Anthony Kusalik

Computational prediction of eukaryotic phosphorylation sites

MOTIVATION Kinase-mediated phosphorylation is the central mechanism of post-translational modification to regulate cellular responses and phenotypes. Signaling defects associated with protein phosphorylation are linked to many diseases, particularly cancer. Characterizing protein kinases and their substrates enhances our ability to understand and treat such diseases and broadens our knowledge of signaling networks in general. While most or all protein kinases have been identified in well-studied eukaryotes, the sites that they phosphorylate have been only partially elucidated. Experimental methods for identifying phosphorylation sites are resource intensive, so the ability to computationally predict potential sites has considerable value. RESULTS Many computational techniques for phosphorylation site prediction have been proposed, most of which are available on the web. These techniques differ in several ways, including the machine learning technique used; the amount of sequence information used; whether or not structural information is used in addition to sequence information; whether predictions are made for specific kinases or for kinases in general; and sources of training and testing data. This review summarizes, categorizes and compares the available methods for phosphorylation site prediction, and provides an overview of the challenges that are faced when designing predictors and how they have been addressed. It should therefore be useful both for those wishing to choose a phosphorylation site predictor for their particular biological application, and for those attempting to improve upon established techniques in the future. CONTACT brett.trost@usask.ca.

Modeling gene expression from microarray expression data with state-space equations.

We describe a new method to model gene expression from time-course gene expression data. The modelling is in terms of state-space descriptions of linear systems. A cell can be considered to be a system where the behaviours (responses) of the cell depend completely on the current internal state plus any external inputs. The gene expression levels in the cell provide information about the behaviours of the cell. In previously proposed methods, genes were viewed as internal state variables of a cellular system and their expression levels were the values of the intemal state variables. This viewpoint has suffered from the underestimation of the model parameters. Instead, we view genes as the observation variables, whose expression values depend on the current intemal state variables and any external input. Factor analysis is used to identify the internal state variables, and Bayesian Information Criterion (BIC) is used to determine the number of the internal state variables. By building dynamic equations of the internal state variables and the relationships between the internal state variables and the observation variables (gene expression profiles), we get state-space descriptions of gene expression model. In the present method, model parameters may be unambiguously identified from time-course gene expression data. We apply the method to two time-course gene expression datasets to illustrate it.

Peptidology: short amino acid modules in cell biology and immunology

Summary.Short amino acid motifs, either linear sequences or discontinuous amino acid groupings, can interact with specific protein domains, so exerting a central role in cell adhesion, signal transduction, hormone activity, regulation of transcript expression, enzyme activity, and antigen-antibody interaction. Here, we analyze the literature for such critical short amino acid motifs to determine the minimal peptide length involved in biologically important interactions. We report the pentapeptide unit as a common minimal amino acid sequence critically involved in peptide-protein interaction and immune recognition. The present survey may have implications in defining the dimensional module for peptide-based therapeutical approaches such as the development of novel antibiotics, enzyme inhibitors/activators, mimetic agonists/antagonists of neuropeptides, thrombolitic agents, specific anti-viral agents, etc. In such a therapeutical context, it is of considerable interest that low molecular weight peptides can easily cross biological barriers, are less susceptible to protease attacks, and can be administered at high concentrations. In addition, small peptides are a rational target for strategies aimed at antigen-specific immunotherapeutical intervention. As an example, specific short peptide fragments might be used to elicit antibodies capable of reacting with the full-length proteins containing the peptide fragment’s amino acid sequence, so abolishing the risk of cross-reactivity.

Strength in numbers: achieving greater accuracy in MHC-I binding prediction by combining the results from multiple prediction tools

BackgroundPeptides derived from endogenous antigens can bind to MHC class I molecules. Those which bind with high affinity can invoke a CD8+ immune response, resulting in the destruction of infected cells. Much work in immunoinformatics has involved the algorithmic prediction of peptide binding affinity to various MHC-I alleles. A number of tools for MHC-I binding prediction have been developed, many of which are available on the web.ResultsWe hypothesize that peptides predicted by a number of tools are more likely to bind than those predicted by just one tool, and that the likelihood of a particular peptide being a binder is related to the number of tools that predict it, as well as the accuracy of those tools. To this end, we have built and tested a heuristic-based method of making MHC-binding predictions by combining the results from multiple tools. The predictive performance of each individual tool is first ascertained. These performance data are used to derive weights such that the predictions of tools with better accuracy are given greater credence. The combined tool was evaluated using ten-fold cross-validation and was found to signicantly outperform the individual tools when a high specificity threshold is used. It performs comparably well to the best-performing individual tools at lower specificity thresholds. Finally, it also outperforms the combination of the tools resulting from linear discriminant analysis.ConclusionA heuristic-based method of combining the results of the individual tools better facilitates the scanning of large proteomes for potential epitopes, yielding more actual high-affinity binders while reporting very few false positives.

