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PubMed2005

Inferring protein-protein interactions through high-throughput interaction data from diverse organisms.

MOTIVATION: Identifying protein-protein interactions is critical for understanding cellular processes. Because protein domains represent binding modules and are responsible for the interactions between proteins, computational approaches have been proposed to predict protein interactions at the domain level. The fact that protein domains are likely evolutionarily conserved allows us to pool information from data across multiple organisms for the inference of domain-domain and protein-protein interaction probabilities. RESULTS: We use a likelihood approach to estimating domain-domain interaction probabilities by integrating large-scale protein interaction data from three organisms, Saccharomyces cerevisiae, Caenorhabditis elegans and Drosophila melanogaster. The estimated domain-domain interaction probabilities are then used to predict protein-protein interactions in S.cerevisiae. Based on a thorough comparison of sensitivity and specificity, Gene Ontology term enrichment and gene expression profiles, we have demonstrated that it may be far more informative to predict protein-protein interactions from diverse organisms than from a single organism. AVAILABILITY: The program for computing the protein-protein interaction probabilities and supplementary material are available at http://bioinformatics.med.yale.edu/interaction.

Binding Sites↗
PubMed2004

InterWeaver: interaction reports for discovering potential protein interaction partners with online evidence.

InterWeaver is a web server for discovering potential protein interactions with online evidence automatically extracted from protein interaction databases, literature abstracts, domain fusion events and domain interactions. Given a new protein sequence, the server identifies potential interaction partners using two approaches. In the homology-based approach, the system performs sequence homology searches to find similar proteins in other species, and then searches the protein interaction databases and the biomedical literature for interaction partners. In the domain-based approach, the system detects the domains in the input protein sequence and searches databases of domain fusion events and putative domain interactions to suggest potential interacting partners. The results are compiled into a personalized and downloadable interaction report to aid biologists in their discovery of protein interactions. InterWeaver is freely available for academic users at http://interweaver.i2r.a-star.edu.sg/.

Computational Biology↗
PubMed1999

Adverse drug interactions in dental practice: interactions associated with vasoconstrictors. Part V of a series.

BACKGROUND: Adrenergic vasoconstrictors are commonly used by dentists to enhance the pain-relieving action of local anesthetics and to control local bleeding. Although normally considered safe for these applications, vasoconstrictors can participate in drug interactions that potentially are harmful to patients. METHODS: The faculty of a March 1998 symposium entitled "Adverse Drug Interactions in Dentistry: Separating the Myths From the Facts" extensively reviewed the literature on drug interactions. They then established a significance rating of alleged adverse drug interactions pertaining to dentistry, based on the quality of documentation and severity of effect. The author of this article focused on the adrenergic vasoconstrictors epinephrine and levonordefrin. RESULTS: Vasoconstrictor drug interactions involving tricyclic antidepressants, nonselective beta-adrenergic blocking drugs, certain general anesthetics and cocaine are well-documented in both humans and animals as having the potential for causing serious morbidity or death. Evidence for adverse interactions involving adrenergic neuronal blocking drugs, drugs with alpha-adrenergic blocking activity, local anesthetics and thyroid hormones is much less compelling, suggesting for the most part that clinically significant reactions may occur only when both the vasoconstrictor and the interacting drug are used in excessive doses. In the case of monoamine oxidase inhibitors, there is no credible evidence of a significant interaction with epinephrine or levonordefrin. CONCLUSIONS: Potentially serious adverse drug interactions involving adrenergic vasoconstrictors can occur in dental practice. In most circumstances, careful administration of small doses of vasoconstrictors and avoidance of gingival retraction cord containing epinephrine, coupled with monitoring of vita signs, will permit these drugs to be used with no risk or only minimally increased risk. Only in the case of cocaine intoxication must adrenergic vasoconstrictors be avoided completely. CLINICAL IMPLICATIONS: For optimal patient safety, dentists must recognize potential drug interactions involving adrenergic vasoconstrictors and modify their use of these agents accordingly.

Adrenergic Agents↗
PubMedDate not supplied

Interactions and non-interactions with ranitidine.

