Inferring protein–protein interaction complexes from immunoprecipitation data

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• 作者：Joachim Kutzera (1) (2)
  Huub CJ Hoefsloot (1) (2)
  Anna Malovannaya (4)
  August B Smit (2) (3)
  Iven Van Mechelen (5)
  Age K Smilde (1) (2)
  
• 关键词：Protein–protein interactions ; Proteomics ; Protein complexes ; Immunoprecipitation
• 刊名：BMC Research Notes
• 出版年：2013
• 出版时间：December 2013
• 年：2013
• 卷：6
• 期：1
• 全文大小：495 KB
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• 作者单位：Joachim Kutzera (1) (2)
  Huub CJ Hoefsloot (1) (2)
  Anna Malovannaya (4)
  August B Smit (2) (3)
  Iven Van Mechelen (5)
  Age K Smilde (1) (2)
  
  1. Biosystems Data Analysis, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands
  2. Netherlands Institute for Systems Biology, University of Amsterdam, Amsterdam, The Netherlands
  4. Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, 77030, USA
  3. Department of Molecular and Cellular Neurobiology, Center for Neurogenomics and Cognitive Research, VU University Amsterdam, Amsterdam, The Netherlands
  5. Faculty of Psychology and Educational Sciences, Katholieke Universiteit Leuven, Leuven, Belgium
  
• ISSN：1756-0500

文摘

Background Protein–protein interactions in cells are widely explored using small–scale experiments. However, the search for protein complexes and their interactions in data from high throughput experiments such as immunoprecipitation is still a challenge. We present "4N", a novel method for detecting protein complexes in such data. Our method is a heuristic algorithm based on Near Neighbor Network (3N) clustering. It is written in R, it is faster than model-based methods, and has only a small number of tuning parameters. We explain the application of our new method to real immunoprecipitation results and two artificial datasets. We show that the method can infer protein complexes from protein immunoprecipitation datasets of different densities and sizes. Findings 4N was applied on the immunoprecipitation dataset that was presented by the authors of the original 3N in Cell 145:787-99, 2011. The test with our method shows that it can reproduce the original clustering results with fewer manually adapted parameters and, in addition, gives direct insight into the complex–complex interactions. We also tested 4N on the human "Tip49a/b" dataset. We conclude that 4N can handle the contaminants and can correctly infer complexes from this very dense dataset. Further tests were performed on two artificial datasets of different sizes. We proved that the method predicts the reference complexes in the two artificial datasets with high accuracy, even when the number of samples is reduced. Conclusions 4N has been implemented in R. We provide the sourcecode of 4N and a user-friendly toolbox including two example calculations. Biologists can use this 4N-toolbox even if they have a limited knowledge of R. There are only a few tuning parameters to set, and each of these parameters has a biological interpretation. The run times for medium scale datasets are in the order of minutes on a standard desktop PC. Large datasets can typically be analyzed within a few hours.