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 1,333 records · Page 74Linked to original sources

Discovering motif pairs at interaction sites from protein sequences on a proteome-wide scale.

MOTIVATION: Protein-protein interaction, mediated by protein interaction sites, is intrinsic to many functional processes in the cell. In this paper, we propose a novel method to discover patterns in protein interaction sites. We observed from protein interaction networks that there exist a kind of significant substructures called interacting protein group pairs, which exhibit an all-versus-all interaction between the two protein-sets in such a pair. The full-interaction between the pair indicates a common interaction mechanism shared by the proteins in the pair, which can be referred as an interaction type. Motif pairs at the interaction sites of the protein group pairs can be used to represent such interaction type, with each motif derived from the sequences of a protein group by standard motif discovery algorithms. The systematic discovery of all pairs of interacting protein groups from large protein interaction networks is a computationally challenging problem. By a careful and sophisticated problem transformation, the problem is solved using efficient algorithms for mining frequent patterns, a problem extensively studied in data mining. RESULTS: We found 5349 pairs of interacting protein groups from a yeast interaction dataset. The expected value of sequence identity within the groups is only 7.48%, indicating non-homology within these protein groups. We derived 5343 motif pairs from these group pairs, represented in the form of blocks. Comparing our motifs with domains in the BLOCKS and PRINTS databases, we found that our blocks could be mapped to an average of 3.08 correlated blocks in these two databases. The mapped blocks occur 4221 out of total 6794 domains (protein groups) in these two databases. Comparing our motif pairs with iPfam consisting of 3045 interacting domain pairs derived from PDB, we found 47 matches occurring in 105 distinct PDB complexes. Comparing with another putative domain interaction database InterDom, we found 203 matches. AVAILABILITY: http://research.i2r.a-star.edu.sg/BindingMotifPairs/resources. SUPPLEMENTARY INFORMATION: http://research.i2r.a-star.edu.sg/BindingMotifPairs and Bioinformatics online.

Algorithms↗

A database and tool, IM Browser, for exploring and integrating emerging gene and protein interaction data for Drosophila.

BACKGROUND: Biological processes are mediated by networks of interacting genes and proteins. Efforts to map and understand these networks are resulting in the proliferation of interaction data derived from both experimental and computational techniques for a number of organisms. The volume of this data combined with the variety of specific forms it can take has created a need for comprehensive databases that include all of the available data sets, and for exploration tools to facilitate data integration and analysis. One powerful paradigm for the navigation and analysis of interaction data is an interaction graph or map that represents proteins or genes as nodes linked by interactions. Several programs have been developed for graphical representation and analysis of interaction data, yet there remains a need for alternative programs that can provide casual users with rapid easy access to many existing and emerging data sets. DESCRIPTION: Here we describe a comprehensive database of Drosophila gene and protein interactions collected from a variety of sources, including low and high throughput screens, genetic interactions, and computational predictions. We also present a program for exploring multiple interaction data sets and for combining data from different sources. The program, referred to as the Interaction Map (IM) Browser, is a web-based application for searching and visualizing interaction data stored in a relational database system. Use of the application requires no downloads and minimal user configuration or training, thereby enabling rapid initial access to interaction data. IM Browser was designed to readily accommodate and integrate new types of interaction data as it becomes available. Moreover, all information associated with interaction measurements or predictions and the genes or proteins involved are accessible to the user. This allows combined searches and analyses based on either common or technique-specific attributes. The data can be visualized as an editable graph and all or part of the data can be downloaded for further analysis with other tools for specific applications. The database is available at http://proteome.wayne.edu/PIMdb.html CONCLUSION: The Drosophila Interactions Database described here places a variety of disparate data into one easily accessible location. The database has a simple structure that maintains all relevant information about how each interaction was determined. The IM Browser provides easy, complete access to this database and could readily be used to publish other sets of interaction data. By providing access to all of the available information from a variety of data types, the program will also facilitate advanced computational analyses.

Database Management Systems↗

Geometric cooperativity and anticooperativity of three-body interactions in native proteins.

