Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “interaction”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,423 records · Page 79Linked to original sources
PubMed2000

The Agrobacterium T-DNA transport pore proteins VirB8, VirB9, and VirB10 interact with one another.

The VirB proteins of Agrobacterium tumefaciens form a transport pore to transfer DNA from bacteria to plants. The assembly of the transport pore will require interaction among the constituent proteins. The identification of proteins that interact with one another can provide clues to the assembly of the transport pore. We studied interaction among four putative transport pore proteins, VirB7, VirB8, VirB9 and VirB10. Using the yeast two-hybrid assay, we observed that VirB8, VirB9, and VirB10 interact with one another. In vitro studies using protein fusions demonstrated that VirB10 interacts with VirB9 and itself. These results suggest that the outer membrane VirB7-VirB9 complex interacts with the inner membrane proteins VirB8 and VirB10 for the assembly of the transport pore. Fusions that contain small, defined segments of the proteins were used to define the interaction domains of VirB8 and VirB9. All interaction domains of both proteins mapped to the N-terminal half of the proteins. Two separate domains at the N- and C-terminal ends of VirB9 are involved in its homotypic interaction, suggesting that VirB9 forms a higher oligomer. We observed that the alteration of serine at position 87 of VirB8 to leucine abolished its DNA transfer function. Studies on the interaction of the mutant protein with the other VirB proteins showed that the VirB8S87L mutant is defective in interaction with VirB9. The mutant, however, interacted efficiently with VirB8 and VirB10, suggesting that the VirB8-VirB9 interaction is essential for DNA transfer.

Bacterial Proteins↗
PubMed2006

Stable evolutionary signal in a yeast protein interaction network.

BACKGROUND: The recently emerged protein interaction network paradigm can provide novel and important insights into the innerworkings of a cell. Yet, the heavy burden of both false positive and false negative protein-protein interaction data casts doubt on the broader usefulness of these interaction sets. Approaches focusing on one-protein-at-a-time have been powerfully employed to demonstrate the high degree of conservation of proteins participating in numerous interactions; here, we expand his 'node' focused paradigm to investigate the relative persistence of 'link' based evolutionary signals in a protein interaction network of S. cerevisiae and point out the value of this relatively untapped source of information. RESULTS: The trend for highly connected proteins to be preferably conserved in evolution is stable, even in the context of tremendous noise in the underlying protein interactions as well as in the assignment of orthology among five higher eukaryotes. We find that local clustering around interactions correlates with preferred evolutionary conservation of the participating proteins; furthermore the correlation between high local clustering and evolutionary conservation is accompanied by a stable elevated degree of coexpression of the interacting proteins. We use this conserved interaction data, combined with P. falciparum/Yeast orthologs, as proof-of-principle that high-order network topology can be used comparatively to deduce local network structure in non-model organisms. CONCLUSION: High local clustering is a criterion for the reliability of an interaction and coincides with preferred evolutionary conservation and significant coexpression. These strong and stable correlations indicate that evolutionary units go beyond a single protein to include the interactions among them. In particular, the stability of these signals in the face of extreme noise suggests that empirical protein interaction data can be integrated with orthologous clustering around these protein interactions to reliably infer local network structures in non-model organisms.

Evolution, Molecular↗
PubMed2006

Measuring additive interaction using odds ratios.

Interaction measured on the additive scale has been argued to be better correlated with biologic interaction than when measured on the multiplicative scale. Measures of interaction on the additive scale have been developed using risk ratios. However, in studies that use odds ratios as the sole measure of effect, the calculation of these measures of additive interaction is usually performed by directly substituting odds ratios for risk ratios. Yet assessing additive interaction based on replacing risk ratios by odds ratios in formulas that were derived using the former may be erroneous. In this paper, we evaluate the extent to which three measures of additive interaction - the interaction contrast ratio (ICR), the attributable proportion due to interaction (AP), and the synergy index (S), estimated using odds ratios versus using risk ratios differ as the incidence of the outcome of interest increases in the source population and/or as the magnitude of interaction increases. Our analysis shows that the difference between the two depends on the measure of interaction used, the type of interaction present, and the baseline incidence of the outcome. Substituting odds ratios for risk ratios, when calculating measures of additive interaction, may result in misleading conclusions. Of the three measures, AP appears to be the most robust to this direct substitution. Formulas that use stratum specific odds and odds ratios to accurately calculate measures of additive interaction are presented.