At present, there are two H2-receptor antagonists available for the treatment of peptic ulcer disease - cimetidine and ranitidine. Cimetidine is well known to interact with a number of concurrently administered drugs. Like cimetidine, ranitidine binds to cytochrome P-450 in the liver where it appears to exert an inhibitory effect, but to a lesser extent than cimetidine. Both H2-receptor antagonists may also reduce hepatic blood flow. Several drugs which are known to interact with cimetidine have been found not to interact significantly with ranitidine, including propranolol, lignocaine, phenytoin and diazepam. However, significant pharmacokinetic interactions between ranitidine and several other drugs have been established. These interactions may be attributed variously to an effect of ranitidine on hepatic metabolism or to an effect on the absorption of concomitantly administered drugs. For example, the bioavailability of midazolam is significantly increased due to the influence of ranitidine on gastric pH and thus on absorption of midazolam, leading to an increased soporific effect of this benzodiazepine; an effect of ranitidine on oxidative liver metabolism also appears to be a contributory factor in this interaction. Conversely, ranitidine distinctly reduced protein-bound cobalamin absorption from a mean of 7.66% prior to ranitidine administration to 0.84% during treatment with ranitidine 300 mg daily. A significant pharmacokinetic interaction has also been demonstrated between ranitidine and procainamide: the AUC of procainamide increased and the renal clearance fell significantly from a mean of 378 to 309 ml/min with ranitidine co-administration. However, this interaction is due to a different mechanism. In this case, ranitidine appears to compete with procainamide for the common renal proximal tubular secretion site. The reported interactions of ranitidine with warfarin, metoprolol, nifedipine, theophylline and fentanyl appear to be due to inhibition of cytochrome P-450. In a clinical study, warfarin clearance was significantly reduced from 66.7 to 48.7 ml/min by ranitidine, and by cimetidine to 42.9 ml/min. Similarly, the elimination half-lives of metoprolol and nifedipine were distinctly prolonged and the AUCs significantly increased by ranitidine. However, the latter pharmacokinetic interactions appear unlikely to be of clinical significance since the clinical effects of metoprolol and nifedipine were unaffected by ranitidine treatment. In therapeutic concentrations, ranitidine inhibited the disappearance of fentanyl from an in vitro microsomal preparation, indicating that it inhibits microsomal drug metabolism.(ABSTRACT TRUNCATED AT 400 WORDS)

Acetylcholinesterase↗
PubMed2004

Drug interaction ontology (DIO) for inferences of possible drug-drug interactions.

Drug Interaction Ontology (DIO) was developed for formal representation of pharmacological knowledge. It provides a fundamental framework for accumulation of reusable knowledge components in molecular pharmacology. Ontology was employed and implemented as a relational model. Some features include: 1) Drug-biomolecule interaction was assumed as a primitive knowledge element. 2) Symbolic representation was developed for drug-biomolecule interaction. Consequences of two conjugated units of interaction were defined by using symbols. These are applied for query development for identification of possible drug-drug interaction. 3) The triadic relationship model was developed as a ground model for bio-logical interactions and/or function, including semantic ones. One application of DIO is to support hypothesis generation of drug interaction by providing new hypotheses from a structured database storing literature information on known drug-biomolecule interactions. A knowledge base using DIO that contains information beginning with anti-cancer drugs is now under development. Detection of possible drug interaction was tested and its capacity to lead clinically known ones was confirmed. The system generated theoretically possible drug-drug interactions, which implies potential usefulness of new drugs to be tested before actual clinical application. In this paper, sorivudine and 5-fluorouracil mediated by dihydropyrimidine dehydrogenase are presented.

Arabinofuranosyluracil↗
PubMed1997

Subunit interactions of Escherichia coli F1-ATPase: mutants of the gamma subunits defective in interaction with the epsilon subunit isolated by the yeast two-hybrid system.

