The InterPro protein families database: the classification resource after 15 years

By Alex L. Mitchell, Hsin-Yu Chang, L. Daugherty, M. Fraser, S. Hunter, Rodrigo Lopez, C. McAnulla, Gift Nuka, Sebastien Pesseat, Amaia Sangrador-Vegas and 25 more. 2014

1,300 citations recorded by the source corpus.

The InterPro database (http://www.ebi.ac.uk/interpro/) is a freely available resource that can be used to classify sequences into protein families and to predict the presence of important domains and sites. Central to the InterPro database are predictive models, known as signatures, from a range of different protein family databases that have different biological focuses and use different methodological approaches to classify protein families and domains. InterPro integrates these signatures, capitalizing on the respective strengths of the individual databases, to produce a powerful protein classification resource. Here, we report on the status of InterPro as it enters its 15th year of operation, and give an overview of new developments with the database and its associated Web interfaces and software. In particular, the new domain architecture search tool is described and the process of mapping of Gene Ontology terms to InterPro is outlined. We also discuss the challenges faced by the resource given the explosive growth in sequence data in recent years. InterPro (version 48.0) contains 36 766 member database signatures integrated into 26 238 InterPro entries, an increase of over 3993 entries (5081 signatures), since 2012.

Subfields: Molecular Biology

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Citations in the corpus

The SWISS-MODEL Repository—new features and functionality (2016, background): “Information about the target protein includes the name of the target protein, links to UniProtKB (26), InterPro (27) and STRING (28).”
UniProt: the universal protein knowledgebase (2016, methodology): “Both UniRule and SAAS use the hierarchical InterPro classification of protein family and domain signatures (18) as a basis for protein classification and functional annotation.”
The Pfam protein families database: towards a more sustainable future (2015, background): “0, in which the underlying sequence database was UniProtKB, the taxonomic root of these entries was: Eukaryota (115 entries), Bacteria (60), Viruses (82), Archaea (1) and unclassified (2).”
InterPro in 2017—beyond protein family and domain annotations (2016, background): “Since the last update paper (35), we have been evaluating a range of different databases at U niersity C olege L odon on D ecem er (1)9, 2016 http://narrdjournals.”
MEGAN Community Edition - Interactive Exploration and Analysis of Large-Scale Microbiome Sequencing Data (2016, methodology): “An important step in the analysis of such data is the alignment of the reads against a protein reference database such as NCBI-nr [4] or InterPro [5].”
Expansion of the Gene Ontology knowledgebase and resources (2016, neutral): “The irst, and most comprehensive, method is InterPro2GO (13), which is based on the curated association of a GO term with a generalized sequence model (‘signature’) of a group of homologous proteins.”
The Transporter Classification Database (TCDB): recent advances (2015, background): “RCSP PDB (26) Provides high-resolution crystal or NMR structures for transport proteins when available Interpro (27) Provides functional analysis of transporters by classifying them according to domains and other important sites or motifs”
FFPred 3: feature-based function prediction for all Gene Ontology domains (2016, methodology): “The GO consortium, for instance, makes wide use of a semi-automated tool for phylogenetic analysis and functional inference 2 , and of mappings between protein domain families to GO terms that are valid for all their members 3 .”
Ensembl Genomes 2016: more genomes, more complexity (2015, methodology): “Functional information is provided through direct curation, import from the UniProt Knowledgebase (2) or imputation from protein sequence (using the classification tool InterProScan (3)).”
CAMPR3: a database on sequences, structures and signatures of antimicrobial peptides (2015, methodology): “These sequences are predicted to be antimicrobial either based on their GO (22)/Pfam (23)/InterPro (24)/UniProtKB/NCBI annotations or they were retrieved based on the AMP family signatures.”
The EMBL-EBI bioinformatics web and programmatic tools framework (2015, methodology): “Through these applications the latest mainstream bioinformatics databases can be searched, for example ENA (11), Ensembl Genomes (12), UniProt (13), InterPro (14) and Pfam (15).”
GOLabeler: Improving Sequence-based Large-scale Protein Function Prediction by Learning to Rank (2017, methodology): “InterPro [21] combines 14 diﬀerent protein and domain family databases, including Pfam [28], CATH-Gene3D [27], CCD [20] and SUPER-FAMILY [8], so covering a large number of protein families, domains and motifs in sequences.”
Genomic and Transcriptomic Features of Response to Anti-PD-1 Therapy in Metastatic Melanoma (2016, positive): “The impact of BRCA2 nsSNVs was visualized using the domain information in the INTERPRO protein domain database (Mitchell et al., 2015).”
FlyBase at 25: looking to the future (2016, methodology): “Protein domain data in FlyBase are obtained from InterPro (28).”