Extracting protein-protein interactions from MEDLINE using the hidden vector state model

A major challenge in text mining for biomedicine is automatically extracting protein-protein interactions from the vast amount of biomedical literature. We have constructed an information extraction system based on the Hidden Vector State (HVS) model for protein-protein interactions. The HVS model can be trained using only lightly annotated data whilst simultaneously retaining sufficient ability to capture the hierarchical structure. When applied in extracting protein-protein interactions, we found that it performed better than other established statistical methods and achieved 61.5% in F-score with balanced recall and precision values. Moreover, the statistical nature of the pure data-driven HVS model makes it intrinsically robust and it can be easily adapted to other domains.

Ontology-based protein-protein interactions extraction from literature using the hidden vector state model

This paper proposes a novel framework of incorporating protein-protein interactions (PPI) ontology knowledge into PPI extraction from biomedical literature in order to address the emerging challenges of deep natural language understanding. It is built upon the existing work on relation extraction using the Hidden Vector State (HVS) model. The HVS model belongs to the category of statistical learning methods. It can be trained directly from un-annotated data in a constrained way whilst at the same time being able to capture the underlying named entity relationships. However, it is difficult to incorporate background knowledge or non-local information into the HVS model. This paper proposes to represent the HVS model as a conditionally trained undirected graphical model in which non-local features derived from PPI ontology through inference would be easily incorporated. The seamless fusion of ontology inference with statistical learning produces a new paradigm to information extraction.

From biomedical literature to knowledge:mining protein-protein interactions

To date, more than 16 million citations of published articles in biomedical domain are available in the MEDLINE database. These articles describe the new discoveries which accompany a tremendous development in biomedicine during the last decade. It is crucial for biomedical researchers to retrieve and mine some specific knowledge from the huge quantity of published articles with high efficiency. Researchers have been engaged in the development of text mining tools to find knowledge such as protein-protein interactions, which are most relevant and useful for specific analysis tasks. This chapter provides a road map to the various information extraction methods in biomedical domain, such as protein name recognition and discovery of protein-protein interactions. Disciplines involved in analyzing and processing unstructured-text are summarized. Current work in biomedical information extracting is categorized. Challenges in the field are also presented and possible solutions are discussed.

Protein-protein interactions classification from text via local learning with class priors

Text classification is essential for narrowing down the number of documents relevant to a particular topic for further pursual, especially when searching through large biomedical databases. Protein-protein interactions are an example of such a topic with databases being devoted specifically to them. This paper proposed a semi-supervised learning algorithm via local learning with class priors (LL-CP) for biomedical text classification where unlabeled data points are classified in a vector space based on their proximity to labeled nodes. The algorithm has been evaluated on a corpus of biomedical documents to identify abstracts containing information about protein-protein interactions with promising results. Experimental results show that LL-CP outperforms the traditional semisupervised learning algorithms such as SVMand it also performs better than local learning without incorporating class priors.

Receptor activity-modifying protein-dependent effects of mutations in the calcitonin receptor-like receptor:implications for adrenomedullin and calcitonin gene-related peptide pharmacology

Background and Purpose Receptor activity-modifying proteins (RAMPs) define the pharmacology of the calcitonin receptor-like receptor (CLR). The interactions of the different RAMPs with this class B GPCR yield high-affinity calcitonin gene-related peptide (CGRP) or adrenomedullin (AM) receptors. However, the mechanism for this is unclear. Experimental Approach Guided by receptor models, we mutated residues in the N-terminal helix of CLR, RAMP2 and RAMP3 hypothesized to be involved in peptide interactions. These were assayed for cAMP production with AM, AM2 and CGRP together with their cell surface expression. Binding studies were also conducted for selected mutants. Key Results An important domain for peptide interactions on CLR from I32 to I52 was defined. Although I41 was universally important for binding and receptor function, the role of other residues depended on both ligand and RAMP. Peptide binding to CLR/RAMP3 involved a more restricted range of residues than that to CLR/RAMP1 or CLR/RAMP2. E101 of RAMP2 had a major role in AM interactions, and F111/W84 of RAMP2/3 was important with each peptide. Conclusions and Implications RAMP-dependent effects of CLR mutations suggest that the different RAMPs control accessibility of peptides to binding residues situated on the CLR N-terminus. RAMP3 appears to alter the role of specific residues at the CLR-RAMP interface compared with RAMP1 and RAMP2. © 2013 The Authors. British Journal of Pharmacology published by John Wiley &. Sons Ltd on behalf of The British Pharmacological Society.

