Predicting N-terminal myristoylation sites in plant proteins

BACKGROUND: N-terminal myristoylation plays a vital role in membrane targeting and signal transduction in plant responses to environmental stress. Although N-myristoyltransferase enzymatic function is conserved across plant, animal, and fungal kingdoms, exact substrate specificities vary, making it difficult to predict protein myristoylation accurately within specific taxonomic groups. RESULTS: A new method for predicting N-terminal myristoylation sites specifically in plants has been developed and statistically tested for sensitivity, specificity, and robustness. Compared to previously available methods, the new model is both more sensitive in detecting known positives, and more selective in avoiding false positives. Scores of myristoylated and non-myristoylated proteins are more widely separated than with other methods, greatly reducing ambiguity and the number of sequences giving intermediate, uninformative results. The prediction model is available at . CONCLUSION: Superior performance of the new model is due to the selection of a plant-specific training set, covering 266 unique sequence examples from 40 different species, the use of a probability-based hidden Markov model to obtain predictive scores, and a threshold cutoff value chosen to provide maximum positive-negative discrimination. The new model has been used to predict 589 plant proteins likely to contain N-terminal myristoylation signals, and to analyze the functional families in which these proteins occur

The megaprior heuristic for discovering protein sequence patterns

Several computer algorithms for discovering patterns in groups of protein sequences are in use that are based on fitting the parameters of a statistical model to a group of related sequences. These include hidden Markov model (HMM) algorithms for multiple sequence alignment, and the MEME and Gibbs sampler aagorithms for discovering motifs. These algorithms axe sometimes prone to producing models that are incorrect because two or more patterns have been tombitted. The statistical model produced in this situation is a convex combination (weighted average) two or more different models. This paper presents a solution to the problem of convex combinations in the form of a heuristic based on using extremely low variance Dirichlet mixture priors as past of the statistical model. This heuristic, which we call the megaprior heuristic, increases the strength (i.e., decreases the variance) of the prior in proportion to the size of the sequence dataset. This causes each column in the final model to strongly resemble the mean of a single component of the prior, regardless of the size of the dataset. We describe the cause of the convex combination problem, analyze it mathematically, motivate and describe the implementation of the megaprior heuristic, and show how it can effectively eliminate the problem of convex combinations in protein sequence pattern discovery

Wiggle—Predicting Functionally Flexible Regions from Primary Sequence

The Wiggle series are support vector machine–based predictors that identify regions of functional flexibility using only protein sequence information. Functionally flexible regions are defined as regions that can adopt different conformational states and are assumed to be necessary for bioactivity. Many advances have been made in understanding the relationship between protein sequence and structure. This work contributes to those efforts by making strides to understand the relationship between protein sequence and flexibility. A coarse-grained protein dynamic modeling approach was used to generate the dataset required for support vector machine training. We define our regions of interest based on the participation of residues in correlated large-scale fluctuations. Even with this structure-based approach to computationally define regions of functional flexibility, predictors successfully extract sequence-flexibility relationships that have been experimentally confirmed to be functionally important. Thus, a sequence-based tool to identify flexible regions important for protein function has been created. The ability to identify functional flexibility using a sequence based approach complements structure-based definitions and will be especially useful for the large majority of proteins with unknown structures. The methodology offers promise to identify structural genomics targets amenable to crystallization and the possibility to engineer more flexible or rigid regions within proteins to modify their bioactivity

A left-handed crossover involved in amidohydrolase catalysis Crystal structure of Erwinia chrysanthemi l-asparaginase with bound l-aspartate

AbstractThe crystal structure of l-asparaginase from Erwinia chrysanthemi in the presence and absence of l-aspartate was determined at 1.8 Å resolution. Conserved residues in a left-handed crossover (a rare occurrence in protein structures) link pairs of dimers into the catalytically active tetrameric form of the enzyme. The structure of ErA containing bound aspartic acid shows that this unusual strand connectivity is an essential part of the active site architecture, responsible for releasing the product of the enzymatic hydrolysis. The orientation of the bound aspartate indicates for the first time a threonine residue as a catalytic nucleophile

Differential gene expression in Varroa jacobsoni mites following a host shift to European honey bees (Apis mellifera)

