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

iSPOT: A Web Tool for the Analysis and Recognition of Protein Domain Specificity

Methods that aim at predicting interaction partners are very likely to play an important role in the interpretation of genomic information. iSPOT (iSpecificity Prediction Of Target) is a web tool (accessible at http://cbm.bio.uniroma2.it/iSPOT) developed for the prediction of protein-protein interaction mediated by families of peptide recognition modules. iSPOT accesses a database of position specific residue-residue interaction frequencies for members of the SH3 and PDZ protein domain families. The software utilises this database to provide a score for any potential domain peptide interaction

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)

A methodology for determining amino-acid substitution matrices from set covers

We introduce a new methodology for the determination of amino-acid substitution matrices for use in the alignment of proteins. The new methodology is based on a pre-existing set cover on the set of residues and on the undirected graph that describes residue exchangeability given the set cover. For fixed functional forms indicating how to obtain edge weights from the set cover and, after that, substitution-matrix elements from weighted distances on the graph, the resulting substitution matrix can be checked for performance against some known set of reference alignments and for given gap costs. Finding the appropriate functional forms and gap costs can then be formulated as an optimization problem that seeks to maximize the performance of the substitution matrix on the reference alignment set. We give computational results on the BAliBASE suite using a genetic algorithm for optimization. Our results indicate that it is possible to obtain substitution matrices whose performance is either comparable to or surpasses that of several others, depending on the particular scenario under consideration