Characterizing multibody interactions of hydrophobic, polar, and ionizable residues in protein is important for understanding the stability of protein structures. We introduce a geometric model for quantifying 3-body interactions in native proteins. With this model, empirical propensity values for many types of 3-body interactions can be reliably estimated from a database of native protein structures, despite the overwhelming presence of pairwise contacts. In addition, we define a nonadditive coefficient that characterizes cooperativity and anticooperativity of residue interactions in native proteins by measuring the deviation of 3-body interactions from 3 independent pairwise interactions. It compares the 3-body propensity value from what would be expected if only pairwise interactions were considered, and highlights the distinction of propensity and cooperativity of 3-body interaction. Based on the geometric model, and what can be inferred from statistical analysis of such a model, we find that hydrophobic interactions and hydrogen-bonding interactions make nonadditive contributions to protein stability, but the nonadditive nature depends on whether such interactions are located in the protein interior or on the protein surface. When located in the interior, many hydrophobic interactions such as those involving alkyl residues are anticooperative. Salt-bridge and regular hydrogen-bonding interactions, such as those involving ionizable residues and polar residues, are cooperative. When located on the protein surface, these salt-bridge and regular hydrogen-bonding interactions are anticooperative, and hydrophobic interactions involving alkyl residues become cooperative. We show with examples that incorporating 3-body interactions improves discrimination of protein native structures against decoy conformations. In addition, analysis of cooperative 3-body interaction may reveal spatial motifs that can suggest specific protein functions.

Computer Simulation↗

Trans interactions between galactosylceramide and cerebroside sulfate across apposed bilayers.

The two glycosphingolipids galactosylceramide (GalC) and its sulfated form, cerebroside sulfate (CBS), are present at high concentrations in the multilayered myelin sheath and are involved in carbohydrate-carbohydrate interactions between the lipid headgroups. In order to study the structure of the complex of these two glycolipids by Fourier transform infrared (FTIR) spectroscopy, GalC dispersions were combined with CBS dispersions in the presence and absence of Ca(2+). The FTIR spectra indicated that a strong interaction occurred between these glycolipids even in the absence of Ca(2+). The interaction resulted in dehydration of the sulfate, changes in the intermolecular hydrogen bonding interactions of the sugar and other oxygens, decreased intermolecular hydrogen bonding of the amide C==O of GalC and dehydration of the amide region of one or both of the lipids in the mixture, and disordering of the hydrocarbon chains of both lipids. The spectra also show that Ca(2+) interacts with the sulfate of CBS. Although they do not reveal which other groups of CBS and GalC interact with Ca(2+) or which groups participate in the interaction between the two lipids, they do show that the sulfate is not directly involved in interaction with GalC, since it can still bind to Ca(2+) in the mixture. The interaction between these two lipids could be either a lateral cis interaction in the same bilayer or a trans interaction between apposed bilayers. The type of interaction between the lipids, cis or trans, was investigated using fluorescent and spin-label probes and anti-glycolipid antibodies. The results confirmed a strong interaction between the GalC and the CBS microstructures. They suggested further that this interaction caused the CBS microstructures to be disrupted so that CBS formed a single bilayer around the GalC multilayered microstructures, thus sequestering GalC from the external aqueous phase. Thus the CBS and GalC interacted via a trans interaction across apposed bilayers, which resulted in dehydration of the headgroup and interface region of both lipid bilayers. The strong interaction between these lipids may be involved in stabilization of the myelin sheath.

Animals↗

Classification of metal-oxide bonded interactions based on local potential- and kinetic-energy densities.

