Abstract: T1DBase (http://www.t1dbase.org) is web platform, which supports the type 1 diabetes (T1D) community. It integrates genetic, genomic and expression data relevant to T1D research across mouse, rat and human and presents this to the user as a set of web pages and tools. This update describes the incorporation of new data sets, tools and curation efforts as well as a new website design to simplify site use. New data sets include curated summary data from four genome-wide association studies relevant to T1D, HaemAtlas-a data set and tool to query gene expression levels in haematopoietic cells and a manually curated table of human T1D susceptibility loci, incorporating genetic overlap with other related diseases. These developments will continue to support T1D research and allow easy access to large and complex T1D relevant data sets.
Abstract: IntAct is an open-source, open data molecular interaction database and toolkit. Data is abstracted from the literature or from direct data depositions by expert curators following a deep annotation model providing a high level of detail. As of September 2009, IntAct contains over 200.000 curated binary interaction evidences. In response to the growing data volume and user requests, IntAct now provides a two-tiered view of the interaction data. The search interface allows the user to iteratively develop complex queries, exploiting the detailed annotation with hierarchical controlled vocabularies. Results are provided at any stage in a simplified, tabular view. Specialized views then allows 'zooming in' on the full annotation of interactions, interactors and their properties. IntAct source code and data are freely available at http://www.ebi.ac.uk/intact.
Abstract: Most cellular processes are enabled by cohorts of interacting proteins that form dynamic networks within the plant proteome. The study of these networks can provide insight into protein function and provide new avenues for research. This article informs the plant science community of the currently available sources of protein interaction data and discusses how they can be useful to researchers. Using our recently curated IntAct Arabidopsis thaliana protein-protein interaction data set as an example, we discuss potentials and limitations of the plant interactomes generated to date. In addition, we present our efforts to add value to the interaction data by using them to seed a proteome-wide map of predicted protein subcellular locations.
Abstract: Establishment of similarities between proteins is very important for the study of the relationship between sequence, structure and function and for the analysis of evolutionary relationships. Motif-based search methods play a crucial role in establishing the connections between proteins that are particularly useful for distant relationships. This paper reports SCANMOT, a web-based server that searches for similarities between proteins by simultaneous matching of multiple motifs. SCANMOT searches for similar sequences in entire sequence databases using multiple conserved regions and utilizes inter-motif spacing as restraints. The SCANMOT server is available via http://www.ncbs.res.in/~faculty/mini/scanmot/scanmot.html.
Abstract: Multiple sequence alignment algorithms are very important tools in molecular biology today. Accurate alignment of proteins is central to several areas such as homology modelling, docking studies, understanding evolutionary trends and study of structure-function relationships. In recent times, improvement of existing progressing programs and implementation of new iterative algorithms have made a significant change in this field.
Abstract: A detailed knowledge of a protein's functional site is an absolute prerequisite for understanding its mode of action at the molecular level. However, the rapid pace at which sequence and structural information is being accumulated for proteins greatly exceeds our ability to determine their biochemical roles experimentally. As a result, computational methods are required which allow for the efficient processing of the evolutionary information contained in this wealth of data, in particular that related to the nature and location of functionally important sites and residues. The method presented here, referred to as conserved functional group (CFG) analysis, relies on a simplified representation of the chemical groups found in amino acid side-chains to identify functional sites from a single protein structure and a number of its sequence homologues. We show that CFG analysis can fully or partially predict the location of functional sites in approximately 96% of the 470 cases tested and that, unlike other methods available, it is able to tolerate wide variations in sequence identity. In addition, we discuss its potential in a structural genomics context, where automation, scalability and efficiency are critical, and an increasing number of protein structures are determined with no prior knowledge of function. This is exemplified by our analysis of the hypothetical protein Ydde_Ecoli, whose structure was recently solved by members of the North East Structural Genomics consortium. Although the proposed active site for this protein needs to be validated experimentally, this example illustrates the scope of CFG analysis as a general tool for the identification of residues likely to play an important role in a protein's biochemical function. Thus, our method offers a convenient solution to rapidly and automatically process the vast amounts of data that are beginning to emerge from structural genomics projects.
