approximate and present both a Fixed Parameter Tractable (FPT) algorithm, and heuristic approximation algorithms for solving it. The running time of these algorithms on simulated inputs with hundreds of protein families and hundreds of co-evolutionary relations was fast (up to four minutes) and it achieved an approximation ratio of <1.3. We use our approach to study the ancestral genome content of the Fungi. To this end, we implement our approach on a dataset of 33, 931 protein families and 20, 317 co-evolutionary relations. Our algorithm added and removed hundreds of proteins from the ancestral genomes inferred by maximum likelihood (ML) or maximum parsimony (MP) while slightly affecting the likelihood/parsimony score of the results. A biological analysis revealed various pieces of evidence that support the biological plausibility of the new solutions. In addition, we showed that our approach reconstructs missing values at the leaves of the Fungi evolutionary tree better than ML or MP.

Trees on networks: resolving statistical patterns of phylogenetic similarities among interacting proteins

Background

Phylogenies capture the evolutionary ancestry linking extant species. Correlations and similarities among a set of species are mediated by and need to be understood in terms of the phylogenic tree. In a similar way it has been argued that biological networks also induce correlations among sets of interacting genes or their protein products.

Results

We develop suitable statistical resampling schemes that can incorporate these two potential sources of correlation into a single inferential framework. To illustrate our approach we apply it to protein interaction data in yeast and investigate whether the phylogenetic trees of interacting proteins in a panel of yeast species are more similar than would be expected by chance.

Conclusions

While we find only negligible evidence for such increased levels of similarities, our statistical approach allows us to resolve the previously reported contradictory results on the levels of co-evolution induced by protein-protein interactions. We conclude with a discussion as to how we may employ the statistical framework developed here in further functional and evolutionary analyses of biological networks and systems.

An integrated view of molecular coevolution in protein-protein interactions

Protein-protein interactions effectively mediate molecular function. They are the result of specific interactions between protein interfaces and are maintained by the action of evolutionary pressure on the regions of the interacting proteins that contribute to binding. For the most part, selection restricts amino acid replacements, accounting for the conservation of binding interfaces. However, in some cases, change in one protein will be mitigated by compensatory change in its binding partner, maintaining function in the face of evolutionary change. There have been several attempts to use correlations in sequence evolution to predict interactions of proteins. Most commonly, these approaches use the entire sequence to identify correlations and so infer probable binding. However, other factors such as shared evolutionary history and similarities in the rates of evolution confound these whole-sequence-based approaches. Here, we discuss recent work on this topic and argue that both site-specific coevolutionary change and whole-sequence evolution contribute to evolutionary signals in sets of interacting proteins. We discuss the relative effects of both types of selection and how they might be identified. This permits an integrated view of protein-protein interactions, their evolution, and coevolution.

PRED_PPI: a server for predicting protein-protein interactions based on sequence data with probability assignment

BACKGROUND

Protein-protein interactions (PPIs) are crucial for almost all cellular processes, including metabolic cycles, DNA transcription and replication, and signaling cascades. Given the importance of PPIs, several methods have been developed to detect them. Since the experimental methods are time-consuming and expensive, developing computational methods for effectively identifying PPIs is of great practical significance.

FINDINGS

Most previous methods were developed for predicting PPIs in only one species, and do not account for probability estimations. In this work, a relatively comprehensive prediction system was developed, based on a support vector machine (SVM), for predicting PPIs in five organisms, specifically humans, yeast, Drosophila, Escherichia coli, and Caenorhabditis elegans. This PPI predictor includes the probability of its prediction in the output, so it can be used to assess the confidence of each SVM prediction by the probability assignment. Using a probability of 0.5 as the threshold for assigning class labels, the method had an average accuracy for detecting protein interactions of 90.67% for humans, 88.99% for yeast, 90.09% for Drosophila, 92.73% for E. coli, and 97.51% for C. elegans. Moreover, among the correctly predicted pairs, more than 80% were predicted with a high probability of >/=0.8, indicating that this tool could predict novel PPIs with high confidence.

CONCLUSIONS

Based on this work, a web-based system, Pred_PPI, was constructed for predicting PPIs from the five organisms. Users can predict novel PPIs and obtain a probability value about the prediction using this tool. Pred_PPI is freely available at http://cic.scu.edu.cn/bioinformatics/predict_ppi/default.html.

Tsetse flies, trypanosomes, humans and animals: what is proteomics revealing about their crosstalks?

