A guideline to proteome-wide α-helical membrane protein topology predictions

For current state-of-the-art methods, the prediction of correct topology of membrane proteins has been reported to be above 80%. However, this performance has only been observed in small and possibly biased data sets obtained from protein structures or biochemical assays. Here, we test a number of topology predictors on an "unseen" set of proteins of known structure and also on four "genome-scale" data sets, including one recent large set of experimentally validated human membrane proteins with glycosylated sites. The set of glycosylated proteins is also used to examine the ability of prediction methods to separate membrane from nonmembrane proteins. The results show that methods utilizing multiple sequence alignments are overall superior to methods that do not. The best performance is obtained by TOPCONS, a consensus method that combines several of the other prediction methods. The best methods to distinguish membrane from nonmembrane proteins belong to the "Phobius" group of predictors. We further observe that the reported high accuracies in the smaller benchmark sets are not quite maintained in larger scale benchmarks. Instead, we estimate the performance of the best prediction methods for eukaryotic membrane proteins to be between 60% and 70%. The low agreement between predictions from different methods questions earlier estimates about the global properties of the membrane proteome. Finally, we suggest a pipeline to estimate these properties using a combination of the best predictors that could be applied in large-scale proteomics studies of membrane proteins.     

Epigenetic age prediction

Advanced age is the main common risk factor for cancer, cardiovascular disease and neurodegeneration. Yet, more is known about the molecular basis of any of these groups of diseases than the changes that accompany ageing itself. Progress in molecular ageing research was slow because the tools predicting whether someone aged slowly or fast (biological age) were unreliable. To understand ageing as a risk factor for disease and to develop interventions, the molecular ageing field needed a quantitative measure; a clock for biological age. Over the past decade, a number of age predictors utilising DNA methylation have been developed, referred to as epigenetic clocks. While they appear to estimate biological age, it remains unclear whether the methylation changes used to train the clocks are a reflection of other underlying cellular or molecular processes, or whether methylation itself is involved in the ageing process. The precise aspects of ageing that the epigenetic clocks capture remain hidden and seem to vary between predictors. Nonetheless, the use of epigenetic clocks has opened the door towards studying biological ageing quantitatively, and new clocks and applications, such as forensics, appear frequently. In this review, we will discuss the range of epigenetic clocks available, their strengths and weaknesses, and their applicability to various scientific queries.     

Delving into the variations in tree species composition and richness across South American subtropical Atlantic and Pampean forests

Aims We analyse here the variations in species composition and richness and the geographic ranges of the tree species occurring in South American subtropical Atlantic and Pampean forests. Our goals were to assess (i) the floristic consistency of usual classifications based on vegetation physiognomy, climate and elevation; (ii) the leading role of temperature-related variables on the variations in species composition and richness; (iii) the predominance of species with tropical–subtropical ranges, possibly as a result of forest expansion over grasslands after the Last Glacial Maximum (LGM); (iv) the restriction of most subtropical endemics to stressful habitats as a possible result of past forest refuges during the LGM.Methods The region was defined by the Tropic of Capricorn to the north, the Rio de la Plata to the south, the Atlantic shoreline to the east and the catchment areas of the upper Paraná and Uruguay Rivers to the west. Multivariate analyses, multiple regression modelling and variance partition analyses were performed on a database containing 63 994 occurrence records of 1555 tree species in 491 forest sites and 48 environmental variables. All species were also classified according to their known geographic range.Important findings A main differentiation in species composition and richness was observed between the eastern windward coastlands (rain and cloud forests) and western leeward hinterlands (Araucaria and semi-deciduous forests). Pre-defined forest types on both sides were consistent with variations in tree species composition, which were significantly related to both environmental variables and spatial proximity, with extremes of low temperature playing a chief role. Tree species richness declined substantially towards the south and also from rain to seasonal forests and towards the highland summits and sandy shores. Species richness was significantly correlated with both minimum temperature and actual evapotranspiration. About 91% of the subtropical flora is shared with the much richer tropical flora, probably extracting species that can cope with frost outbreaks. The 145 subtropical endemics were not concentrated in harsher habitats.     

Prion-like proteins and their computational identification in proteomes

Introduction: The aberrant or misfolded forms of the prion protein have been described as the causative agents of rare transmissible spongiform encephalopathies. In addition, proteins associated with frequently occurring neurodegenerative disorders, such as Alzheimer’s and Parkinson’s, are shown to share prion-like properties and to spread the disease in the brain.

Areas covered: Interest in the prion phenomenon has crystallized in a series of computational methods aimed at uncovering prion-like proteins at the proteome level. These programs rely on the identification of sequence signatures similar to those of yeast prions, whose structural conversion is driven by specific domains enriched in glutamine/asparagine residues. A myriad of prion-like candidates, similar to those in yeast, are predicted to exist in organisms across all kingdoms of life. We review here the role of prions, prionoids and prion-like proteins in health and disease, with a special focus on the algorithms and databases developed for their prediction and classification.

Expert commentary: Computational approaches provide novel insights into prion-like protein functions, their regulation and their role in disease.     

