Horizontal transfer, not duplication, drives the expansion of protein families in prokaryotes

Gene duplication followed by neo- or sub-functionalization deeply impacts the evolution of protein families and is regarded as the main source of adaptive functional novelty in eukaryotes. While there is ample evidence of adaptive gene duplication in prokaryotes, it is not clear whether duplication outweighs the contribution of horizontal gene transfer in the expansion of protein families. We analyzed closely related prokaryote strains or species with small genomes (Helicobacter, Neisseria, Streptococcus, Sulfolobus), average-sized genomes (Bacillus, Enterobacteriaceae), and large genomes (Pseudomonas, Bradyrhizobiaceae) to untangle the effects of duplication and horizontal transfer. After removing the effects of transposable elements and phages, we show that the vast majority of expansions of protein families are due to transfer, even among large genomes. Transferred genes--xenologs--persist longer in prokaryotic lineages possibly due to a higher/longer adaptive role. On the other hand, duplicated genes--paralogs--are expressed more, and, when persistent, they evolve slower. This suggests that gene transfer and gene duplication have very different roles in shaping the evolution of biological systems: transfer allows the acquisition of new functions and duplication leads to higher gene dosage. Accordingly, we show that paralogs share most protein-protein interactions and genetic regulators, whereas xenologs share very few of them. Prokaryotes invented most of life's biochemical diversity. Therefore, the study of the evolution of biology systems should explicitly account for the predominant role of horizontal gene transfer in the diversification of protein families.     

Increasing confidence of protein interactomes using network topological metrics

MOTIVATION: Experimental limitations in high-throughput protein-protein interaction detection methods have resulted in low quality interaction datasets that contained sizable fractions of false positives and false negatives. Small-scale, focused experiments are then needed to complement the high-throughput methods to extract true protein interactions. However, the naturally vast interactomes would require much more scalable approaches. RESULTS: We describe a novel method called IRAP* as a computational complement for repurification of the highly erroneous experimentally derived protein interactomes. Our method involves an iterative process of removing interactions that are confidently identified as false positives and adding interactions detected as false negatives into the interactomes. Identification of both false positives and false negatives are performed in IRAP* using interaction confidence measures based on network topological metrics. Potential false positives are identified amongst the detected interactions as those with very low computed confidence values, while potential false negatives are discovered as the undetected interactions with high computed confidence values. Our results from applying IRAP* on large-scale interaction datasets generated by the popular yeast-two-hybrid assays for yeast, fruit fly and worm showed that the computationally repurified interaction datasets contained potentially lower fractions of false positive and false negative errors based on functional homogeneity. AVAILABILITY: The confidence indices for PPIs in yeast, fruit fly and worm as computed by our method can be found at our website http://www.comp.nus.edu.sg/~chenjin/fpfn.     

Deciphering protein-protein interactions. Part II. Computational methods to predict protein and domain interaction partners

Recent advances in high-throughput experimental methods for the identification of protein interactions have resulted in a large amount of diverse data that are somewhat incomplete and contradictory. As valuable as they are, such experimental approaches studying protein interactomes have certain limitations that can be complemented by the computational methods for predicting protein interactions. In this review we describe different approaches to predict protein interaction partners as well as highlight recent achievements in the prediction of specific domains mediating protein-protein interactions. We discuss the applicability of computational methods to different types of prediction problems and point out limitations common to all of them.     

Specificity and evolvability in eukaryotic protein interaction networks

Progress in uncovering the protein interaction networks of several species has led to questions of what underlying principles might govern their organization. Few studies have tried to determine the impact of protein interaction network evolution on the observed physiological differences between species. Using comparative genomics and structural information, we show here that eukaryotic species have rewired their interactomes at a fast rate of approximately 10⁻⁵ interactions changed per protein pair, per million years of divergence. For Homo sapiens this corresponds to 10³ interactions changed per million years. Additionally we find that the specificity of binding strongly determines the interaction turnover and that different biological processes show significantly different link dynamics. In particular, human proteins involved in immune response, transport, and establishment of localization show signs of positive selection for change of interactions. Our analysis suggests that a small degree of molecular divergence can give rise to important changes at the network level. We propose that the power law distribution observed in protein interaction networks could be partly explained by the cell's requirement for different degrees of protein binding specificity.     