A classification of the hydrogen fluoride H-F-bonded interactions comprising a large number of molecules has been proposed by Espinosa et al. [J. Chem. Phys. 117, 5529 (2002)] based on the ratio /Vr(c)/ / Gr(c) where /Vr(c)/ is the magnitude of the local potential-energy density and Gr(c) is the local kinetic-energy density, each evaluated at a bond critical point r(c). A calculation of the ratio for the M-O bonded interactions comprising a relatively large number of oxide molecules and earth materials, together with the constraints imposed by the values of inverted Delta2rho r(c) and the local electronic energy density, Hr(c) = Gr(c) + Vr(c), in the H-F study, yielded practically the same classification for the oxides. This is true despite the different trends that hold between the bond critical point and local energy density properties with the bond lengths displayed by the H-F and M-O bonded interactions. On the basis of the ratio, Li-O, Na-O, and Mg-O bonded interactions classify as closed-shell ionic bonds, Be-O, Al-O, Si-O, B-O, and P-O interactions classify as bonds of intermediate character with the covalent character increasing from Be-O to P-O. N-O interactions classify as shared covalent bonds. C-O and S-O bonded interactions classify as both intermediate and covalent bonded interactions. The C-O double- and triple-bonded interactions classify as intermediate-bonded interactions, each with a substantial component of covalent character and the C-O single-bonded interaction classifies as a covalent bond whereas their local electronic energy density values indicate that they are each covalent bonded interactions. The ratios for the Be-O, Al-O, and Si-O bonded interactions indicate that they have a substantial component of ionic character despite their classification as bonds of intermediate character. The trend between the ratio and the character of the bonded interactions is consistent with trends expected from electronegativity considerations. The ratio increases as the net charges and the coordination numbers for the atoms for several Ni-sulfides decrease. On the contrary, the ratio for the Si-O bonded interactions for the orthosilicate, forsterite, Mg2SiO4, and the high-pressure silica polymorph, stishovite, decreases as the observed net atomic charges and the coordination numbers of Si and O increase in value. The ratio for the Ni-Ni bonded interactions for the Ni-sulfides and bulk Ni metal indicate that the interactions are intermediate in character with a substantial component of ionic character.

Journal Article↗

Interaction energy analyses of folate analog binding to human dihydrofolate reductase: contribution of the antifolate substructural regions to complex stability.

The three-dimensional structure of the human dihydrofolate reductase (DHFR), methotrexate tetrazole, and NADPH ternary complex was used to model the corresponding ternary complexes with methotrexate tetrazole replaced by methotrexate, methotrexate-polyglutamate with three glutamyl residues, and 5,10-deazaaminopterin, respectively. Each complex was solvated in a 60-angstrom cube of explicit water and subjected to structural minimization followed by interaction energy analyses. Interaction energy calculations were performed for the antifolate interaction with water, NADPH, the DHFR binding site residues, the entire DHFR protein, and the solvated NADPH:DHFR complex. These studies revealed that methotrexate-polyglutamate exhibited the most stable interactions and that approximately one half of antifolate:DHFR stability could be accounted for by the interaction of the antifolate with the binding site residues. The antifolate structures were also subdivided into heterocyclic, phenyl, and glutamyl substructural regions. Interaction energies were subsequently calculated for the interactions of the subregions with water, NADPH, the DHFR binding site residues, the DHFR protein, and the solvated NADPH:DHFR complex. The glutamyl substructural region showed the greatest contribution to overall antifolate binding stability due to its interaction with the DHFR protein. The heterocyclic and phenyl substructural regions generally showed much less stable interactions. These results suggest that the primary stabilizing factor of the antifolate interaction is the interaction of glutamyl with the DHFR protein. Additionally, interaction energy analyses were performed for specific groups of atoms within the substructural regions. These studies indicated that the stability of the glutamyl interaction is due to the interaction of glutamyl oxygen atoms with the DHFR protein. In the case of the methotrexate tetrazole complex, the tetrazole nitrogens also contribute significantly to the stability of the glutamyl interaction. The carbon atoms of the heterocyclic and phenyl groups both showed more stable interactions with NADPH than with water, while the nitrogen atoms showed more stable interactions with water than with NADPH. Collectively, these results indicate that the glutamyl region is the most important in antifolate binding stability.

Aminopterin↗

Pharmacoepidemiologic study of potential drug interactions in outpatients of a university hospital in Thailand.