Journal Article↗
PubMed2006

Estrogen receptor beta (ESR2) polymorphisms in interaction with estrogen receptor alpha (ESR1) and insulin-like growth factor I (IGF1) variants influence the risk of fracture in postmenopausal women.

UNLABELLED: In this large population-based cohort study, variants in ESR2 were associated with increased risk of vertebral and incident fragility fracture in postmenopausal women. Interaction of ESR2 with ESR1 and IGF1 was determined and revealed a deleterious genetic combination that enhances the risk of osteoporotic fracture. INTRODUCTION: Osteoporosis is a complex disease with strong genetic influence, but the genes involved are ill-defined. We examined estrogen receptor beta (ESR2) polymorphisms in interaction with estrogen receptor alpha (ESR1) and insulin-like growth factor I (IGF1) variants in relation to the risk of osteoporotic fracture, BMD, and bone geometry. MATERIALS AND METHODS: In the Rotterdam study, a prospective population-based cohort of elderly white individuals, we studied six single nucleotide polymorphisms (SNPs) in ESR2 (n = 6343, 60% women). We analyzed the genetic variants in the form of haplotypes reconstructed by a statistical method. Results refer to the most frequent ESR2 haplotype 1 estimated from two SNPs in intron 2 and the 3'-untranslated region (UTR). Outcomes included vertebral and incident nonvertebral fractures, BMD, and hip structural analysis (HSA). We also studied the interaction with (the most frequent) ESR1 haplotype 1 estimated from the PvuII and XbaI polymorphisms and an IGF1 promoter CA-repeat. RESULTS: Compared with ESR2 haplotype 1 noncarriers, female homozygous carriers had a 1.8- and 1.4-fold increased risk of vertebral and fragility fractures. HSA showed that ESR2 haplotype 1 homozygote women had 2.6% thinner cortices, 1.0% increased neck width, and 4.3% higher bone instability (buckling ratios). For testing the gene interaction, we assumed a recessive model of ESR2 haplotype 1. Female homozygous carriers of ESR2 haplotype 1 and noncarriers of ESR1 haplotype 1 had a 3.5- and 1.8-fold increased risk of vertebral and fragility fractures (p(interaction) = 0.10). Such effects and interactions were stronger in women homozygous for the IGF1 192-bp allele, with 9.3-fold increased risk (p(interaction) = 0.002) for vertebral and 4.0-fold increased risk (p(interaction) = 0.01) for fragility fractures. Multilocus interaction analyses of fracture endured correction for multiple testing using Monte-Carlo simulations (p(interaction) = 0.02 for vertebral and p(interaction) = 0.03 for fragility fractures). Similar patterns of interaction were observed for BMD, cortical thickness, bone strength (section modulus), and instability (buckling ratio). In men, no such effects were observed. CONCLUSIONS: Variants of ESR2 alone and in interaction with ESR1 and IGF1 influence the risk of fracture in postmenopausal women. These findings reinforce the polygenic and complex character of osteoporosis.

Aged↗
PubMed2004

Correlation of dominance as determined by agonistic interactions with feeding order in cats.

OBJECTIVE: To determine whether the direction of dominance as determined by agonistic interactions away from food was different from the direction of dominance as determined by access to a resource in cats. ANIMALS: 28 cats. PROCEDURE: Dyadic relationships and hierarchy formed from observation of agonistic interactions away from food were compared with those formed from interactions at the food bowl. A cat was scored as subordinate to another cat if it lost 3 of 3 interactions or lost > or = 75% of the interactions when > 3 interactions occurred. RESULTS: Cats were observed for 449.4 hours. Hierarchy rank determined by agonistic interactions away from food was significantly correlated with rank determined by interactions at the food bowl. In 27 of 31 dyads, the direction of dominance was the same for food bowl and agonistic relationships, which was significant. In post hoc analyses, when considering the relationship between 2 cats, the heavier cat most likely ranked higher in each hierarchy; however, age was not significantly correlated with either hierarchy. On the basis of dyadic information, the older cat in a dyad was more often dominant in agonistic interactions. Males had a higher mean dominance rank than females; however, sex had no effect on rank determined by interactions at the food bowl. CONCLUSIONS AND CLINICAL RELEVANCE: Factors influencing dominant-subordinate relationships are of interest for understanding and treating behavior problems such as aggression and resource control. The outcome of agonistic interactions away from food was related to, but not perfectly correlated with, the outcome of interactions at the food bowl, although winners of those agonistic interactions tended to have control of food.

