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PubMed2004

Predicting protein-peptide interactions via a network-based motif sampler.

MOTIVATION: Many protein-protein interactions are mediated by peptide recognition modules (PRMs), compact domains that bind to short peptides, and play a critical role in a wide array of biological processes. Recent experimental protein interaction data provide us with an opportunity to examine whether we may explain, or even predict their interactions by computational sequence analysis. Such a question was recently posed by the use of random peptide screens to characterize the ligands of one such PRM, the SH3 domain. RESULTS: We describe a general computational procedure for identifying the ligand peptides of PRMs by combining protein sequence information and observed physical interactions into a simple probabilistic model and from it derive an interaction-mediated de novo motif-finding framework. Using a recent all-versus-all yeast two-hybrid SH3 domain interaction network, we demonstrate that our technique can be used to derive independent predictions of interactions mediated by SH3 domains. We show that only when sequence information is combined with such all versus all protein interaction datasets, are we capable of identifying motifs with sufficient sensitivity and specificity for predicting interactions. The algorithm is general so that it may be applied to other PRM domains (e.g. SH2, WW, PDZ). AVAILABILITY: The Netmotsa software and source code, as part of a general Gibbs motif sampling library, are available at http://sf.net/projects/netmotsa

Algorithms↗
PubMed2005

Kernel methods for predicting protein-protein interactions.

MOTIVATION: Despite advances in high-throughput methods for discovering protein-protein interactions, the interaction networks of even well-studied model organisms are sketchy at best, highlighting the continued need for computational methods to help direct experimentalists in the search for novel interactions. RESULTS: We present a kernel method for predicting protein-protein interactions using a combination of data sources, including protein sequences, Gene Ontology annotations, local properties of the network, and homologous interactions in other species. Whereas protein kernels proposed in the literature provide a similarity between single proteins, prediction of interactions requires a kernel between pairs of proteins. We propose a pairwise kernel that converts a kernel between single proteins into a kernel between pairs of proteins, and we illustrate the kernel's effectiveness in conjunction with a support vector machine classifier. Furthermore, we obtain improved performance by combining several sequence-based kernels based on k-mer frequency, motif and domain content and by further augmenting the pairwise sequence kernel with features that are based on other sources of data. We apply our method to predict physical interactions in yeast using data from the BIND database. At a false positive rate of 1% the classifier retrieves close to 80% of a set of trusted interactions. We thus demonstrate the ability of our method to make accurate predictions despite the sizeable fraction of false positives that are known to exist in interaction databases. AVAILABILITY: The classification experiments were performed using PyML available at http://pyml.sourceforge.net. Data are available at: http://noble.gs.washington.edu/proj/sppi.

Algorithms↗
PubMed2005

Equivalent binding sites reveal convergently evolved interaction motifs.

MOTIVATION: Much research has been devoted to the characterization of interaction interfaces found in complexes with known structure. In this context, the interactions of non-homologous domains at equivalent binding sites are of particular interest, as they can reveal convergently evolved interface motifs. Such motifs are an important source of information to formulate rules for interaction specificity and to design ligands based on the common features shared among diverse partners. RESULTS: We develop a novel method to identify non-homologous structural domains which bind at equivalent sites when interacting with a common partner. We systematically apply this method to all pairs of interactions with known structure and derive a comprehensive database for these interactions. Of all non-homologous domains, which bind with a common interaction partner, 4.2% use the same interface of the common interaction partner (excluding immunoglobulins and proteases). This rises to 16% if immunoglobulin and proteases are included. We demonstrate two applications of our database: first, the systematic screening for viral protein interfaces, which can mimic native interfaces and thus interfere; and second, structural motifs in enzymes and its inhibitors. We highlight several cases of virus protein mimicry: viral M3 protein interferes with a chemokine dimer interface. The virus has evolved the motif SVSPLP, which mimics the native SSDTTP motif. A second example is the regulatory factor Nef in HIV which can mimic a kinase when interacting with SH3. Among others the virus has evolved the kinase's PxxP motif. Further, we elucidate motif resemblances in Baculovirus p35 and HIV capsid proteins. Finally, chymotrypsin is subject to scrutiny wrt. its structural similarity to subtilisin and wrt. its inhibitor's similar recognition sites. SUPPLEMENTARY INFORMATION: A database is online at scoppi.biotec.tu-dresden.de/abac/.

