Utente:Alesssia/Sandbox

L' interazione proteina-proteina avviene quando due o più proteine si legano assieme, spesso per svolgere la loro funzione biologica. La maggior parte dei dei più importanti processi molecolari che avvengono all'interno della cellula sono svolti da grandi macchinari costruiti a partire da un grande numero di componenti proteici. L'interazione proteica è stata studiata da diverse prospettive: biochimica, chimica quantistica, dinamica molecolare, e a partire da altre reti di interazione (reti metaboliche, genetiche/epigenetiche). Infatti, le interazioni proteiche sono alla base di ogni cellula.

Le interazioni tra le proteine sono importanti per la maggior parte delle funzioni biologiche. Per esempio, i segnali all'esterno della cellula sono mediati dall'interno attraverso l'interazione di molecole relative alla segnalazione cellulare. Questo processo ha un ruolo fondamentale in molti processi biologici e in molte malattie (es., cancro). Le proteine possono interagire per un lungo periodo per formare un complesso proteico, una proteina può trasportarne un'altra (per esempio dal citoplasma verso il nucleo o viceversa), oppure una proteina può interagire brevemente con un'altra, modificandola (per esempio una chinasi aggiunge un fosfato ad un'altra proteina). Queste modifiche possono a loro volta cambiare le interazioni proteiche. Per esempio, alcune proteine con dominio SH2 legano altre proteine solo quando sono fosforilate nell'amminoacido tirosina.

Per concludere, le interazioni proteina-proteina sono effettivamente di centrali importanza per ciascun processo cellulare. Le informazioni riguardanti queste interazioni possono accrescere la comprensione sullo sviluppo di malattie e possono fornire le basi per lo sviluppo di nuovi approcci terapeutici.

Metodi

Poiché le interazioni proteina-proteina sono così importanti sono stati sviluppati molto metodi per individuarle. Ogni metodo ha i propri punti di forza e debolezze, specialmente per ciò che riguarda la sensitività e la specificità. Un'alta sensitività indica che molte delle interazioni che sono presenti nella realtà vengono individuate. Un'alta specificità indica che la maggior parte delle interazioni individuate sono anche presenti nei sistemi reali. Metodi come il yeast two-hybrid screening possono essere usati per scoprire nuove interazioni. A livello teorico, i dati provenienti da esperimenti di larga scala sono spesso modellati con grafi.

Usando la proximity ligation assay (PLA), singole interazioni proteiche possono essere individuate, localizzate e quantificate in cellule e tessuti. Utilizzando solo poche cellule, eventi sub-cellulari, interazioni deboli o transienti sono individuati in situ, mentre le sub-popolazioni cellulare possono venire differenziate e caratterizzate.

Visualizzazione

La visualizzazione delle reti di interazione proteica è un'applicazione popolare all'interno delle tecniche di visualizzazione in ambito scientifico.

Visualization of protein–protein interaction networks is a popular application of scientific visualization techniques. Although protein interaction diagrams are common in textbooks, diagrams of whole cell protein interaction networks were not as common since the level of complexity made them difficult to generate. One example of a manually produced molecular interaction map is Kurt Kohn's 1999 map of cell cycle control. Drawing on Kohn's map, in 2000 Schwikowski, Uetz, and Fields published a paper on protein–protein interactions in yeast, linking together 1,548 interacting proteins determined by two-hybrid testing. They used a force-directed (Sugiyama) graph drawing algorithm to automatically generate an image of their network. (see also external links below).

Database collections

Methods for identifying interacting proteins have defined hundreds of thousands of interactions. These interactions are collected together in specialised biological databases that allow the interactions to be assembled and studied further. The first of these databases was DIP, the database of interacting proteins. Since that time a large number of further database collections have been created such as BioGRID, STRING, and Database of Interacting Proteins.

See also

• Interactomics
• Complex systems biology
• Protein–protein interaction prediction
• Protein–protein interaction screening

Further reading

1. De Las Rivas J, Fontanillo C, Protein-protein interactions essentials: key concepts to building and analyzing interactome networks, in PLoS Comput Biol, vol. 6, n. 6, 2010, DOI:10.1371/journal.pcbi.1000807.
2. Phizicky EM, Fields S, Protein-protein interactions: methods for detection and analysis, in Microbiol. Rev., 1995, pp. 94–123.

External links

KEGG è composto da sette diverse banche dati:

• KEGG Pathway, un insieme di mappe di interazioni molecolari curate manualmente;
• KEGG Genes, una collezione di geni per tutti i genomi completi, generata a partire da risorse pubbliche (es. NCBI RefSeq);
• KEGG Ligand, un insieme di sostanze chimiche che sono rilevanti per la vita;
• KEGG BRITE, una collezione di classificazioni gerarchiche, rappresentanti vari aspetti dei sistemi biologici;
• KEGG Module, un'insieme di unità funzionali, quali pathway, complessi strutturali, firme genomiche...;
• KEGG Drugs, l'insieme di tutti i farmaci approvato in Giappone, USA ed europa, organizzati secondo la loro struttura e/o composizione chimica e associati al loro bersaglio e alle reti con le quali interagiscono;
• KEGG Genome, un insieme di organismi il cui genoma è completamente conosciuto.

