Ian M. Donaldson

Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry

The recent abundance of genome sequence data has brought an urgent need for systematic proteomics to decipher the encoded protein networks that dictate cellular function. To date, generation of large-scale protein–protein interaction maps has relied on the yeast two-hybrid system, which detects binary interactions through activation of reporter gene expression. With the advent of ultrasensitive mass spectrometric protein identification methods, it is feasible to identify directly protein complexes on a proteome-wide scale. Here we report, using the budding yeast Saccharomyces cerevisiae as a test case, an example of this approach, which we term high-throughput mass spectrometric protein complex identification (HMS-PCI). Beginning with 10% of predicted yeast proteins as baits, we detected 3,617 associated proteins covering 25% of the yeast proteome. Numerous protein complexes were identified, including many new interactions in various signalling pathways and in the DNA damage response. Comparison of the HMS-PCI data set with interactions reported in the literature revealed an average threefold higher success rate in detection of known complexes compared with large-scale two-hybrid studies. Given the high degree of connectivity observed in this study, even partial HMS-PCI coverage of complex proteomes, including that of humans, should allow comprehensive identification of cellular networks.

iRefIndex: A consolidated protein interaction database with provenance

BackgroundInteraction data for a given protein may be spread across multiple databases. We set out to create a unifying index that would facilitate searching for these data and that would group together redundant interaction data while recording the methods used to perform this grouping.ResultsWe present a method to generate a key for a protein interaction record and a key for each participant protein. These keys may be generated by anyone using only the primary sequence of the proteins, their taxonomy identifiers and the Secure Hash Algorithm. Two interaction records will have identical keys if they refer to the same set of identical protein sequences and taxonomy identifiers. We define records with identical keys as a redundant group. Our method required that we map protein database references found in interaction records to current protein sequence records. Operations performed during this mapping are described by a mapping score that may provide valuable feedback to source interaction databases on problematic references that are malformed, deprecated, ambiguous or unfound. Keys for protein participants allow for retrieval of interaction information independent of the protein references used in the original records.ConclusionWe have applied our method to protein interaction records from BIND, BioGrid, DIP, HPRD, IntAct, MINT, MPact, MPPI and OPHID. The resulting interaction reference index is provided in PSI-MITAB 2.5 format at http://irefindex.uio.no. This index may form the basis of alternative redundant groupings based on gene identifiers or near sequence identity groupings.

PreBIND and Textomy – mining the biomedical literature for protein-protein interactions using a support vector machine

BackgroundThe majority of experimentally verified molecular interaction and biological pathway data are present in the unstructured text of biomedical journal articles where they are inaccessible to computational methods. The Biomolecular interaction network database (BIND) seeks to capture these data in a machine-readable format. We hypothesized that the formidable task-size of backfilling the database could be reduced by using Support Vector Machine technology to first locate interaction information in the literature. We present an information extraction system that was designed to locate protein-protein interaction data in the literature and present these data to curators and the public for review and entry into BIND.ResultsCross-validation estimated the support vector machines test-set precision, accuracy and recall for classifying abstracts describing interaction information was 92%, 90% and 92% respectively. We estimated that the system would be able to recall up to 60% of all non-high throughput interactions present in another yeast-protein interaction database. Finally, this system was applied to a real-world curation problem and its use was found to reduce the task duration by 70% thus saving 176 days.ConclusionsMachine learning methods are useful as tools to direct interaction and pathway database back-filling; however, this potential can only be realized if these techniques are coupled with human review and entry into a factual database such as BIND. The PreBIND system described here is available to the public at http://bind.ca. Current capabilities allow searching for human, mouse and yeast protein-interaction information.

PSICQUIC and PSISCORE: accessing and scoring molecular interactions

To study proteins in the context of a cellular system, it is essential that the molecules with which a protein interacts are identified and the functional consequence of each interaction is understood. A plethora of resources now exist to capture molecular interaction data from the many laboratories generating…

iRefWeb: interactive analysis of consolidated protein interaction data and their supporting evidence

We present iRefWeb, a web interface to protein interaction data consolidated from 10 public databases: BIND, BioGRID, CORUM, DIP, IntAct, HPRD, MINT, MPact, MPPI and OPHID. iRefWeb enables users to examine aggregated interactions for a protein of interest, and presents various statistical summaries of the data across databases, such as the number of organism-specific interactions, proteins and cited publications. Through links to source databases and supporting evidence, researchers may gauge the reliability of an interaction using simple criteria, such as the detection methods, the scale of the study (high- or low-throughput) or the number of cited publications. Furthermore, iRefWeb compares the information extracted from the same publication by different databases, and offers means to follow-up possible inconsistencies. We provide an overview of the consolidated protein–protein interaction landscape and show how it can be automatically cropped to aid the generation of meaningful organism-specific interactomes. iRefWeb can be accessed at: http://wodaklab.org/iRefWeb. Database URL: http://wodaklab.org/iRefWeb/

