Search PubMed⌕ Search

PubMed · 11125103

BIND--The Biomolecular Interaction Network Database.

Abstract

The Biomolecular Interaction Network Database (BIND; http://binddb. org) is a database designed to store full descriptions of interactions, molecular complexes and pathways. Development of the BIND 2.0 data model has led to the incorporation of virtually all components of molecular mechanisms including interactions between any two molecules composed of proteins, nucleic acids and small molecules. Chemical reactions, photochemical activation and conformational changes can also be described. Everything from small molecule biochemistry to signal transduction is abstracted in such a way that graph theory methods may be applied for data mining. The database can be used to study networks of interactions, to map pathways across taxonomic branches and to generate information for kinetic simulations. BIND anticipates the coming large influx of interaction information from high-throughput proteomics efforts including detailed information about post-translational modifications from mass spectrometry. Version 2.0 of the BIND data model is discussed as well as implementation, content and the open nature of the BIND project. The BIND data specification is available as ASN.1 and XML DTD.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

G D Bader, I Donaldson, C Wolting, B F Ouellette, T Pawson, C W Hogue. 2001-01-01. BIND--The Biomolecular Interaction Network Database.. https://doi.org/10.1093/nar%2F29.1.242

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Affinity ultrafiltration: effects of ligand binding on selectivity and process optimization.

The effective design of affinity ultrafiltration processes using a selective macroligand requires a detailed understanding of the effects of ligand-binding interactions on product yield and purification. Theoretical calculations were performed to evaluate the performance of affinity diafiltration separations with both competitive and independent binding interactions for the product and impurity. The intrinsic selectivity for independent binding decreased during the diafiltration due to the increase in fractional impurity binding as the impurity is selectively removed. The opposite behavior was seen for competitive binding because the strongly bound product displaces the impurity from the binding sites. Purification-yield diagrams were used to examine the effects of affinity-ligand concentration and binding constants on the separation. Model calculations were in excellent agreement with experimental data for the separation of tryptophan isomers using bovine serum albumin as the steroselective macroligand. Simulations with a fixed number of diavolumes show a clear optimum in product yield and purification factor at an intermediate ligand concentration due to the competing effects of the intrinsic selectivity and the rate of impurity removal. These results provide an appropriate framework for the design and optimization of affinity ultrafiltration systems.

Binding, Competitive↗

Characterization of bradykinin receptors in a human osteoblastic cell line.

Bradykinin receptor subtypes linked to prostaglandin release have been assessed in a human osteosarcoma cell line with osteoblastic phenotype (MG-63). Bradykinin (BK; 1 micromol/l) caused a burst of prostaglandin E(2) release that was maximal at 10 min. When the effect on the burst of PGE(2) and PGI(2) release by a variety of kinins and kinin analogues was assessed, the following rank order of response was found: Lys-BK>BK> or =Met-Lys-BK>Ile-Ser-BK>[Tyr(8)]-BK> or =[Hyp(3)]-BK>>>des-Arg(9)-BK=des-Arg(10)-Lys-BK=des-Arg(1)-BK, [Thi(5,8),D-Phe(7)]-BK=Sar-[D-Phe(8)]-des-Arg(9)-BK=Tyr-Gly-Lys-Aca-Lys-des-Arg(9)-BK. The rapid effect of BK on PGE(2) and PGI(2) release was unaffected by des-Arg(9)-[Leu(8)]-BK, des-Arg(10)-[Leu(9)]-Lys-BK and des-Arg(10)-[Hoe 140], but strongly inhibited by Hoe 140 in a concentration-dependent manner. When the incubation time was extended to 48 h, it was found that des-Arg(9)-BK and des-Arg(10)-Lys-BK caused a delayed enhancement of the formation of PGE(2). When PGE(2) formation was assessed in 24-h experiments, the following rank order of response was obtained: Tyr-Gly-Lys-Aca-Lys-des-Arg(9)-BK>>BK=Lys-BK>>des-Arg(10)-Lys-BK>Sar[D-Phe(8)]-des-Arg(9)-BK>des-Arg(9)-BK. The stimulatory effect of BK at 24 h was unaffected by des-Arg(9)-[Leu(8)]-BK, des-Arg(10)-[Leu(9)]-Lys-BK and des-Arg(10)-[Hoe 140] but inhibited by Hoe 140. The stimulatory effect of des-Arg(10)-Lys-BK in 24-h experiments was inhibited by des-Arg(9)-[Leu(8)]-BK, des-Arg(10)-[Leu(9)]-Lys-BK and des-Arg(10)-[Hoe 140]. Similarly, the stimulatory effects of Sar[D-Phe(8)]-des-Arg(9)-BK and Tyr-Gly-Lys-Aca-Lys-des-Arg(9)-BK was inhibited by des-Arg(10)-[Hoe 140]. The following rank order of response was seen for inhibition of [3H]-BK binding to MG-63 cells: Lys-BK=BK=Hoe 140>>>>>>des-Arg(10)-Hoe 140=des-Arg(10)-Lys-BK=des-Arg(9)-BK=Tyr-Gly-Lys-Aca-Lys-des-Arg(9)-BK. Using [3H]-des-Arg(10)-Lys-BK, the following rank order of response for inhibition of binding was seen: des-Arg(10)-Lys-BK=Tyr-Gly-Lys-Aca-Lys-des-Arg(9)-BK>des-Arg(10)-Hoe 140>des-Arg(9)-BK=Lys-BK=BK=Hoe 140. MG-63 cells expressed mRNAs for BK B1 and B2 receptors, as assessed by RT-PCR. These data indicate that the human osteoblastic osteosarcoma cell line MG-63 is equipped with functional BK receptors of both B1 and B2 receptor subtypes. The B2 receptors are linked to a burst of prostanoid release, whereas the B1 receptors mediate a delayed prostaglandin response, indicating that the two receptor subtypes are linked to different signal transducing mechanisms or that the molecular mechanisms involved in prostaglandin release are different.

Binding, Competitive↗

Study of competitive binding of enantiomers to protein by affinity capillary electrochromatography.

Affinity capillary electrochromatography (CEC) with zonal elution method was used to probe the competitive interactions of enantiomers with protein. In this approach, a known concentration of a competing agent is continuously applied to a CEC column with bovine serum albumin (BSA) physically adsorbed on SAX packing while injections of a small amount of analyte are made. The binding sites of solutes on the BSA molecule were determined by the changes in the retention factors of the solutes resulted from the addition of competitive agent. By using D- or L-tryptophan as competitive agents and D-, L-tryptophan and benzoin enantiomers as injected analytes showed that BSA molecule has a primary site to strongly bind L-tryptophan, but D-tryptophan dose not bind at this site; D- and L-tryptophan share a weak binding site on the BSA molecule. Benzoin enantiomers do not share any binding sites with either D- or L-tryptophan. Non-chiral compounds of trichloroacetic acid and n-hexanoic acid were applied as the competitive agents to study the binding of warfarin enantiomers to BSA, it was observed that trichloroacetic acid and n-hexanoic acid had a same binding site for warfarin enantiomers binding to BSA molecule.

Binding, Competitive↗