» Articles » PMID: 11125103

BIND--The Biomolecular Interaction Network Database

Overview
Specialty Biochemistry
Date 2000 Jan 11
PMID 11125103
Citations 182
Authors
Affiliations
Soon will be listed here.
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.

Citing Articles

Heterogeneous network approaches to protein pathway prediction.

Nayar G, Altman R Comput Struct Biotechnol J. 2024; 23:2727-2739.

PMID: 39035835 PMC: 11260399. DOI: 10.1016/j.csbj.2024.06.022.


Software and Databases for Protein-Protein Docking.

Jaronczyk M, Abagyan R, Totrov M Methods Mol Biol. 2024; 2780:129-138.

PMID: 38987467 DOI: 10.1007/978-1-0716-3985-6_8.


In Silico Analysis of Protein-Protein Interactions of Putative Endoplasmic Reticulum Metallopeptidase 1 in .

Gonzalez-Esparragoza D, Carrasco-Carballo A, Rosas-Murrieta N, Pena L, Luna F, Herrera-Camacho I Curr Issues Mol Biol. 2024; 46(5):4609-4629.

PMID: 38785548 PMC: 11120530. DOI: 10.3390/cimb46050280.


Identify Biomarkers and Design Effective Multi-Target Drugs in Ovarian Cancer: Hit Network-Target Sets Model Optimizing.

Esmaeilzadeh A, Kashian M, Mahmood Salman H, Alsaffar M, Musa Jaber M, Soltani S Biology (Basel). 2022; 11(12).

PMID: 36552360 PMC: 9776135. DOI: 10.3390/biology11121851.


Systems Drug Design for Muscle Invasive Bladder Cancer and Advanced Bladder Cancer by Genome-Wide Microarray Data and Deep Learning Method with Drug Design Specifications.

Su P, Chen B Int J Mol Sci. 2022; 23(22).

PMID: 36430344 PMC: 9692470. DOI: 10.3390/ijms232213869.


References
1.
Pawson T . Protein modules and signalling networks. Nature. 1995; 373(6515):573-80. DOI: 10.1038/373573a0. View

2.
CASSMAN M, Hunter T, Pawson T . Proteins suggest form of their own database. Nature. 2000; 403(6770):591-2. DOI: 10.1038/35001230. View

3.
Salcini A, McGlade J, Pelicci G, Nicoletti I, Pawson T, Pelicci P . Formation of Shc-Grb2 complexes is necessary to induce neoplastic transformation by overexpression of Shc proteins. Oncogene. 1994; 9(10):2827-36. View

4.
Marcotte E, Pellegrini M, Ng H, Rice D, Yeates T, Eisenberg D . Detecting protein function and protein-protein interactions from genome sequences. Science. 1999; 285(5428):751-3. DOI: 10.1126/science.285.5428.751. View

5.
Wheeler D, Chappey C, Lash A, Leipe D, Madden T, Schuler G . Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 1999; 28(1):10-4. PMC: 102437. DOI: 10.1093/nar/28.1.10. View