Age Factors↗
PubMed1997

Assessing the relevance of rodent data on chemical interactions for health risk assessment purposes: a case study with dichloromethane-toluene mixture.

Several descriptive studies have reported the occurrence of infra-additive and supra-additive toxic interactions in rodents given high doses of chemicals by routes different from anticipated human exposures. In order to assess the relevance of such rodent data on chemical interactions for humans, the route, species, and dose extrapolations need to be conducted on the basis of proven/hypothetical interaction mechanisms. The present study initially developed a physiologically based model of the toxicological interaction reported in rats receiving high oral doses of dichloromethane (DCM) and toluene (TOL). This predictive model was then used to asses the relevance of DCM-TOL interaction for humans exposed to threshold limit values (TLVs) of these chemicals, following the conduct of the various, essential extrapolations (i.e., rat to human, oral to inhalation, high dose to low dose). The interaction modeling approach involved (i) obtaining validated rat and human physiologically based pharmacokinetic (PBPK) models for TOL and DCM from the literature, and (ii) linking them via the modified Michaelis-Menten equation accounting for hypothetical mechanisms of interactions (no interaction, competitive inhibition, noncompetitive inhibition, and uncompetitive inhibition). Of the various interaction mechanisms investigated, the noncompetitive and uncompetitive metabolic inhibitions were found to adequately describe the reduction of carboxyhemoglobinemia (COHB) observed in rats during combined exposures (18.8 mmol/kg TOL, +6.2 mmol/kg DCM, po; 0.005 mmol/kg TOL, ip +5000 ppm DCM, 1 hr). The simulation model, based on noncompetitive and uncompetitive inhibition mechanisms, suggests that only < 10% reduction in the area under the COHB vs time curve (AUCCOHB) is likely to occur in humans exposed to the current TLVs of DCM and TOL (compared to AUCCOHB resulting from an 8-hr exposure to TLV of DCM alone). The present modeling approach, based on hypothetical mechanisms of interaction, then indicates that rodent data on DCM-TOL interaction are not relevant for humans, particularly with respect to the COHB effect. The application of this kind of a predictive modeling approach should be useful in screening the available reports on chemical interactions for identifying those of greater concern at relevant human exposure levels (RfD, RfC, TLV).

Administration, Inhalation↗
PubMed2003

Homo- or hetero-dimerization of muscarinic receptor subtypes is not mediated by direct protein-protein interaction through intracellular and extracellular regions.

The oligomerization of G-proteincoupled receptors (GPCRs) has been shown to occur by various mechanisms, such as via disulfide covalent linkages, noncovalent (ionic, hydrophobic) interactions of the N-terminal, and/or transmembrane and/or intracellular domains. Interactions between GPCRs could involve an association between identical proteins (homomers) or non-identical proteins (heteromers), or between two monomers (to form dimers) or multiple monomers (to form oligomers). It is believed that muscarinic receptors may also be arranged into dimeric or oigomeric complexes, but no systematic experimental evidence exists concerning the direct physical interaction between receptor proteins as its mechanism. We undertook this study to determine whether muscarinic receptors form homomers or a heteromers by direct protein-protein interaction within the same or within different subtypes using a yeast two-hybrid system. Intracellular loops (i1, i2 and i3) and the C-terminal cytoplasmic tails (C) of human muscarinic (Hm) receptor subtypes, Hm1, Hm2 and Hm3, were cloned into the vectors (pB42AD and pLexA) of a two-hybrid system and examined for heteromeric or homodimeric interactions between the cytoplasmic domains. No physical interaction was observed between the intracellular domains of any of the Hm/Hm receptor sets tested. The results of our study suggest that the Hm1, Hm2 and Hm3 receptors do not form dimers or oligomers by interacting directly through either the hydrophilic intracellular domains or the C-terminal tail domains. To further investigate extracellular domain interactions, the N-terminus (N) and extracellular loops (o1 and o2) were also cloned into the two-hybrid vectors. Interactions of Hm2N with Hm2N, Hm2o1, Hm2o2, Hm3N, Hm3o1 or Hm3o2 were examined. The N-terminal domain of Hm2 was found to have no direct interaction with any extracellular domain. From our results, we excluded the possibility of a direct interaction between the muscarinic receptor subtypes (Hm1, Hm2 and Hm3) as a mechanism for homo- or hetero-meric dimerization/oligomerization. On the other hand, it remains a possibility that interaction may occur indirectly or require proper conformation or subunit formation or hydrophobic region involvement.

