Detecting Coevolution in and among Protein Domains

Correlated changes of nucleic or amino acids have provided strong information about the structures and interactions of molecules. Despite the rich literature in coevolutionary sequence analysis, previous methods often have to trade off between generality, simplicity, phylogenetic information, and specific knowledge about interactions. Furthermore, despite the evidence of coevolution in selected protein families, a comprehensive screening of coevolution among all protein domains is still lacking. We propose an augmented continuous-time Markov process model for sequence coevolution. The model can handle different types of interactions, incorporate phylogenetic information and sequence substitution, has only one extra free parameter, and requires no knowledge about interaction rules. We employ this model to large-scale screenings on the entire protein domain database (Pfam). Strikingly, with 0.1 trillion tests executed, the majority of the inferred coevolving protein domains are functionally related, and the coevolving amino acid residues are spatially coupled. Moreover, many of the coevolving positions are located at functionally important sites of proteins/protein complexes, such as the subunit linkers of superoxide dismutase, the tRNA binding sites of ribosomes, the DNA binding region of RNA polymerase, and the active and ligand binding sites of various enzymes. The results suggest sequence coevolution manifests structural and functional constraints of proteins. The intricate relations between sequence coevolution and various selective constraints are worth pursuing at a deeper level

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

DIMA 3.0: Domain Interaction Map

Domain Interaction MAp (DIMA, available at http://webclu.bio.wzw.tum.de/dima) is a database of predicted and known interactions between protein domains. It integrates 5807 structurally known interactions imported from the iPfam and 3did databases and 46 900 domain interactions predicted by four computational methods: domain phylogenetic profiling, domain pair exclusion algorithm correlated mutations and domain interaction prediction in a discriminative way. Additionally predictions are filtered to exclude those domain pairs that are reported as non-interacting by the Negatome database. The DIMA Web site allows to calculate domain interaction networks either for a domain of interest or for entire organisms, and to explore them interactively using the Flash-based Cytoscape Web software

Correlated evolution of androgen receptor and aromatase revisited

Author Posting. © The Authors, 2010. This is the author's version of the work. It is posted here by permission of Oxford University Press for personal use, not for redistribution. The definitive version was published in Molecular Biology and Evolution 27 (2010): 2211-2215, doi:10.1093/molbev/msq129.Conserved interactions among proteins or other molecules can provide strong evidence for coevolution across their evolutionary history. Diverse phylogenetic methods have been applied to identify potential coevolutionary relationships. In most cases, these methods minimally require comparisons of orthologous sequences and appropriate controls to separate effects of selection from the overall evolutionary relationships. In vertebrates, androgen receptor (AR) and cytochrome p450 aromatase (CYP19) share an affinity for androgenic steroids, which serve as receptor ligands and enzyme substrates. In a recent study, Tiwary and Li (2009) reported that AR and CYP19 displayed a signature of ancient and conserved interactions throughout all of the Eumetazoa (i.e., cnidarians, protostomes, and deuterostomes). Because these findings conflicted with a number of previous studies, we reanalyzed the data set used by Tiwary and Li. First, our analyses demonstrate that the invertebrate genes used in the previous analysis are not orthologous sequences, but instead represent a diverse set of nuclear receptors and cytochrome p450 enzymes with no confirmed or hypothesized relationships with androgens. Second, we show that (1) their analytical approach, which measures correlations in evolutionary distances between proteins, potentially led to spurious significant relationships due simply to conserved domains and (2) control comparisons provide positive evidence for a strong influence of evolutionary history. We discuss how corrections to this method and analysis of key taxa (e.g., duplications in the teleost fish and suiform lineages) can inform investigations of the coevolutionary relationships between androgen receptor and aromatase.AMR was supported by the Postdoctoral Scholar Program at the Woods Hole Oceanographic Institution, with funding provided by The Beacon Institute for Rivers and Estuaries, and AMT was supported by WHOI Assistant Scientist Endowed Support

The Coevolution of Phycobilisomes: Molecular Structure Adapting to Functional Evolution

Phycobilisome is the major light-harvesting complex in cyanobacteria and red alga. It consists of phycobiliproteins and their associated linker peptides which play key role in absorption and unidirectional transfer of light energy and the stability of the whole complex system, respectively. Former researches on the evolution among PBPs and linker peptides had mainly focused on the phylogenetic analysis and selective evolution. Coevolution is the change that the conformation of one residue is interrupted by mutation and a compensatory change selected for in its interacting partner. Here, coevolutionary analysis of allophycocyanin, phycocyanin, and phycoerythrin and covariation analysis of linker peptides were performed. Coevolution analyses reveal that these sites are significantly correlated, showing strong evidence of the functional and structural importance of interactions among these residues. According to interprotein coevolution analysis, less interaction was found between PBPs and linker peptides. Our results also revealed the correlations between the coevolution and adaptive selection in PBS were not directly related, but probably demonstrated by the sites coupled under physical-chemical interactions