La Gene Ontology, o GO, è una delle più importanti inizative in ambito bioinformatico. Essa si pone come obiettivo una rappresentazione unica degli attributi che descrivono i geni e i loro prodotti (sia proteine sia RNA)., rendendo possibile la ricerca di informazioni che sarebbe invece costosa sia in termini di tempo, sia di fatica. In dettaglio, il progetto ha come scopi:

1. il mantenimento e lo sviluppo di un vocabolario condiviso degli attributi che descrivono i geni e i loro prodotti;
2. l'annotazione dei geni e gene products
3. lo sviluppo di uno strumento per accedere con semplicità a tutti gli aspetti dei dati.

La GO fa parte di un più ampio progetto di classificazione, il Open Biomedical Ontologies (OBO).

GO terms

Nel campo della bioinformatica uno dei maggiori colli di bottiglia nella ricerca di informazione è insito nella mancanza di un accordo sulla terminologia comune. Per esempio, diversi database medici possono contenere descrizioni inconsistenti, che rendono difficile tanto la condivisione dei dati quanto l'applicazione di tecniche di tecniche di analisi automatiche. Il progetto Gene Ontology fornisce un'ontologia di termini ben definiti, rappresentanti le propietà di geni, proteine e RNA. L'ontolgia copre tre dominii:

• cellular component, la parte della cellula o il suo ambiente extra cellulare;
• molecular function, l'attività di un prodotto genico a livello molecolare, quale la catalisi;
• biological process, insieme di oprezioni o di attività molecolari con uno specifica successione, pertinenti al funzionamento di cellule, tessuti, organi, e organismi.

Each GO term within the ontology has a term name, which may be a word or string of words; a unique alphanumeric identifier; a definition with cited sources; and a namespace indicating the domain to which it belongs. Terms may also have synonyms, which are classed as being exactly equivalent to the term name, broader, narrower, or related; references to equivalent concepts in other databases; and comments on term meaning or usage. The GO ontology is structured as a directed acyclic graph, and each term has defined relationships to one or more other terms in the same domain, and sometimes to other domains. The GO vocabulary is designed to be species-neutral, and includes terms applicable to prokaryotes and eukaryotes, single and multicellular organisms.

The GO ontology is not static, and additions, corrections and alterations are suggested by, and solicited from, members of the research and annotation communities, as well as by those directly involved in the GO project. For example, an annotator may request a specific term to represent a metabolic pathway, or a section of the ontology may be revised with the help of community experts (e.g.). Suggested edits are reviewed by the ontology editors, and implemented where appropriate.

The GO ontology file is freely available from the GO website in a number of formats, or can be accessed online using the GO browser AmiGO. The Gene Ontology project also provides downloadable mappings of its terms to other classification systems.

Example GO term

id:         GO:0000016
name:       lactase activity
namespace:  molecular_function
def:        "Catalysis of the reaction: lactose + H2O = D-glucose + D-galactose." [EC:3.2.1.108]
synonym:    "lactase-phlorizin hydrolase activity" BROAD [EC:3.2.1.108]
synonym:    "lactose galactohydrolase activity" EXACT [EC:3.2.1.108]
xref:       EC:3.2.1.108
xref:       MetaCyc:LACTASE-RXN
xref:       Reactome:20536
is_a:       GO:0004553 ! hydrolase activity, hydrolyzing O-glycosyl compounds

Data source:

Annotation

In GO, ontology defines what types and relationships in it, but doesn’t define what instances in every type, thus we need to develop a method so that we can put every gene product into its term properly. And this process is called annotation. Genome annotation is the practice of capturing data about a gene product, and GO annotations use terms from the GO ontology to do so. The members of the GO Consortium submit their annotation for integration and dissemination on the GO website, where they can be downloaded directly or viewed online using AmiGO. In addition to the gene product identifier and the relevant GO term, GO annotations have the following data:

• The reference used to make the annotation (e.g. a journal article)
• An evidence code denoting the type of evidence upon which the annotation is based
• The date and the creator of the annotation

The evidence code comes from the Evidence Code Ontology, a controlled vocabulary of codes covering both manual and automated annotation methods. For example, Traceable Author Statement (TAS) means a curator has read a published scientific paper and the metadata for that annotation bears a citation to that paper; Inferred from Sequence Similarity (ISS) means a human curator has reviewed the output from a sequence similarity search and verified that it is biologically meaningful. Annotations from automated processes (for example, remapping annotations created using another annotation vocabulary) are given the code Inferred from Electronic Annotation (IEA). As of April 1st, 2010, over 98% of all GO annotations were inferred computationally, not by curators. As these annotations are not checked by a human, the GO Consortium considers them to be less reliable and does not include them in the data available online in AmiGO. Full annotation data sets can be downloaded from the GO website. To support the development of annotation, GO Consortium provide study camps and mentors to new groups of developers.