Base Sequence↗
PubMed2005

Information deficits in the summary of product characteristics preclude an optimal management of drug interactions: a comparison with evidence from the literature.

OBJECTIVE: To compare comprehensiveness and accuracy of drug interaction information in the German summary of product characteristics (SPC) with current evidence from the literature and to evaluate the SPC's usefulness with respect to management of drug interactions. METHODS: Information on clinically relevant drug interactions was compared between the SPC and three standard information sources on drug interactions (DRUGDEX, Hansten/Horn's Drug Interactions Analysis and Management, Stockley's Drug Interactions) according to five consecutive criteria (inclusion, appropriateness of class labelling, effect description, management recommendation, explicit dose adjustment). Using medication data of an outpatient population (n=4,949), we determined what percentage of insufficiently characterized combinations indeed occurred in outpatients treated with combination drug therapy. RESULTS: Only for 33% (192/579) of the evaluated combinations did SPCs provide drug interaction information equivalent to the evidence from the published literature. Of the clinically relevant drug interactions, 16% were completely missing and 51% were insufficiently characterized compared with standard sources. Explicit management recommendations were either missing or differed from standard sources in 18% of the evaluated pairs of compounds. Of these missing or insufficiently characterized combinations, 12% (47/387) were indeed prescribed to outpatients. Those drug combinations for which the interaction potential was not mentioned in the SPC were received by 0.6% (32/4,949) of patients, and 4% (192/4,949) of patients received combinations that had insufficiently characterized drug interactions. CONCLUSIONS: If physicians only rely on SPC information for drug interactions, adverse events due to lacking management recommendations may occur. To meet the SPCs claim of being the basis of information for health professionals on how to use medicinal products safely and effectively, information on drug interactions should be thoroughly up-dated and expanded.

Databases as Topic↗
PubMed2004

Drug interactions in primary care: impact of a new algorithm on risk determination.

BACKGROUND: If managed adequately, many drug interactions do not result in clinical manifestations. Earlier studies may have overestimated the risk arising from drug interactions because they usually reported interaction frequencies and the severity of potential outcomes irrespective of their manageability. OBJECTIVE: Our objective was to estimate the risk associated with drug interactions in a large population when not only the severity of possible clinical events but also measures of their prevention (manageability, modulating factors) are considered. METHODS: We evaluated all drug pairs concurrently prescribed to 9481 adults aged 50 to 75 years who participated in a health-screening examination. Drug interactions were evaluated by use of an algorithm with 4 decision layers (severity, manageability, risk/benefit assessment, and patient-related risk factors), and this risk evaluation was compared with the conventional evaluation solely on the basis of severity. RESULTS: More than 52% of the patients received combination therapy. Interaction information was available in a standard source (DRUGDEX; Thomson MICROMEDEX, Greenwood Village, Colo) for only 1029 of all 13,672 individual prescribed drug pairs. Of the drug pairs, 881 (6.4%) were identified as interacting. Of these 881 interactions, 132 (15.0%) were of major severity but 101 of 132 (76.5%) were considered manageable. Only 31 (23.5%) of 132 major interactions (ie, 31/881 [3.5% of all interacting pairs]) offered no management options and should thus be avoided. CONCLUSION: For many commonly prescribed drug pairs, explicit drug interaction information is currently not available in DRUGDEX. For major interactions with published evidence, the overwhelming majority were manageable, and therefore risk estimates only based on the severity of potential outcomes will grossly overestimate the risk associated with such combinations.

Aged↗
PubMed2002

The importance of drug interactions in epilepsy therapy.

