DNA-binding protein prediction using plant specific support vector machines:validation and application of a new genome annotation tool

There are currently 151 plants with draft genomes available but levels of functional annotation for putative protein products are low. Therefore, accurate computational predictions are essential to annotate genomes in the first instance, and to provide focus for the more costly and time consuming functional assays that follow. DNA-binding proteins are an important class of proteins that require annotation, but current computational methods are not applicable for genome wide predictions in plant species. Here, we explore the use of species and lineage specific models for the prediction of DNA-binding proteins in plants. We show that a species specific support vector machine model based on Arabidopsis sequence data is more accurate (accuracy 81%) than a generic model (74%), and based on this we develop a plant specific model for predicting DNA-binding proteins. We apply this model to the tomato proteome and demonstrate its ability to perform accurate high-throughput prediction of DNA-binding proteins. In doing so, we have annotated 36 currently uncharacterised proteins by assigning a putative DNA-binding function. Our model is publically available and we propose it be used in combination with existing tools to help increase annotation levels of DNA-binding proteins encoded in plant genomes

Deriving a mutation index of carcinogenicity using protein structure and protein interfaces

With the advent of Next Generation Sequencing the identification of mutations in the genomes of healthy and diseased tissues has become commonplace. While much progress has been made to elucidate the aetiology of disease processes in cancer, the contributions to disease that many individual mutations make remain to be characterised and their downstream consequences on cancer phenotypes remain to be understood. Missense mutations commonly occur in cancers and their consequences remain challenging to predict. However, this knowledge is becoming more vital, for both assessing disease progression and for stratifying drug treatment regimes. Coupled with structural data, comprehensive genomic databases of mutations such as the 1000 Genomes project and COSMIC give an opportunity to investigate general principles of how cancer mutations disrupt proteins and their interactions at the molecular and network level. We describe a comprehensive comparison of cancer and neutral missense mutations; by combining features derived from structural and interface properties we have developed a carcinogenicity predictor, InCa (Index of Carcinogenicity). Upon comparison with other methods, we observe that InCa can predict mutations that might not be detected by other methods. We also discuss general limitations shared by all predictors that attempt to predict driver mutations and discuss how this could impact high-throughput predictions. A web interface to a server implementation is publicly available at http://inca.icr.ac.uk/

New trends in peptide-based anti-biofilm strategies : a review of recent achievements and bioinformatics approaches

Antimicrobial peptides (AMPs) have a broad spectrum of activity and unspecific mechanisms of action. Therefore, they are seen as valid alternatives to overcome clinically relevant biofilms and reduce the chance of acquired resistance. This paper reviews AMPs and anti-biofilm AMP-based strategies and discusses ongoing and future work. Recent studies report successful AMP-based prophylactic and therapeutic strategies, several databases catalogue AMP information and analysis tools, and novel bioinformatics tools are supporting AMP discovery and design. However, most AMP studies are performed with planktonic cultures, and most studies on sessile cells test AMPs on growing rather than mature biofilms. Promising preliminary synergistic studies have to be consubstantiated and the study of functionalized coatings with AMPs must be further explored. Standardized operating protocols, to enforce the repeatability and reproducibility of AMP anti-biofilm tests, and automated means of screening and processing the ever-expanding literature are still missing.Financial support from IBB-CEB and Fundacao para a Ciencia e Tecnologia (FCT) and European Community fund FEDER, through Program COMPETE, in the ambit of the FCT project 'PTDC/SAU-SAP/113196/2009/ FCOMP-01-0124-FEDER-016012' is gratefully acknowledged

OREMPdb: a semantic dictionary of computational pathway models

<p>Abstract</p> <p>Background</p> <p>The information coming from biomedical ontologies and computational pathway models is expanding continuously: research communities keep this process up and their advances are generally shared by means of dedicated resources published on the web. In fact, such models are shared to provide the characterization of molecular processes, while biomedical ontologies detail a semantic context to the majority of those pathways. Recent advances in both fields pave the way for a scalable information integration based on aggregate knowledge repositories, but the lack of overall standard formats impedes this progress. Indeed, having different objectives and different abstraction levels, most of these resources "speak" different languages. Semantic web technologies are here explored as a means to address some of these problems.</p> <p>Methods</p> <p>Employing an extensible collection of interpreters, we developed OREMP (Ontology Reasoning Engine for Molecular Pathways), a system that abstracts the information from different resources and combines them together into a coherent ontology. Continuing this effort we present OREMPdb; once different pathways are fed into OREMP, species are linked to the external ontologies referred and to reactions in which they participate. Exploiting these links, the system builds species-sets, which encapsulate species that operate together. Composing all of the reactions together, the system computes all of the reaction paths from-and-to all of the species-sets.</p> <p>Results</p> <p>OREMP has been applied to the curated branch of BioModels (2011/04/15 release) which overall contains 326 models, 9244 reactions, and 5636 species. OREMPdb is the semantic dictionary created as a result, which is made of 7360 species-sets. For each one of these sets, OREMPdb links the original pathway and the link to the original paper where this information first appeared. </p