Previously, we established a method to detect subunit interactions of F1-ATPase by the yeast two-hybrid system (Moritani, C., et al. Biochim. Biophys. Acta 1274, 67-72, 1996). Here, we isolated mutants of the gamma subunits defective in interaction with the epsilon subunit by this new procedure to study the molecular basis of coupling mechanisms of the F1F0-ATPase. Based on the intensities of the reporter gene expression in this system, five mutants of the gamma subunit with different levels of gamma-epsilon interactions were isolated and their single base substitutions were determined. Mutants with a substitution of Pro-55 for Leu, Thr-102 for Met, Val-141 for Asp, or Gln-235 for Leu exhibited decreased reporter gene expression, suggesting decreased levels of interaction, while Asp-85 for Gly mutation caused a higher level of expression, suggesting increased interaction. Among these point mutations, G85D, M102T, or D141V mutations were introduced into the gamma subunit gene in the plasmid carrying whole unc operon. Transformants carrying a deletion mutant of the whole unc operon with these expression plasmids were analyzed. Mutations M102T and D141V with decreased gamma-epsilon interaction caused increases of membrane-bound F1-ATPase activity and proton pumping activity, while G85D with increased gamma-epsilon interaction exhibited lower levels of F1-ATPase activity in the membranes. Molecular assembly of the F1 subunits on the mutant membranes detected by Western blotting exhibited no defect for all three mutants. These results suggested that the correlation between the ATPase activity and gamma-epsilon interaction is reciprocal and this interaction may regulate the ATPase activity. The topological and functional importance of Gly-85, Met-102, and Asp-141 together with Leu-55 and Leu-235 in gamma-epsilon interaction is discussed.

Amino Acid Substitution↗
PubMed1999

Mapping the APP/presenilin (PS) binding domains: the hydrophilic N-terminus of PS2 is sufficient for interaction with APP and can displace APP/PS1 interaction.

Mutations in presenilin 1 and presenilin 2 (PS1 and PS2, respectively) genes cause the large majority of familial forms of early-onset Alzheimer's disease. The physical interaction between presenilins and APP has been recently described using coimmunoprecipitation. With a similar technique, we confirmed this interaction and have mapped the interaction domains on both PS2 and APP. Using several carboxy-terminal truncated forms of PS2, we demonstrated that the hydrophilic amino terminus of PS2 (residues 1 to 87, PS2NT) was sufficient for interaction with APP. Interestingly, only a construct with a leader peptide for secretion (SecPS2NT) and not its cytosolic counterpart was shown to interact with APP. For APP, we could demonstrate interaction of PS2 with the last 100 but not the last 45 amino acids of APP, including therefore the A beta region. Accordingly, SecPS2NT is capable of binding to A beta-immunoreactive species in conditioned medium. In addition, a second region in the extracellular domain of APP also interacted with PS2. Comparable results with PS1 indicate that the two presenilins share similar determinants of binding to APP. Confirming these results, SecPS2NT is able to inhibit PS1/APP interaction. Such a competition makes it unlikely that the PS/APP interaction results from nonspecific aggregation of PS in transfected cells. The physical interaction of presenilins with a region encompassing the A beta sequence of APP could be causally related to the misprocessing of APP and the production of A beta1-42.

Alzheimer Disease↗
PubMed2001

Further analysis of the interactions between the Brassica S receptor kinase and three interacting proteins (ARC1, THL1 and THL2) in the yeast two-hybrid system.

The yeast two-hybrid system was used to further characterize the interactions between the Brassica S receptor kinase (SRK) and three putative substrates, ARC1 and the two thioredoxin h proteins, THL1 and THL2. Interactions were generally detectable with kinase domains of both Class I and Class II SRKs. Chimeric constructs were made between the SRK910 kinase domain and the non-interacting Arabidopsis RLK5 kinase domain. Only one chimeric construct, SRR2, interacted with THL1 and THL2, while none of the chimeras were able to interact with ARC1. SRR2 is largely made up of RLK5 kinase domain with the N-terminal end being derived from the SRK910 kinase domain and was the only chimeric construct that retained kinase activity. Deletion or substitution of a conserved cysteine at the N-terminal end of the SRK910 kinase domain resulted in loss of interaction with THL1 and THL2, while the addition of this cysteine to a related receptor kinase, SFR1, conferred the ability to interact with the thioredoxin h proteins. In addition, substitution of the cysteines in the THL1 active site abolished the interaction. Lastly, the two Arabidopsis thioredoxin h clones most closely related to THL1 and THL2 were found to interact with the SRK kinase domains. Thus, the nature of the interaction of the thioredoxin h clones with SRK involves the reducing activity of these proteins and is restricted to the class of thioredoxin h proteins which have the variant CPPC active site.

