A novel method for assigning functional linkages to proteins using enhanced phylogenetic trees

MOTIVATION: Functional linkages implicate pairwise relationships between proteins that work together to implement biological tasks. During evolution, functionally linked proteins are likely to be preserved or eliminated across a range of genomes in a correlated fashion. Based on this hypothesis, phylogenetic profiling-based approaches try to detect pairs of protein families that show similar evolutionary patterns. Traditionally, the evolutionary pattern of a protein is encoded by either a binary profile of presence and absence of this protein across species or an occurrence profile that indicates the distribution of copies of this protein across species. RESULTS: In our study, we characterize each protein by its enhanced phylogenetic tree, a novel graphical model of the evolution of a protein family with explicitly marked by speciation and duplication events. By topological comparison between enhanced phylogenetic trees, we are able to detect the functionally associated protein pairs. Because the enhanced phylogenetic trees contain more evolutionary information of proteins, our method shows greater performance and discovers functional linkages among proteins more reliably compared with the conventional approaches.     

Discovery of uncharacterized cellular systems by genome-wide analysis of functional linkages

We introduce a general computational method, applicable on a genome-wide scale, for the systematic discovery of uncharacterized cellular systems. Quantitative analysis of the coinheritance of pairs of genes among different organisms, calculated using phylogenetic profiles, allows the prediction of thousands of functional linkages between the corresponding proteins. A comparison of these functional linkages to known pathways reveals that calculated linkages are comparable in accuracy to genome-wide yeast two-hybrid screens or mass spectrometry interaction assays. In aggregate, these linkages describe the structure of large-scale networks, with the resulting yeast network composed of 3,875 linkages among 804 proteins, and the resulting pathogenic Escherichia coli network composed of 2,043 linkages among 828 proteins. The search of such networks for groups of uncharacterized, linked proteins led to the identification of 27 novel cellular systems from one nonpathogenic and three pathogenic bacterial genomes.     

Accurate extraction of functional associations between proteins based on common interaction partners and common domains

MOTIVATION: Genomic and proteomic approaches have accumulated a huge amount of data which provide clues to protein function. However, interpreting single omic data for predicting uncharacterized protein functions has been a challenging task, because the data contain a lot of false positives. To overcome this problem, methods for integrating data from various omic approaches are needed for more accurate function prediction. RESULT: In this paper, we have developed a method which extracts functionally similar proteins with high confidence by integrating protein-protein interaction data and domain information. We used this method to analyze publicly available data from Saccharomyces cerevisiae. We identified 1042 functional associations, involving 765 proteins of which 98 (12.8%) had no previously ascribed function. Our method extracts functionally similar protein pairs more accurately than conventional methods, and predicting function for previously uncharacterized proteins can be achieved. Our method can of course be applied to protein-protein interaction data for any species.     

Use of contiguity on the chromosome to predict functional coupling

The availability of a growing number of completely sequenced genomes opens new opportunities for understanding of complex biological systems. Success of genome-based biology will, to a large extent, depend on the development of new approaches and tools for efficient comparative analysis of the genomes and their organization. We have developed a technique for detecting possible functional coupling between genes based on detection of potential operons. The approach involves computation of "pairs of close bidirectional best hits", which are pairs of genes that apparently occur within operons in multiple genomes. Using these pairs, one can compose evidence (based on the number of distinct genomes and the phylogenetic distance between the orthologous pairs) that a pair of genes is potentially functionally coupled. The technique has revealed a surprisingly rich and apparently accurate set of functionally coupled genes. The approach depends on the use of a relatively large number of genomes, and the amount of detected coupling grows dramatically as the number of genomes increases.     

Functional linkages can reveal protein complexes for structure determination

In the study of protein complexes, is there a computational method for inferring which combinations of proteins in an organism are likely to form a crystallizable complex? Here we attempt to answer this question, using the Protein Data Bank (PDB) to assess the usefulness of inferred functional protein linkages from the Prolinks database. We find that of the 242 nonredundant prokaryotic protein complexes shared between the current PDB and Prolinks, 44% (107/242) contain proteins linked at high confidence by one or more methods of computed functional linkages. Similarly, high-confidence linkages detect 47% of known Escherichia coli protein complexes, with 45% accuracy. Together these findings suggest that functional linkages will be useful in defining protein complexes for structural studies, including for structural genomics. We offer a database of inferred linkages corresponding to likely protein complexes for some 629,952 pairs of proteins in 154 prokaryotes and archaea.     

