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Waardenberg AJ, Homan B, Mohamed S, et al. Prediction and validation of protein-protein interactors from genome-wide DNA-binding data using a knowledge-based machine-learning approach. Open Biol. 2016;6(9)doi: 10.1098/rsob.160183.
Waardenberg, A. J., Homan, B., Mohamed, S., Harvey, R. P., & Bouveret, R. (2016). Prediction and validation of protein-protein interactors from genome-wide DNA-binding data using a knowledge-based machine-learning approach. Open biology, 6(9), . https://doi.org/10.1098/rsob.160183
Waardenberg, Ashley J, et al. "Prediction and validation of protein-protein interactors from genome-wide DNA-binding data using a knowledge-based machine-learning approach." Open biology vol. 6,9 (2016). doi: https://doi.org/10.1098/rsob.160183
Waardenberg AJ, Homan B, Mohamed S, Harvey RP, Bouveret R. Prediction and validation of protein-protein interactors from genome-wide DNA-binding data using a knowledge-based machine-learning approach. Open Biol. 2016 Sep;6(9). doi: 10.1098/rsob.160183. PMID: 27683156; PMCID: PMC5043580.
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Open Biol. 2016 Sep;6(9). doi: 10.1098/rsob.160183.

Prediction and validation of protein-protein interactors from genome-wide DNA-binding data using a knowledge-based machine-learning approach.

Open biology

Ashley J Waardenberg, Bernou Homan, Stephanie Mohamed, Richard P Harvey, Romaric Bouveret

Affiliations

  1. Victor Chang Cardiac Research Institute, Darlinghurst, New South Wales 2010, Australia Children's Medical Research Institute, University of Sydney, Westmead, New South Wales 2145, Australia [email protected].
  2. Victor Chang Cardiac Research Institute, Darlinghurst, New South Wales 2010, Australia.
  3. Victor Chang Cardiac Research Institute, Darlinghurst, New South Wales 2010, Australia St Vincent's Clinical School, University of Sydney, Westmead, New South Wales 2145, Australia School of Biotechnology and Biomolecular Science, University of New South Wales, Kensington, New South Wales 2052, Australia [email protected].
  4. Victor Chang Cardiac Research Institute, Darlinghurst, New South Wales 2010, Australia St Vincent's Clinical School, University of Sydney, Westmead, New South Wales 2145, Australia.

PMID: 27683156 PMCID: PMC5043580 DOI: 10.1098/rsob.160183

Abstract

The ability to accurately predict the DNA targets and interacting cofactors of transcriptional regulators from genome-wide data can significantly advance our understanding of gene regulatory networks. NKX2-5 is a homeodomain transcription factor that sits high in the cardiac gene regulatory network and is essential for normal heart development. We previously identified genomic targets for NKX2-5 in mouse HL-1 atrial cardiomyocytes using DNA-adenine methyltransferase identification (DamID). Here, we apply machine learning algorithms and propose a knowledge-based feature selection method for predicting NKX2-5 protein : protein interactions based on motif grammar in genome-wide DNA-binding data. We assessed model performance using leave-one-out cross-validation and a completely independent DamID experiment performed with replicates. In addition to identifying previously described NKX2-5-interacting proteins, including GATA, HAND and TBX family members, a number of novel interactors were identified, with direct protein : protein interactions between NKX2-5 and retinoid X receptor (RXR), paired-related homeobox (PRRX) and Ikaros zinc fingers (IKZF) validated using the yeast two-hybrid assay. We also found that the interaction of RXRα with NKX2-5 mutations found in congenital heart disease (Q187H, R189G and R190H) was altered. These findings highlight an intuitive approach to accessing protein-protein interaction information of transcription factors in DNA-binding experiments.

© 2016 The Authors.

Keywords: gene regulatory networks; machine learning; protein–protein interactions; transcription factors

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