Protein interactions studied by SAXS:effect of ionic strength and protein concentration for BSA in aqueous solutions

We have studied a series of samples of bovine serum albumin (BSA) solutions with protein concentration, c, ranging from 2 to 500 mg/mL and ionic strength, I, from 0 to 2 M by small-angle X-ray scattering (SAXS). The scattering intensity distribution was compared to simulations using an oblate ellipsoid form factor with radii of 17 x 42 x 42 A, combined with either a screened Coulomb, repulsive structure factor, S-SC(q), or an attractive square-well structure factor, S-SW(q). At pH = 7, BSA is negatively charged. At low ionic strength, I <0.3 M, the total interaction exhibits a decrease of the repulsive interaction when compared to the salt-free solution, as the net surface charge is screened, and the data can be fitted by assuming an ellipsoid form factor and screened Coulomb interaction. At moderate ionic strength (0.3-0.5 M), the interaction is rather weak, and a hard-sphere structure factor has been used to simulate the data with a higher volume fraction. Upon further increase of the ionic strength (I >= 1.0 M), the overall interaction potential was dominated by an additional attractive potential, and the data could be successfully fitted by an ellipsoid form factor and a square-well potential model. The fit parameters, well depth and well width, indicate that the attractive potential caused by a high salt concentration is weak and long-ranged. Although the long-range, attractive potential dominated the protein interaction, no gelation or precipitation was observed in any of the samples. This is explained by the increase of a short-range, repulsive interaction between protein molecules by forming a hydration layer with increasing salt concentration. The competition between long-range, attractive and short-range, repulsive interactions accounted for the stability of concentrated BSA solution at high ionic strength.

Tear analysis and lens-tear interactions:Part I. Protein fingerprinting with microfluidic technology

The purpose of this work is to establish the application of a fully automated microfluidic chip based protein separation assay in tear analysis. It is rapid, requires small sample volumes and is vastly superior to, and more convenient than, comparable conventional gel electrophoresis assays. The protein sizing chip technology was applied to three specific fields of analysis. Firstly tear samples were collected regularly from subjects establishing the baseline effects of tear stimulation, tear state and patient health. Secondly tear samples were taken from lens wearing eyes and thirdly the use of microfluidic technology was assessed as a means to investigate a novel area of tear analysis, which we have termed the 'tear envelope'. Utilising the Agilent 2100 Bioanalyzer in combination with the Protein 200 Plus LabChip kit, these studies investigated tear proteins in the range of 14-200 kDa. Particular attention was paid to the relative concentrations of lysozyme, tear lipocalin, secretory IgA (sIgA), IgG and lactoferrin, together with the overall tear electropherogram 'fingerprint'. Furthermore, whilst lens-tear interaction studies are generally thought of as an investigation into the effects of tears components on the contact lens material, i.e. deposition studies, this report addresses the reverse phenomenon-the effect of the lens, and particularly the newly inserted lens, on the tear fluid composition and dynamics. The use of microfluidic technology provides a significant advance in tear studies and should prove invaluable in tear diagnostics and contact lens performance analysis.