Amino Acid Sequence↗
PubMed2006

Assessment of the quality of interaction in distance learning programmes utilizing the Internet or interactive television: perceptions of students and lecturers.

WebCT, a web-based virtual learning environment (VLE) and Interactive TV (ITV) are relatively new technologies that are used to deliver distance education at the Faculty of Health Sciences, Stellenbosch University. This study explores how effective current approaches to instructional design and interaction have been in utilizing these two technologies to support interaction and what lessons can be learnt. Five focus-group interviews were held with students and lecturers to assess the perceived quality of student-lecturer/student-student interactions. All students were invited to complete a questionnaire at the end of every module to assess their perceptions of interaction. Interaction was highly valued by students and lecturers participating in distance-learning programmes utilizing either VLE or ITV. Students rated courses using both technologies as moderately interactive. Significant differences between VLE and ITV were detected in student-lecturer and student-student interactions, use of additional modes of communication, instructional design, technological interactivity and social rapport activities. The groups did not differ across a number of likely barriers to interaction and both also reported the need for more flexible and better paced instructional designs.

Adult↗
PubMed1999

Quantitative drug interactions prediction system (Q-DIPS): a computer-based prediction and management support system for drug metabolism interactions.

OBJECTIVE: Drug biotransformation and interactions are a major source of variability in the response to drugs. The superfamily of cytochromes P450 plays a key role in this phenomenon but, because of the complexity of interactions between drugs and isozymes, it becomes more and more difficult for clinicians to master the knowledge required to predict the occurrence of such drug interactions. To predict and help manage the occurrence of cytochrome P450-dependent interactions, we developed an original computer application: Q-DIPS (quantitative drug interactions prediction system). METHODS: A multidisciplinary work team was created, associating clinical pharmacologists, pharmacists and a computer scientist. Major steps of investigation were: (1) the creation of a database to collect qualitative and quantitative data describing substrates, inhibitors and inducers of specific cytochrome P450 isozymes, with quality assessments; (2) the development of multi-access to these data and (3) their incorporation into extrapolation systems allowing the prediction of in vivo drug interactions on the basis of in vitro data. As an example, prediction and validation studies of CYP3A4 inhibition by ketoconazole and fluconazole will be discussed. RESULTS: Q-DIPS gives up-to-date information, in dynamic tables, describing which specific P450 isozymes metabolise a given drug, as well as which drugs may inhibit or induce a given isozyme. To better answer common clinical questions and help to rapidly evaluate the risk of interactions, it is possible to obtain an overview of substances causing interactions with a specific drug or to focus on drugs taken by a patient ("clinical case"). For each question, key references, relevant quantitative data and quality indices are easily accessible. Two modules allowing input with commercial names and the anatomical therapeutic chemical classification were also included. On the basis of enzymatic and pharmacokinetic data generated in vitro or collected in vivo, the extrapolation module integrates quantitative models to predict the impact of a treatment on enzymatic activities. The simplest model predicted a strong but fluctuating inhibition of CYP3A4 by ketoconazole, whereas the impact of fluconazole was lower. Validations with published in vivo data suggested an appropriate prediction of the risk. CONCLUSION: The current Q-DIPS prototype shows promising potential for helping to improve the management of drug interactions involving metabolism. Validation of extrapolation techniques need to be completed, in view of including important factors such as intrahepatocyte drug accumulation, contribution of metabolites to inhibition as well as in vitro non-specific binding to microsomal proteins. The final goal will be to help select the most judicious clinical studies to be performed so as to avoid useless, expensive and unethical investigations in man.

Antifungal Agents↗
PubMed2002

Real-time analysis of molecular interaction of retinoid receptors and receptor-interacting protein 140 (RIP140).

