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Combining Protein-Protein Interaction (PPI) Network and Sequence Attributes for Predicting Hypertension Related Proteins

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Bioinformatics Research and Development (BIRD 2008)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 13))

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Abstract

Cardiovascular disease is set to become the number one cause of deaths worldwide. It is therefore important to understand the etiologic mechanisms for hypertension, in order to identify new routes to improved treatment. Human hypertension arises from a combination of genetic factors and lifestyle influences. Here we study hypertension related proteins from the perspective of protein-protein interaction (PPI) networks, pathways, Gene Ontology (GO) categories and sequence properties. We find that hypertension related proteins are not generally associated with network hubs and do not exhibit high clustering coefficients. Despite this, they tend to be closer and better connected to other hypertension proteins on the interaction network than we would expect, with 23% directly interacting. We find that molecular function category ‘oxidoreductase’ and biological process categories ‘response to stimulus’ and ‘electron transport’ are overrepresented. We also find that functional similarity does not correlate strongly with PPI distance separating hypertension related protein pairs and known hypertension related proteins are spread across 36 KEGG pathways. Finally, weighted Bagged PART classifiers were used to build predictive models that combined amino acid sequence with PPI network and GO properties.

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Authors and Affiliations

  1. The Genome Centre, St Barts and The London School of Medicine and Dentistry, Charterhouse Sq., London, EC1 6BQ

    Richard J. B. Dobson, Patricia B. Munroe, Charles A. Mein, Mark J. Caulfield & Mansoor A. S. Saqi

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  1. Richard J. B. Dobson
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  2. Patricia B. Munroe
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  3. Charles A. Mein
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  4. Mark J. Caulfield
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  5. Mansoor A. S. Saqi
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Editor information

Mourad Elloumi Josef Küng Michal Linial Robert F. Murphy Kristan Schneider Cristian Toma

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© 2008 Springer-Verlag Berlin Heidelberg

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Dobson, R.J.B., Munroe, P.B., Mein, C.A., Caulfield, M.J., Saqi, M.A.S. (2008). Combining Protein-Protein Interaction (PPI) Network and Sequence Attributes for Predicting Hypertension Related Proteins. In: Elloumi, M., Küng, J., Linial, M., Murphy, R.F., Schneider, K., Toma, C. (eds) Bioinformatics Research and Development. BIRD 2008. Communications in Computer and Information Science, vol 13. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70600-7_28

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  • DOI: https://doi.org/10.1007/978-3-540-70600-7_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-70598-7

  • Online ISBN: 978-3-540-70600-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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