Dynein light chain LC8 is a dimerization hub essential in diverse protein networks

The operations within a living cell depend on the collective activity of networks of proteins, sometimes termed "interactomes". Within these networks, most proteins interact with few partners, while a small proportion of proteins, called hubs, participate in a large number of interactions and play a central role in organizing these interactomes. LC8 was first discovered as an essential component of the microtubule-based molecular motor dynein and as such is involved in fundamental processes, including retrograde vesicular trafficking, ciliary/flagellar motility, and cell division. More recently, evidence has accumulated that LC8 also interacts with proteins that are not clearly connected with dynein or microtubule-based transport, including some with roles in apoptosis, viral pathogenesis, enzyme regulation, and kidney development. Here, we introduce the idea that LC8 is a hub protein essential in diverse protein networks, and its function as a dynein light chain is but one of many. We further propose that the crucial regulatory roles of LC8 in various systems are due to its ability to promote dimerization of partially disordered proteins.     

From subgenome analysis to protein structure

Groups of related genes abound in large eukaryotic genomes. In such 'subgenomes', homology modeling carried out for a few genes will probably have relevance to the entire group. Subgenomes also afford unique ways of determining protein structural information. In addition to analyses based on the quantification of residue variability in paralogs, two-way comparisons, both within and among species, help to disclose functional amino acids. Comparative studies of gene families throughout the mammalian genome will also help elucidate the functional significance of single nucleotide polymorphisms in coding regions.     

High-throughput analysis of peptide-binding modules

Modular protein interaction domains (PIDs) that recognize linear peptide motifs are found in hundreds of proteins within the human genome. Some PIDs such as SH2, 14-3-3, Chromo, and Bromo domains serve to recognize posttranslational modification (PTM) of amino acids (such as phosphorylation, acetylation, methylation, etc.) and translate these into discrete cellular responses. Other modules such as SH3 and PSD-95/Discs-large/ZO-1 (PDZ) domains recognize linear peptide epitopes and serve to organize protein complexes based on localization and regions of elevated concentration. In both cases, the ability to nucleate-specific signaling complexes is in large part dependent on the selectivity of a given protein module for its cognate peptide ligand. High-throughput (HTP) analysis of peptide-binding domains by peptide or protein arrays, phage display, mass spectrometry, or other HTP techniques provides new insight into the potential protein-protein interactions prescribed by individual or even whole families of modules. Systems level analyses have also promoted a deeper understanding of the underlying principles that govern selective protein-protein interactions and how selectivity evolves. Lastly, there is a growing appreciation for the limitations and potential pitfalls associated with HTP analysis of protein-peptide interactomes. This review will examine some of the common approaches utilized for large-scale studies of PIDs and suggest a set of standards for the analysis and validation of datasets from large-scale studies of peptide-binding modules. We will also highlight how data from large-scale studies of modular interaction domain families can provide insight into systems level properties such as the linguistics of selective interactions.     

Flexible nets. The roles of intrinsic disorder in protein interaction networks

Proteins participate in complex sets of interactions that represent the mechanistic foundation for much of the physiology and function of the cell. These protein-protein interactions are organized into exquisitely complex networks. The architecture of protein-protein interaction networks was recently proposed to be scale-free, with most of the proteins having only one or two connections but with relatively fewer 'hubs' possessing tens, hundreds or more links. The high level of hub connectivity must somehow be reflected in protein structure. What structural quality of hub proteins enables them to interact with large numbers of diverse targets? One possibility would be to employ binding regions that have the ability to bind multiple, structurally diverse partners. This trait can be imparted by the incorporation of intrinsic disorder in one or both partners. To illustrate the value of such contributions, this review examines the roles of intrinsic disorder in protein network architecture. We show that there are three general ways that intrinsic disorder can contribute: First, intrinsic disorder can serve as the structural basis for hub protein promiscuity; secondly, intrinsically disordered proteins can bind to structured hub proteins; and thirdly, intrinsic disorder can provide flexible linkers between functional domains with the linkers enabling mechanisms that facilitate binding diversity. An important research direction will be to determine what fraction of protein-protein interaction in regulatory networks relies on intrinsic disorder.     