BACKGROUND: Drug-drug interaction is a potential cause of adverse drug reactions. The incidence of such drug interactions in university hospitals in Thailand is unknown. PURPOSE: To estimate the rate of potential drug-drug interactions in outpatients of a typical Thai university hospital, and to identify risk factors for such interactions in Thai patients. METHODS: One-year outpatients' prescription data were retrieved from the hospital computer records. Potential drug interactions were identified using the existing drug-interaction database system. Potential interactions within a specific prescription and involving drugs prescribed 1-, 3- and 7-day earlier were searched for. Possible associations between occurrence of an interaction and a patient's age and gender and the number of items on the prescription were explored. RESULTS: The overall rate of potential drug interactions was 27.9% with a maximal value of 57.8% at the Department of Psychiatry. The rate of the most potentially significant interactions was 2.6%, being the highest in the Department of Medicine (6.0%), with isoniazid vs. rifampin as the most common interacting combination. The rate increased with the patient's age and prescription size (P=0.000). The odd's ratio of having at least one potential drug interaction was 1.8 (64.2%) when age increased by 20 years (P=0.000) and 2.8 (165.7%) when another drug was added (P=0.000). The rate of potential drug interactions was the same for both genders. The rate of potential drug interactions detected across prescriptions was higher than within prescriptions and was dependent on the time interval between prescriptions. CONCLUSIONS: Potential drug interactions were common in our sample of patients. The rate of such interactions increased with the number of drugs prescribed and the patient's age.

Adult↗

Systematic overview of drug interactions with antidepressant medications.

OBJECTIVE: Antidepressants are commonly used drugs with potential for numerous drug interactions. This study aims to systematically review the literature on drug interactions with antidepressants. METHODS: We searched MEDLINE (1966 to November 2003) and EMBASE (1980 to 2003), using the heading drug interactions combined with individual antidepressant names. We restricted searches to English-language articles and human studies. We screened drug interaction texts and review articles for relevant studies. We included articles reporting original human data on drug interactions with antidepressants commonly used in North America. Articles were independently evaluated by 2 reviewers on clinical effect, clinical significance, and quality of evidence. Discrepancies were resolved by consensus. RESULTS: There were 904 eligible interactions, involving 9509 patients, for a total of 598 summary interactions. Of these, 439 (73%) demonstrated an interaction, 148 (25%) had no effect, and 11 (2%) had conflicting evidence. For 510 interactions (85%), the quality of evidence was poor. It was fair for 67 (11%) interactions and good for 10 (2%) interactions. There were no interactions with excellent quality of evidence. There were 145 (24%) interactions of major clinical significance. These were predominantly hypertensive emergencies and serotonin syndrome. Most interacting drugs had central nervous system (CNS) activity. As expected, monoamine oxidase inhibitors (MAOIs) appear to be the most problematic family in terms of potential for serious drug interactions. CONCLUSIONS: Drug interactions with antidepressants are an important cause for concern, but this concern is based primarily on poor evidence. We recommend caution when combining antidepressants with other CNS drugs, particularly when coadministering MAOIs with other substances.

Cytochrome P-450 Enzyme System↗

Adverse drug interactions in dental practice. Professional and educational implications.

BACKGROUND: Rapid progress in dental pharmacotherapeutics requires that clinicians constantly update their knowledge of new drugs, drug interactions and useful therapeutic trends. This article is the first 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 goal of the series is to identify specific adverse drug interactions that are relevant to the therapeutic agents commonly used in general dental practice: analgesics, antibiotics, sedatives, local anesthetics and vasoconstrictors. METHODS: A group of dentist/clinical pharmacologists, with documented expertise in specific areas of dental therapeutics, reviewed the current literature regarding adverse drug interactions in dentistry. This expert panel evaluated the quality of information used to document these drug interactions and assess the severity of these drug reactions with respect to the drugs' use in dental practice. RESULTS: On the basis of the quality and severity of each reported interaction, the authors summarized the clinical importance of these drug interactions using a Significance Rating for Dental Drug Interactions. The participants presented their recommendations at the above-mentioned IADR symposium. CONCLUSIONS: Although thousands of drug interactions are described in the literature, the authors found many to be poorly documented or of minor importance to dental practitioners. For interactions that they determined to be relevant, the participants provided recommendations and precautions for preventing these potential complications. This article discusses the professional impact of drug interactions on dental practice; the classification and documentation of drug interactions; the determination of causality between drug interactions and adverse effects; risk factors; and unique characteristics of dental therapeutics. Subsequent articles will present specific summary recommendations for drug interactions associated with the use of antibiotics, analgesics, sedatives, and local anesthetics and vasoconstrictors. CLINICAL IMPLICATIONS: Although thousands of drug interactions have been reported in the literature, only a few are significantly associated with dental therapeutic agents. Avoiding these drug interactions will prevent potentially severe reactions in dental practice.