Human and animal African trypanosomoses, or sleeping sickness and Nagana, are neglected vector-borne parasitic diseases caused by protozoa belonging to the Trypanosoma genus. Advances in proteomics offer new tools to better understand host-vector-parasite crosstalks occurring during the complex parasitic developmental cycle, and to determine the outcome of both transmission and infection. In this review, we summarize proteomics studies performed on African trypanosomes and on the interactions with their vector and mammalian hosts. We discuss the contributions and pitfalls of using diverse proteomics tools, and argue about the interest of pathogenoproteomics, both to generate advances in basic research on the best knowledge and understanding of host-vector-pathogen interactions, and to lead to the concrete development of new tools to improve diagnosis and treatment management of trypanosomoses in the near future.

Inference of functional relations in predicted protein networks with a machine learning approach

Background

Molecular biology is currently facing the challenging task of functionally characterizing the proteome. The large number of possible protein-protein interactions and complexes, the variety of environmental conditions and cellular states in which these interactions can be reorganized, and the multiple ways in which a protein can influence the function of others, requires the development of experimental and computational approaches to analyze and predict functional associations between proteins as part of their activity in the interactome.

Methodology/Principal Findings

We have studied the possibility of constructing a classifier in order to combine the output of the several protein interaction prediction methods. The AODE (Averaged One-Dependence Estimators) machine learning algorithm is a suitable choice in this case and it provides better results than the individual prediction methods, and it has better performances than other tested alternative methods in this experimental set up. To illustrate the potential use of this new AODE-based Predictor of Protein InterActions (APPIA), when analyzing high-throughput experimental data, we show how it helps to filter the results of published High-Throughput proteomic studies, ranking in a significant way functionally related pairs. Availability: All the predictions of the individual methods and of the combined APPIA predictor, together with the used datasets of functional associations are available at http://ecid.bioinfo.cnio.es/.

Conclusions

We propose a strategy that integrates the main current computational techniques used to predict functional associations into a unified classifier system, specifically focusing on the evaluation of poorly characterized protein pairs. We selected the AODE classifier as the appropriate tool to perform this task. AODE is particularly useful to extract valuable information from large unbalanced and heterogeneous data sets. The combination of the information provided by five prediction interaction prediction methods with some simple sequence features in APPIA is useful in establishing reliability values and helpful to prioritize functional interactions that can be further experimentally characterized.

Construction and analysis of protein-protein interaction networks

Protein–protein interactions form the basis for a vast majority of cellular events, including signal transduction and transcriptional regulation. It is now understood that the study of interactions between cellular macromolecules is fundamental to the understanding of biological systems. Interactions between proteins have been studied through a number of high-throughput experiments and have also been predicted through an array of computational methods that leverage the vast amount of sequence data generated in the last decade. In this review, I discuss some of the important computational methods for the prediction of functional linkages between proteins. I then give a brief overview of some of the databases and tools that are useful for a study of protein–protein interactions. I also present an introduction to network theory, followed by a discussion of the parameters commonly used in analysing networks, important network topologies, as well as methods to identify important network components, based on perturbations.

Discovering local patterns of co-evolution: computational aspects and biological examples

Background

Co-evolution is the process in which two (or more) sets of orthologs exhibit a similar or correlative pattern of evolution. Co-evolution is a powerful way to learn about the functional interdependencies between sets of genes and cellular functions and to predict physical interactions. More generally, it can be used for answering fundamental questions about the evolution of biological systems.

Orthologs that exhibit a strong signal of co-evolution in a certain part of the evolutionary tree may show a mild signal of co-evolution in other branches of the tree. The major reasons for this phenomenon are noise in the biological input, genes that gain or lose functions, and the fact that some measures of co-evolution relate to rare events such as positive selection. Previous publications in the field dealt with the problem of finding sets of genes that co-evolved along an entire underlying phylogenetic tree, without considering the fact that often co-evolution is local.

Results

In this work, we describe a new set of biological problems that are related to finding patterns of local co-evolution. We discuss their computational complexity and design algorithms for solving them. These algorithms outperform other bi-clustering methods as they are designed specifically for solving the set of problems mentioned above.

We use our approach to trace the co-evolution of fungal, eukaryotic, and mammalian genes at high resolution across the different parts of the corresponding phylogenetic trees. Specifically, we discover regions in the fungi tree that are enriched with positive evolution. We show that metabolic genes exhibit a remarkable level of co-evolution and different patterns of co-evolution in various biological datasets.

In addition, we find that protein complexes that are related to gene expression exhibit non-homogenous levels of co-evolution across different parts of the fungi evolutionary line. In the case of mammalian evolution, signaling pathways that are related to neurotransmission exhibit a relatively higher level of co-evolution along the primate subtree.

