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. 2014 Jan;42(Database issue):D396-400.
doi: 10.1093/nar/gkt1079. Epub 2013 Nov 8.

Negatome 2.0: a database of non-interacting proteins derived by literature mining, manual annotation and protein structure analysis

Philipp Blohm  1 , Goar Frishman, Pawel Smialowski, Florian Goebels, Benedikt Wachinger, Andreas Ruepp, Dmitrij Frishman
Affiliations

Negatome 2.0: a database of non-interacting proteins derived by literature mining, manual annotation and protein structure analysis

Philipp Blohm et al. Nucleic Acids Res. 2014 Jan.

Abstract

Knowledge about non-interacting proteins (NIPs) is important for training the algorithms to predict protein-protein interactions (PPIs) and for assessing the false positive rates of PPI detection efforts. We present the second version of Negatome, a database of proteins and protein domains that are unlikely to engage in physical interactions (available online at http://mips.helmholtz-muenchen.de/proj/ppi/negatome). Negatome is derived by manual curation of literature and by analyzing three-dimensional structures of protein complexes. The main methodological innovation in Negatome 2.0 is the utilization of an advanced text mining procedure to guide the manual annotation process. Potential non-interactions were identified by a modified version of Excerbt, a text mining tool based on semantic sentence analysis. Manual verification shows that nearly a half of the text mining results with the highest confidence values correspond to NIP pairs. Compared to the first version the contents of the database have grown by over 300%.

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Figures

Figure 1.
Figure 1.
Manual assessment of the text mining performance. The figure shows the number of sentences proposed by the text mining system that were tagged as containing a negative interaction by a human expert (acceptance rate) and the number of negative interactions by the human expert from other sentences stemming from the paper selected by the text mining system (addition rate). Both rates are displayed in relation to the confidence score that was calculated for the text mining results.
Figure 2.
Figure 2.
Flowchart explaining how Negatome 2.0 data are generated, merged with Negatome 1.0 and filtered against known interactions to produce stringent datasets.
See this image and copyright information in PMC

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