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Predicting Protein-Protein Interactions from Primary Protein Sequences Using a Novel Multi-Scale Local Feature Representation Scheme and the Random Forest

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  • Zhu-Hong You
  • Keith C C Chan
  • Pengwei Hu

Abstract

The study of protein-protein interactions (PPIs) can be very important for the understanding of biological cellular functions. However, detecting PPIs in the laboratories are both time-consuming and expensive. For this reason, there has been much recent effort to develop techniques for computational prediction of PPIs as this can complement laboratory procedures and provide an inexpensive way of predicting the most likely set of interactions at the entire proteome scale. Although much progress has already been achieved in this direction, the problem is still far from being solved. More effective approaches are still required to overcome the limitations of the current ones. In this study, a novel Multi-scale Local Descriptor (MLD) feature representation scheme is proposed to extract features from a protein sequence. This scheme can capture multi-scale local information by varying the length of protein-sequence segments. Based on the MLD, an ensemble learning method, the Random Forest (RF) method, is used as classifier. The MLD feature representation scheme facilitates the mining of interaction information from multi-scale continuous amino acid segments, making it easier to capture multiple overlapping continuous binding patterns within a protein sequence. When the proposed method is tested with the PPI data of Saccharomyces cerevisiae, it achieves a prediction accuracy of 94.72% with 94.34% sensitivity at the precision of 98.91%. Extensive experiments are performed to compare our method with existing sequence-based method. Experimental results show that the performance of our predictor is better than several other state-of-the-art predictors also with the H. pylori dataset. The reason why such good results are achieved can largely be credited to the learning capabilities of the RF model and the novel MLD feature representation scheme. The experiment results show that the proposed approach can be very promising for predicting PPIs and can be a useful tool for future proteomic studies.

Suggested Citation

  • Zhu-Hong You & Keith C C Chan & Pengwei Hu, 2015. "Predicting Protein-Protein Interactions from Primary Protein Sequences Using a Novel Multi-Scale Local Feature Representation Scheme and the Random Forest," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-19, May.
  • Handle: RePEc:plo:pone00:0125811
    DOI: 10.1371/journal.pone.0125811
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    References listed on IDEAS

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    1. Luis P Fernandes & Alessia Annibale & Jens Kleinjung & Anthony C C Coolen & Franca Fraternali, 2010. "Protein Networks Reveal Detection Bias and Species Consistency When Analysed by Information-Theoretic Methods," PLOS ONE, Public Library of Science, vol. 5(8), pages 1-14, August.
    2. Benjamin A Shoemaker & Anna R Panchenko, 2007. "Deciphering Protein–Protein Interactions. Part II. Computational Methods to Predict Protein and Domain Interaction Partners," PLOS Computational Biology, Public Library of Science, vol. 3(4), pages 1-7, April.
    3. Qing-Ju Jiao & Yan-Kai Zhang & Lu-Ning Li & Hong-Bin Shen, 2011. "BinTree Seeking: A Novel Approach to Mine Both Bi-Sparse and Cohesive Modules in Protein Interaction Networks," PLOS ONE, Public Library of Science, vol. 6(11), pages 1-12, November.
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    Cited by:

    1. Ingoo Lee & Jongsoo Keum & Hojung Nam, 2019. "DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences," PLOS Computational Biology, Public Library of Science, vol. 15(6), pages 1-21, June.

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