Long-term antiepileptic drug (AED) therapy is the reality for the majority of patients diagnosed with epilepsy. One AED will usually be sufficient to control seizures effectively, but a significant proportion of patients will need to receive a multiple AED regimen. Furthermore, polytherapy may be necessary for the treatment of concomitant disease. The fact that over-the-counter drugs and nutritional supplements are increasingly being self-administered by patients also must be considered. Therefore the probability of patients with epilepsy experiencing drug interactions is high, particularly with the traditional AEDs, which are highly prone to drug interactions. Physicians prescribing AEDs to patients with epilepsy must, therefore, be aware of the potential for drug interactions and the effects (pharmacokinetic and pharmacodynamic) that can occur both during combination therapy and on drug discontinuation. Although pharmacokinetic interactions are numerous and well described, pharmacodynamic interactions are few and usually concluded by default. Perhaps the most clinically significant pharmacodynamic interaction is that of lamotrigine (LTG) and valproic acid (VPA); these drugs exhibit synergistic efficacy when coadministered in patients with refractory partial and generalised seizures. Hepatic metabolism is often the target for pharmacokinetic drug interactions, and enzyme-inducing drugs such as phenytoin (PHT), phenobarbitone (PB), and carbamazepine (CBZ) will readily enhance the metabolism of other AEDs [e.g., LTG, topiramate (TPM), and tiagabine (TGB)]. The enzyme-inducing AEDs also enhance the metabolism of many other drugs (e.g., oral contraceptives, antidepressants, and warfarin) so that therapeutic efficacy of coadministered drugs is lost unless the dosage is increased. VPA inhibits the metabolism of PB and LTG, resulting in an elevation in the plasma concentrations of the inhibited drugs and consequently an increased risk of toxicity. The inhibition of the metabolism of CBZ by VPA results in an elevation of the metabolite CBZ-epoxide, which also increases the risk of toxicity. Other examples include the inhibition of PHT and CBZ metabolism by cimetidine and CBZ metabolism by erythromycin. In recent years, a more rational approach has been taken with regard to metabolic drug interactions because of our enhanced understanding of the cytochrome P450 system that is responsible for the metabolism of many drugs, including AEDs. The review briefly discusses the mechanisms of drug interactions and then proceeds to highlight some of the more clinically relevant drug interactions between AEDs and between AEDs and non-AEDs. Understanding the fundamental principles that contribute to a drug interaction may help the physician to better anticipate a drug interaction and allow a graded and planned therapeutic response and, therefore, help to enhance the management of patients with epilepsy who may require treatment with polytherapy regimens.

Anticonvulsants↗
PubMed2002

Community-wide distribution of predator-prey interaction strength in kelp forests.

The strength of interactions between predators and their prey (interaction strength) varies enormously among species within ecological communities. Understanding the community-wide distribution of interaction strengths is vital, given that communities dominated by weak interactions may be more stable and resistant to invasion. In the oceans, previous studies have reported log-normal distributions of per capita interaction strength. We estimated the distribution of predator-prey interaction strengths within a subtidal speciose herbivore community (45 species). Laboratory experiments were used to determine maximum per capita interaction strengths for eight species of herbivores (including amphipods, isopods, gastropods, and sea urchins) that graze on giant kelp (Macrocystis pyrifera) microscopic stages. We found that maximum per capita interaction strength saturated as a function of individual herbivore biomass, likely caused by predator/prey size thresholds. Incorporating this nonlinearity, we predicted maximum per capita interaction strength for the remaining herbivore species. The resulting distribution of per capita interaction strengths was bimodal, in striking contrast to previous reports from other communities. Although small herbivores often had per capita interaction strengths similar to larger herbivores, their tendency to have greater densities in the field increased their potential impact as grazers. These results indicate that previous conclusions about the distributions of interaction strength in natural communities are not general, and that intermediate-sized predators can under realistic circumstances represent the most effective consumers in natural communities.

Animals↗
PubMed1999

Characterization of receptor-interacting protein 140 in retinoid receptor activities.

Receptor-interacting protein 140 (RIP140) contains multiple receptor interaction domains and interacts with retinoic acid receptors in a ligand-dependent manner. Nine LXXLL receptor-interacting motifs are organized into two clusters within this molecule, each differentially interacting with retinoic acid receptor (RAR) and retinoid X receptor (RXR). RAR interacts with the 5' cluster, whereas RXR interacts with both clusters. Additionally, a third ligand-dependent receptor-interacting domain is assigned to the very C terminus of this molecule, which contains no LXXLL motif. In mammalian cells, receptor heterodimerization is required for efficient interaction of RAR/RXR with RIP140. Furthermore, the heterodimeric, holoreceptors cooperatively interact with RIP140, which requires the activation function 2 domains of both receptors. By using different retinoic acid reporter systems, it is demonstrated that RIP140 strongly suppresses retinoic acid induction of reporter activities, but coactivator SRC-1 enhances it. Furthermore, an intrinsic repressive activity of RIP140 is demonstrated in a GAL4 fusion system. Unlike receptor corepressor, which interacts with antagonist-bound RAR/RXRs, RIP140 does not interact with antagonist-occupied RAR/RXR dimers. These data suggest that RIP140 represents a third coregulator category that is able to suppress the activation of certain agonist-bound hormone receptors.