Visualization and interpretation of protein networks in Mycobacterium tuberculosis based on hierarchical clustering of genome-wide functional linkage maps

Genome-wide functional linkages among proteins in cellular complexes and metabolic pathways can be inferred from high throughput experimentation, such as DNA microarrays, or from bioinformatic analyses. Here we describe a method for the visualization and interpretation of genome-wide functional linkages inferred by the Rosetta Stone, Phylogenetic Profile, Operon and Conserved Gene Neighbor computational methods. This method involves the construction of a genome-wide functional linkage map, where each significant functional linkage between a pair of proteins is displayed on a two-dimensional scatter-plot, organized according to the order of genes along the chromosome. Subsequent hierarchical clustering of the map reveals clusters of genes with similar functional linkage profiles and facilitates the inference of protein function and the discovery of functionally linked gene clusters throughout the genome. We illustrate this method by applying it to the genome of the pathogenic bacterium Mycobacterium tuberculosis, assigning cellular functions to previously uncharacterized proteins involved in cell wall biosynthesis, signal transduction, chaperone activity, energy metabolism and polysaccharide biosynthesis.     

Inferring functional linkages between proteins from evolutionary scenarios

Identifying potential protein interactions is of great importance in understanding the topologies of cellular networks, which is much needed and valued in current systematic biological studies. The development of our computational methods to predict protein-protein interactions have been spurred on by the massive sequencing efforts of the genomic revolution. Among these methods is phylogenetic profiling, which assumes that proteins under similar evolutionary pressures with similar phylogenetic profiles might be functionally related. Here, we introduce a method for inferring functional linkages between proteins from their evolutionary scenarios. The term evolutionary scenario refers to a series of events that occurred in speciation over time, which can be reconstructed given a phylogenetic profile and a species tree. Common evolutionary pressures on two proteins can then be inferred by comparing their evolutionary scenarios, which is a direct indication of their functional linkage. This scenario method has proven to have better performance compared with the classical phylogenetic profile method, when applied to the same test set. In addition, predicted results of the two methods are found to be fairly different, suggesting the possibility of merging them in order to achieve a better performance. We analyzed the influence of the topology of the phylogenetic tree on the performance of this method, and found it to be robust to perturbations in the topology of the tree. However, if a completely random tree is incorporated, performance will decline significantly. The evolutionary scenario method was used for inferring functional linkages in 67 species, and 40,006 linkages were predicted. We examine our prediction for budding yeast and find that almost all predicted linkages are supported by further evidence.     

A novel method for detecting intramolecular coevolution: adding a further dimension to selective constraints analyses

Protein evolution depends on intramolecular coevolutionary networks whose complexity is proportional to the underlying functional and structural interactions among sites. Here we present a novel approach that vastly improves the sensitivity of previous methods for detecting coevolution through a weighted comparison of divergence between amino acid sites. The analysis of the HIV-1 Gag protein detected convergent adaptive coevolutionary events responsible for the selective variability emerging between subtypes. Coevolution analysis and functional data for heat-shock proteins, Hsp90 and GroEL, highlight that almost all detected coevolving sites are functionally or structurally important. The results support previous suggestions pinpointing the complex interdomain functional interactions within these proteins and we propose new amino acid sites as important for interdomain functional communication. Three-dimensional information sheds light on the functional and structural constraints governing the coevolution between sites. Our covariation analyses propose two types of coevolving sites in agreement with previous reports: pairs of sites spatially proximal, where compensatory mutations could maintain the local structure stability, and clusters of distant sites located in functional domains, suggesting a functional dependency between them. All sites detected under adaptive evolution in these proteins belong to coevolution groups, further underlining the importance of testing for coevolution in selective constraints analyses.     

