Qualitative and Quantitative Protein Complex Prediction Through Proteome-Wide Simulations.

Simone Rizzetto,Corrado Priami,Attila Csikász-Nagy

doi:10.1371/journal.pcbi.1004424

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.

Highlights

IntroductionMass-spectrometry (MS) techniques solved many fundamental issues in the identification of protein complexes [1,2,3] and other high-throughput techniques allowed the identification of Protein-Protein Interactions (PPI) and Domain-Domain Interactions (DDI), which paved the way for computational methods to predict protein complexes [4, 5]
In this article we propose an integrative computational approach able to predict protein complexes from existing data sources on protein-protein and domain-domain interactions
Mass-spectrometry (MS) techniques solved many fundamental issues in the identification of protein complexes [1,2,3] and other high-throughput techniques allowed the identification of Protein-Protein Interactions (PPI) and Domain-Domain Interactions (DDI), which paved the way for computational methods to predict protein complexes [4, 5]

Summary

Introduction

Mass-spectrometry (MS) techniques solved many fundamental issues in the identification of protein complexes [1,2,3] and other high-throughput techniques allowed the identification of Protein-Protein Interactions (PPI) and Domain-Domain Interactions (DDI), which paved the way for computational methods to predict protein complexes [4, 5]. We consider DDIs only between proteins with a corresponding PPI, but the same domain of a given protein can be bound by multiple proteins with matching DDI and PPI leading to competition for binding sites and limiting formation of unrealistically large complexes. This ensures that proteins with high number of possible interactors do not interact with all possible partners at the same time and limits the size of such complexes [18]. This served as a proof of concept towards protein complex prediction based drug design [19, 20]

Methods

Results

Discussion

Conclusion