Algorithms↗
PubMed2025

Roles of microbial interactions in determining the establishment and function of synthetic consortium inoculants for soil applications.

Synthetic microbial consortium inoculants are emerging nature-based solutions for promoting sustainable agriculture and mitigating environmental challenges. However, despite promising results in simpler lab-scale trials, many inoculants fail to establish or perform satisfactorily in field conditions. One most critical yet least understood factor influencing inoculant effectiveness is the complex microbial interactions, both within consortium inoculants ("within-community" interactions) and between consortium inoculants and native soil communities ("cross-community" interactions). Here, we first discuss major negative and positive "within-community" interactions and highlight the importance to design consortium inoculants with positive interactions for improved stability and functionality. We then examine the bidirectional "cross-community" interactions once introducing consortium inoculants to soils. Soil native communities often create strong resistance to the invasion of inoculants. We discuss major drivers controlling the invasibility of native communities and various strategies increasing the invasiveness of consortium inoculants. We then discuss how consortium inoculants can reshape native communities, with implications for long-term ecosystem resilience and functioning. We propose future research efforts including advancing strategies for harnessing natural species from relatively untapped soil reservoirs and using high-throughput interaction profiling with multi-omics and computational tools to build compatible synthetic consortia with desirable functions; leveraging positive interactions and prebiotics to facilitate inoculant establishment; and assessing fully soil functional resilience over longer terms, including recognizing the importance of rare keystone taxa. By integrating with ecological theory, this review provides a comprehensive insight into microbial interactions to advance the design, application, and monitoring of synthetic consortium inoculants for enhancing soil health and ecosystem sustainability.

establishment↗
PubMed1988

Overview of drug interactions with the quinolones.

Drug interactions with the quinolones are of two types: pharmacokinetic and pharmacodynamic. Pharmacokinetic interactions include inhibition of absorption of quinolones by aluminium and magnesium containing antacids and inhibition of metabolism of other drugs by quinolones. Norfloxacin and ofloxacin are not extensively metabolized and do not inhibit drug metabolism; ciprofloxacin and enoxacin reduce theophylline clearance in normal subjects by less than 50% and greater than 50% respectively. Ciprofloxacin inhibits the metabolism of caffeine, theophylline and antipyrine. The latter is a marker of broad substrate specificity and, until proved otherwise, it would be prudent to avoid combination of ciprofloxacin with drugs which are metabolized and have a low therapeutic index. In addition to theophylline, these include cyclosporin, phenytoin and warfarin. There is evidence that the elderly and patients with liver disease are particularly susceptible to kinetic interactions with ciprofloxacin. In contrast, there is no evidence to suggest that ofloxacin is likely to impair hepatic drug elimination. Enoxacin does not impair the metabolism of chlorpropamide or glibenclamide, it is therefore unlikely that any of the quinolones will interact with sulphonylurea hypoglycaemic drugs. A pharmacodynamic interaction has been demonstrated in vitro between quinolones and non-steroidal anti-inflammatory drugs (NSAIDS) or theophylline. All of these drugs inhibit binding of radio-labelled gamma-amino-butyric acid to mouse synaptic membranes and combinations of quinolones with NSAIDS or theophylline are synergistic. Convulsions have been reported in patients who received a combination of enoxacin with either fenbufen, a NSAID, or theophylline. Like theophylline, NSAIDS undergo hepatic metabolism, so the clinical interaction may be the result of combined pharmacokinetic and pharmacodynamic interactions. Drug-interactions with quinolones are a clinically important problem. Drugs, such as ofloxacin, which do not impair hepatic metabolism of other drugs, have a clinically significant advantage over other quinolones. The pharmacodynamic interaction between quinolones and other GABA inhibitors is extremely poorly documented; further in-vitro, animal and clinical studies are urgently required.

4-Quinolones↗
PubMed1996

Pharmacokinetic interactions related to the chemical structures of fluoroquinolones.