Adverse Drug Reaction Reporting Systems↗

Clinical risk management in Dutch community pharmacies: the case of drug-drug interactions.

BACKGROUND: The prevention of drug-drug interactions requires a systematic approach for which the concept of clinical risk management can be used. The objective of our study was to measure the frequency, nature and management of drug-drug interaction alerts as these occur in daily practice of Dutch community pharmacies. METHODS: In total, 63 Dutch pharmacies collected all drug-drug interaction alerts during 153 research days (on average 2.4 days/pharmacy), as well as variables related to these alerts, such as involved medicines, first time or recurrent drug-drug interaction, same or different prescribers, patient data (age, sex) and information about the management of drug-drug interactions by the pharmacy. The latter was discriminated into internal procedures only and external action, such as communication with the patient, the prescriber or the anticoagulation clinic and prescription modification. All drug-drug interactions were classified into categories of clinical relevance (A-F) and available evidence (0-4). RESULTS: A total of 43,129 prescription-only medicines were dispensed during the study period. On average, 16.8 interaction alerts per day per pharmacy were collected. Approximately 6% of all prescriptions generated a drug-drug interaction alert. Of all alerts (n = 2572), 31.1% occurred for the first time and with 21% two different prescribers were involved. The 20 most frequently occurring drug-drug interaction alerts accounted for approximately 76% of all alerts. Cardiovascular drugs, NSAIDs, oral contraceptives and antibacterials were most frequently involved. External action was taken in response to 27.3% of the alerts, meaning either a modification of one of the concerned prescriptions (n = 65; 9.3%), communication with the prescriber or anticoagulation clinic (n = 90; 12.8%) or communication with the patient or a relative (n = 547; 77.9%). Where there was no external action (n = 1860; 72.3%), pharmacists concluded in about two-thirds of cases that the drug-drug interaction had been managed in the past. Other reasons not to intervene externally were for instance: incorrect alert; acceptable drug-drug interaction; or outcome of the interaction considered irrelevant. Adjusted for several variables, a first alert was found to be a main determinant for external action. After stratifying for first alerts no other significant determinants were found. CONCLUSIONS: A high frequency of drug-drug interaction alerts was found. Most concerned recurrent alerts, which were the main reason not to act externally. Concerning the assessment phase in the risk-management process, drug-drug interactions with no or low evidence/relevance should be reconsidered. Concerning the management of drug-drug interactions in pharmacies, the opportunity to actively suppress alerts for a certain period of time should be studied in more detail. There are indicators that the management of patient-orientated advice could be improved and a greater degree of consistency developed for the management of first and recurrent interaction alerts.

Adolescent↗

Intra- and intermolecular interactions between cyclic-AMP receptor protein and DNA: ab initio fragment molecular orbital study.

The ab initio fragment molecular orbital (FMO) calculations were performed for the cAMP receptor protein (CRP) complexed with a cAMP and DNA duplex to elucidate their sequence-specific binding and the stability of the DNA duplex, as determined by analysis of their inter- and intramolecular interactions. Calculations were performed with the AMBER94 force field and at the HF and MP2 levels with several basis sets. The interfragment interaction energies (IFIEs) were analyzed for interactions of CRP-cAMP with each base pair, DNA duplex with each amino acid residue, and each base pair with each residue. In addition, base-base interactions were analyzed including hydrogen bonding and stacking of DNA. In the interaction between DNA and CRP-cAMP, there was a significant charge transfer (CT) from the DNA to CRP, and this CT interaction played an important role as well as the electrostatic interactions. It is necessary to apply a quantum mechanical approach beyond the "classical" force-field approach to describe the sequence specificity. In the DNA intramolecular interaction, the dispersion interactions dominated the stabilization of the base-pair stacking interactions. Strong, attractive 1,2-stacking interactions and weak, repulsive 1,3-stacking interactions were observed. Comparison of the intramolecular interactions of free and complexed DNA revealed that the base-pairing interactions were stronger, and the stacking interactions were weaker, in the complexed structure. Therefore, the DNA duplex stability appears to change due to both the electrostatic and the CT interactions that take place under conditions of DNA-CRP binding.