Synthesis, self-assembly and (absence of) protein interactions of poly(glycerol methacrylate)-silicone macro-amphiphiles

The present study focuses on the synthesis of amphiphilic block copolymers containing poly(glycerol monomethacrylate) (PGMMA), showing the advantages of a protection/deprotection strategy based on silyl groups. PGMMA blocks were synthesized via ATRP started by a double functional poly(dimethyl siloxane) (PDMS) macroinitiator of molecular weight ≈7000 g mol-1. The resulting triblock copolymers were characterized by low polydispersity (generally ≤1.1) and their aggregation concentration in water was essentially dominated by the PDMS block length (critical aggregation concentration substantially invariant for GMMA degree of polymerization ≥30). For GMMA blocks with DP > 50, the self-assembly in water produced 35-50 nm spherical micelles, while shorter hydrophilic chains produced larger aggregates apparently displaying worm-like morphologies. Block copolymers with long GMMA chains (DP ≈ 200) produced particularly stable micellar aggregates, which were then selected for a preliminary assessment of the possibility of adsorption of plasma proteins (albumin and fibrinogen); using diffusion NMR as an analytical technique, no significant adsorption was recorded both on micelles and on soluble PGMMA employed as a control, indicating the possibility of a "stealth" behaviour. This journal is © 2013 The Royal Society of Chemistry.

IUPHAR-DB:the IUPHAR database of G protein-coupled receptors and ion channels

The IUPHAR database (IUPHAR-DB) integrates peer-reviewed pharmacological, chemical, genetic, functional and anatomical information on the 354 nonsensory G protein-coupled receptors (GPCRs), 71 ligand-gated ion channel subunits and 141 voltage-gated-like ion channel subunits encoded by the human, rat and mouse genomes. These genes represent the targets of approximately one-third of currently approved drugs and are a major focus of drug discovery and development programs in the pharmaceutical industry. IUPHAR-DB provides a comprehensive description of the genes and their functions, with information on protein structure and interactions, ligands, expression patterns, signaling mechanisms, functional assays and biologically important receptor variants (e.g. single nucleotide polymorphisms and splice variants). In addition, the phenotypes resulting from altered gene expression (e.g. in genetically altered animals or in human genetic disorders) are described. The content of the database is peer reviewed by members of the International Union of Basic and Clinical Pharmacology Committee on Receptor Nomenclature and Drug Classification (NC-IUPHAR); the data are provided through manual curation of the primary literature by a network of over 60 subcommittees of NC-IUPHAR. Links to other bioinformatics resources, such as NCBI, Uniprot, HGNC and the rat and mouse genome databases are provided. IUPHAR-DB is freely available at http://www.iuphar-db.org. © 2008 The Author(s).

Immunological protein-protein interactions:a critical reflection

Life, and the biochemistry of which it is ultimately comprised, is built from the interactions of proteins, and the study of protein-protein interactions is fast becoming a central feature of molecular bioscience. This is as true of immunobiology as it is of other areas of the wider biological milieu. Protein-protein interactions within an immunological setting comprise both the kind familiar from other areas of biology and instantiations of protein-protein interactions special to the immune arena. Of the generic kind of protein-protein interaction, co-stimulatory receptors, such as CD28, and the interaction of accessory proteins, such as CD4 or CD8, are amongst the most prevalent and apposite of examples. The key examples of special immunological instantiations of protein-protein interactions are the binding of antigens by antibodies and the formation of peptide-MHC-TCR complexes; both prime examples of vital molecular recognition events mediated by protein-protein interactions. In this brief review, and within the context of this burgeoning field, we examine immunological protein-protein interactions, focussing on the problematic nature of defining such interactions. © 2011 by Nova Science Publishers, Inc. All rights reserved.

