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Evaluation of Function Predictions for Moonlighting Proteins

By Ishita K. Khan1, Meghana Chitale1, Catherine Rayon2, Daisuke Kihara1

1. Purdue University 2. Université de Picardie Jules Verne

Supplemental datasets used for evaluation of function predictions for moonlighting proteins.

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Version 1.0 - published on 14 Feb 2013 doi:10.4231/D3T727G0X - cite this Archived on 25 Oct 2016

Licensed under CC0 1.0 Universal


The performances of PFP, ESG, and PSI-BLAST in predicting the functional diversity of moonlighting proteins were analyzed. PFP shows overall better performance in predicting diverse moonlighting functions as compared with PSI-BLAST and ESG. Recall by PSI-BLAST greatly improved when BLOSUM45 was used. This analysis indicates that considering weakly similar sequences in prediction enhances the performance of sequence based AFP methods in predicting functional diversity of moonlighting proteins. The current study will also motivate development of novel computational frameworks for automatic identification of such proteins. This dataset was used for the evaluation of function predictions for moonlighting proteins. The dataset provides a set of 19 moonlighting proteins from Huberts et al. (2010) with GO annotations taken for each protein from Uniprot. The GO annotations for the proteins are classified as in “Function 1”, “Function 2”, “Function Common to Both” and “Function Not Clear”. The dataset is available in two excel files: the first file contains the protein names and uniprot ID, and the second file contains the current GO annotations for the proteins from Uniprot, functional description, and functional classification of the GO terms.

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Supplementary data for: Ishita K Khan, Meghana Chitale, Catherine Rayon & Daisuke Kihara. Evaluation of function predictions by PFP, ESG, and PSI-BLAST for moonlighting proteins.'' BMC Proceedings'' 2012, '''6'''(Suppl 7):S5. doi:10.1186/1753-6561-6-S7-S5.