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Database to explore known and predicted interactions of chemicals and proteins. It integrates information about interactions from metabolic pathways, crystal structures, binding experiments and drug-target relationships. Inferred information from phenotypic effects, text mining and chemical structure similarity is used to predict relations between chemicals. STITCH further allows exploring the network of chemical relations, also in the context of associated binding proteins. Each proposed interaction can be traced back to the original data sources. The database contains interaction information for over 68,000 different chemicals, including 2200 drugs, and connects them to 1.5 million genes across 373 genomes and their interactions contained in the STRING database.
Proper citation: Search Tool for Interactions of Chemicals (RRID:SCR_007947) Copy
http://bioinformatics.biol.uoa.gr/human_gpdb/
A publicly accessible, relational database of human G-Proteins and their interactions with human GPCRs and Effectors. Advanced data integration techniques make Human-gpDB very rich in context since all of the bioentities are linked to a rich variety of external data sources. High quality visualization methods make the networks more informative and the extraction of information easier. Human-gpDB is currently a very useful tool for drug targeting investigation. The sequences of G-Proteins and GPCRs are classified according to a hierarchy of different classes, families and sub-families, whereas the Effectors sequences are classified in families, subfamilies and types, based on extensive literature search. The classification of GPCRs follows the IUPHAR classification, while the Effectors classification is a unique feature and is based on their function. The database currently holds information about 713 human GPCRs, 36 human G-Proteins and 99 human Effectors. The collection of the information about the interactions between these molecules was done manually and the current status of Human-gpDB reveals information about 1663 connections between GPCRs and G-Proteins and 1618 connections between G-Proteins and Effectors.
Proper citation: Human-gpDB (RRID:SCR_006223) Copy
The main focus of this Computational Biology group is to predict function and to gain insights into evolution by comparative analysis of complex molecular data. The group currently works on three different scales: * genes and proteins, * protein networks and cellular processes, and * phenotypes and environments. They require both tool development and applications. Some selected projects include comparative gene, genome and metagenome analysis, mapping interactions to proteins and pathways as well as the study of temporal and spatial protein network aspects. All are geared towards the bridging of genotype and phenotype through a better understanding of molecular and cellular processes. The services - resources & tools, developed by Bork Group, are mainly designed and maintained for research & academic purposes. Most of services are published and documented in one or more papers. All our tools can be completely customized and integrated into your existing framework. This service is provided by the company biobyte solutions GmbH. Please visit their tools and services pages for full details and more information. Standard commercial licenses for our tools are also available through biobyte solutions GmbH. The group is partially associated with Max Delbr��ck Center for Molecular Medicine (MDC), Berlin.
Proper citation: EMBL - Bork Group (RRID:SCR_000810) Copy
A computer algorithm that provides a fast and quantitative estimation of the importance of the interactions contributing to the stability of proteins and protein complexes. The predictive power of FOLDEF has been tested on a very large set of point mutants (1088 mutants) spanning most of the structural environments found in proteins . FoldX uses a full atomic description of the structure of the proteins. The different energy terms taken into account in FoldX have been weighted using empirical data obtained from protein engineering experiments.
Proper citation: FoldX (RRID:SCR_008522) Copy
Database of known and predicted protein interactions. The interactions include direct (physical) and indirect (functional) associations and are derived from four sources: Genomic Context, High-throughput experiments, (Conserved) Coexpression, and previous knowledge. STRING quantitatively integrates interaction data from these sources for a large number of organisms, and transfers information between these organisms where applicable. The database currently covers 5''214''234 proteins from 1133 organisms. (2013)
Proper citation: STRING (RRID:SCR_005223) Copy
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The NIDDK Information Network (dkNET) serves the needs of basic and clinical investigators by providing seamless access to large pools of data and research resources relevant to the mission of The National Institute of Diabetes Digestive and Kidney Diseases (NIDDK). dkNET is hosted at University of California San Diego, and is supported by NIH NIDDK grant U24DK097771.
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