Qualitative and Quantitative Protein Complex Prediction Through Proteome-Wide Simulations.
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.
The Microsoft Research-University of Trento Centre for Computational Systems Biology, Rovereto, Italy.
Simone
S
Rizzetto
The Microsoft Research-University of Trento Centre for Computational Systems Biology, Rovereto, Italy; Department of Mathematics, University of Trento, Povo (TN), Italy.
Corrado
C
Priami
Department of Computational Biology, Research and Innovation Centre, Fondazione Edmund Mach, San Michele all'Adige, Italy; Randall Division of Cell and Molecular Biophysics and Institute for Mathematical and Molecular Biomedicine, King's College London, London, United Kingdom.
Attila
A
Csikász-Nagy
D000465:Algorithms; D001665:Binding Sites; D006801:Humans; D008956:Models, Chemical; D062105:Molecular Docking Simulation; D011485:Protein Binding; D025941:Protein Interaction Mapping / Q000379:methods*; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism* / Q000648:ultrastructure; D013329:Structure-Activity Relationship
D011506:Proteins