Slow Co-Evolution of the MAGO and Y14 Protein Families Is Required for the Maintenance of Their Obligate Heterodimerization Mode

The exon junction complex (EJC) plays important roles in RNA metabolisms and the development of eukaryotic organisms. MAGO (short form of MAGO NASHI) and Y14 (also Tsunagi or RBM8) are the EJC core components. Their biological roles have been well investigated in various species, but the evolutionary patterns of the two gene families and their protein-protein interactions are poorly known. Genome-wide survey suggested that the MAGO and Y14 two gene families originated in eukaryotic organisms with the maintenance of a low copy. We found that the two protein families evolved slowly; however, the MAGO family under stringent purifying selection evolved more slowly than the Y14 family that was under relative relaxed purifying selection. MAGO and Y14 were obliged to form heterodimer in a eukaryotic organism, and this obligate mode was plesiomorphic. Lack of binding of MAGO to Y14 as functional barrier was observed only among distantly species, suggesting that a slow co-evolution of the two protein families. Inter-protein co-evolutionary signal was further quantified in analyses of the Tol-MirroTree and co-evolution analysis using protein sequences. About 20% of the 41 significantly correlated mutation groups (involving 97 residues) predicted between the two families was clade-specific. Moreover, around half of the predicted co-evolved groups and nearly all clade-specific residues fell into the minimal interaction domains of the two protein families. The mutagenesis effects of the clade-specific residues strengthened that the co-evolution is required for obligate MAGO-Y14 heterodimerization mode. In turn, the obliged heterodimerization in an organism serves as a strong functional constraint for the co-evolution of the MAGO and Y14 families. Such a co-evolution allows maintaining the interaction between the proteins through large evolutionary time scales. Our work shed a light on functional evolution of the EJC genes in eukaryotes, and facilitates to understand the co-evolutionary processes among protein families.     

Evolutionarily Conserved Herpesviral Protein Interaction Networks

Herpesviruses constitute a family of large DNA viruses widely spread in vertebrates and causing a variety of different diseases. They possess dsDNA genomes ranging from 120 to 240 kbp encoding between 70 to 170 open reading frames. We previously reported the protein interaction networks of two herpesviruses, varicella-zoster virus (VZV) and Kaposi''s sarcoma-associated herpesvirus (KSHV). In this study, we systematically tested three additional herpesvirus species, herpes simplex virus 1 (HSV-1), murine cytomegalovirus and Epstein-Barr virus, for protein interactions in order to be able to perform a comparative analysis of all three herpesvirus subfamilies. We identified 735 interactions by genome-wide yeast-two-hybrid screens (Y2H), and, together with the interactomes of VZV and KSHV, included a total of 1,007 intraviral protein interactions in the analysis. Whereas a large number of interactions have not been reported previously, we were able to identify a core set of highly conserved protein interactions, like the interaction between HSV-1 UL33 with the nuclear egress proteins UL31/UL34. Interactions were conserved between orthologous proteins despite generally low sequence similarity, suggesting that function may be more conserved than sequence. By combining interactomes of different species we were able to systematically address the low coverage of the Y2H system and to extract biologically relevant interactions which were not evident from single species.     

Genome and protein evolution in eukaryotes

The past year has seen the completion of the genome sequence of the flowering plant Arabidopsis thaliana and the initial sequence reports of the human genome. The availability of completely sequenced eukaryotic genomes from disparate phylogenetic lineages has opened the door to comparative analyses and a better understanding of the evolutionary processes shaping genomes. Complex many-to-many relationships between genes from different species appear to be the norm, suggesting that transfer of detailed functional annotation will not be straightforward. In addition to expansion and contraction of gene families, new genes evolve from recombination of pre-existing domains, although some domain families do appear to have evolved recently and to be specific to restricted phylogenetic lineages. The overall picture is of a huge diversity of gene content within eukaryotic genomes, reflecting different functional demands in different species.     