Algorithms↗

Lecture versus standardized patient interaction in the surgical clerkship: a randomized prospective cross-over study.

BACKGROUND: Medical schools increasingly are incorporating the standardized patient (SP) interaction as a teaching and testing modality to prepare students for United States Medical Licensing Examination Step 2 Clinical Skills. Although SP interactions provide a safe environment in which to practice clinical skills, little is known about medical students' perceptions of the instructional SP interaction in comparison with the classic didactic lecture. We hypothesized that students would enjoy and value an instructional SP interaction more than a didactic lecture, and that this perception would be enhanced if the lecture immediately preceded the instructional SP interaction. METHODS: Students on the junior surgery clerkship from January to December 2004 were randomized into 2 groups. Group A (n = 71) received an hour-long didactic lecture about peripheral vascular disease followed by a 20-minute instructional SP interaction in which the SP portrayed a patient with lower-extremity claudication. Group B (n = 72) received the SP interaction first, followed by the lecture. All students received an identical quiz about peripheral vascular disease after the second educational encounter, whether the SP interaction or the lecture. Student perceptions were surveyed by written questionnaire before and after the session by using a 5-point Likert scale, with 5 being the most favorable rating. Data (perceptions, performances on the quiz and the SP interaction, clerkship grades) were compared using the Student t test or the Mann-Whitney rank sums test. RESULTS: Although all students on average enjoyed and valued the lecture more than the SP interaction (P <.001), group A students enjoyed the SP interaction more than group B (3.3 +/- .9 vs. 2.8 +/- 1.0, P = .008), and they perceived the SP interaction as having more value to their overall education (3.3 +/- 1.0 vs. 2.8 +/- 1.0, P = .004). Group A students performed statistically better on the physical examination (66% +/- 19% vs. 40% +/- 16%, P <.001) and communication portions (90% +/- 11% vs. 79% +/- 12%, P <.001) of the SP checklist than group B, but not on the history portion. Student confidence in their own history and physical examination skills increased similarly for both groups. CONCLUSIONS: The classic lecture format not only is enjoyed and valued as a learning tool more by our surgical clerkship students than the instructional SP interaction, but having a lecture just before an SP interaction increased perceived enjoyment and value of the SP interaction and enhanced performance on the SP checklist. These data suggest that educators can improve student perceptions and attitudes surrounding the instructional SP interaction by using strategically timed lectures.

Clinical Clerkship↗

Interaction and intervention modeling: predicting and extrapolating the impact of multiple interventions.

PURPOSE: Methods called interaction and intervention modeling are presented. Interaction modeling examines the interactions between variables as the basis for predicting the impact of multiple variables on a target population and on populations with difference distributions of risk factors. Intervention modeling incorporates these interactions and aims to extrapolate the impact of multiple interventions to new populations. The aim is to develop methods that will be useful for modeling and comparing intervention strategies using existing data and standard statistical methods. METHODS: Traditional hypothesis testing methods used for randomized clinical trials and cohort studies and extrapolating the results to new populations are compared with interaction and intervention modeling methods. Interaction and intervention modeling utilizes the same data as the traditional approach but examines the impact of multiple simultaneous interactions and allows extrapolation of the results to populations with different prevalences and distributions of risk factors. An example using real data demonstrates the potential of interaction and intervention modeling to predict the impact of multiple interacting variables and to compare the impact of alternative interventions. RESULTS: The methods outlined take into account the impact of the magnitude of the relative risks, prevalence of risk factors, and interaction of risk variables when predicting the impact on a new population or extrapolating the results of one or more interventions on a new population. Traditional methods that do not take into account interactions are shown to produce different conclusions from the intervention modeling approach that incorporates interactions. The impact of the intervention modeling approach compared with the traditional approach will be quite variable depending on the prevalence of the risk factors and their extent of interaction. CONCLUSIONS: Studies designed to test a hypothesis treat most variables as potential confounding variables adjusting for their impact and their interactions as part of the analysis using traditional regression methods. Interaction and intervention modeling focuses on the interactions themselves and allows comparison of the effectiveness of alternative interventions.