Conclusions

We show that finding local patterns of co-evolution is a computationally challenging task and we offer novel algorithms that allow us to solve this problem, thus opening a new approach for analyzing the evolution of biological systems.

Comparative classification of species and the study of pathway evolution based on the alignment of metabolic pathways

BACKGROUND

Pathways provide topical descriptions of cellular circuitry. Comparing analogous pathways reveals intricate insights into individual functional differences among species. While previous works in the field performed genomic comparisons and evolutionary studies that were based on specific genes or proteins, whole genomic sequence, or even single pathways, none of them described a genomic system level comparative analysis of metabolic pathways. In order to properly implement such an analysis one should overcome two specific challenges: how to combine the effect of many pathways under a unified framework and how to appropriately analyze co-evolution of pathways. Here we present a computational approach for solving these two challenges. First, we describe a comprehensive, scalable, information theory based computational pipeline that calculates pathway alignment information and then compiles it in a novel manner that allows further analysis. This approach can be used for building phylogenies and for pointing out specific differences that can then be analyzed in depth. Second, we describe a new approach for comparing the evolution of metabolic pathways. This approach can be used for detecting co-evolutionary relationships between metabolic pathways.

RESULTS

We demonstrate the advantages of our approach by applying our pipeline to data from the MetaCyc repository (which includes a total of 205 organisms and 660 metabolic pathways). Our analysis revealed several surprising biological observations. For example, we show that the different habitats in which Archaea organisms reside are reflected by a pathway based phylogeny. In addition, we discover two striking clusters of metabolic pathways, each cluster includes pathways that have very similar evolution.

CONCLUSION

We demonstrate that distance measures that are based on the topology and the content of metabolic networks are useful for studying evolution and co-evolution.

A novel method to detect proteins evolving at correlated rates: identifying new functional relationships between coevolving proteins

Interacting proteins evolve at correlated rates, possibly as the result of evolutionary pressures shared by functional groups and/or coevolution between interacting proteins. This evolutionary signature can be exploited to learn more about protein networks and to infer functional relationships between proteins on a genome-wide scale. Multiple methods have been introduced that detect correlated evolution using amino acid distances. One assumption made by these methods is that the neutral rate of nucleotide substitution is uniform over time; however, this is unlikely and such rate heterogeneity would adversely affect amino acid distance methods. We explored alternative methods that detect correlated rates using protein-coding nucleotide sequences in order to better estimate the rate of nonsynonymous substitution at each branch (d(N)) normalized by the underlying synonymous substitution rate (d(S)). Our novel likelihood method, which was robust to realistic simulation parameters, was tested on Drosophila nuclear pore proteins, which form a complex with well-documented physical interactions. The method revealed significantly correlated evolution between nuclear pore proteins, where members of a stable subcomplex showed stronger correlations compared with those proteins that interact transiently. Furthermore, our likelihood approach was better able to detect correlated evolution among closely related species than previous methods. Hence, these sequence-based methods are a complementary approach for detecting correlated evolution and could be applied genome-wide to provide candidate protein-protein interactions and functional group assignments using just coding sequences.

Covariation of branch lengths in phylogenies of functionally related genes

Recent studies have shown evidence for the coevolution of functionally-related genes. This coevolution is a result of constraints to maintain functional relationships between interacting proteins. The studies have focused on the correlation in gene tree branch lengths of proteins that are directly interacting with each other. We here hypothesize that the correlation in branch lengths is not limited only to proteins that directly interact, but also to proteins that operate within the same pathway. Using generalized linear models as a basis of identifying correlation, we attempted to predict the gene ontology (GO) terms of a gene based on its gene tree branch lengths. We applied our method to a dataset consisting of proteins from ten prokaryotic species. We found that the degree of accuracy to which we could predict the function of the proteins from their gene tree varied substantially with different GO terms. In particular, our model could accurately predict genes involved in translation and certain ribosomal activities with the area of the receiver-operator curve of up to 92%. Further analysis showed that the similarity between the trees of genes labeled with similar GO terms was not limited to genes that physically interacted, but also extended to genes functioning within the same pathway. We discuss the relevance of our findings as it relates to the use of phylogenetic methods in comparative genomics.

Critical assessment of sequence-based protein-protein interaction prediction methods that do not require homologous protein sequences

Background

Protein-protein interactions underlie many important biological processes. Computational prediction methods can nicely complement experimental approaches for identifying protein-protein interactions. Recently, a unique category of sequence-based prediction methods has been put forward - unique in the sense that it does not require homologous protein sequences. This enables it to be universally applicable to all protein sequences unlike many of previous sequence-based prediction methods. If effective as claimed, these new sequence-based, universally applicable prediction methods would have far-reaching utilities in many areas of biology research.