Using phylogeny to improve genome-wide distant homology recognition

The gap between the number of known protein sequences and structures continues to widen, particularly as a result of sequencing projects for entire genomes. Recently there have been many attempts to generate structural assignments to all genes on sets of completed genomes using fold-recognition methods. We developed a method that detects false positives made by these genome-wide structural assignment experiments by identifying isolated occurrences. The method was tested using two sets of assignments, generated by SUPERFAMILY and PSI-BLAST, on 150 completed genomes. A phylogeny of these genomes was built and a parsimony algorithm was used to identify isolated occurrences by detecting occurrences that cause a gain at leaf level. Isolated occurrences tend to have high e-values, and in both sets of assignments, a sudden increase in isolated occurrences is observed for e-values >10⁻⁸ for SUPERFAMILY and >10⁻⁴ for PSI-BLAST. Conditions to predict false positives are based on these results. Independent tests confirm that the predicted false positives are indeed more likely to be incorrectly assigned. Evaluation of the predicted false positives also showed that the accuracy of profile-based fold-recognition methods might depend on secondary structure content and sequence length. We show that false positives generated by fold-recognition methods can be identified by considering structural occurrence patterns on completed genomes; occurrences that are isolated within the phylogeny tend to be less reliable. The method provides a new independent way to examine the quality of fold assignments and may be used to improve the output of any genome-wide fold assignment method.     

Two-hybrid arrays

The two-hybrid system is a genetic method for detecting protein-protein interactions. The assay can be applied to random libraries or arrays of colonies that express defined pairs of proteins. Arrays enable the testing of all possible protein pairs for interactions in a systematic fashion. The array format makes a large number of individual assays comparable and thus greatly simplifies the identification of false positives. Two-hybrid arrays have been used to study interactions among the proteins of yeast, hepatitis C virus, vaccinia virus, Drosophila, Caenorhabditis elegans, mouse and other species, and have already identified thousands of interactions.     

Protein function prediction using the Protein Link EXplorer (PLEX)

We introduce the Protein Link EXplorer (PLEX), a web-based environment that allows the construction of a phylogenetic profile for any given amino acid sequence, and its comparison with profiles of approximately 350,000 predicted genes from 89 genomes, as a means of interactively identifying functionally linked genes and predicting protein function. PLEX can be searched iteratively and also enables searches for chromosomal gene neighbors and Rosetta Stone linkages. PLEX search results are accompanied by quantitative estimates of linkage confidence, enabling users to take advantage of coinheritance, operon and gene fusion-based methods for inferring gene function and reconstructing cellular systems and pathways. AVAILABILITY: http://bioinformatics.icmb.utexas.edu/plex     

Assembly rules for protein networks derived from phylogenetic-statistical analysis of whole genomes

Background

We report an analysis of a protein network of functionally linked proteins, identified from a phylogenetic statistical analysis of complete eukaryotic genomes. Phylogenetic methods identify pairs of proteins that co-evolve on a phylogenetic tree, and have been shown to have a high probability of correctly identifying known functional links.

Results

The eukaryotic correlated evolution network we derive displays the familiar power law scaling of connectivity. We introduce the use of explicit phylogenetic methods to reconstruct the ancestral presence or absence of proteins at the interior nodes of a phylogeny of eukaryote species. We find that the connectivity distribution of proteins at the point they arise on the tree and join the network follows a power law, as does the connectivity distribution of proteins at the time they are lost from the network. Proteins resident in the network acquire connections over time, but we find no evidence that 'preferential attachment' – the phenomenon of newly acquired connections in the network being more likely to be made to proteins with large numbers of connections – influences the network structure. We derive a 'variable rate of attachment' model in which proteins vary in their propensity to form network interactions independently of how many connections they have or of the total number of connections in the network, and show how this model can produce apparent power-law scaling without preferential attachment.

Conclusion

A few simple rules can explain the topological structure and evolutionary changes to protein-interaction networks: most change is concentrated in satellite proteins of low connectivity and small phenotypic effect, and proteins differ in their propensity to form attachments. Given these rules of assembly, power law scaled networks naturally emerge from simple principles of selection, yielding protein interaction networks that retain a high-degree of robustness on short time scales and evolvability on longer evolutionary time scales.

     

How much can we learn about the function of bacterial rRNA modification by mining large-scale experimental datasets?