Comprehensive genome analysis of 203 genomes provides structural genomics with new insights into protein family space

We present an analysis of 203 completed genomes in the Gene3D resource (including 17 eukaryotes), which demonstrates that the number of protein families is continually expanding over time and that singleton-sequences appear to be an intrinsic part of the genomes. A significant proportion of the proteomes can be assigned to fewer than 6000 well-characterized domain families with the remaining domain-like regions belonging to a much larger number of small uncharacterized families that are largely species specific. Our comprehensive domain annotation of 203 genomes enables us to provide more accurate estimates of the number of multi-domain proteins found in the three kingdoms of life than previous calculations. We find that 67% of eukaryotic sequences are multi-domain compared with 56% of sequences in prokaryotes. By measuring the domain coverage of genome sequences, we show that the structural genomics initiatives should aim to provide structures for less than a thousand structurally uncharacterized Pfam families to achieve reasonable structural annotation of the genomes. However, in large families, additional structures should be determined as these would reveal more about the evolution of the family and enable a greater understanding of how function evolves.     

Systematic functional prioritization of protein posttranslational modifications

Protein function is often regulated by posttranslational modifications (PTMs), and recent advances in mass spectrometry have resulted in an exponential increase in PTM identification. However, the functional significance of the vast majority of these modifications remains unknown. To address this problem, we compiled nearly 200,000 phosphorylation, acetylation, and ubiquitination sites from 11 eukaryotic species, including 2,500 newly identified ubiquitylation sites for Saccharomyces cerevisiae. We developed methods to prioritize the functional relevance of these PTMs by predicting those that likely participate in cross-regulatory events, regulate domain activity, or mediate protein-protein interactions. PTM conservation within domain families identifies regulatory "hot spots" that overlap with functionally important regions, a concept that we experimentally validated on the HSP70 domain family. Finally, our analysis of the evolution of PTM regulation highlights potential routes for neutral drift in regulatory interactions and suggests that only a fraction of modification sites are likely to have a significant biological role.     

Prediction of putative protein interactions through evolutionary analysis of osmotic stress response in the model yeast Saccharomyces cerevisae

The osmotic stress response signalling pathway of the model yeast Saccharomyces cerevisae is crucial for the survival of cells under osmotic stress, and is preserved to varying degrees in other related fungal species. We apply a method for inference of ancestral states of characteristics over a phylogeny to 17 fungal species to infer the maximum likelihood estimate of presence or absence in ancestral genomes of genes involved in osmotic stress response. The same method allows us furthermore to perform a statistical test for correlated evolution between genes. Where such correlations exist within the osmotic stress response pathway of S. cerevisae, we have used this in order to predict and subsequently test for the presence of physical protein-protein interactions in an attempt to detect novel interactions. Finally we assess the relevance of observed evolutionary correlations in predicting protein interactions in light of the experimental results. We do find that correlated evolution provides some useful information for the prediction of protein-protein interactions, but that these alone are not sufficient to explain detectable patterns of correlated evolution.     

Virion-wide protein interactions of Kaposi's sarcoma-associated herpesvirus

Herpesvirus virions are highly organized structures built through specific protein-protein interactions. Thus, revelation of the protein interactions among virion proteins will shed light on the processes and the mechanisms of virion formation. Recently, we identified 24 virion proteins of Kaposi's sarcoma-associated herpesvirus (KSHV), using a proteomic approach (F. X. Zhu et al., J. Virol. 79:800-811, 2005). In the current study, a comprehensive analysis of protein-protein interaction between KSHV virion proteins was carried out using yeast two-hybrid (Y2H) and coimmunoprecipitation (co-IP) approaches. Every pairwise combination between KSHV tegument and capsid proteins, between tegument and envelope proteins, and among tegument proteins was tested for possible binary interaction. Thirty-seven protein-protein interactions were identified by both Y2H and co-IP analyses. The results revealed interactions between tegument and capsid proteins such as that of open reading frame 64 (ORF64) with ORF25 (major capsid protein [MCP]), ORF62 (triplex-1 [TRI-1]), and ORF26 (TRI-2). Many interactions were detected among the tegument proteins. ORF64 was found to interact with several tegument proteins including ORF11, ORF21, ORF33, ORF45, ORF63, ORF75, and ORF64 itself, suggesting that ORF64 may serve as a hub protein and play a role in recruiting tegument proteins during tegumentation and virion assembly. Our investigation also revealed redundant interactions between tegument proteins and envelope glycoproteins. These interactions are believed to contribute to final envelopment in virion assembly. Overall, this study allows us to establish a virion-wide protein interaction map, which provides insight into the architecture of the KSHV virion and sets up a foundation for exploring the functions of these proteins in viral particle assembly.     