Fluoroquinolone derivatives interact with methylxanthines (theophylline, caffeine) and metallic ion-containing drugs to different degrees. The rat appears to be a suitable model for predicting such interactions in man. It has been possible to determine the relationship between the chemical structure of the fluoroquinolone and the magnitude of the interaction. Fluoroquinolones with a bulky substituent at the position 8, such as sparfloxacin, lomefloxacin and fieroxacin, are less prone to interact with theophylline than those without an 8-substituent, such as enoxacin. This substituent determines the planarity of the whole fluoroquinolone molecule and the interaction tends to be more significant for planar fluoroquinolones. Furthermore, a 4'-nitrogen atom in the 7-piperazinyl group is essential for the interaction to occur. The nitrogen atom is possibly the site that binds cytochrome P-450, which catalyses theophylline metabolism. The reduction in bioavailability of fluoroquinolones by concurrent administration of aluminium hydroxide is more striking for derivatives with fewer substituents on the essential structure and on the piperazinyl group, such as norfloxacin, ciprofloxacin and enoxacin. Substitution at the 5-position diminishes the interaction, which suggests that the 5-substituent may affect the formation and/or stability of unabsorbable chelate complex which is the probable cause of the interaction. These findings are potentially useful in designing fluoroquinolones less prone to drug interactions.

Aluminum Hydroxide↗
PubMed2003

BIND: the Biomolecular Interaction Network Database.

The Biomolecular Interaction Network Database (BIND: http://bind.ca) archives biomolecular interaction, complex and pathway information. A web-based system is available to query, view and submit records. BIND continues to grow with the addition of individual submissions as well as interaction data from the PDB and a number of large-scale interaction and complex mapping experiments using yeast two hybrid, mass spectrometry, genetic interactions and phage display. We have developed a new graphical analysis tool that provides users with a view of the domain composition of proteins in interaction and complex records to help relate functional domains to protein interactions. An interaction network clustering tool has also been developed to help focus on regions of interest. Continued input from users has helped further mature the BIND data specification, which now includes the ability to store detailed information about genetic interactions. The BIND data specification is available as ASN.1 and XML DTD.

Amino Acid Sequence↗
PubMed2006

Genome-wide prediction and characterization of interactions between transcription factors in Saccharomyces cerevisiae.

Combinatorial regulation by transcription factor complexes is an important feature of eukaryotic gene regulation. Here, we propose a new method for identification of interactions between transcription factors (TFs) that relies on the relationship of their binding sites, and we test it using Saccharomyces cerevisiae as a model system. The algorithm predicts interacting TF pairs based on the co-occurrence of their binding motifs and the distance between the motifs in promoter sequences. This allows investigation of interactions between TFs without known binding motifs or expression data. With this approach, 300 significant interactions involving 77 TFs were identified. These included more than 70% of the known protein-protein interactions. Approximately half of the detected interacting motif pairs showed strong preferences for particular distances and orientations in the promoter sequences. These one dimensional features may reflect constraints on allowable spatial arrangements for protein-protein interactions. Evidence for biological relevance of the observed characteristic distances is provided by the finding that target genes with the same characteristic distances show significantly higher co-expression than those without preferred distances. Furthermore, the observed interactions were dynamic: most of the TF pairs were not constitutively active, but rather showed variable activity depending on the physiological condition of the cells. Interestingly, some TF pairs active in multiple conditions showed preferences for different distances and orientations depending on the condition. Our prediction and characterization of TF interactions may help to understand the transcriptional regulatory networks in eukaryotic systems.

Algorithms↗
PubMedDate not supplied

Drug-herb interaction among commonly used conventional medicines: a compendium for health care professionals.

The objective of the review was to consolidate the clinical and pharmacologic aspects of drug-herb interactions to develop a compendium of information to provide prescribers with a measure of the risk of interactions, a description of the clinical consequences, and an assessment of the quality (ie, validity) of evidence. A variety of electronic databases and hand-searched references were used to identify documentation of interactions between herbal products and drugs from the most commonly used therapeutic classes. MEDLINE, Allied and Complementary Medicine Database, CINHAL, HealthSTAR, and EMBASE were searched from 1966 to the present. One hundred sixty-two citations were identified. Only 22 citations met the inclusion criteria. Using a matrix of 165 possible drug-herb interaction pairs (15 therapeutic drug classes by 11 herbal products), we identified 51 (31%) interactions discussed in the literature. Twenty-two of these 51 drug-herb pairs (43%) were supported by randomized clinical trials, case-control studies, cohort studies, case series, or case studies. The remaining interaction pairs reflected theoretic reasoning in the absence of clinical data. Most interactions were pharmacokinetic, with most actually or theoretically affecting the metabolism of the affected product by way of the cytochrome p450 enzymes. In this review, warfarin was the most common drug and St. John's wort was the most common herbal product reported in drug-herb interactions. To create a comprehensive and valid list of herb-drug interactions would require a substantial increase in research activities in this area. Improvements in the quality of methodology used are also necessary.