An application of isotonic longitudinal marginal regression to monitoring the healing process

This paper discusses marginal regression for repeated ordinal measurements that are isotonic over time. Such data are often observed in longitudinal studies on healing processes in which, due to recovery, the status of patients only improves or remains the same. We show how this prior information can be used to construct appropriate and parsimoniously parametrized marginal models. As a second aspect, we also incorporate nonparametric fitting of covariate effects via a penalized quasi-likelihood or general estimating equation approach. We illustrate our methods by an application to sports-related injuries.     

3D-SHOTGUN: a novel,cooperative, fold-recognition meta-predictor

To gain a better understanding of the biological role of proteins encoded in genome sequences, knowledge of their three-dimensional (3D) structure and function is required. The computational assignment of folds is becoming an increasingly important complement to experimental structure determination. In particular, fold-recognition methods aim to predict approximate 3D models for proteins bearing no sequence similarity to any protein of known structure. However, fully automated structure-prediction methods can currently produce reliable models for only a fraction of these sequences. Using a number of semiautomated procedures, human expert predictors are often able to produce more and better predictions than automated methods. We describe a novel, fully automatic, fold-recognition meta-predictor, named 3D-SHOTGUN, which incorporates some of the strategies human predictors have successfully applied. This new method is reminiscent of the so-called cooperative algorithms of Computer Vision. The input to 3D-SHOTGUN are the top models predicted by a number of independent fold-recognition servers. The meta-predictor consists of three steps: (i) assembly of hybrid models, (ii) confidence assignment, and (iii) selection. We have applied 3D-SHOTGUN to an unbiased test set of 77 newly released protein structures sharing no sequence similarity to proteins previously released. Forty-six correct rank-1 predictions were obtained, 30 of which had scores higher than that of the first incorrect prediction-a significant improvement over the performance of all individual servers. Furthermore, the predicted hybrid models were, on average, more similar to their corresponding native structures than those produced by the individual servers. This opens the possibility of generating more accurate, full-atom homology models for proteins with no sequence similarity to proteins of known structure. These improvements represent a step forward toward the wider applicability of fully automated structure-prediction methods at genome scales.     

Bias in the detection of negative density dependence in plant communities

Regression dilution is a statistical inference bias that causes underestimation of the strength of dependency between two variables when the predictors are error‐prone proxies (EPPs). EPPs are widely used in plant community studies focused on negative density‐dependence (NDD) to quantify competitive interactions. Because of the nature of the bias, conspecific NDD is often overestimated in recruitment analyses, and in some cases, can be erroneously detected when absent. In contrast, for survival analyses, EPPs typically cause NDD to be underestimated, but underestimation is more severe for abundant species and for heterospecific effects, thereby generating spurious negative relationships between the strength of NDD and the abundances of con‐ and heterospecifics. This can explain why many studies observed rare species to suffer more severely from conspecific NDD, and heterospecific effects to be disproportionally smaller than conspecific effects. In general, such species‐dependent bias is often related to traits associated with likely mechanisms of NDD, which creates false patterns and complicates the ecological interpretation of the analyses. Classic examples taken from literature and simulations demonstrate that this bias has been pervasive, which calls into question the emerging paradigm that intraspecific competition has been demonstrated by direct field measurements to be generally stronger than interspecific competition.     

Landscape fragmentation in South Coast Renosterveld,South Africa,in relation to rainfall and topography

The South Coast Renosterveld has been fragmented extensively by agriculture. The extent of this fragmentation in terms of overall habitat loss, fragment sizes and fragment numbers has not been described previously, thereby limiting the development of conservation strategies for this vegetation type. Patterns of renosterveld loss in three sectors along a west–east gradient were described using LANDSAT imagery and a Geographical Information System‐based program (FRAGSTATS) for spatial pattern analysis. These patterns were then correlated with rainfall and topography measures, which are indicators of agricultural potential. Over 80% of the South Coast Renosterveld has been cultivated. Fragmentation levels increased significantly from east to west, with 33% of natural vegetation remaining in the east and only 4% in the west. Topographical variables were the strongest predictors of patterns of renosterveld loss, with fragments being largely confined to slopes too steep for ploughing; they therefore face little risk of future cultivation. These results have implications for conservation planning options for the South Coast Renosterveld. There is the potential for large reserves in the east, as well as corridor reserves along major river valleys, but for only small, isolated reserves in the west.     

Predicting climate-induced range shifts: model differences and model reliability

Predicted changes in the global climate are likely to cause large shifts in the geographic ranges of many plant and animal species. To date, predictions of future range shifts have relied on a variety of modeling approaches with different levels of model accuracy. Using a common data set, we investigated the potential implications of alternative modeling approaches for conclusions about future range shifts and extinctions. Our common data set entailed the current ranges of 100 randomly selected mammal species found in the western hemisphere. Using these range maps, we compared six methods for modeling predicted future ranges. Predicted future distributions differed markedly across the alternative modeling approaches, which in turn resulted in estimates of extinction rates that ranged between 0% and 7%, depending on which model was used. Random forest predictors, a model‐averaging approach, consistently outperformed the other techniques (correctly predicting >99% of current absences and 86% of current presences). We conclude that the types of models used in a study can have dramatic effects on predicted range shifts and extinction rates; and that model‐averaging approaches appear to have the greatest potential for predicting range shifts in the face of climate change.