A network medicine approach to human disease

High-throughput interaction discovery initiatives are providing thousands of novel protein interactions which are unveiling many unexpected links between apparently unrelated biological processes. In particular, analyses of the first draft human interactomes highlight a strong association between protein network connectivity and disease. Indeed, recent exciting studies have exploited the information contained within protein networks to disclose some of the molecular mechanisms underlying complex pathological processes. These findings suggest that both protein-protein interactions and the networks themselves could emerge as a new class of targetable entities, boosting the quest for novel therapeutic strategies.     

Integrated prediction of the helical membrane protein interactome in yeast

At least a quarter of all genes in most genomes contain putative transmembrane (TM) helices, and helical membrane protein interactions are a major component of the overall cellular interactome. However, current experimental techniques for large-scale detection of protein-protein interactions are biased against membrane proteins. Here, we define protein-protein interaction broadly as co-complexation, and develop a weighted-voting procedure to predict interactions among yeast helical membrane proteins by optimally combining evidence based on diverse genome-wide information such as sequence, function, localization, abundance, regulation, and phenotype. We use logistic regression to simultaneously optimize the weights of all evidence sources for best discrimination based on a set of known helical membrane protein interactions. The resulting integrated classifier not only significantly outperforms classifiers based on any single genomic feature, but also does better than a benchmark Na?ve Bayes classifier (using a simplifying assumption of conditional independence among features). Finally, we apply the optimized classifier genome-wide, and construct a comprehensive map of predicted helical membrane protein interactome in yeast. This can serve as a guide for prioritizing further experimental validation efforts.     

Genome compaction and stability in microsporidian intracellular parasites

Microsporidian genomes are extraordinary among eukaryotes for their extreme reduction: although they are similar in form to other eukaryotic genomes, they are typically smaller than many prokaryotic genomes. At the same time, their rates of sequence evolution are among the highest for eukaryotic organisms. To explore the effects of compaction on nuclear genome evolution, we sequenced 685,000 bp of the Antonospora locustae genome (formerly Nosema locustae) and compared its organization with the recently completed genome of the human parasite Encephalitozoon cuniculi. Despite being very distantly related, the genomes of these two microsporidian species have retained an unexpected degree of synteny: 13% of genes are in the same context, and 30% of the genes were separated by a small number of short rearrangements. Microsporidian genomes are, therefore, paradoxically composed of rapidly evolving sequences harbored within a slowly evolving genome, although these two processes are sometimes considered to be coupled. Microsporidian genomes show that eukaryotic genomes (like genes) do not evolve in a clock-like fashion, and genome stability may result from compaction in addition to a lack of recombination, as has been traditionally thought to occur in bacterial and organelle genomes.     

Pairwise interactions of the six human MCM protein subunits

The eukaryotic minichromosome maintenance (MCM) proteins have six subunits, Mcm2 to 7p. Together they play essential roles in the initiation and elongation of DNA replication, and the human MCM proteins present attractive targets for potential anticancer drugs. The six MCM subunits interact and form a ring-shaped heterohexameric complex containing one of each subunit in a variety of eukaryotes, and subcomplexes have also been observed. However, the architecture of the human MCM heterohexameric complex is still unknown. We systematically studied pairwise interactions of individual human MCM subunits by using the yeast two-hybrid system and in vivo protein-protein crosslinking with a non-cleavable crosslinker in human cells followed by co-immunoprecipitation. In the yeast two-hybrid assays, we revealed multiple binary interactions among the six human MCM proteins, and a subset of these interactions was also detected as direct interactions in human cells. Based on our results, we propose a model for the architecture of the human MCM protein heterohexameric complex. We also propose models for the structures of subcomplexes. Thus, this study may serve as a foundation for understanding the overall architecture and function of eukaryotic MCM protein complexes and as clues for developing anticancer drugs targeted to the human MCM proteins.