Results

Upon close survey, I realized that many of these new methods were ill-tested. In addition, newer methods were often published without performance comparison with previous ones. Thus, it is not clear how good they are and whether there are significant performance differences among them. In this study, I have implemented and thoroughly tested 4 different methods on large-scale, non-redundant data sets. It reveals several important points. First, significant performance differences are noted among different methods. Second, data sets typically used for training prediction methods appear significantly biased, limiting the general applicability of prediction methods trained with them. Third, there is still ample room for further developments. In addition, my analysis illustrates the importance of complementary performance measures coupled with right-sized data sets for meaningful benchmark tests.

Conclusions

The current study reveals the potentials and limits of the new category of sequence-based protein-protein interaction prediction methods, which in turn provides a firm ground for future endeavours in this important area of contemporary bioinformatics.

Phylogeny-guided interaction mapping in seven eukaryotes

BACKGROUND

The assembly of reliable and complete protein-protein interaction (PPI) maps remains one of the significant challenges in systems biology. Computational methods which integrate and prioritize interaction data can greatly aid in approaching this goal.

RESULTS

We developed a Bayesian inference framework which uses phylogenetic relationships to guide the integration of PPI evidence across multiple datasets and species, providing more accurate predictions. We apply our framework to reconcile seven eukaryotic interactomes: H. sapiens, M. musculus, R. norvegicus, D. melanogaster, C. elegans, S. cerevisiae and A. thaliana. Comprehensive GO-based quality assessment indicates a 5% to 44% score increase in predicted interactomes compared to the input data. Further support is provided by gold-standard MIPS, CYC2008 and HPRD datasets. We demonstrate the ability to recover known PPIs in well-characterized yeast and human complexes (26S proteasome, endosome and exosome) and suggest possible new partners interacting with the putative SWI/SNF chromatin remodeling complex in A. thaliana.

CONCLUSION

Our phylogeny-guided approach compares favorably to two standard methods for mapping PPIs across species. Detailed analysis of predictions in selected functional modules uncovers specific PPI profiles among homologous proteins, establishing interaction-based partitioning of protein families. Provided evidence also suggests that interactions within core complex subunits are in general more conserved and easier to transfer accurately to other organisms, than interactions between these subunits.

Reconstructing ancestral gene content by coevolution

Inferring the gene content of ancestral genomes is a fundamental challenge in molecular evolution. Due to the statistical nature of this problem, ancestral genomes inferred by the maximum likelihood (ML) or the maximum-parsimony (MP) methods are prone to considerable error rates. In general, these errors are difficult to abolish by using longer genomic sequences or by analyzing more taxa. This study describes a new approach for improving ancestral genome reconstruction, the ancestral coevolver (ACE), which utilizes coevolutionary information to improve the accuracy of such reconstructions over previous approaches. The principal idea is to reduce the potentially large solution space by choosing a single optimal (or near optimal) solution that is in accord with the coevolutionary relationships between protein families. Simulation experiments, both on artificial and real biological data, show that ACE yields a marked decrease in error rate compared with ML or MP. Applied to a large data set (95 organisms, 4873 protein families, and 10,000 coevolutionary relationships), some of the ancestral genomes reconstructed by ACE were remarkably different in their gene content from those reconstructed by ML or MP alone (more than 10% in some nodes). These reconstructions, while having almost similar likelihood/parsimony scores as those obtained with ML/MP, had markedly higher concordance with the coevolutionary information. Specifically, when ACE was implemented to improve the results of ML, it added a large number of proteins to those encoded by LUCA (last universal common ancestor), most of them ribosomal proteins and components of the F(0)F(1)-type ATP synthase/ATPases, complexes that are vital in most living organisms. Our analysis suggests that LUCA appears to have been bacterial-like and had a genome size similar to the genome sizes of many extant organisms.

