A structural annotation resource for the selection of putative target proteins in the malaria parasite

<p>Abstract</p> <p>Background</p> <p>Protein structure plays a pivotal role in elucidating mechanisms of parasite functioning and drug resistance. Moreover, protein structure aids the determination of protein function, which can together with the structure be used to identify novel drug targets in the parasite. However, various structural features in <it>Plasmodium falciparum </it>proteins complicate the experimental determination of protein structures. Limited similarity to proteins in the Protein Data Bank and the shortage of solved protein structures in the malaria parasite necessitate genome-scale structural annotation of <it>P. falciparum </it>proteins. Additionally, the annotation of a range of structural features facilitates the identification of suitable targets for experimental and computational studies.</p> <p>Methods</p> <p>An integrated structural annotation system was developed and applied to <it>P. falciparum</it>, <it>Plasmodium vivax </it>and <it>Plasmodium yoelii</it>. The annotation included searches for sequence similarity, patterns and domains in addition to the following predictions: secondary structure, transmembrane helices, protein disorder, low complexity, coiled-coils and small molecule interactions. Subsequently, candidate proteins for further structural studies were identified based on the annotated structural features.</p> <p>Results</p> <p>The annotation results are accessible through a web interface, enabling users to select groups of proteins which fulfil multiple criteria pertaining to structural and functional features <abbrgrp><abbr bid="B1">1</abbr></abbrgrp>. Analysis of features in the <it>P. falciparum </it>proteome showed that protein-interacting proteins contained a higher percentage of predicted disordered residues than non-interacting proteins. Proteins interacting with 10 or more proteins have a disordered content concentrated in the range of 60–100%, while the disorder distribution for proteins having only one interacting partner, was more evenly spread.</p> <p>Conclusion</p> <p>A series of <it>P. falciparum </it>protein targets for experimental structure determination, comparative modelling and <it>in silico </it>docking studies were putatively identified. The system is available for public use, where researchers may identify proteins by querying with multiple physico-chemical, sequence similarity and interaction features.</p

The Long March: A Sample Preparation Technique that Enhances Contig Length and Coverage by High-Throughput Short-Read Sequencing

High-throughput short-read technologies have revolutionized DNA sequencing by drastically reducing the cost per base of sequencing information. Despite producing gigabases of sequence per run, these technologies still present obstacles in resequencing and de novo assembly applications due to biased or insufficient target sequence coverage. We present here a simple sample preparation method termed the “long march” that increases both contig lengths and target sequence coverage using high-throughput short-read technologies. By incorporating a Type IIS restriction enzyme recognition motif into the sequencing primer adapter, successive rounds of restriction enzyme cleavage and adapter ligation produce a set of nested sub-libraries from the initial amplicon library. Sequence reads from these sub-libraries are offset from each other with enough overlap to aid assembly and contig extension. We demonstrate the utility of the long march in resequencing of the Plasmodium falciparum transcriptome, where the number of genomic bases covered was increased by 39%, as well as in metagenomic analysis of a serum sample from a patient with hepatitis B virus (HBV)-related acute liver failure, where the number of HBV bases covered was increased by 42%. We also offer a theoretical optimization of the long march for de novo sequence assembly

Advances of genomic science and systems biology in renal transplantation: a review

The diagnosis of rejection in kidney transplant patients is based on histologic classification of a graft biopsy. The current “gold standard” is the Banff 97 criteria; however, there are several limitations in classifying rejection based on biopsy samples. First, a biopsy involves an invasive procedure. Second, there is significant variance among blinded pathologists in the interpretation of a biopsy. And third, there is also variance between the histology and the molecular profiles of a biopsy. To increase the positive predictive value of classifiers of rejection, a Banff committee is developing criteria that integrate histologic and molecular data into a unified classifier that could diagnose and prognose rejection. To develop the most appropriate molecular criteria, there have been studies by multiple groups applying omics technologies in attempts to identify biomarkers of rejection. In this review, we discuss studies using genome-wide data sets of the transcriptome and proteome to investigate acute rejection, chronic allograft dysfunction, and tolerance. We also discuss studies which focus on genetic biomarkers in urine and peripheral blood, which will provide clinicians with minimally invasive methods for monitoring transplant patients. We also discuss emerging technologies, including whole-exome sequencing and RNA-Seq and new bioinformatic and systems biology approaches, which should increase the ability to develop both biomarkers and mechanistic understanding of the rejection process

