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Qualitative and Quantitative Protein Complex Prediction Through Proteome-Wide Simulations

Research output: Contribution to journalArticle

Simone Rizzetto, Corrado Priami, Attila Csikász-Nagy

Original languageEnglish
Pages (from-to)e1004424
Number of pages16
JournalPL o S Computational Biology
Volume11
Issue number10
DOIs
Publication statusPublished - 22 Oct 2015

Documents

  • journal.pcbi.1004424

    journal.pcbi.1004424.pdf, 2.99 MB, application/pdf

    1/12/2015

    Final published version

    CC BY

King's Authors

Abstract

Despite recent progress in proteomics most protein complexes are still unknown. Identification of these complexes will help us understand cellular regulatory mechanisms and support development of new drugs. Therefore it is really important to establish detailed information about the composition and the abundance of protein complexes but existing algorithms can only give qualitative predictions. Herein, we propose a new approach based on stochastic simulations of protein complex formation that integrates multi-source data-such as protein abundances, domain-domain interactions and functional annotations-to predict alternative forms of protein complexes together with their abundances. This method, called SiComPre (Simulation based Complex Prediction), achieves better qualitative prediction of yeast and human protein complexes than existing methods and is the first to predict protein complex abundances. Furthermore, we show that SiComPre can be used to predict complexome changes upon drug treatment with the example of bortezomib. SiComPre is the first method to produce quantitative predictions on the abundance of molecular complexes while performing the best qualitative predictions. With new data on tissue specific protein complexes becoming available SiComPre will be able to predict qualitative and quantitative differences in the complexome in various tissue types and under various conditions.

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