How different from random are docking predictions when ranked by scoring functions?

Docking algorithms predict the structure of protein–protein interactions. They sample the orientation of two unbound proteins to produce various predictions about their interactions, followed by a scoring step to rank the predictions. We present a statistical assessment of scoring functions used to rank near‐native orientations, applying our statistical analysis to a benchmark dataset of decoys of protein–protein complexes and assessing the statistical significance of the outcome in the Critical Assessment of PRedicted Interactions (CAPRI) scoring experiment. A P value was assigned that depended on the number of near‐native structures in the sampling. We studied the effect of filtering out redundant structures and tested the use of pair‐potentials derived using ZDock and ZRank. Our results show that for many targets, it is not possible to determine when a successful reranking performed by scoring functions results merely from random choice. This analysis reveals that changes should be made in the design of the CAPRI scoring experiment. We propose including the statistical assessment in this experiment either at the preprocessing or the evaluation step. Proteins 2010. © 2010 Wiley‐Liss, Inc.     

Experimental optimization of protein refolding with a genetic algorithm

Refolding of proteins from solubilized inclusion bodies still represents a major challenge for many recombinantly expressed proteins and often constitutes a major bottleneck. As in vitro refolding is a complex reaction with a variety of critical parameters, suitable refolding conditions are typically derived empirically in extensive screening experiments. Here, we introduce a new strategy that combines screening and optimization of refolding yields with a genetic algorithm (GA). The experimental setup was designed to achieve a robust and universal method that should allow optimizing the folding of a variety of proteins with the same routine procedure guided by the GA. In the screen, we incorporated a large number of common refolding additives and conditions. Using this design, the refolding of four structurally and functionally different model proteins was optimized experimentally, achieving 74–100% refolding yield for all of them. Interestingly, our results show that this new strategy provides optimum conditions not only for refolding but also for the activity of the native enzyme. It is designed to be generally applicable and seems to be eligible for all enzymes.     

The Intrasectional Chemotaxonomic Placement of Hypericum elegans Stephan ex Willd. Inferred from the Essential‐Oil Chemical Composition

Here we report, for the first time, the results of detailed GC and GC/MS analyses of the essential oil of a rare taxon in Serbia, Hypericum elegans Stephan ex Willd . One hundred and sixty two constituents identified accounted for 98.6% of the oil. The major components of the oil were undecane (31.9%), α‐pinene (16.7%), nonane (6.1%), bicyclogermacrene (5.8%), 2‐methyloctane (3.7%), and germacrene D (3.6%). Non‐terpenoids as chemotaxonomic markers constituted the main fraction of H. elegans oil, whereby n‐alkanes were the most abundant contributors of this fraction. Based on these results and previously published ones, we performed an intrasectional multivariate statistical comparison of corresponding essential‐oil chemical compositions. Principal component analysis (PCA) and agglomerative hierarchical clustering (AHC) of the data on the volatile profiles of section Hypericum taxa revealed that H. elegans either represents an oil chemotype of its own (AHC) or could be considered related to H. perforatum (PCA).     

Pairwise Variable Selection for High‐Dimensional Model‐Based Clustering

Summary Variable selection for clustering is an important and challenging problem in high‐dimensional data analysis. Existing variable selection methods for model‐based clustering select informative variables in a “one‐in‐all‐out” manner; that is, a variable is selected if at least one pair of clusters is separable by this variable and removed if it cannot separate any of the clusters. In many applications, however, it is of interest to further establish exactly which clusters are separable by each informative variable. To address this question, we propose a pairwise variable selection method for high‐dimensional model‐based clustering. The method is based on a new pairwise penalty. Results on simulated and real data show that the new method performs better than alternative approaches that use ?₁ and ?_∞ penalties and offers better interpretation.     

Ecological biogeography of Malagasy non‐volant mammals: community structure is correlated with habitat

Aim To examine the relationship between ecoregions, as a proxy for regional climate and habitat type, and mammalian community structure, defined by species composition and richness (e.g. taxonomic structure) and ecological diversity (e.g. ecological structure) of non‐volant species. Location Madagascar. Methods Faunal lists of non‐volant mammal species occurring in 35 communities from five World Wildlife Fund ecoregions were collected from published and unpublished sources. Species were assigned to ecological groups defined by trophic status, locomotor habits, activity cycle and body mass. We used Mantel tests, cluster analysis and principal coordinates analysis to evaluate geographic patterning in taxonomic composition and species richness. We used stepwise multiple discriminant analysis to characterize patterns in the ecological diversity of the mammalian communities from each ecoregion. Communities from transitional habitats (e.g. representing more than one ecoregion) were used to test the predictive power of the analyses. Results Non‐volant mammal communities divided into clusters that correspond to ecoregions. There was a strong distance effect in the taxonomic structure of communities across the island and within both humid and dry forest communities, but this effect was weak within humid forest communities. Mammalian species richness was significantly lower in dry forest than in humid forest communities. The ecological structure of communities was also correlated with ecoregions. Changes in the relative percentages of omnivory, arboreal quadrupedalism, terrestrial/arboreal quadrupedalism and two body mass classes accounted for 98.1% of the variation in ecological structure. Transitional communities were projected in intermediate positions by the discriminant model. Main conclusions Our analysis demonstrates that the broad‐scale habitat and climate variables captured by the ecoregion model have shaped the assembly of non‐volant mammal communities in Madagascar over evolutionary time. The spatial pattern is consistent with ecological sorting of species ranges along environmental gradients. Historical processes, such as recent extinction and migration, may have also affected the structure of mammal communities, although these factors have played a secondary role.     