Inflammation driven by tumour-specific Th1 cells protects against B-cell cancer

The immune system can both promote and suppress cancer. Chronic inflammation and proinflammatory cytokines such as interleukin (IL)-1 and IL-6 are considered to be tumour promoting. In contrast, the exact nature of protective antitumour immunity remains obscure. Here, we quantify locally secreted cytokines during primary immune responses against myeloma and B-cell lymphoma in mice. Strikingly, successful cancer immunosurveillance mediated by tumour-specific CD4+ T cells is consistently associated with elevated local levels of both proinflammatory (IL-1α, IL-1β and IL-6) and T helper 1 (Th1)-associated cytokines (interferon-γ (IFN-γ), IL-2 and IL-12). Cancer eradication is achieved by a collaboration between tumour-specific Th1 cells and tumour-infiltrating, antigen-presenting macrophages. Th1 cells induce secretion of IL-1β and IL-6 by macrophages. Th1-derived IFN-γ is shown to render macrophages directly cytotoxic to cancer cells, and to induce macrophages to secrete the angiostatic chemokines CXCL9/MIG and CXCL10/IP-10. Thus, inflammation, when driven by tumour-specific Th1 cells, may prevent rather than promote cancer.

Literature curation of protein interactions: measuring agreement across major public databases

Literature curation of protein interaction data faces a number of challenges. Although curators increasingly adhere to standard data representations, the data that various databases actually record from the same published information may differ significantly. Some of the reasons underlying these differences are well known, but their global impact on the interactions collectively curated by major public databases has not been evaluated. Here we quantify the agreement between curated interactions from 15 471 publications shared across nine major public databases. Results show that on average, two databases fully agree on 42% of the interactions and 62% of the proteins curated from the same publication. Furthermore, a sizable fraction of the measured differences can be attributed to divergent assignments of organism or splice isoforms, different organism focus and alternative representations of multi-protein complexes. Our findings highlight the impact of divergent curation policies across databases, and should be relevant to both curators and data consumers interested in analyzing protein-interaction data generated by the scientific community. Database URL: http://wodaklab.org/iRefWeb

Interaction databases on the same page

391 to drive continued investment in their development. If healthcare payers fail to incentivize the sector and set overwhelming barriers to innovation, it will considerably hinder progress in personalized medicine. Test developers as well as regulatory authorities are accepting risks by developing, approving and championing molecular diagnostics; healthcare payers must now also share an element of this risk by adopting a positive stance toward coverage and reimbursement of molecular diagnostic technology.

iRefR: an R package to manipulate the iRefIndex consolidated protein interaction database

BackgroundThe iRefIndex addresses the need to consolidate protein interaction data into a single uniform data resource. iRefR provides the user with access to this data source from an R environment.ResultsThe iRefR package includes tools for selecting specific subsets of interest from the iRefIndex by criteria such as organism, source database, experimental method, protein accessions and publication identifier. Data may be converted between three representations (MITAB, edgeList and graph) for use with other R packages such as igraph, graph and RBGL.The user may choose between different methods for resolving redundancies in interaction data and how n-ary data is represented. In addition, we describe a function to identify binary interaction records that possibly represent protein complexes. We show that the user choice of data selection, redundancy resolution and n-ary data representation all have an impact on graphical analysis.ConclusionsThe package allows the user to control how these issues are dealt with and communicate them via an R-script written using the iRefR package - this will facilitate communication of methods, reproducibility of network analyses and further modification and comparison of methods by researchers.

Proteome analysis of microtubule-associated proteins and their interacting partners from mammalian brain.

The microtubule (MT) cytoskeleton is essential for a variety of cellular processes. MTs are finely regulated by distinct classes of MT-associated proteins (MAPs), which themselves bind to and are regulated by a large number of additional proteins. We have carried out proteome analyses of tubulin-rich and tubulin-depleted MAPs and their interacting partners isolated from bovine brain. In total, 573 proteins were identified giving us unprecedented access to brain-specific MT-associated proteins from mammalian brain. Most of the standard MAPs were identified and at least 500 proteins have been reported as being associated with MTs. We identified protein complexes with a large number of subunits such as brain-specific motor/adaptor/cargo complexes for kinesins, dynein, and dynactin, and proteins of an RNA-transporting granule. About 25% of the identified proteins were also found in the synaptic vesicle proteome. Analysis of the MS/MS data revealed many posttranslational modifications, amino acid changes, and alternative splice variants, particularly in tau, a key protein implicated in Alzheimer’s disease. Bioinformatic analysis of known protein–protein interactions of the identified proteins indicated that the number of MAPs and their associated proteins is larger than previously anticipated and that our database will be a useful resource to identify novel binding partners.