Modification of ribosomal RNA is ubiquitous among living organisms. Its functional role is well established for only a limited number of modified nucleotides. There are examples of rRNA modification involvement in the gene expression regulation in the cell. There is a need for large data set analysis in the search for potential functional partners for rRNA modification. In this study, we extracted phylogenetic profile, genome neighbourhood, co-expression and phenotype profile and co-purification data regarding Escherichia coli rRNA modification enzymes from public databases. Results were visualized as graphs using Cytoscape and analysed. Majority linked genes/proteins belong to translation apparatus. Among co-purification partners of rRNA modification enzymes are several candidates for experimental validation. Phylogenetic profiling revealed links of pseudouridine synthetases with RF2, RsmH with translation factors IF2, RF1 and LepA and RlmM with RdgC. Genome neighbourhood connections revealed several putative functionally linked genes, e.g. rlmH with genes coding for cell wall biosynthetic proteins and others. Comparative analysis of expression profiles (Gene Expression Omnibus) revealed two main associations, a group of genes expressed during fast growth and association of rrmJ with heat shock genes. This study might be used as a roadmap for further experimental verification of predicted functional interactions.     

Utilizing logical relationships in genomic data to decipher cellular processes

The wealth of available genomic data has spawned a corresponding interest in computational methods that can impart biological meaning and context to these experiments. Traditional computational methods have drawn relationships between pairs of proteins or genes based on notions of equality or similarity between their patterns of occurrence or behavior. For example, two genes displaying similar variation in expression, over a number of experiments, may be predicted to be functionally related. We have introduced a natural extension of these approaches, instead identifying logical relationships involving triplets of proteins. Triplets provide for various discrete kinds of logic relationships, leading to detailed inferences about biological associations. For instance, a protein C might be encoded within an organism if, and only if, two other proteins A and B are also both encoded within the organism, thus suggesting that gene C is functionally related to genes A and B. The method has been applied fruitfully to both phylogenetic and microarray expression data, and has been used to associate logical combinations of protein activity with disease state phenotypes, revealing previously unknown ternary relationships among proteins, and illustrating the inherent complexities that arise in biological data.     

Codon Usage Methods for Horizontal Gene Transfer Detection Generate an Abundance of False Positive and False Negative Results

Bacteria acquire new DNA in a process known as horizontal gene transfer (HGT). To investigate the evolutionary impact of this transfer of DNA, various methods have been developed to detect past HGT events. For example, codon usage-based methods detect the presence of transferred genes by identifying atypical patterns of codon usage. However, some inherited genes exhibit atypical codon usage and some transferred genes have codon usage patterns similar to those of the inherited genes. In this study, we used a comparative phylogenetic approach with Methylobacterium and Caulobacter species to demonstrate that even well-designed codon usage methods fail to detect many HGT events and generate a high rate of false positives (60–75 %) and false negatives (23–61 %). Therefore, we recommend caution when employing codon usage methods to identify transferred genes and suggest that the rapidly increasing availability of bacterial genome sequences makes the phylogenetic approach the method of choice.     

Detection of evolutionarily stable fragments of cellular pathways by hierarchical clustering of phyletic patterns

Background

Phyletic patterns denote the presence and absence of orthologous genes in completely sequenced genomes and are used to infer functional links between genes, on the assumption that genes involved in the same pathway or functional system are co-inherited by the same set of genomes. However, this basic premise has not been quantitatively tested, and the limits of applicability of the phyletic-pattern method remain unknown.

Results

We characterized a hierarchy of 3,688 phyletic patterns encompassing more than 5,000 known protein-coding genes from 66 complete microbial genomes, using different distances, clustering algorithms, and measures of cluster quality. The most sensitive set of parameters recovered 223 clusters, each consisting of genes that belong to the same metabolic pathway or functional system. Fifty-six clusters included unexpected genes with plausible functional links to the rest of the cluster. Only a small percentage of known pathways and multiprotein complexes are co-inherited as one cluster; most are split into many clusters, indicating that gene loss and displacement has occurred in the evolution of most pathways.

Conclusions

Phyletic patterns of functionally linked genes are perturbed by differential gains, losses and displacements of orthologous genes in different species, reflecting the high plasticity of microbial genomes. Groups of genes that are co-inherited can, however, be recovered by hierarchical clustering, and may represent elementary functional modules of cellular metabolism. The phyletic patterns approach alone can confidently predict the functional linkages for about 24% of the entire data set.