Mycobacterium avium subsp. paratuberculosis Inhibits Gamma Interferon-Induced Signaling in Bovine Monocytes: Insights into the Cellular Mechanisms of Johne's Disease

ABSTRACT Mycobacterium avium subsp. paratuberculosis is the causative agent of Johnes disease in cattle and may have implications for human health. Establishment of chronic infection by M. avium subsp. paratuberculosis depends on its subversion of host immune responses. This includes blocking the ability of infected macrophages to be activated by gamma interferon (IFN-γ) for clearance of this intracellular pathogen. To define the mechanism by which M. avium subsp. paratuberculosis subverts this critical host cell function, patterns of signal transduction to IFN-γ stimulation of uninfected and M. avium subsp. paratuberculosis-infected bovine monocytes were determined through bovine-specific peptide arrays for kinome analysis. Pathway analysis of the kinome data indicated activation of the JAK-STAT pathway, a hallmark of IFN-γ signaling, in uninfected monocytes. In contrast, IFN-γ stimulation of M. avium subsp. paratuberculosis-infected monocytes failed to induce patterns of peptide phosphorylation consistent with JAK-STAT activation. The inability of IFN-γ to induce differential phosphorylation of peptides corresponding to early JAK-STAT intermediates in infected monocytes indicates that M. avium subsp. paratuberculosis blocks responsiveness at, or near, the IFN-γ receptor. Consistent with this hypothesis, increased expression of negative regulators of the IFN-γ receptors SOCS1 and SOCS3 as well as decreased expression of IFN-γ receptor chains 1 and 2 is observed in M. avium subsp. paratuberculosis-infected monocytes. These patterns of expression are functionally consistent with the kinome data and offer a mechanistic explanation for this critical M. avium subsp. paratuberculosis behavior. Understanding this mechanism may contribute to the rational design of more effective vaccines and/or therapeutics for Johnes disease.

Massive peptide sharing between viral and human proteomes

Abstract Thirty viral proteomes were examined for amino acid sequence similarity to the human proteome, and, in parallel, a control of 30 sets of human proteins was analyzed for internal human overlapping. We find that all of the analyzed 30 viral proteomes, independently of their structural or pathogenic characteristics, present a high number of pentapeptide overlaps to the human proteome. Among the examined viruses, human T-lymphotropic virus 1, Rubella virus, and hepatitis C virus present the highest number of viral overlaps to the human proteome. The widespread and ample distribution of viral amino acid sequences through the human proteome indicates that viral and human proteins are formed of common peptide backbone units and suggests a fluid compositional chimerism in phylogenetic entities canonically classified distantly as viruses and Homo sapiens. Importantly, the massive viral to human peptide overlapping calls into question the possibility of a direct causal association between virus–host sharing of amino acid sequences and incitement to autoimmune reactions through molecular recognition of common motifs.

A Systematic Approach for Analysis of Peptide Array Kinome Data

A new method of analysis of kinome data takes account of the differences between peptide arrays and DNA microarrays. The central roles of kinases in cellular processes and diseases make them highly attractive as indicators of biological responses and as therapeutic targets. Peptide arrays are emerging as an important means of characterizing kinome activity. Currently, the computational tools used to perform high-throughput kinome analyses are not specifically tailored to the nature of the data, which hinders extraction of biological information and overall progress in the field. We have developed a method for kinome analysis, which is implemented as a software pipeline in the R environment. Components and parameters were chosen to address the technical and biological characteristics of kinome microarrays. We performed comparative analysis of kinome data sets that corresponded to stimulation of immune cells with ligands of well-defined signaling pathways: bovine monocytes treated with interferon-γ (IFN-γ), CpG-containing nucleotides, or lipopolysaccharide (LPS). The data sets for each of the treatments were analyzed with our methodology as well as with three other commonly used approaches. The methods were evaluated on the basis of statistical confidence of calculated values with respect to technical and biological variability, and the statistical confidence (P values) by which the known signaling pathways could be independently identified by the pathway analysis of InnateDB (a Web-based resource for innate immunity interactions and pathways). By considering the particular attributes of kinome data, we found that our approach identified more of the peptides involved in the pathways than did the other compared methods and that it did so at a much higher degree of statistical confidence.