Adaptor Proteins, Signal Transducing↗
PubMed2003

Exposure to potential drug interactions in primary health care.

OBJECTIVE: To analyse the prevalence of potential drug interactions, and to identify patients particularly prone to drug interaction. DESIGN: Database study (Odense University Pharmacoepidemiologic Database). SETTING: Individuals exposed to polypharmacy in 1999 were examined for potential drug interactions. SUBJECTS: Inhabitants of the County of Funen (n = 471 732). MAIN OUTCOME MEASURES: Prevalence of potential drug interactions. RESULTS: One-third of the population were exposed to polypharmacy and among these 15% were exposed to drugs carrying a risk of harmful interaction. Among the elderly with polypharmacy, 25% aged 60-79 years and 36% over 80 years received drugs carrying the risk of interaction. Among individuals exposed to potential drug interaction, 62% were exposed only to one drug interaction and 38% to two or more different drug interactions. The drugs accounting for the highest number of potential interactions were diuretics, NSAIDs, ACE-inhibitors, digoxin, oral antidiabetics, calcium channel blockers, anticoagulants and beta-blockers. When focusing only on major drug interactions, potassium-sparing diuretics and oral anticoagulants were the most frequently involved drugs. CONCLUSION: Elderly patients exposed to polypharmacy should be kept under intensified monitoring as they are at increased risk of clinically significant drug interactions.

Adult↗
PubMed2004

HPID: the Human Protein Interaction Database.

UNLABELLED: The Human Protein Interaction Database (http://www.hpid.org) was designed (1) to provide human protein interaction information pre-computed from existing structural and experimental data, (2) to predict potential interactions between proteins submitted by users and (3) to provide a depository for new human protein interaction data from users. Two types of interaction are available from the pre-computed data: (1) interactions at the protein superfamily level and (2) those transferred from the interactions of yeast proteins. Interactions at the superfamily level were obtained by locating known structural interactions of the PDB in the SCOP domains and identifying homologs of the domains in the human proteins. Interactions transferred from yeast proteins were obtained by identifying homologs of the yeast proteins in the human proteins. For each human protein in the database and each query submitted by users, the protein superfamilies and yeast proteins assigned to the protein are shown, along with their interacting partners. We have also developed a set of web-based programs so that users can visualize and analyze protein interaction networks in order to explore the networks further. AVAILABILITY: http://www.hpid.org.

Algorithms↗
PubMed2005

Prediction of protein-protein interactions by combining structure and sequence conservation in protein interfaces.

MOTIVATION: Elucidation of the full network of protein-protein interactions is crucial for understanding of the principles of biological systems and processes. Thus, there is a need for in silico methods for predicting interactions. We present a novel algorithm for automated prediction of protein-protein interactions that employs a unique bottom-up approach combining structure and sequence conservation in protein interfaces. RESULTS: Running the algorithm on a template dataset of 67 interfaces and a sequentially non-redundant dataset of 6170 protein structures, 62 616 potential interactions are predicted. These interactions are compared with the ones in two publicly available interaction databases (Database of Interacting Proteins and Biomolecular Interaction Network Database) and also the Protein Data Bank. A significant number of predictions are verified in these databases. The unverified ones may correspond to (1) interactions that are not covered in these databases but known in literature, (2) unknown interactions that actually occur in nature and (3) interactions that do not occur naturally but may possibly be realized synthetically in laboratory conditions. Some unverified interactions, supported significantly with studies found in the literature, are discussed. AVAILABILITY: http://gordion.hpc.eng.ku.edu.tr/prism CONTACT: agursoy@ku.edu.tr; okeskin@ku.edu.tr.

Algorithms↗
PubMed2005

Filtering high-throughput protein-protein interaction data using a combination of genomic features.

