TAP score: torsion angle propensity normalization applied to local protein structure evaluation

Background  

Experimentally determined protein structures may contain errors and require validation. Conformational criteria based on the Ramachandran plot are mainly used to distinguish bet ween distorted and adequately refined models. While the readily available criteria are sufficient to detect totally wrong structures, establishing the more subtle differences between plausible structures remains more challenging.     

Shift from facilitative to neutral interactions by the cushion plant Silene acaulis along a primary succession gradient

Background

The stress‐gradient hypothesis predicts a shift from facilitative to competitive plant interactions with decreasing abiotic stress. This has been supported by studies along elevation and temperature gradients, but also challenged by the hypothesis of a facilitation collapse at extremely harsh sites. Although facilitation is known to be important in primary succession, few studies have examined these hypotheses along primary succession gradients.

Aim

To examine whether there is a relationship between the presence of the circumpolar cushion plant Silene acaulis and other species, and if so, whether there is a shift between positive and negative interactions along a primary succession gradient in a glacier foreland.

Location

Finse, southern Norway.

Methods

We examined the performance of the common alpine forb Bistorta vivipara, species richness of vascular plants, bryophytes and lichens, and the number of seedlings and fertile vascular plants in S. acaulis cushions, and control plots without S. acaulis, along a succession gradient with increasing distance from a glacier front, and thus decreasing abiotic stress. To examine if S. acaulis cushions modify the abiotic environment, we recorded soil temperature, moisture, organic content and pH in cushions and control plots.

Results

Bistorta vivipara performed better, as shown by bigger leaves in S. acaulis cushions compared to control plots in the harshest part of the gradient close to the glacier. There were few differences in B. vivipara performance between cushion and control plots in the more benign environment further away from the glacier. This suggests a shift from facilitative to mainly neutral interactions by S. acaulis on the performance of B. vivipara with decreasing abiotic stress. A trend, although not significant, of higher vascular species richness and fertility inside S. acaulis cushions along the whole gradient, suggests that S. acaulis also facilitates community‐level species richness. The causal mechanism of this facilitation is likely that the cushions buffer extreme temperatures.

Conclusions

Our results support the stress‐gradient hypothesis for the relationship between the cushion plant S. acaulis and the performance of a single species along a primary succession gradient in a glacier foreland. S. acaulis also tended to increase vascular plant species richness and fertility regardless of stress level along the gradient, suggesting facilitation at the community level. We found no collapse of facilitation at the most stressful end of the gradient in this alpine glacier foreland.     

Applying an empirical hydropathic forcefield in refinement may improve low-resolution protein X-ray crystal structures

Background

The quality of X-ray crystallographic models for biomacromolecules refined from data obtained at high-resolution is assured by the data itself. However, at low-resolution, >3.0 Å, additional information is supplied by a forcefield coupled with an associated refinement protocol. These resulting structures are often of lower quality and thus unsuitable for downstream activities like structure-based drug discovery.

Methodology

An X-ray crystallography refinement protocol that enhances standard methodology by incorporating energy terms from the HINT (Hydropathic INTeractions) empirical forcefield is described. This protocol was tested by refining synthetic low-resolution structural data derived from 25 diverse high-resolution structures, and referencing the resulting models to these structures. The models were also evaluated with global structural quality metrics, e.g., Ramachandran score and MolProbity clashscore. Three additional structures, for which only low-resolution data are available, were also re-refined with this methodology.

Results

The enhanced refinement protocol is most beneficial for reflection data at resolutions of 3.0 Å or worse. At the low-resolution limit, ≥4.0 Å, the new protocol generated models with Cα positions that have RMSDs that are 0.18 Å more similar to the reference high-resolution structure, Ramachandran scores improved by 13%, and clashscores improved by 51%, all in comparison to models generated with the standard refinement protocol. The hydropathic forcefield terms are at least as effective as Coulombic electrostatic terms in maintaining polar interaction networks, and significantly more effective in maintaining hydrophobic networks, as synthetic resolution is decremented. Even at resolutions ≥4.0 Å, these latter networks are generally native-like, as measured with a hydropathic interactions scoring tool.     