Background: Varroa mites are widely considered the biggest honey bee health problem worldwide. Until recently, Varroa jacobsoni has been found to live and reproduce only in Asian honey bee (Apis cerana) colonies, while V. destructor successfully reproduces in both A. cerana and A. mellifera colonies. However, we have identified an island population of V. jacobsoni that is highly destructive to A. mellifera, the primary species used for pollination and honey production. The ability of these populations of mites to cross the host species boundary potentially represents an enormous threat to apiculture, and is presumably due to genetic variation that exists among populations of V. jacobsoni that influences gene expression and reproductive status. In this work, we investigate differences in gene expression between populations of V. jacobsoni reproducing on A. cerana and those either reproducing or not capable of reproducing on A. mellifera, in order to gain insight into differences that allow V. jacobsoni to overcome its normal species tropism. Results: We sequenced and assembled a de novo transcriptome of V. jacobsoni. We also performed a differential gene expression analysis contrasting biological replicates of V. jacobsoni populations that differ in their ability to reproduce on A. mellifera. Using the edgeR, EBSeq and DESeq R packages for differential gene expression analysis, we found 287 differentially expressed genes (FDR ≤ 0.05), of which 91% were up regulated in mites reproducing on A. mellifera. In addition, mites found reproducing on A. mellifera showed substantially more variation in expression among replicates. We searched for orthologous genes in public databases and were able to associate 100 of these 287 differentially expressed genes with a functional description. Conclusions: There is differential gene expression between the two mite groups, with more variation in gene expression among mites that were able to reproduce on A. mellifera. A small set of genes showed reduced expression in mites on the A. mellifera host, including putative transcription factors and digestive tract developmental genes. The vast majority of differentially expressed genes were up-regulated in this host. This gene set showed enrichment for genes associated with mitochondrial respiratory function and apoptosis, suggesting that mites on this host may be experiencing higher stress, and may be less optimally adapted to parasitize it. Some genes involved in reproduction and oogenesis were also overexpressed, which should be further studied in regards to this host shift. © 2016 The Author(s)

Analysis of Gap Gene Regulation in a 3D Organism-Scale Model of the Drosophila melanogaster Embryo

The axial bodyplan of Drosophila melanogaster is determined during a process called morphogenesis. Shortly after fertilization, maternal bicoid mRNA is translated into Bicoid (Bcd). This protein establishes a spatially graded morphogen distribution along the anterior-posterior (AP) axis of the embryo. Bcd initiates AP axis determination by triggering expression of gap genes that subsequently regulate each other's expression to form a precisely controlled spatial distribution of gene products. Reaction-diffusion models of gap gene expression on a 1D domain have previously been used to infer complex genetic regulatory network (GRN) interactions by optimizing model parameters with respect to 1D gap gene expression data. Here we construct a finite element reaction-diffusion model with a realistic 3D geometry fit to full 3D gap gene expression data. Though gap gene products exhibit dorsal-ventral asymmetries, we discover that previously inferred gap GRNs yield qualitatively correct AP distributions on the 3D domain only when DV-symmetric initial conditions are employed. Model patterning loses qualitative agreement with experimental data when we incorporate a realistic DV-asymmetric distribution of Bcd. Further, we find that geometry alone is insufficient to account for DV-asymmetries in the final gap gene distribution. Additional GRN optimization confirms that the 3D model remains sensitive to GRN parameter perturbations. Finally, we find that incorporation of 3D data in simulation and optimization does not constrain the search space or improve optimization results

Characterization of the yeast ionome: a genome-wide analysis of nutrient mineral and trace element homeostasis in Saccharomyces cerevisiae

BACKGROUND: Nutrient minerals are essential yet potentially toxic, and homeostatic mechanisms are required to regulate their intracellular levels. We describe here a genome-wide screen for genes involved in the homeostasis of minerals in Saccharomyces cerevisiae. Using inductively coupled plasma-atomic emission spectroscopy (ICP-AES), we assayed 4,385 mutant strains for the accumulation of 13 elements (calcium, cobalt, copper, iron, potassium, magnesium, manganese, nickel, phosphorus, selenium, sodium, sulfur, and zinc). We refer to the resulting accumulation profile as the yeast 'ionome'. RESULTS: We identified 212 strains that showed altered ionome profiles when grown on a rich growth medium. Surprisingly few of these mutants (four strains) were affected for only one element. Rather, levels of multiple elements were altered in most mutants. It was also remarkable that only six genes previously shown to be involved in the uptake and utilization of minerals were identified here, indicating that homeostasis is robust under these replete conditions. Many mutants identified affected either mitochondrial or vacuolar function and these groups showed similar effects on the accumulation of many different elements. In addition, intriguing positive and negative correlations among different elements were observed. Finally, ionome profile data allowed us to correctly predict a function for a previously uncharacterized gene, YDR065W. We show that this gene is required for vacuolar acidification. CONCLUSION: Our results indicate the power of ionomics to identify new aspects of mineral homeostasis and how these data can be used to develop hypotheses regarding the functions of previously uncharacterized genes