How reliably can we predict the reliability of protein structure predictions?

Background: Comparative methods have been the standard techniques for in silico protein structure prediction. The prediction is based on a multiple alignment that contains both reference sequences with known structures and the sequence whose unknown structure is predicted. Intensive research has been made to improve the quality of multiple alignments, since misaligned parts of the multiple alignment yield misleading predictions. However, sometimes all methods fail to predict the correct alignment, because the evolutionary signal is too weak to find the homologous parts due to the large number of mutations that separate the sequences. Results: Stochastic sequence alignment methods define a posterior distribution of possible multiple alignments. They can highlight the most likely alignment, and above that, they can give posterior probabilities for each alignment column. We made a comprehensive study on the HOMSTRAD database of structural alignments, predicting secondary structures in four different ways. We showed that alignment posterior probabilities correlate with the reliability of secondary structure predictions, though the strength of the correlation is different for different protocols. The correspondence between the reliability of secondary structure predictions and alignment posterior probabilities is the closest to the identity function when the secondary structure posterior probabilities are calculated from the posterior distribution of multiple alignments. The largest deviation from the identity function has been obtained in the case of predicting secondary structures from a single optimal pairwise alignment. We also showed that alignment posterior probabilities correlate with the 3D distances between C α amino acids in superimposed tertiary structures. Conclusion: Alignment posterior probabilities can be used to a priori detect errors in comparative models on the sequence alignment level. </p

Meta-Alignment with Crumble and Prune: Partitioning very large alignment problems for performance and parallelization

<p>Abstract</p> <p>Background</p> <p>Continuing research into the global multiple sequence alignment problem has resulted in more sophisticated and principled alignment methods. Unfortunately these new algorithms often require large amounts of time and memory to run, making it nearly impossible to run these algorithms on large datasets. As a solution, we present two general methods, Crumble and Prune, for breaking a phylogenetic alignment problem into smaller, more tractable sub-problems. We call Crumble and Prune <it>meta-alignment </it>methods because they use existing alignment algorithms and can be used with many current alignment programs. Crumble breaks long alignment problems into shorter sub-problems. Prune divides the phylogenetic tree into a collection of smaller trees to reduce the number of sequences in each alignment problem. These methods are orthogonal: they can be applied together to provide better scaling in terms of sequence length and in sequence depth. Both methods partition the problem such that many of the sub-problems can be solved independently. The results are then combined to form a solution to the full alignment problem.</p> <p>Results</p> <p>Crumble and Prune each provide a significant performance improvement with little loss of accuracy. In some cases, a gain in accuracy was observed. Crumble and Prune were tested on real and simulated data. Furthermore, we have implemented a system called Job-tree that allows hierarchical sub-problems to be solved in parallel on a compute cluster, significantly shortening the run-time.</p> <p>Conclusions</p> <p>These methods enabled us to solve gigabase alignment problems. These methods could enable a new generation of biologically realistic alignment algorithms to be applied to real world, large scale alignment problems.</p

Behavioral responses to injury and death in wild Barbary macaques (Macaca sylvanus)

The wounding or death of a conspecific has been shown to elicit varied behavioral responses throughout thanatology. Recently, a number of reports have presented contentious evidence of epimeletic behavior towards the dying and dead among non-human animals, a behavioral trait previously considered uniquely human. Here, we report on the behavioral responses of Barbary macaques, a social, non-human primate, to the deaths of four group members (one high-ranking adult female, one high-ranking adult male, one juvenile male, and one female infant), all caused by road traffic accidents. Responses appeared to vary based on the nature of the death (protracted or instant) and the age class of the deceased. Responses included several behaviors with potential adaptive explanations or consequences. These included exploration, caretaking (guarding, carrying, and grooming), and proximity to wounded individuals or corpses, and immediate as well as longer-lasting distress behaviors from other group members following death, all of which have been reported in other non-human primate species. These observations add to a growing body of comparative evolutionary analysis of primate thanatology and help to highlight the multifaceted impacts of human-induced fatalities on an endangered and socially complex primate. © 2016, Japan Monkey Centre and Springer Japan

Split-based computation of majority-rule supertrees

<p>Abstract</p> <p>Background</p> <p>Supertree methods combine overlapping input trees into a larger supertree. Here, I consider split-based supertree methods that first extract the split information of the input trees and subsequently combine this split information into a phylogeny. Well known split-based supertree methods are matrix representation with parsimony and matrix representation with compatibility. Combining input trees on the same taxon set, as in the consensus setting, is a well-studied task and it is thus desirable to generalize consensus methods to supertree methods.</p> <p>Results</p> <p>Here, three variants of majority-rule (MR) supertrees that generalize majority-rule consensus trees are investigated. I provide simple formulas for computing the respective score for bifurcating input- and supertrees. These score computations, together with a heuristic tree search minmizing the scores, were implemented in the python program PluMiST (Plus- and Minus SuperTrees) available from <url>http://www.cibiv.at/software/plumist</url>. The different MR methods were tested by simulation and on real data sets. The search heuristic was successful in combining compatible input trees. When combining incompatible input trees, especially one variant, MR(-) supertrees, performed well.</p> <p>Conclusions</p> <p>The presented framework allows for an efficient score computation of three majority-rule supertree variants and input trees. I combined the score computation with a heuristic search over the supertree space. The implementation was tested by simulation and on real data sets and showed promising results. Especially the MR(-) variant seems to be a reasonable score for supertree reconstruction. Generalizing these computations to multifurcating trees is an open problem, which may be tackled using this framework.</p