PETALS: Proteomic Evaluation and Topological Analysis of a mutated Locus' Signaling

<p>Abstract</p> <p>Background</p> <p>Colon cancer is driven by mutations in a number of genes, the most notorious of which is <it>Apc</it>. Though much of <it>Apc</it>'s signaling has been mechanistically identified over the years, it is not always clear which functions or interactions are operative in a particular tumor. This is confounded by the presence of mutations in a number of other putative cancer driver (CAN) genes, which often synergize with mutations in <it>Apc</it>.</p> <p>Computational methods are, thus, required to predict which pathways are likely to be operative when a particular mutation in <it>Apc </it>is observed.</p> <p>Results</p> <p>We developed a pipeline, PETALS, to predict and test likely signaling pathways connecting <it>Apc </it>to other CAN-genes, where the interaction network originating at <it>Apc </it>is defined as a "blossom," with each <it>Apc</it>-CAN-gene subnetwork referred to as a "petal." Known and predicted protein interactions are used to identify an Apc blossom with 24 petals. Then, using a novel measure of bimodality, the coexpression of each petal is evaluated against proteomic (2 D differential In Gel Electrophoresis, 2D-DIGE) measurements from the <it>Apc</it>^{<it>1638N</it>+/-}mouse to test the network-based hypotheses.</p> <p>Conclusions</p> <p>The predicted pathways linking <it>Apc </it>and <it>Hapln1 </it>exhibited the highest amount of bimodal coexpression with the proteomic targets, prioritizing the <it>Apc-Hapln1 </it>petal over other CAN-gene pairs and suggesting that this petal may be involved in regulating the observed proteome-level effects. These results not only demonstrate how functional 'omics data can be employed to test in <it>silico </it>predictions of CAN-gene pathways, but also reveal an approach to integrate models of upstream genetic interference with measured, downstream effects.</p

From principal component to direct coupling analysis of coevolution in proteins: Low-eigenvalue modes are needed for structure prediction

Various approaches have explored the covariation of residues in multiple-sequence alignments of homologous proteins to extract functional and structural information. Among those are principal component analysis (PCA), which identifies the most correlated groups of residues, and direct coupling analysis (DCA), a global inference method based on the maximum entropy principle, which aims at predicting residue-residue contacts. In this paper, inspired by the statistical physics of disordered systems, we introduce the Hopfield-Potts model to naturally interpolate between these two approaches. The Hopfield-Potts model allows us to identify relevant 'patterns' of residues from the knowledge of the eigenmodes and eigenvalues of the residue-residue correlation matrix. We show how the computation of such statistical patterns makes it possible to accurately predict residue-residue contacts with a much smaller number of parameters than DCA. This dimensional reduction allows us to avoid overfitting and to extract contact information from multiple-sequence alignments of reduced size. In addition, we show that low-eigenvalue correlation modes, discarded by PCA, are important to recover structural information: the corresponding patterns are highly localized, that is, they are concentrated in few sites, which we find to be in close contact in the three-dimensional protein fold.Comment: Supporting information can be downloaded from: http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.100317

Revealing pancrustacean relationships: Phylogenetic analysis of ribosomal protein genes places Collembola (springtails) in a monophyletic Hexapoda and reinforces the discrepancy between mitochondrial and nuclear DNA markers

<p>Abstract</p> <p>Background</p> <p>In recent years, several new hypotheses on phylogenetic relations among arthropods have been proposed on the basis of DNA sequences. One of the challenged hypotheses is the monophyly of hexapods. This discussion originated from analyses based on mitochondrial DNA datasets that, due to an unusual positioning of Collembola, suggested that the hexapod body plan evolved at least twice. Here, we re-evaluate the position of Collembola using ribosomal protein gene sequences.</p> <p>Results</p> <p>In total 48 ribosomal proteins were obtained for the collembolan <it>Folsomia candida</it>. These 48 sequences were aligned with sequence data on 35 other ecdysozoans. Each ribosomal protein gene was available for 25% to 86% of the taxa. However, the total sequence information was unequally distributed over the taxa and ranged between 4% and 100%. A concatenated dataset was constructed (5034 inferred amino acids in length), of which ~66% of the positions were filled. Phylogenetic tree reconstructions, using Maximum Likelihood, Maximum Parsimony, and Bayesian methods, resulted in a topology that supports monophyly of Hexapoda.</p> <p>Conclusion</p> <p>Although ribosomal proteins in general may not evolve independently, they once more appear highly valuable for phylogenetic reconstruction. Our analyses clearly suggest that Hexapoda is monophyletic. This underpins the inconsistency between nuclear and mitochondrial datasets when analyzing pancrustacean relationships. Caution is needed when applying mitochondrial markers in deep phylogeny.</p