Clinical Trials as Topic↗

Model chemistry calculations of thiophene dimer interactions: origin of pi-stacking.

The intermolecular interaction energies of thiophene dimers have been calculated by using an aromatic intermolecular interaction (AIMI) model (a model chemistry for the evaluation of intermolecular interactions between aromatic molecules). The CCSD(T) interaction energy at the basis set limit has been estimated from the MP2 interaction energy near the basis set limit and the CCSD(T) correction term obtained by using a medium-size basis set. The calculated interaction energies of the parallel and perpendicular thiophene dimers are -1.71 and -3.12 kcal/mol, respectively. The substantial attractive interaction in the thiophene dimer, even where the molecules are well separated, shows that the major source of attraction is not short-range interactions such as charge transfer but rather long-range interactions such as electrostatic and dispersion. The inclusion of electron correlation increases the attraction significantly. The dispersion interaction is found to be the major source of attraction in the thiophene dimer. The calculated total interaction energy of the thiophene dimer is highly orientation dependent. Although electrostatic interaction is substantially weaker than dispersion interaction, it is highly orientation dependent, and therefore electrostatic interaction play an important role in the orientation dependence of the total interaction energy. The large attractive interaction in the perpendicular dimer is the cause of the preference for the herringbone structure in the crystals of nonsubstituted oligothiophenes (alpha-terthienyls), and the steric repulsion between the beta-substituents is the cause of the pi-stacked structure in the crystals of some beta-substituted oligothiophenes.

Journal Article↗

Virus-specific interaction between the human cytomegalovirus major capsid protein and the C terminus of the assembly protein precursor.

We previously identified a minimal 12-amino-acid domain in the C terminus of the herpes simplex virus type 1 (HSV-1) scaffolding protein which is required for interaction with the HSV-1 major capsid protein. An alpha-helical structure which maximizes the hydropathicity of the minimal domain is required for the interaction. To address whether cytomegalovirus (CMV) utilizes the same strategy for capsid assembly, several glutathione S-transferase fusion proteins to the C terminus of the CMV assembly protein precursor were produced and purified from bacterial cells. The study showed that the glutathione S-transferase fusion containing 16 amino acids near the C-terminal end was sufficient to interact with the major capsid protein. Interestingly, no cross-interaction between HSV-1 and CMV could be detected. Mutation analysis revealed that a three-amino-acid region at the N-terminal side of the central Phe residue of the CMV interaction domain played a role in determining the viral specificity of the interaction. When this region was converted so as to correspond to that of HSV-1, the CMV assembly protein domain lost its ability to interact with the CMV major capsid protein but gained full interaction with the HSV-1 major capsid protein. To address whether the minimal interaction domain of the CMV assembly protein forms an alpha-helical structure similar to that in HSV-1, peptide competition experiments were carried out. The results showed that a cyclic peptide derived from the interaction domain with a constrained (alpha-helical structure competed for interaction with the major capsid protein much more efficiently than the unconstrained linear peptide. In contrast, a cyclic peptide containing an Ala substitution for the critical Phe residue did not compete for the interaction at all. The results of this study suggest that (i) CMV may have developed a strategy similar to that of HSV-1 for capsid assembly; (ii) the minimal interaction motif in the CMV assembly protein requires an alpha-helix for efficient interaction with the major capsid protein; and (iii) the Phe residue in the CMV minimal interaction domain is critical for interaction with the major capsid protein.

Animals↗

Macrolide drug interactions: an update.

