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InterPro in 2017—beyond protein family and domain annotations

2016; Oxford University Press; Volume: 45; Issue: D1 Linguagem: Inglês

10.1093/nar/gkw1107

1362-4962

ROBERT FINN, Teresa K. Attwood, Patricia C. Babbitt, Alex Bateman, Peer Bork, Alan Bridge, Hsin-Yu Chang, Zsuzsanna Dosztányi, Sara El-Gebali, Matthew Fraser, Julian Gough, David R Haft, Gemma L. Holliday, Hongzhan Huang, Xiaosong Huang, Ivica Letunić, Rodrigo López, Shennan Lu, Aron Marchler‐Bauer, Huaiyu Mi, Jaina Mistry, Darren A. Natale, Marco Necci, Gift Nuka, Christine Orengo, Young Mi Park, Sebastien Pesseat, Damiano Piovesan, Simon Potter, Neil D. Rawlings, Nicole Redaschi, Lorna Richardson, Catherine Rivoire, Amaia Sangrador‐Vegas, Christian Sigrist, Ian Sillitoe, Ben Smithers, Silvano Squizzato, Granger Sutton, Narmada Thanki, Paul D. Thomas, Silvio C. E. Tosatto, Cathy Wu, Ioannis Xénarios, Lai-Su Yeh, Siew-Yit Young, Alex Mitchell,

Machine Learning in Bioinformatics

InterPro (http://www.ebi.ac.uk/interpro/) is a freely available database used to classify protein sequences into families and to predict the presence of important domains and sites. InterProScan is the underlying software that allows both protein and nucleic acid sequences to be searched against InterPro's predictive models, which are provided by its member databases. Here, we report recent developments with InterPro and its associated software, including the addition of two new databases (SFLD and CDD), and the functionality to include residue-level annotation and prediction of intrinsic disorder. These developments enrich the annotations provided by InterPro, increase the overall number of residues annotated and allow more specific functional inferences.