Grading pistachio nuts using a neural network approach

A multi-structure neural network (MSNN) classifier was proposed and applied to classify four varieties (classes) of pistachio nuts. The MSNN classifier consisted of four parallel discriminators (one per class), followed by a maximum selector. Each discriminator was a feed-forward neural network with two hidden layers and a single-neuron output layer. The discriminators were individually trained using physical attributes of the nuts extracted from their images as input. The performance of MSNN classifier was compared with the performance of a multi-layer feed-forward neural network (MLNN) classifier. The average classification accuracy of MSNN classifier was 95.9%, an increase of over 8.9% of the performance of MLNN.

Altered Toll-Like Receptor 9 Signaling in Mycobacterium avium subsp. paratuberculosis-Infected Bovine Monocytes Reveals Potential Therapeutic Targets

ABSTRACT Mycobacterium avium subsp. paratuberculosis is the causative agent of Johnes disease in cattle. The complex, multifaceted interaction of M. avium subsp. paratuberculosis with its host includes dampening the ability of infected cells to respond to stimuli that promote M. avium subsp. paratuberculosis clearance. By disrupting host defenses, M. avium subsp. paratuberculosis creates an intracellular environment that favors the establishment and maintenance of infection. Toll-like receptors (TLRs) are important sensors that initiate innate immune responses to microbial challenge and are also immunotherapeutic targets. For example, TLR9 contributes to host defense against M. avium subsp. paratuberculosis, and its agonists (CpG oligodeoxynucleotides [ODNs]) are under investigation for treatment of Johnes disease and other infections. Here we demonstrate that M. avium subsp. paratuberculosis infection changes the responsiveness of bovine monocytes to TLR9 stimulation. M. avium subsp. paratuberculosis inhibits classical TLR9-mediated responses despite a 10-fold increase in TLR9 expression and maintained uptake of CpG ODNs. Other TLR9-mediated responses, such as oxidative burst, which occur through noncanonical signaling, remain functional. Kinome analysis verifies that classic TLR9 signaling is blocked by M. avium subsp. paratuberculosis infection and that signaling instead proceeds through a Pyk2-mediated mechanism. Pyk2-mediated signaling does not hinder infection, as CpG ODNs fail to promote M. avium subsp. paratuberculosis clearance. Indeed, Pyk2 signaling appears to be an important aspect of M. avium subsp. paratuberculosis infection, as Pyk2 inhibitors significantly reduce the number of intracellular M. avium subsp. paratuberculosis bacteria. The actions of M. avium subsp. paratuberculosis on TLR9 signaling may represent a strategy to generate a host environment which is better suited for infection, revealing potential new targets for therapeutic intervention.

DYNAMIC MODEL-BASED CLUSTERING FOR TIME–COURSE GENE EXPRESSION DATA

Microarray technology has produced a huge body of time-course gene expression data. Such gene expression data has proved useful in genomic disease diagnosis and genomic drug design. The challenge is how to uncover useful information in such data. Cluster analysis has played an important role in analyzing gene expression data. Many distance/correlation- and static model-based clustering techniques have been applied to time-course expression data. However, these techniques are unable to account for the dynamics of such data. It is the dynamics that characterize the data and that should be considered in cluster analysis so as to obtain high quality clustering. This paper proposes a dynamic model-based clustering method for time-course gene expression data. The proposed method regards a time-course gene expression dataset as a set of time series, generated by a number of stochastic processes. Each stochastic process defines a cluster and is described by an autoregressive model. A relocation-iteration algorithm is proposed to identity the model parameters and posterior probabilities are employed to assign each gene to an appropriate cluster. A bootstrapping method and an average adjusted Rand index (AARI) are employed to measure the quality of clustering. Computational experiments are performed on a synthetic and three real time-course gene expression datasets to investigate the proposed method. The results show that our method allows the better quality clustering than other clustering methods (e.g. k-means) for time-course gene expression data, and thus it is a useful and powerful tool for analyzing time-course gene expression data.