Drosophila cbl Is Essential for Control of Cell Death and Cell Differentiation during Eye Development

Activation of cell surface receptors transduces extracellular signals into cellular responses such as proliferation, differentiation and survival. However, as important as the activation of these receptors is their appropriate spatial and temporal down-regulation for normal development and tissue homeostasis. The Cbl family of E3-ubiquitin ligases plays a major role for the ligand-dependent inactivation of receptor tyrosine kinases (RTKs), most notably the Epidermal Growth Factor Receptor (EGFR) through ubiquitin-mediated endocytosis and lysosomal degradation.Here, we report the mutant phenotypes of Drosophila cbl (D-cbl) during eye development. D-cbl mutants display overgrowth, inhibition of apoptosis, differentiation defects and increased ommatidial spacing. Using genetic interaction and molecular markers, we show that most of these phenotypes are caused by increased activity of the Drosophila EGFR. Our genetic data also indicate a critical role of ubiquitination for D-cbl function, consistent with biochemical models.These data may provide a mechanistic model for the understanding of the oncogenic activity of mammalian cbl genes

A Genome-Wide Scan of Ashkenazi Jewish Crohn's Disease Suggests Novel Susceptibility Loci

Crohn's disease (CD) is a complex disorder resulting from the interaction of intestinal microbiota with the host immune system in genetically susceptible individuals. The largest meta-analysis of genome-wide association to date identified 71 CD–susceptibility loci in individuals of European ancestry. An important epidemiological feature of CD is that it is 2–4 times more prevalent among individuals of Ashkenazi Jewish (AJ) descent compared to non-Jewish Europeans (NJ). To explore genetic variation associated with CD in AJs, we conducted a genome-wide association study (GWAS) by combining raw genotype data across 10 AJ cohorts consisting of 907 cases and 2,345 controls in the discovery stage, followed up by a replication study in 971 cases and 2,124 controls. We confirmed genome-wide significant associations of 9 known CD loci in AJs and replicated 3 additional loci with strong signal (p<5×10−6). Novel signals detected among AJs were mapped to chromosomes 5q21.1 (rs7705924, combined p = 2×10−8; combined odds ratio OR = 1.48), 2p15 (rs6545946, p = 7×10−9; OR = 1.16), 8q21.11 (rs12677663, p = 2×10−8; OR = 1.15), 10q26.3 (rs10734105, p = 3×10−8; OR = 1.27), and 11q12.1 (rs11229030, p = 8×10−9; OR = 1.15), implicating biologically plausible candidate genes, including RPL7, CPAMD8, PRG2, and PRG3. In all, the 16 replicated and newly discovered loci, in addition to the three coding NOD2 variants, accounted for 11.2% of the total genetic variance for CD risk in the AJ population. This study demonstrates the complementary value of genetic studies in the Ashkenazim

Efficient representation of uncertainty in multiple sequence alignments using directed acyclic graphs

Background A standard procedure in many areas of bioinformatics is to use a single multiple sequence alignment (MSA) as the basis for various types of analysis. However, downstream results may be highly sensitive to the alignment used, and neglecting the uncertainty in the alignment can lead to significant bias in the resulting inference. In recent years, a number of approaches have been developed for probabilistic sampling of alignments, rather than simply generating a single optimum. However, this type of probabilistic information is currently not widely used in the context of downstream inference, since most existing algorithms are set up to make use of a single alignment. Results In this work we present a framework for representing a set of sampled alignments as a directed acyclic graph (DAG) whose nodes are alignment columns; each path through this DAG then represents a valid alignment. Since the probabilities of individual columns can be estimated from empirical frequencies, this approach enables sample-based estimation of posterior alignment probabilities. Moreover, due to conditional independencies between columns, the graph structure encodes a much larger set of alignments than the original set of sampled MSAs, such that the effective sample size is greatly increased. Conclusions The alignment DAG provides a natural way to represent a distribution in the space of MSAs, and allows for existing algorithms to be efficiently scaled up to operate on large sets of alignments. As an example, we show how this can be used to compute marginal probabilities for tree topologies, averaging over a very large number of MSAs. This framework can also be used to generate a statistically meaningful summary alignment; example applications show that this summary alignment is consistently more accurate than the majority of the alignment samples, leading to improvements in downstream tree inference. Implementations of the methods described in this article are available at http://statalign.github.io/WeaveAlign webcite

AMPK in Pathogens

During host–pathogen interactions, a complex web of events is crucial for the outcome of infection. Pathogen recognition triggers powerful cellular signaling events that is translated into the induction and maintenance of innate and adaptive host immunity against infection. In opposition, pathogens employ active mechanisms to manipulate host cell regulatory pathways toward their proliferation and survival. Among these, subversion of host cell energy metabolism by pathogens is currently recognized to play an important role in microbial growth and persistence. Extensive studies have documented the role of AMP-activated protein kinase (AMPK) signaling, a central cellular hub involved in the regulation of energy homeostasis, in host–pathogen interactions. Here, we highlight the most recent advances detailing how pathogens hijack cellular metabolism by suppressing or increasing the activity of the host energy sensor AMPK. We also address the role of lower eukaryote AMPK orthologues in the adaptive process to the host microenvironment and their contribution for pathogen survival, differentiation, and growth. Finally, we review the effects of pharmacological or genetic AMPK modulation on pathogen growth and persistence.CIHR -Canadian Institutes of Health Researc