Case-Control Studies↗
PubMed2005

Natural health product-drug interactions: a systematic review of clinical trials.

Interactions between natural health products (NHP) and prescription medications are of increasing concern. This paper aims to identify all clinical trials of NHP-drug interactions. To determine the prevalence and outcomes of clinical investigations of NHP-drug pharmacokinetic interactions, electronic databases were searched from inception through March 2004, as well as reference lists from published reports and experts in the field for unpublished studies. Eligible studies were clinical investigations of the interaction between a NHP and the metabolism of a regulated medication in humans. Studies were excluded that only investigated the metabolism of an NHP or examined food-drug or NHP-NHP interactions. Two reviewers selected studies for inclusion and independently extracted data. Forty-seven trials were identified, studying an average of 14 participants/study (95% confidence interval [CI] 11-18), examined drug interactions with 19 different herbal preparations. All trials were pharmacokinetic studies, 41 of healthy volunteers and 6 of patients. Ten different herbal medicines as well as 5 different traditional herbal concoctions were studied. Potentially clinically significant drug interactions were observed with St. John wort (16/24 studies), garlic (2/5 studies), and American ginseng (1 study). Research on NHP-drug interactions is limited in number and scope. With the exception of St. John wort, clinicians and the public do not have information that permits strong inferences about interactions between NHPs and conventional medications.

Clinical Trials as Topic↗
PubMed2004

A protein interaction framework for human mRNA degradation.

The degradation of mRNA is an important regulatory step in the control of gene expression. However, mammalian RNA decay pathways remain poorly characterized. To provide a framework for studying mammalian RNA decay, a two-hybrid protein interaction map was generated using 54 constructs from 38 human proteins predicted to function in mRNA decay. The results provide evidence for interactions between many different proteins required for mRNA decay. Of particular interest are interactions between the poly(A) ribonuclease and the exosome and between the Lsm complex, decapping factors, and 5'-->3' exonucleases. Moreover, multiple interactions connect 5'-->3' and 3'-->5' decay proteins to each other and to nonsense-mediated decay factors, providing the opportunity for coordination between decay pathways. The interaction network also predicts the internal organization of the exosome and Lsm complexes. Additional interactions connect mRNA decay factors to many novel proteins and to proteins required for other steps in gene expression. These results provide an experimental insight into the organization of proteins required for mRNA decay and their coupling to other cellular processes, and the physiological relevance of many of these interactions are supported by their evolutionary conservation. The interactions also provide a wealth of hypotheses to guide future research on mRNA degradation and demonstrate the power of exhaustive protein interaction mapping in aiding understanding of uncharacterized protein complexes and pathways.

Carrier Proteins↗
PubMed2004

Potential for drug interactions involving cytochromes P450 2D6 and 3A4 on general adult psychiatric and functional elderly psychiatric wards.

AIMS: To assess the potential for interactions involving cytochromes P450 2D6 (CYP2D6) and 3A4 (CYP3A4) between drugs prescribed in a city in-patient psychiatric service. METHODS: Prescription information was obtained from all 236 patients in general adult wards and all 87 patients in functional elderly wards under a city psychiatric service. The frequencies with which combinations of drugs expected to interact via CYP2D6 or CYP3A4 were documented and compared between these two settings. RESULTS: All 2089 drug prescriptions, of which 1237 (59%) were administered, were analyzed. One hundred and seventy-two patients (73%) on adult wards and 59 (68%) on functional elderly wards were prescribed at least one drug metabolized by and/or inhibiting CYP2D6, the difference being nonsignificant (95% confidence interval on the difference -6.3%, 16.4%). Anticipated interactions from 62/82 CYP2D6-related combinations prescribed on adult wards (27/100 patients) and 19/30 prescribed to elderly patients (22/100 patients) were judged to be clinically important or potentially clinically important. The proportion of patients on functional elderly wards prescribed at least one drug interacting with CYP3A4 (87%) was significantly greater than that for patients on adult wards (57%, P < 0.001). The frequency of interactions involving CYP3A4 was significantly greater on functional elderly than adult wards (43/100 vs 22/100 patients, P < 0.025, 95% confidence interval on the difference 4, 38/100). CONCLUSIONS: Our findings confirm extensive polypharmacy on general adult psychiatric and functional elderly psychiatric wards. A substantial proportion of patients were receiving combinations of drugs that interact with CYP2D6 and/or CYP3A4, many of which are known to produce clinically important interactions. Doctors practising in old age psychiatry should be aware that patients on functional elderly wards are at increased risk of clinically important CYP3A4 interactions. Psychiatrists should consider the pharmacokinetic implications of drugs prescribed for use 'as needed', because of the potential for unpredictable interactions.