Spatial genetic structure of a small rodent in a heterogeneous landscape

Gene flow in natural populations may be strongly influenced by landscape features. The integration of landscape characteristics in population genetic studies may thus improve our understanding of population functioning. In this study, we investigated the population genetic structure and gene flow pattern for the common vole, Microtus arvalis, in a heterogeneous landscape characterised by strong spatial and temporal variation. The studied area is an intensive agricultural zone of approximately 500 km² crossed by a motorway. We used individual-based Bayesian methods to define the number of population units and their spatial borders without prior delimitation of such units. Unexpectedly, we determined a single genetic unit that covered the entire area studied. In particular, the motorway considered as a likely barrier to dispersal was not associated with any spatial genetic discontinuity. Using computer simulations, we demonstrated that recent anthropogenic barriers to effective dispersal are difficult to detect through analysis of genetic variation for species with large effective population sizes. We observed a slight, but significant, pattern of isolation by distance over the whole study site. Spatial autocorrelation analyses detected genetic structuring on a local scale, most probably due to the social organisation of the study species. Overall, our analysis suggests intense small-scale dispersal associated with a large effective population size. High dispersal rates may be imposed by the strong spatio-temporal heterogeneity of habitat quality, which characterises intensive agroecosystems.     

Ceramide: physiological and pathophysiological aspects

Ceramide generated in the cell membrane has been shown to be central for the induction of apoptosis by death receptors and many stress stimuli such as gamma-irradiation, UV-light or infection with pathogens. Ceramide reorganizes cell membranes and forms large ceramide-enriched membrane domains that serve the spatial and temporal organization of the cellular signalosome upon activation. Thus, ceramide-enriched membrane domains mediate clustering of CD95 and DR5 to facilitate apoptosis, and they are also critically involved in apoptosis after irradiation, UV-light and infection with Pseudomonas aeruginosa. Since ceramide-enriched membrane domains amplify signals, their function is not restricted to the induction of apoptosis and it was shown that ceramide-enriched membrane domains are also involved in internalization of pathogens and the control of cytokine release from infected epithelial cells. Recent studies support the notion that changes of the ceramide metabolism are also critically involved in human diseases, for instance neurological disorders, cancer, infectious diseases and Wilson's disease.     

Genetic interference reduces the evolvability of modular and non-modular visual neural networks

The aim of this paper is to propose an interdisciplinary evolutionary connectionism approach for the study of the evolution of modularity. It is argued that neural networks as a model of the nervous system and genetic algorithms as simulative models of biological evolution would allow us to formulate a clear and operative definition of module and to simulate the different evolutionary scenarios proposed for the origin of modularity. I will present a recent model in which the evolution of primate cortical visual streams is possible starting from non-modular neural networks. Simulation results not only confirm the existence of the phenomenon of neural interference in non-modular network architectures but also, for the first time, reveal the existence of another kind of interference at the genetic level, i.e. genetic interference, a new population genetic mechanism that is independent from the network architecture. Our simulations clearly show that genetic interference reduces the evolvability of visual neural networks and sexual reproduction can at least partially solve the problem of genetic interference. Finally, it is shown that entrusting the task of finding the neural network architecture to evolution and that of finding the network connection weights to learning is a way to completely avoid the problem of genetic interference. On the basis of this evidence, it is possible to formulate a new hypothesis on the origin of structural modularity, and thus to overcome the traditional dichotomy between innatist and empiricist theories of mind.     

iGibbs: improving Gibbs motif sampler for proteins by sequence clustering and iterative pattern sampling

The motif prediction problem is to predict short, conserved subsequences that are part of a family of sequences, and it is a very important biological problem. Gibbs is one of the first successful motif algorithms and it runs very fast compared with other algorithms, and its search behavior is based on the well-studied Gibbs random sampling. However, motif prediction is a very difficult problem and Gibbs may not predict true motifs in some cases. Thus, the authors explored a possibility of improving the prediction accuracy of Gibbs while retaining its fast runtime performance. In this paper, the authors considered Gibbs only for proteins, not for DNA binding sites. The authors have developed iGibbs, an integrated motif search framework for proteins that employs two previous techniques of their own: one for guiding motif search by clustering sequences and another by pattern refinement. These two techniques are combined to a new double clustering approach to guiding motif search. The unique feature of their framework is that users do not have to specify the number of motifs to be predicted when motifs occur in different subsets of the input sequences since it automatically clusters input sequences into clusters and predict motifs from the clusters. Tests on the PROSITE database show that their framework improved the prediction accuracy of Gibbs significantly. Compared with more exhaustive search methods like MEME, iGibbs predicted motifs more accurately and runs one order of magnitude faster.