Mirrortree Method Research Articles

Bioinformatic Analysis of Na+, K+-ATPase Regulation through Phosphorylation of the Alpha-Subunit N-Terminus.

The Na+, K+-ATPase is an integral membrane protein which uses the energy of ATP hydrolysis to pump Na+ and K+ ions across the plasma membrane of all animal cells. It plays crucial roles in numerous physiological processes, such as cell volume regulation, nutrient reabsorption in the kidneys, nerve impulse transmission, and muscle contraction. Recent data suggest that it is regulated via an electrostatic switch mechanism involving the interaction of its lysine-rich N-terminus with the cytoplasmic surface of its surrounding lipid membrane, which can be modulated through the regulatory phosphorylation of the conserved serine and tyrosine residues on the protein's N-terminal tail. Prior data indicate that the kinases responsible for phosphorylation belong to the protein kinase C (PKC) and Src kinase families. To provide indications of which particular enzyme of these families might be responsible, we analysed them for evidence of coevolution via the mirror tree method, utilising coevolution as a marker for a functional interaction. The results obtained showed that the most likely kinase isoforms to interact with the Na+, K+-ATPase were the θ and η isoforms of PKC and the Src kinase itself. These theoretical results will guide the direction of future experimental studies.

A co-evolutionary approach of serpin and serine proteases using Mirrortree method

A co-evolutionary approach of serpin and serine proteases using Mirrortree method

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.

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.

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.

Human protein–protein interaction prediction by a novel sequence-based co-evolution method: co-evolutionary divergence

Protein-protein interaction (PPI) plays an important role in understanding gene functions, and many computational PPI prediction methods have been proposed in recent years. Despite the extensive efforts, PPI prediction still has much room to improve. Sequence-based co-evolution methods include the substitution rate method and the mirror tree method, which compare sequence substitution rates and topological similarity of phylogenetic trees, respectively. Although they have been used to predict PPI in species with small genomes like Escherichia coli, such methods have not been tested in large scale proteome like Homo sapiens. In this study, we propose a novel sequence-based co-evolution method, co-evolutionary divergence (CD), for human PPI prediction. Built on the basic assumption that protein pairs with similar substitution rates are likely to interact with each other, the CD method converts the evolutionary information from 14 species of vertebrates into likelihood ratios and combined them together to infer PPI. We showed that the CD method outperformed the mirror tree method in three independent human PPI datasets by a large margin. With the arrival of more species genome information generated by next generation sequencing, the performance of the CD method can be further improved. Source code and support are available at http://mib.stat.sinica.edu.tw/LAP/tmp/CD.rar.

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

BackgroundThe 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.ResultsMore 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.ConclusionsThe 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.

Correlated Evolution of Interacting Proteins: Looking Behind the Mirrortree

It has been observed that the evolutionary distances of interacting proteins often display a higher level of similarity than those of noninteracting proteins. This finding indicates that interacting proteins are subject to common evolutionary constraints and constitutes the basis of a method to predict protein interactions known as mirrortree. It has been difficult, however, to identify the direct cause of the observed similarities between evolutionary trees. One possible explanation is the existence of compensatory mutations between partners' binding sites to maintain proper binding. This explanation, though, has been recently challenged, and it has been suggested that the signal of correlated evolution uncovered by the mirrortree method is unrelated to any correlated evolution between binding sites. We examine the contribution of binding sites to the correlation between evolutionary trees of interacting domains. We show that binding neighborhoods of interacting proteins have, on average, higher coevolutionary signal compared with the regions outside binding sites; however, when the binding neighborhood is removed, the remaining domain sequence still contains some coevolutionary signal. In conclusion, the correlation between evolutionary trees of interacting domains cannot exclusively be attributed to the correlated evolution of the binding sites or to common evolutionary pressure exerted on the whole protein domain sequence, each of which contributes to the signal measured by the mirrortree approach.