The Plasmodium Export Element Revisited

We performed a bioinformatical analysis of protein export elements (PEXEL) in the putative proteome of the malaria parasite Plasmodium falciparum. A protein family-specific conservation of physicochemical residue profiles was found for PEXEL-flanking sequence regions. We demonstrate that the family members can be clustered based on the flanking regions only and display characteristic hydrophobicity patterns. This raises the possibility that the flanking regions may contain additional information for a family-specific role of PEXEL. We further show that signal peptide cleavage results in a positional alignment of PEXEL from both proteins with, and without, a signal peptide

Gene-Specific Signatures of Elevated Non-Synonymous Substitution Rates Correlate Poorly across the Plasmodium Genus

BACKGROUND: Comparative genome analyses of parasites allow large scale investigation of selective pressures shaping their evolution. An acute limitation to such analysis of Plasmodium falciparum is that there is only very partial low-coverage genome sequence of the most closely related species, the chimpanzee parasite P. reichenowi. However, if orthologous genes have been under similar selective pressures throughout the Plasmodium genus then positive selection on the P. falciparum lineage might be predicted to some extent by analysis of other lineages. PRINCIPAL FINDINGS: Here, three independent pairs of closely related species in different sub-generic clades (P. falciparum and P. reichenowi; P. vivax and P. knowlesi; P. yoelii and P. berghei) were compared for a set of 43 candidate ligand genes considered likely to be under positive directional selection and a set of 102 control genes for which there was no selective hypothesis. The ratios of non-synonymous to synonymous substitutions (dN/dS) were significantly elevated in the candidate ligand genes compared to control genes in each of the three clades. However, the rank order correlation of dN/dS ratios for individual candidate genes was very low, less than the correlation for the control genes. SIGNIFICANCE: The inability to predict positive selection on a gene in one lineage by identifying elevated dN/dS ratios in the orthologue within another lineage needs to be noted, as it reflects that adaptive mutations are generally rare events that lead to fixation in individual lineages. Thus it is essential to complete the genome sequences of particular species of phylogenetic importance, such as P. reichenowi

A Kernel for Open Source Drug Discovery in Tropical Diseases

Open source drug discovery, a promising alternative avenue to conventional patent-based drug development, has so far remained elusive with few exceptions. A major stumbling block has been the absence of a critical mass of preexisting work that volunteers can improve through a series of granular contributions. This paper introduces the results from a newly assembled computational pipeline for identifying protein targets for drug discovery in ten organisms that cause tropical diseases. We have also experimentally tested two promising targets for their binding to commercially available drugs, validating one and invalidating the other. The resulting kernel provides a base of drug targets and lead candidates around which an open source community can nucleate. We invite readers to donate their judgment and in silico and in vitro experiments to develop these targets to the point where drug optimization can begin

HECTOR: Enabling Microarray Experiments over the Hellenic Grid Infrastructure

Biologists, medical experts, biochemical engineers and researchers working on DNA microarray experiments are increasingly turning on Grid computing with the scope of leveraging the Grid's computing power, immense storage resources, and quality of service to the expedient processing of a wide range of datasets. In this paper we present a combined experience of grid application experts and bioinformatics scientists in deploying a pilot service enabling computationally efficient processing and analysis of data stemming from microarray experiments. This pilot service is accessible over the Hellenic portion of the EGEE grid and has been demonstrated in the scope of several public events. We highlight the process of grid application enablement, grid deployment challenges, as well as lessons learnt from a bi-annual effort to port and deploy a MATLAB DNA microarray application on a production grid. In addition to describing the parallelization of the application, we also emphasize on the development of a distributed federated database for storing and post-processing the results of the microarray experiments. Overall we believe that our experience could be proven valuable not only to microarray data scientists but also to other Grid users that intend to Grid-enable and deploy their applications