Abnormal cognition, sleep, EEG and brain metabolism in a novel knock-in Alzheimer mouse, PLB1

Peer reviewedPublisher PD

Combinatorial Clustering of Residue Position Subsets Predicts Inhibitor Affinity across the Human Kinome

The protein kinases are a large family of enzymes that play fundamental roles in propagating signals within the cell. Because of the high degree of binding site similarity shared among protein kinases, designing drug compounds with high specificity among the kinases has proven difficult. However, computational approaches to comparing the 3-dimensional geometry and physicochemical properties of key binding site residue positions have been shown to be informative of inhibitor selectivity. The Combinatorial Clustering Of Residue Position Subsets (CCORPS) method, introduced here, provides a semi-supervised learning approach for identifying structural features that are correlated with a given set of annotation labels. Here, CCORPS is applied to the problem of identifying structural features of the kinase ATP binding site that are informative of inhibitor binding. CCORPS is demonstrated to make perfect or near-perfect predictions for the binding affinity profile of 8 of the 38 kinase inhibitors studied, while only having overall poor predictive ability for 1 of the 38 compounds. Additionally, CCORPS is shown to identify shared structural features across phylogenetically diverse groups of kinases that are correlated with binding affinity for particular inhibitors; such instances of structural similarity among phylogenetically diverse kinases are also shown to not be rare among kinases. Finally, these function-specific structural features may serve as potential starting points for the development of highly specific kinase inhibitors

TDP-43 stabilises the processing intermediates of mitochondrial transcripts

The 43-kDa trans-activating response region DNA-binding protein 43 (TDP-43) is a product of a causative gene for amyotrophic lateral sclerosis (ALS). Despite of accumulating evidence that mitochondrial dysfunction underlies the pathogenesis of TDP-43–related ALS, the roles of wild-type TDP-43 in mitochondria are unknown. Here, we show that the small TDP-43 population present in mitochondria binds directly to a subset of mitochondrial tRNAs and precursor RNA encoded in L-strand mtDNA. Upregulated expression of TDP-43 stabilised the processing intermediates of mitochondrial polycistronic transcripts and their products including the components of electron transport and 16S mt-rRNA, similar to the phenotype observed in cells deficient for mitochondrial RNase P. Conversely, TDP-43 deficiency reduced the population of processing intermediates and impaired mitochondrial function. We propose that TDP-43 has a novel role in maintaining mitochondrial homeostasis by regulating the processing of mitochondrial transcripts

Maternal microchimerism in the livers of patients with Biliary atresia

BACKGROUND: Biliary atresia (BA) is a neonatal cholestatic disease of unknown etiology. It is the leading cause of liver transplantation in children. Many similarities exist between BA and graft versus host disease suggesting engraftment of maternal cells during gestation could result in immune responses that lead to BA. The aim of this study was to determine the presence and extent of maternal microchimerism (MM) in the livers of infants with BA. METHODS: Using fluorescent in situ hybridization (FISH), 11 male BA & 4 male neonatal hepatitis (NH) livers, which served as controls, were analyzed for X and Y-chromosomes. To further investigate MM in BA, 3 patients with BA, and their mothers, were HLA typed. Using immunohistochemical stains, the BA livers were examined for MM. Four additional BA livers underwent analysis by polymerase chain reaction (PCR) for evidence of MM. RESULTS: By FISH, 8 BA and 2 NH livers were interpretable. Seven of eight BA specimens showed evidence of MM. The number of maternal cells ranged from 2–4 maternal cells per biopsy slide. Neither NH specimen showed evidence of MM. In addition, immunohistochemical stains confirmed evidence of MM. Using PCR, a range of 1–142 copies of maternal DNA per 25,000 copies of patients DNA was found. CONCLUSIONS: Maternal microchimerism is present in the livers of patients with BA and may contribute to the pathogenesis of BA

The YARHG Domain: An Extracellular Domain in Search of a Function

We have identified a new bacterial protein domain that we hypothesise binds to peptidoglycan. This domain is called the YARHG domain after the most highly conserved sequence-segment. The domain is found in the extracellular space and is likely to be composed of four alpha-helices. The domain is found associated with protein kinase domains, suggesting it is associated with signalling in some bacteria. The domain is also found associated with three different families of peptidases. The large number of different domains that are found associated with YARHG suggests that it is a useful functional module that nature has recombined multiple times