Predicting protein domain interactions from coevolution of conserved regions

The knowledge of protein and domain interactions provide crucial insights into their function within a cell. Several computational methods have been proposed to detect interactions between proteins and their constitutive domains. In this work, we focus on approaches based on correlated evolution (coevolution) of sequences of interacting proteins. In this type of approach, often referred to as the mirrortree method, a high correlation of evolutionary histories of two proteins is used as an indicator to predict protein interactions. Recently, it has been observed that subtracting the underlying speciation process by separating coevolution due to common speciation divergence from that due to common function of interacting pairs greatly improves the predictive power of the mirrortree approach. In this article, we investigate possible improvements and limitations of this method. In particular, we demonstrate that the performance of the mirrortree method that can be further improved by restricting the coevolution analysis to the relatively conserved regions in the protein domain sequences (disregarding highly divergent regions). We provide a theoretical validation of our results leading to new insights into the interplay between coevolution and speciation of interacting proteins.

Partial correlation coefficient between distance matrices as a new indicator of protein–protein interactions

The computational prediction of protein-protein interactions is currently a major issue in bioinformatics. Recently, a variety of co-evolution-based methods have been investigated toward this goal. In this study, we introduced a partial correlation coefficient as a new measure for the degree of co-evolution between proteins, and proposed its use to predict protein-protein interactions. The accuracy of the prediction by the proposed method was compared with those of the original mirror tree method and the projection method previously developed by our group. We found that the partial correlation coefficient effectively reduces the number of false positives, as compared with other methods, although the number of false negatives increased in the prediction by the partial correlation coefficient. The R script for the prediction of protein-protein interactions reported in this manuscript is available at http://timpani.genome.ad.jp/~parco/

The inference of protein-protein interactions by co-evolutionary analysis is improved by excluding the information about the phylogenetic relationships

The prediction of protein-protein interactions is currently an important issue in bioinformatics. The mirror tree method uses evolutionary information to predict protein-protein interactions. However, it has been recognized that predictions by the mirror tree method lead to many false positives. The incentive of our study was to solve this problem by improving the method of extracting the co-evolutionary information regarding the protein pairs. We developed a novel method to predict protein-protein interactions from co-evolutionary information in the framework of the mirror tree method. The originality is the use of the projection operator to exclude the information about the phylogenetic relationships among the source organisms from the distance matrix. Each distance matrix was transformed into a vector for the operation. The vector is referred to as a 'phylogenetic vector'. We have proposed three ways to extract the phylogenetic information: (1) using the 16S rRNA from the same source organisms as the proteins under consideration, (2) averaging the phylogenetic vectors and (3) analyzing the principal components of the phylogenetic vectors. We examined the performance of the proposed methods to predict interacting protein pairs from Escherichia coli, using experimentally verified data. Our method was successful, and it drastically reduced the number of false positives in the prediction. The R script for the prediction of protein-protein interactions reported in this manuscript is available at http://timpani.genome.ad.jp/~proj/ sato@kuicr.kyoto-u.ac.jp The information is also available at the same site as the R script.

Prediction of Protein-Protein Interactions from Phylogenetic Trees Using Partial Correlation Coefficient

[2] has been proposed as a generalization of the idea of the phylogenetic prole in order to measure the evolutionary distance between proteins more precisely. Both methods basically stem from the assumption that functionally correlated proteins evolve in a correlated manner. In the mirror tree method, the intensity of the correlation between proteins is evaluated by Peason’s correlation coecient based on phylogenetic trees, but it has been pointed out that a number of false positives tend to be introduced in the prediction. This paper presents a new method to predict protein-protein interactions from the evolutionary information by using partial correlation coecien t in order to extract direct interactions rather than indirect interactions between proteins. Our method is successfully tested on its ability to predict physical protein interactions, from the comparison of phylogenetic trees of proteins. Using the dataset containing known interactions, we show that our approach removes false positives to a large extent and improves the specicit y for predicting physical protein-protein interactions. 2 Method