Receptor activity-modifying proteins; multifunctional G protein-coupled receptor accessory proteins

Receptor activity-modifying proteins (RAMPs) are single pass membrane proteins initially identified by their ability to determine the pharmacology of the calcitonin receptor-like receptor (CLR), a family B G protein-coupled receptor (GPCR). It is now known that RAMPs can interact with a much wider range of GPCRs. This review considers recent developments on the structure of the complexes formed between the extracellular domains (ECDs) of CLR and RAMP1 or RAMP2 as these provide insights as to how the RAMPs direct ligand binding. The range of RAMP interactions is also considered; RAMPs can interact with numerous family B GPCRs as well as examples of family A and family C GPCRs. They influence receptor expression at the cell surface, trafficking, ligand binding and G protein coupling. The GPCR-RAMP interface offers opportunities for drug targeting, illustrated by examples of drugs developed for migraine.

The redoxomics of PTEN: walking a fine line between damage and signaling:mass spectrometry-based approaches to study the effect of oxidation on PTEN function, structure, and protein-protein interactions

The research described in this PhD thesis focuses on proteomics approaches to study the effect of oxidation on the modification status and protein-protein interactions of PTEN, a redox-sensitive phosphatase involved in a number of cellular processes including metabolism, apoptosis, cell proliferation, and survival. While direct evidence of a redox regulation of PTEN and its downstream signaling has been reported, the effect of cellular oxidative stress or direct PTEN oxidation on PTEN structure and interactome is still poorly defined. In a first study, GST-tagged PTEN was directly oxidized over a range of hypochlorous acid (HOCl) concentration, assayed for phosphatase activity, and oxidative post-translational modifications (oxPTMs) were quantified using LC-MS/MS-based label-free methods. In a second study, GSTtagged PTEN was prepared in a reduced and reversibly H2O2-oxidized form, immobilized on a resin support and incubated with HCT116 cell lysate to capture PTEN interacting proteins, which were analyzed by LC-MS/MS and comparatively quantified using label-free methods. In parallel experiments, HCT116 cells transfected with a GFP-tagged PTEN were treated with H2O2 and PTENinteracting proteins immunoprecipitated using standard methods. Several high abundance HOCl-induced oxPTMs were mapped, including those taking place at amino acids known to be important for PTEN phosphatase activity and protein-protein interactions, such as Met35, Tyr155, Tyr240 and Tyr315. A PTEN redox interactome was also characterized, which identified a number of PTEN-interacting proteins that vary with the reversible inactivation of PTEN caused by H2O2 oxidation. These included new PTEN interactors as well as the redox proteins peroxiredoxin-1 (Prdx1) and thioredoxin (Trx), which are known to be involved in the recycling of PTEN active site following H2O2-induced reversible inactivation. The results suggest that the oxidative modification of PTEN causes functional alterations in PTEN structure and interactome, with fundamental implications for the PTEN signaling role in many cellular processes, such as those involved in the pathophysiology of disease and ageing.

Receptor activity-modifying protein-directed G protein signaling specificity for the calcitonin gene-related peptide family of receptors

The calcitonin gene-related peptide (CGRP) family of G protein- coupled receptors (GPCRs) is formed through the association of the calcitonin receptor-like receptor (CLR) and one of three receptor activity-modifying proteins (RAMPs). Binding of one of the three peptide ligands, CGRP, adrenomedullin (AM), and intermedin/adrenomedullin 2 (AM2), is well known to result in aGαs-mediated increase in cAMP. Here we used modified yeast strains that couple receptor activation to cell growth, via chimeric yeast/Gα subunits, and HEK-293 cells to characterize the effect of different RAMP and ligand combinations on this pathway. We not only demonstrate functional couplings to both Gαs and Gαq but also identify a Gαi component to CLR signaling in both yeast and HEK-293 cells, which is absent in HEK-293S cells. We show that the CGRP family of receptors displays both ligand- and RAMPdependent signaling bias among the Gαs, Gαi, and Gαq/11 pathways. The results are discussed in the context of RAMP interactions probed through molecular modeling and molecular dynamics simulations of the RAMP-GPCR-G protein complexes. This study further highlights the importance of RAMPs to CLR pharmacology and to bias in general, as well as identifying the importance of choosing an appropriate model system for the study of GPCR pharmacology.