An image processing approach to computing distances between RNA secondary structures dot plots

Background  

Computing the distance between two RNA secondary structures can contribute in understanding the functional relationship between them. When used repeatedly, such a procedure may lead to finding a query RNA structure of interest in a database of structures. Several methods are available for computing distances between RNAs represented as strings or graphs, but none utilize the RNA representation with dot plots. Since dot plots are essentially digital images, there is a clear motivation to devise an algorithm for computing the distance between dot plots based on image processing methods.     

Comparative modeling of DNA and RNA polymerases from Moniliophthora perniciosa mitochondrial plasmid

Background  

The filamentous fungus Moniliophthora perniciosa (Stahel) Aime & Phillips-Mora is a hemibiotrophic Basidiomycota that causes witches' broom disease of cocoa (Theobroma cacao L.). This disease has resulted in a severe decrease in Brazilian cocoa production, which changed the position of Brazil in the market from the second largest cocoa exporter to a cocoa importer. Fungal mitochondrial plasmids are usually invertrons encoding DNA and RNA polymerases. Plasmid insertions into host mitochondrial genomes are probably associated with modifications in host generation time, which can be involved in fungal aging. This association suggests activity of polymerases, and these can be used as new targets for drugs against mitochondrial activity of fungi, more specifically against witches' broom disease. Sequencing and modeling: DNA and RNA polymerases of M. perniciosa mitochondrial plasmid were completely sequenced and their models were carried out by Comparative Homology approach. The sequences of DNA and RNA polymerase showed 25% of identity to 1XHX and 1ARO (pdb code) using BLASTp, which were used as templates. The models were constructed using Swiss PDB-Viewer and refined with a set of Molecular Mechanics (MM) and Molecular Dynamics (MD) in water carried out with AMBER 8.0, both working under the ff99 force fields, respectively. Ramachandran plots were generated by Procheck 3.0 and exhibited models with 97% and 98% for DNA and RNA polymerases, respectively. MD simulations in water showed models with thermodynamic stability after 2000 ps and 300 K of simulation.     

Non‐destructive allometric estimates of above‐ground and below‐ground biomass of high‐mountain vegetation in the Andes

Aim

Studies that monitor high‐mountain vegetation, such as paramo grasslands in the Andes, lack non‐destructive biomass estimation methods. We aimed to develop and apply allometric models for above‐ground, below‐ground and total biomass of paramo plants.

Location

The paramo of southern Colombia between 1°09′N and 077°50′W, at 3,400 and 3,700 m a.s.l.

Methods

We established 61 1‐m² plots at random locations, excluding disturbed, inaccessible and peat bog areas. We measured heights and basal diameters of all vascular plants in these plots and classified them into seven growth forms. Near each plot, we sampled the biomass from plants of abundant genera, after having measured their height and basal diameter. Hence, we measured the biomass of 476 plants (allometric set). For each growth form we applied power‐law functions to develop allometric models of biomass against basal diameter, height, height x basal diameter and height × basal area. The best models were selected using AIC_c weights. Using the observed and predicted plant biomass of the allometric set we calculated absolute percentage errors using cross‐validation. The biomass of a plot was estimated by summing the predicted biomass of all plants in a plot. Confidence limits around these sums were calculated by bootstrapping.

Results

For groups of <20 plants the biomass predictions yielded large (>15%) errors. Applying groups that resembled the 1‐m² plots in density and composition, the errors for above‐ground and total biomass estimates were <15%. Across all plots, we obtained an above‐ground, below‐ground and total plot biomass of 329 ± 190, 743 ± 486 and 1011 ± 627 g/m² (mean ± SD), respectively. These values were within the range of biomass estimates obtained destructively in the tropical Andes.

Conclusions

In new applications, if target vegetation samples are similar regarding growth forms and genera to our allometric set, their biomass might be predicted applying our equations, provided they contain at least 50–100 plants. In other situations, we would recommend gathering additional biomass measurements from local plants to evaluate new regression equations.     

Reuse of structural domain–domain interactions in protein networks

Background  

Protein interactions are thought to be largely mediated by interactions between structural domains. Databases such as iPfam relate interactions in protein structures to known domain families. Here, we investigate how the domain interactions from the iPfam database are distributed in protein interactions taken from the HPRD, MPact, BioGRID, DIP and IntAct databases.     