BACKGROUND: Protein-protein interaction data used in the creation or prediction of molecular networks is usually obtained from large scale or high-throughput experiments. This experimental data is liable to contain a large number of spurious interactions. Hence, there is a need to validate the interactions and filter out the incorrect data before using them in prediction studies. RESULTS: In this study, we use a combination of 3 genomic features -- structurally known interacting Pfam domains, Gene Ontology annotations and sequence homology -- as a means to assign reliability to the protein-protein interactions in Saccharomyces cerevisiae determined by high-throughput experiments. Using Bayesian network approaches, we show that protein-protein interactions from high-throughput data supported by one or more genomic features have a higher likelihood ratio and hence are more likely to be real interactions. Our method has a high sensitivity (90%) and good specificity (63%). We show that 56% of the interactions from high-throughput experiments in Saccharomyces cerevisiae have high reliability. We use the method to estimate the number of true interactions in the high-throughput protein-protein interaction data sets in Caenorhabditis elegans, Drosophila melanogaster and Homo sapiens to be 27%, 18% and 68% respectively. Our results are available for searching and downloading at http://helix.protein.osaka-u.ac.jp/htp/. CONCLUSION: A combination of genomic features that include sequence, structure and annotation information is a good predictor of true interactions in large and noisy high-throughput data sets. The method has a very high sensitivity and good specificity and can be used to assign a likelihood ratio, corresponding to the reliability, to each interaction.

Animals↗
PubMed2005

Protein-protein interactions of the hyperthermophilic archaeon Pyrococcus horikoshii OT3.

BACKGROUND: Although 2,061 proteins of Pyrococcus horikoshii OT3, a hyperthermophilic archaeon, have been predicted from the recently completed genome sequence, the majority of proteins show no similarity to those from other organisms and are thus hypothetical proteins of unknown function. Because most proteins operate as parts of complexes to regulate biological processes, we systematically analyzed protein-protein interactions in Pyrococcus using the mammalian two-hybrid system to determine the function of the hypothetical proteins. RESULTS: We examined 960 soluble proteins from Pyrococcus and selected 107 interactions based on luciferase reporter activity, which was then evaluated using a computational approach to assess the reliability of the interactions. We also analyzed the expression of the assay samples by western blot, and a few interactions by in vitro pull-down assays. We identified 11 hetero-interactions that we considered to be located at the same operon, as observed in Helicobacter pylori. We annotated and classified proteins in the selected interactions according to their orthologous proteins. Many enzyme proteins showed self-interactions, similar to those seen in other organisms. CONCLUSION: We found 13 unannotated proteins that interacted with annotated proteins; this information is useful for predicting the functions of the hypothetical Pyrococcus proteins from the annotations of their interacting partners. Among the heterogeneous interactions, proteins were more likely to interact with proteins within the same ortholog class than with proteins of different classes. The analysis described here can provide global insights into the biological features of the protein-protein interactions in P. horikoshii.

Genes, Archaeal↗
PubMed2004

Evaluation of drug interaction software to identify alerts for transplant medications.

BACKGROUND: The ability of computerized physician order entry (CPOE) systems to identify clinically significant drug interactions is dependent upon the integrity of the drug information populating the software. A CPOE system with incomplete or inaccurate drug information will fail to identify clinically important drug interactions and, therefore, fail to reduce preventable adverse drug events (pADEs). OBJECTIVE: To evaluate, from the prescribers' perspective, the ability of a common drug interaction database to identify clinically important drug interactions involving drugs used in transplantation. METHODS: The clinical significance of drug interactions involving 5 transplant drugs was evaluated by an expert panel to determine whether alerts should be generated for physicians not involved in the transplant at the time of order entry. Drug interactions included in the analysis were generated from the expert panel, a common drug interaction database, and 2 standard drug interaction references. Responses on the clinical significance were used to calculate the sensitivity, specificity, and positive and negative predictive values for each severity setting of a common electronic drug interaction database. RESULTS: Overall, the database failed to identify approximately 70% of interactions considered significant by the expert panel. Of the alerts that were generated, >85% were considered clinically significant. The database was most deficient in identifying interactions resulting from additive toxicity. CONCLUSIONS: To expect a decrease in pADEs caused by drug interactions, the information used to populate CPOE systems must be validated. Establishing consistency and integrity of this information may be a future role for pharmacists.

Databases as Topic↗