Adolescent↗
PubMed1984

[Pharmaceutical, pharmacokinetic and dynamic interactions in tumor drug therapy].

Because of unfavourable therapeutic index application of cytostatic agents is often leading to adverse drug interactions. To avoid that kind of results it is important to know risks, early symptoms and possibilities of prevention. Recent observations show a potential occupational hazard for personnel handling cytostatic drugs by aerosols or direct contact (in-vitro-interactions). In cancer patients multimorbidity is not unusual. Drugs for the treatment of these diseases or the treatment or prevention of toxicity may interact with cytostatics (in-vivo-interactions) by influencing the absorption, protein binding, active drug transport, metabolism and excretion (pharmacokinetic interactions) as well as by changing mechanisms of the intracellular uptake of drugs or regulation of physiological functions and by blocking receptors (pharmacodynamic interactions). Inducing synergism or antagonism loss or potentiation of effectively as well as toxic reactions may result. In medical practice we have to pay attention to known clinically important interactions. The importance of other possible but still unknown interactions has to be investigated. To avoid adverse drug interactions in patients the application of drugs should be restricted to only a few but indicated drugs of established effectivity.

Antineoplastic Agents↗
PubMed2004

Interactions of prednisolone and other immunosuppressants used in dual treatment of systemic lupus erythematosus in lymphocyte proliferation assays.

Systemic lupus erythematosus is an autoimmune disease primarily affecting women. Currently, systemic lupus erythematosus therapy is suboptimal due to adverse effects of immunosuppressants, particularly corticosteroids. This study determines the single effects of prednisolone, dehydroepiandrosterone, bromocriptine, tamoxifen, mycophenolic acid, 2-chloro-2'-deoxyadenosine, azathioprine, and chloroquine on lectin-stimulated proliferation of human T lymphocytes, as well as determining whether there are interactions in the joint effects of prednisolone and these agents. The T lymphocytes from the whole blood of 10 middle-aged women were stimulated by phytohemagglutinin and cultured with varying drug concentrations. The Hill function was used to evaluate single-drug response data. Isobolograms were constructed to qualitatively analyze interactions. Parametric analysis based on competitive and noncompetitive interaction models was further applied to quantify the joint interactions and predict steroid-sparing potential. The surface interaction parameter (psi) estimated from parametric analysis was in concordance with isobolographic inspection for all interactions studied. All interactions favored the noncompetitive model. Results suggest that dehydroepiandrosterone is additive in its effect with prednisolone, whereas tamoxifen interacts synergistically, both providing steroid-sparing effects. Novel immuno-suppressants such as mycophenolic acid may still provide added pharmacologic benefit during therapy despite a slight antagonistic interaction with prednisolone. These studies help rationalize actual or potential use of other drugs with prednisolone in the treatment of systemic lupus erythematosus.

Adult↗
PubMed1996

Azathioprine and allopurinol: the price of an avoidable drug interaction.