Analysis on multi-domain cooperation for predicting protein-protein interactions

Background  

Domains are the basic functional units of proteins. It is believed that protein-protein interactions are realized through domain interactions. Revealing multi-domain cooperation can provide deep insights into the essential mechanism of protein-protein interactions at the domain level and be further exploited to improve the accuracy of protein interaction prediction.     

A genetic ensemble approach for gene-gene interaction identification

Background  

It has now become clear that gene-gene interactions and gene-environment interactions are ubiquitous and fundamental mechanisms for the development of complex diseases. Though a considerable effort has been put into developing statistical models and algorithmic strategies for identifying such interactions, the accurate identification of those genetic interactions has been proven to be very challenging.     

Unsupervised assessment of microarray data quality using a Gaussian mixture model

Background  

Quality assessment of microarray data is an important and often challenging aspect of gene expression analysis. This task frequently involves the examination of a variety of summary statistics and diagnostic plots. The interpretation of these diagnostics is often subjective, and generally requires careful expert scrutiny.     

MegaSNPHunter: a learning approach to detect disease predisposition SNPs and high level interactions in genome wide association study

Background  

The interactions of multiple single nucleotide polymorphisms (SNPs) are highly hypothesized to affect an individual's susceptibility to complex diseases. Although many works have been done to identify and quantify the importance of multi-SNP interactions, few of them could handle the genome wide data due to the combinatorial explosive search space and the difficulty to statistically evaluate the high-order interactions given limited samples.     

Protein identification from two-dimensional gel electrophoresis analysis of Klebsiella pneumoniae by combined use of mass spectrometry data and raw genome sequences

Background  

Hotspots are defined as the minimal functional domains involved in protein:protein interactions and sufficient to induce a biological response.     

Neutral evolution of Protein-protein interactions: a computational study using simple models

Background  

Protein-protein interactions are central to cellular organization, and must have appeared at an early stage of evolution. To understand better their role, we consider a simple model of protein evolution and determine the effect of an explicit selection for Protein-protein interactions.     

An integrated approach to the prediction of domain-domain interactions

Background  

The development of high-throughput technologies has produced several large scale protein interaction data sets for multiple species, and significant efforts have been made to analyze the data sets in order to understand protein activities. Considering that the basic units of protein interactions are domain interactions, it is crucial to understand protein interactions at the level of the domains. The availability of many diverse biological data sets provides an opportunity to discover the underlying domain interactions within protein interactions through an integration of these biological data sets.     

A regulatory network of two galectins mediates the earliest steps of avian limb skeletal morphogenesis

Background  

The skeletal elements of vertebrate embryonic limbs are prefigured by rod- and spot-like condensations of precartilage mesenchymal cells. The formation of these condensations depends on cell-matrix and cell-cell interactions, but how they are initiated and patterned is as yet unresolved.     

Semantic integration to identify overlapping functional modules in protein interaction networks

Background  

The systematic analysis of protein-protein interactions can enable a better understanding of cellular organization, processes and functions. Functional modules can be identified from the protein interaction networks derived from experimental data sets. However, these analyses are challenging because of the presence of unreliable interactions and the complex connectivity of the network. The integration of protein-protein interactions with the data from other sources can be leveraged for improving the effectiveness of functional module detection algorithms.     

Computational verification of protein-protein interactions by orthologous co-expression

Background  

High-throughput methods identify an overwhelming number of protein-protein interactions. However, the limited accuracy of these methods results in the false identification of many spurious interactions. Accordingly, the resulting interactions are regarded as hypothetical and computational methods are needed to increase their confidence. Several methods have recently been suggested for this purpose including co-expression as a confidence measure for interacting proteins, but their performance is still quite poor.     

SNAVI: Desktop application for analysis and visualization of large-scale signaling networks

Background  

Studies of cellular signaling indicate that signal transduction pathways combine to form large networks of interactions. Viewing protein-protein and ligand-protein interactions as graphs (networks), where biomolecules are represented as nodes and their interactions are represented as links, is a promising approach for integrating experimental results from different sources to achieve a systematic understanding of the molecular mechanisms driving cell phenotype. The emergence of large-scale signaling networks provides an opportunity for topological statistical analysis while visualization of such networks represents a challenge.