Computer applications for prediction of protein-protein interactions and rational drug design

In recent years, protein-protein interactions are becoming the object of increasing attention in many different fields, such as structural biology, molecular biology, systems biology, and drug discovery. From a structural biology perspective, it would be desirable to integrate current efforts into the structural proteomics programs. Given that experimental determination of many protein-protein complex structures is highly challenging, and in the context of current high-performance computational capabilities, different computer tools are being developed to help in this task. Among them, computational docking aims to predict the structure of a protein-protein complex starting from the atomic coordinates of its individual components, and in recent years, a growing number of docking approaches are being reported with increased predictive capabilities. The improvement of speed and accuracy of these docking methods, together with the modeling of the interaction networks that regulate the most critical processes in a living organism, will be essential for computational proteomics. The ultimate goal is the rational design of drugs capable of specifically inhibiting or modifying protein-protein interactions of therapeutic significance. While rational design of protein-protein interaction inhibitors is at its very early stage, the first results are promising.

Predicting protein-protein interactions in the context of protein evolution

Here we review the methods for the prediction of protein interactions and the ideas in protein evolution that relate to them. The evolutionary assumptions implicit in many of the protein interaction prediction methods are elucidated. We draw attention to the caution needed in deploying certain evolutionary assumptions, in particular cross-organism transfer of interactions by sequence homology, and discuss the known issues in deriving interaction predictions from evidence of co-evolution. We also conject that there is evolutionary knowledge yet to be exploited in the prediction of interactions, in particular the heterogeneity of interactions, the increasing availability of interaction data from multiple species, and the models of protein interaction network growth.

The human protein coevolution network

Coevolution maintains interactions between phenotypic traits through the process of reciprocal natural selection. Detecting molecular coevolution can expose functional interactions between molecules in the cell, generating insights into biological processes, pathways, and the networks of interactions important for cellular function. Prediction of interaction partners from different protein families exploits the property that interacting proteins can follow similar patterns and relative rates of evolution. Current methods for detecting coevolution based on the similarity of phylogenetic trees or evolutionary distance matrices have, however, been limited by requiring coevolution over the entire evolutionary history considered and are inaccurate in the presence of paralogous copies. We present a novel method for determining coevolving protein partners by finding the largest common submatrix in a given pair of distance matrices, with the size of the largest common submatrix measuring the strength of coevolution. This approach permits us to consider matrices of different size and scale, to find lineage-specific coevolution, and to predict multiple interaction partners. We used MatrixMatchMaker to predict protein-protein interactions in the human genome. We show that proteins that are known to interact physically are more strongly coevolving than proteins that simply belong to the same biochemical pathway. The human coevolution network is highly connected, suggesting many more protein-protein interactions than are currently known from high-throughput and other experimental evidence. These most strongly coevolving proteins suggest interactions that have been maintained over long periods of evolutionary time, and that are thus likely to be of fundamental importance to cellular function.

Evolution in protein interaction networks: co-evolution, rewiring and the role of duplication

Molecular function is the result of proteins working together, mediated by highly specific interactions. Maintenance and change of protein interactions can thus be considered one of the main links between molecular function and mutation. As a consequence, protein interaction datasets can be used to study functional evolution directly. In terms of constraining change, the co-evolution of interacting molecules is a very subtle process. This has implications for the signal being used to predict protein–protein interactions. In terms of functional change, the ‘rewiring’ of interaction networks, gene duplication is critically important. Interestingly, once duplication has occurred, the genes involved have different probabilities of being retained related to how they were generated. In the present paper, we discuss some of our recent work in this area.

Intra-protein compensatory mutations analysis highlights the tRNA recognition regions in aminoacyl-tRNA synthetases

The aminoacyl-tRNA synthetases (aaRSs) covalently attach amino acids to their corresponding nucleic acid adapter molecules, tRNAs. The interactions in the tRNA-aaRSs complexes are mostly non-specific, and largely electrostatic. Tracing a way of aaRS-tRNA mutual adaptation throughout evolution offers a clearer view of understanding how aaRS-tRNA systems preserve patterns of tRNA recognition and binding. In this study, we used the compensatory mutations analysis to explore adaptation of aaRSs in respond to random mutations that can occur in the tRNA-recognition area. We showed that the frequency of compensatory mutations among residues that belong to the recognition region is 1.75-fold higher than that of the exposed residues. The highest frequencies of compensatory mutations are observed for pairs of charged residues, wherein one residue is located within the tRNA-recognition area, while the second is placed outside of the area, and contributes to the formation of the aaRS electrostatic landscape. Given charged residues are compensated by buried charge residues in more than 60% of the analyzed mutations. The cytoplasmatic and mitochondrial aaRSs preserve similar patterns of compensatory mutations in the tRNA recognition areas. Moreover, we found that mitochondrial aaRSs demonstrate a significant increase in the frequency of compensatory mutations in the area. Our findings shed light on the physical nature of compensatory mutations in aaRSs, thereby keeping unchanged tRNA-recognition patterns.