Receptor interacting protein 140 (RIP140) is a coregulator for a large number of transcription factors. RIP140 interacts with retinoic acid receptor (RAR) and retinoid X receptor (RXR) with or without ligands. The C-terminal domain of RIP140 (RIP-C') contains a novel sequence (1063-1076, LTKTNPILYYMLQK) and has been shown to interact with RAR and RXR ligand dependently in two-hybrid interaction and pull-down assays. To examine the kinetic characteristics of molecular interaction of RIP-C' with RAR and RXR, a surface plasmon resonance technology (BIAcore) was applied for real-time analyses of this molecular interaction with highly purified proteins. A modified pull-down assay using purified proteins was also conducted to obtain supporting data. The effect of retinoid ligands on this type of interaction was addressed. By using receptor mutants, it was demonstrated that the activation function-2 domain and the ability to form dimers of the receptors are required for an efficient interaction of receptor with RIP140. Finally, with a mutagenesis approach, we determined the effects of specific point mutations on the kinetics of RIP-C' interaction with RAR/RXR.

Adaptor Proteins, Signal Transducing↗
PubMed1999

Adverse drug interactions in dental practice: interactions involving antibiotics. Part II of a series.

BACKGROUND: The prudent use of antibiotics is an integral part of dental practice. While these agents generally are considered safe in the dental setting, their use can result in interactions that can lead to serious morbidity in dental patients. METHODS: The faculty of a symposium entitled "Adverse Drug Interactions in Dentistry: Separating the Myths From the Facts" did an extensive literature review on drug interactions. Through this, they were able to establish a significance rating of alleged adverse drug interactions as they relate to dentistry, based on their scientific documentation and severity of effect. The author of this article focused on antibiotics. RESULTS: Most of the reported drug interactions discussed in this article are well-documented by clinical studies. It is particularly important that dentists be aware of the potentially serious and life-threatening interactions of the antibiotics erythromycin, clarithromycin and metronidazole, and of the antifungal agents ketoconazole and itraconazole, with a host of other drugs whose metabolism is impaired by these antimicrobial agents. In contrast, the alleged ability of commonly employed antibiotics to reduce the effectiveness of oral contraceptive agents is not adequately supported by clinical research. It still is recommended, however, that clinicians discuss this possible interaction with their patients, as it might represent a relatively rare event that cannot be discerned in clinical trials. CONCLUSIONS: Potentially serious adverse drug interactions can occur between antimicrobial agents used in dental practice and other drugs patients are taking for a variety of medical conditions. CLINICAL IMPLICATIONS: It is important that dentists stay abreast of potential drug interactions involving antibiotics to avoid serious morbidity among their patients.

Anti-Bacterial Agents↗
PubMed1999

Adverse drug interactions in dental practice: interactions associated with local anesthetics, sedatives and anxiolytics. Part IV of a series.

BACKGROUND: This article is the fourth in a five-part series based on a 1998 International Association for Dental Research symposium entitled "Adverse Drug Interactions in Dentistry: Separating the Myths From the Facts." The symposium evaluated the significance of various drug interactions associated with dental therapeutics. METHODS: Local anesthesia and preoperative oral sedative/anxiolytic therapy often are indicated for routine oral surgery and restorative dentistry. The author conducted a literature review of the drug interactions associated with the use of local anesthetics and sedatives. The quality of the information used to document these interactions and the severity of the possible adverse outcome were assessed using a significance rating scale for dental drug interactions. RESULTS: Many of the frequently described drug interactions were found to be poorly documented in the dental and medical literature. Others were determined not to be relevant to current dental practice. The use of local anesthetics, sedatives or anxiolytic agents in combination with other central nervous system depressant agents or in combination with drugs that inhibit their metabolism was associated with a few serious adverse drug interactions or complications. CONCLUSIONS: The adverse drug interactions associated with the use of local anesthetics and oral sedative/anxiolytic agents in general practice vary in significance. An understanding of possible adverse drug interactions in dentistry may help practitioners avoid and prevent these complications.

Aged↗
PubMed1992

Zero interaction response surfaces, interaction functions and difference response surfaces for combinations of biologically active agents.