OBJECTIVE: To report the price of a drug interaction between azathioprine and allopurinol that resulted in pancytopenia in a patient who had undergone a heart transplant. CASE SUMMARY: A 63-year-old white man who received an orthotopic heart transplant in 1987 was hospitalized in June 1991 with a diagnosis of pancytopenia. His immunosuppressive medications on admission included cyclosporine 125 mg bid, azathioprine (AZA) 200 mg/d, and prednisone 2.5 mg/5 mg every other day. Six weeks prior to admission, the patient's local physician prescribed allopurinol for left wrist pain suspected to be gout. It was determined that the pancytopenia was caused by the drug interaction between AZA and allopurinol, both of which were withheld on admission. During hospitalization, the patient's white blood cell count dropped to 1.1 x 10(3)/mm3 with an absolute neutrophil count of less than 0.5 x 10(3)/mm3, a platelet count of less than 20 x 10(3)/mm3, and a hemoglobin of 3.7 g/dL. Four units of packed red blood cells were transfused and regramostim (GM-CSF) therapy was begun on hospital day 3 to speed the marrow recovery process. The patient was discharged on hospital day 8 and AZA, which had been withheld since admission, was restarted. The dosage was titrated to 200 mg/d over the following 2 weeks. The price of this patient's hospital stay was $13,042. DISCUSSION: Not included in this price was the effect this drug interaction had on the patient's quality of life. Even after discharge from the hospital, it was estimated that it would take up to 3 months for the patient to fully recover his previous level of strength and functional capability. This interaction between AZA and allopurinol could easily have been avoided. Both the physician and the pharmacist missed this well-documented and potentially life-threatening drug interaction. Also, the patient failed to notify the transplant team when allopurinol was prescribed by his local physician. The importance of patient responsibility for medication therapy must be stressed to help avoid unnecessary drug interactions. CONCLUSIONS: Undetected drug interactions can be life-threatening to patients as well as costly to the healthcare system. Drug interactions also can have a profound negative effect on the patients' quality of life, the price of which cannot be measured in dollars alone. It is vital that the physician, pharmacist, and patient work together to optimize therapeutic outcomes and avoid unnecessary drug interactions.

Allopurinol↗
PubMed2003

Interactions between co-expressed Arabidopsis sucrose transporters in the split-ubiquitin system.

BACKGROUND: The Arabidopsis genome contains nine sucrose transporter paralogs falling into three clades: SUT1-like, SUT2 and SUT4. The carriers differ in their kinetic properties. Many transport proteins are known to exist as oligomers. The yeast-based split ubiquitin system can be used to analyze the ability of membrane proteins to interact. RESULTS: Promoter-GUS fusions were used to analyze the cellular expression of the three transporter genes in transgenic Arabidopsis plants. All three fusion genes are co-expressed in companion cells. Protein-protein interactions between Arabidopsis sucrose transporters were tested using the split ubiquitin system. Three paralogous sucrose transporters are capable of interacting as either homo- or heteromers. The interactions are specific, since a potassium channel and a glucose transporter did not show interaction with sucrose transporters. Also the biosynthetic and metabolizing enzymes, sucrose phosphate phosphatase and sucrose synthase, which were found to be at least in part bound to the plasma membrane, did not specifically interact with sucrose transporters. CONCLUSIONS: The split-ubiquitin system provides a powerful tool to detect potential interactions between plant membrane proteins by heterologous expression in yeast, and can be used to screen for interactions with membrane proteins as baits. Like other membrane proteins, the Arabidopsis sucrose transporters are able to form oligomers. The biochemical approaches are required to confirm the in planta interaction.

Arabidopsis Proteins↗
PubMed2003

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

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

Algorithms↗
PubMed2005

A protein domain interaction interface database: InterPare.

BACKGROUND: Most proteins function by interacting with other molecules. Their interaction interfaces are highly conserved throughout evolution to avoid undesirable interactions that lead to fatal disorders in cells. Rational drug discovery includes computational methods to identify the interaction sites of lead compounds to the target molecules. Identifying and classifying protein interaction interfaces on a large scale can help researchers discover drug targets more efficiently. DESCRIPTION: We introduce a large-scale protein domain interaction interface database called InterPare http://interpare.net. It contains both inter-chain (between chains) interfaces and intra-chain (within chain) interfaces. InterPare uses three methods to detect interfaces: 1) the geometric distance method for checking the distance between atoms that belong to different domains, 2) Accessible Surface Area (ASA), a method for detecting the buried region of a protein that is detached from a solvent when forming multimers or complexes, and 3) the Voronoi diagram, a computational geometry method that uses a mathematical definition of interface regions. InterPare includes visualization tools to display protein interior, surface, and interaction interfaces. It also provides statistics such as the amino acid propensities of queried protein according to its interior, surface, and interface region. The atom coordinates that belong to interface, surface, and interior regions can be downloaded from the website. CONCLUSION: InterPare is an open and public database server for protein interaction interface information. It contains the large-scale interface data for proteins whose 3D-structures are known. As of November 2004, there were 10,583 (Geometric distance), 10,431 (ASA), and 11,010 (Voronoi diagram) entries in the Protein Data Bank (PDB) containing interfaces, according to the above three methods. In the case of the geometric distance method, there are 31,620 inter-chain domain-domain interaction interfaces and 12,758 intra-chain domain-domain interfaces.

Computers, Molecular↗

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