SPAR: A random forest-based predictor for self-interacting proteins with fine-grained domain information

Xuhan Liu, Shiping Yang, Chen Li, Ziding Zhang, Jiangning Song

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25 Citations (Scopus)


Protein self-interaction, i.e. the interaction between two or more identical proteins expressed by one gene, plays an important role in the regulation of cellular functions. Considering the limitations of experimental self-interaction identification, it is necessary to design specific bioinformatics tools for self-interacting protein (SIP) prediction from protein sequence information. In this study, we proposed an improved computational approach for SIP prediction, termed SPAR (Self-interacting Protein Analysis serveR). Firstly, we developed an improved encoding scheme named critical residues substitution (CRS), in which the fine-grained domain–domain interaction information was taken into account. Then, by employing the Random Forest algorithm, the performance of CRS was evaluated and compared with several other encoding schemes commonly used for sequence-based protein–protein interaction prediction. Through the tenfold cross-validation tests on a balanced training dataset, CRS performed the best, with the average accuracy up to 72.01 %. We further integrated CRS with other encoding schemes and identified the most important features using the mRMR (the minimum redundancy maximum relevance) feature selection method. Our SPAR model with selected features achieved an average accuracy of 92.09 % on the human-independent test set (the ratio of positives to negatives was about 1:11). Besides, we also evaluated the performance of SPAR on an independent yeast test set (the ratio of positives to negatives was about 1:8) and obtained an average accuracy of 76.96 %. The results demonstrate that SPAR is capable of achieving a reasonable performance in cross-species application.
Original languageEnglish
Pages (from-to)1655-1665
Number of pages11
JournalAmino Acids
Issue number7
Publication statusPublished - Jul 2016


  • Self-interacting protein
  • Prediction
  • Machine learning
  • Feature selection
  • Domain–domain interaction

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