In the field of combination experiments there is wide-spread confusion over definitions, terminology and methods for the evaluation of interaction between biologically active agents. According to our view the widely used isobole approach is the method of choice. In this contribution it is shown how the combination of the classical isobole approach with response surface modeling and computer graphics leads to powerful new methods for the assessment of interaction of biologically active agents. In particular, zero interaction response surfaces, difference response surfaces and interaction functions are proposed. Zero interaction response surfaces represent surfaces which display zero interaction in the whole dose range. Difference response surfaces display the difference between an actual response surface and the corresponding zero interaction response surface. Interaction functions are a generalization of the index of interaction, which describe the dose dependence of this quantity.

Computer Graphics↗
PubMed1992

Competition between cell-substratum interactions and cell-cell interactions.

Clusterin, a glycoprotein which elicits the aggregation of a wide variety of cells (Fritz, I. B., and Burdy, K.:J. Cell Physiol., 140:18-28, 1989), has been utilized to investigate some of the factors modulating the competition between cell-substratum interactions and cell-cell interactions. We compared the responses to clusterin by anchorage-independent cells (erythrocytes) with those by anchorage-dependent TM4 cells (a cell line derived from neonatal mouse testis cells). Cells were maintained in culture in the presence of various substrata chosen to enhance cell-substratum interactions (laminin-coated wells), or to diminish cell-substratum interactions (agarose-coated wells). Results obtained showed that the aggregation of erythrocytes elicited by clusterin was independent of the nature of the substratum. In contrast, clusterin addition resulted in aggregation of anchorage-dependent TM4 cells only when TM4 cell-substratum interactions were weak. Thus, clusterin did not aggregate TM4 cells plated upon a laminin substratum, but readily aggregated TM4 cells plated upon an agarose-coated substratum, independent of the sequence of addition of cells and clusterin to the culture dish. We utilized YIGSR, a peptide which competes with laminin for laminin receptors, to determine the possible role of laminin receptors on TM4 cells in the competition between cell-substratum interactions and cell-cell interactions. The presence of YIGSR did not alter responses of erythrocytes to clusterin under all conditions examined. In contrast, the responses of TM4 cells to clusterin were greatly changed. YIGSR addition resulted in the inhibition of aggregation of TM4 cells otherwise elicited by clusterin. YIGSR also prevented attachment of TM4 cells to a laminin-coated surface, but this was reversed by the presence of clusterin. We discuss the possible roles of clusterin and laminin in altering the balance in the competition between cell to cell interactions and cell to substratum interactions.

Animals↗
PubMed1991

Quantitative evaluation of solution equilibrium binding interactions by affinity partitioning: application to specific and nonspecific protein-heparin interactions.

A variation of the quantitative affinity chromatography (QAC) method of Winzor, Chaiken, and co-workers for the analysis of protein-ligand interactions has been developed and used to characterize sequence-specific and nonspecific protein-heparin interactions relevant to blood coagulation. The method allows quantitation of the binding of two components, A and B, from the competitive effect of one component, B, on the partitioning of the other component, A, between an immobilized acceptor phase and solution phase at equilibrium. Under the conditions employed, the differences in total A concentrations yielding an equivalent degree of saturation of the immobilized acceptor in the absence and presence of B defines the concentration of A bound to B in solution, thereby enabling conventional Scatchard or nonlinear least-squares analysis of the A-B equilibrium interaction. Like the QAC method, quantitation of the competitor interaction does not depend on the nature of the affinity matrix interaction, which need only be described empirically. The additional advantage of the difference method is that only the total rather than the free competitor ligand concentration need be known. The method requires that the partitioning component A be univalent, but allows for multivalency in the competitor, B, and can in principle be used to study binding interactions involving nonidentical, interacting, or nonspecific overlapping sites. Both the binding constant and the stoichiometry for the specific antithrombin-heparin interaction as well as the apparent binding constant for the nonspecific thrombin-heparin interaction at low thrombin binding densities obtained using this technique were in excellent agreement with values determined using spectroscopic probes.

Antithrombin III↗