Example annotation

Gene product:    Actin, alpha cardiac muscle 1, UniProtKB:P68032
GO term:         heart contraction ; GO:0060047 (biological process)
Evidence code:   Inferred from Mutant Phenotype (IMP)
Reference:       PMID 17611253
Assigned by:     UniProtKB, June 6, 2008

Data source:

Tools

There is a large number of tools available both online and to download that use the data provided by the GO project. The vast majority of these come from third parties; the GO Consortium develops and supports two tools, AmiGO and OBO-Edit.

AmiGO is a web-based application that allows users to query, browse and visualize ontologies and gene product annotation data. In addition, it also has a BLAST tool, tools allowing analysis of larger data sets, and an interface to query the GO database directly. If you are knowledgeable of SQL, you could choose to query database. For example, if you want to search for some information about a term of which the identifier is already know, you could type a query like: SELECT * FROM term WHERE acc= ‘GO:0008572’; Or just use the term name: SELECT * FROM term WHERE name= ‘cell’;

AmiGO can be used online at the GO website to access the data provided by the GO Consortium, or can be downloaded and installed for local use on any database employing the GO database schema (e.g. ). It is free open source software and is available as part of the go-dev software distribution.

OBO-Edit is an open source, platform-independent ontology editor developed and maintained by the Gene Ontology Consortium. It is implemented in Java, and uses a graph-oriented approach to display and edit ontologies. OBO-Edit includes a comprehensive search and filter interface, with the option to render subsets of terms to make them visually distinct; the user interface can also be customized according to user preferences. OBO-Edit also has a reasoner that can infer links that have not been explicitly stated, based on existing relationships and their properties. Although it was developed for biomedical ontologies, OBO-Edit can be used to view, search and edit any ontology. It is freely available to download.

Consortium

The GO Consortium is the set of biological databases and research groups actively involved in the GO project. This includes a number of model organism databases and multi-species protein databases, software development groups, and a dedicated editorial office.

History

The Gene Ontology was originally constructed in 1998 by a consortium of researchers studying the genome of three model organisms: Drosophila melanogaster (fruit fly), Mus musculus (mouse), and Saccharomyces cerevisiae (brewers' or bakers' yeast). Many other model organism databases have joined the Gene Ontology consortium, contributing not only annotation data, but also contributing to the development of the ontologies and tools to view and apply the data. Until now, most of major databases in plant, animal and microorganism make a contribution towards this project. As of January 2008, GO contains over 24,500 terms applicable to a wide variety of biological organisms. There is a significant body of literature on the development and use of GO, and it has become a standard tool in the bioinformatics arsenal. Their objectives have three aspects, building gene ontology, assigning ontology to gene/gene products and develop software and database for first two objects.

See also

• Comparative Toxicogenomics Database - CTD integrates Gene Ontology terms with toxicogenomic and disease data
• DAVID bioinformatics - A free online bioinformatics resources provides functional interpretation of large lists of genes derived from genomic studies
• FatiGO
• GoPubMed - Explore PubMed/MEDLINE with Gene Ontology
• Interferome
• PAMGO, the Plant-Associated Microbe Gene Ontology

External links

• Gene Ontology Consortium — Provides access to the ontologies, software tools, annotated gene product lists, and reference documents describing the GO and its uses.
• The OBO Foundry: Coordinated Evolution of Ontologies to Support Biomedical Data Integration, Nature Biotechnology, 25 (11), 2007.
• OBO Foundry library of interoperable gold standard reference ontologies.
• GONUTS Wiki — Third party GO term documentation, including links to GO annotations at many major model organism databases.
• GOCat - An Automatic GO Categorizer/Browser to help Functional Annotation of Biomedical Texts; also useful to functionally characterize protein and gene names lists generated by high-throughput experiments [1]
• Protein Ontology Project — Provides access to the Protein Ontology (PO) and reference documents describing the PO and its uses.
• EAGLi - A Terminology-powered (Gene Ontology, Swiss-Prot keywords...) biomedical question answering engine for MEDLINE [2]
• PubOnto Medline Exploration based on Gene Ontology and other ontologies.
• Semantic Systems Biology
• National Center for Biomedical Ontology
• WikiProfessional - Disambiguation, knowledge generation and collaborative intelligence - genes and proteins.