OBJECTIVE: To describe the current drug interaction profiles for the commonly used macrolides in the US and Europe, and to comment on the clinical impact of these interactions. DATA SOURCES: A MEDLINE search (1975-1998) was performed to identify all pertinent studies, review articles, and case reports. When appropriate information was not available in the literature, data were obtained from the product manufacturers. STUDY SELECTION: All available data were reviewed to provide an unbiased account of possible drug interactions. DATA EXTRACTION: Data for some of the interactions were not available from the literature, but were available from abstracts or company-supplied materials. Although the data were not always explicit, the best attempt was made to deliver pertinent information that clinical practitioners would need to formulate practice opinions. When more in-depth information was supplied in the form of a review or study report, a thorough explanation of pertinent methodology was supplied. DATA SYNTHESIS: Several clinically significant drug interactions have been identified since the approval of erythromycin. These interactions usually were related to the inhibition of the cytochrome P450 enzyme systems, which are responsible for the metabolism of many drugs. The decreased metabolism by the macrolides has in some instances resulted in potentially severe adverse events. The development and marketing of newer macrolides are hoped to improve the drug interaction profile associated with this class. However, this has produced variable success. Some of the newer macrolides demonstrated an interaction profile similar to that of erythromycin; others have improved profiles. The most success in avoiding drug interactions related to the inhibition of cytochrome P450 has been through the development of the azalide subclass, of which azithromycin is the first and only to be marketed. Azithromycin has not been demonstrated to inhibit the cytochrome P450 system in studies using a human liver microsome model, and to date has produced none of the classic drug interactions characteristic of the macrolides. CONCLUSIONS: Most of the available data regarding macrolide drug interactions are from studies in healthy volunteers and case reports. These data suggest that clarithromycin appears to have an interaction profile similar to that of erythromycin. Given this similarity, it is important to consider the interaction profile of clarithromycin when using erythromycin. This is especially necessary as funds for further studies of a medication available in generic form (e.g., erythromycin) are limited. Azithromycin has produced few clinically significant interactions with any agent cleared through the cytochrome P450 enzyme system. Although the available data are promising, the final test should come from studies conducted in patients who are taking potentially interacting compounds on a chronic basis.

Anti-Bacterial Agents↗

Characterization and prediction of protein-protein interactions within and between complexes.

Databases of experimentally determined protein interactions provide information on binary interactions and on involvement in multiprotein complexes. These data are valuable for understanding the general properties of the interaction between proteins as well as for the development of prediction schemes for unknown interactions. Here we analyze experimentally determined protein interactions by measuring various sequence, genomic, transcriptomic, and proteomic attributes of each interacting pair in the yeast Saccharomyces cerevisiae. We find that dividing the data into two groups, one that includes binary interactions within protein complexes (stable) and another that includes binary interactions that are not within complexes (transient), enables better characterization of the interactions by the different attributes and improves the prediction of new interactions. This analysis revealed that most attributes were more indicative in the set of intracomplex interactions. Using this data set for training, we integrated the different attributes by logistic regression and developed a predictive scheme that distinguishes between interacting and noninteracting protein pairs. Analysis of the logistic-regression model showed that one of the strongest contributors to the discrimination between interacting and noninteracting pairs is the presence of distinct pairs of domain signatures that were suggested previously to characterize interacting proteins. The predictive algorithm succeeds in identifying both intracomplex and other interactions (possibly the more stable ones), and its correct identification rate is 2-fold higher than that of large-scale yeast two-hybrid experiments.

Algorithms↗

Comparative assessment of four drug interaction compendia.

AIMS: To assess the consistency of inclusion and grading of major drug interactions for 50 drugs in four leading international drug interaction compendia. METHODS Four international drug interaction compendia were compared: the drug interactions appendix of the British National Formulary, the interaction supplement in the French drug compendium Vidal, and two US drug interaction compendia, Drug Interaction Facts and the Micromedex (Drug-Reax) program. Major interactions were defined as potentially hazardous in BNF or with the warning 'contraindication' or 'avoid' in Vidal or with the significance grading 1 or 2 in DIF. Major interactions for a list of 50 drugs were searched in all four compendia. RESULTS: A total of 1264 interactions meeting the inclusion criteria were identified for these 50 drugs. After deletion of 169 duplicates, 1095 interactions were included in the analysis. Of the drug interactions classified as major in any one compendium between 14% and 44% were not listed in the other compendia. The grading systems used for the severity and the quality of the supporting evidence in Micromedex and DIF were inconsistent. CONCLUSIONS: There is a lack of consistency in the inclusion and grading of drug interactions of major significance for 50 drugs across the four drug compendia examined. This may reflect the lack of standardization of the terminology used to classify drug interactions and the lack of good epidemiological evidence on which to base the assessment of the clinical relevance of drug interactions.

Adverse Drug Reaction Reporting Systems↗