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Qualitative and Quantitative Protein Complex Prediction Through Proteome-Wide Simulations

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Qualitative and Quantitative Protein Complex Prediction Through Proteome-Wide Simulations. / Rizzetto, Simone; Priami, Corrado; Csikász-Nagy, Attila.

In: PL o S Computational Biology, Vol. 11, No. 10, 22.10.2015, p. e1004424.

Research output: Contribution to journalArticle

Harvard

Rizzetto, S, Priami, C & Csikász-Nagy, A 2015, 'Qualitative and Quantitative Protein Complex Prediction Through Proteome-Wide Simulations', PL o S Computational Biology, vol. 11, no. 10, pp. e1004424. https://doi.org/10.1371/journal.pcbi.1004424

APA

Rizzetto, S., Priami, C., & Csikász-Nagy, A. (2015). Qualitative and Quantitative Protein Complex Prediction Through Proteome-Wide Simulations. PL o S Computational Biology, 11(10), e1004424. https://doi.org/10.1371/journal.pcbi.1004424

Vancouver

Rizzetto S, Priami C, Csikász-Nagy A. Qualitative and Quantitative Protein Complex Prediction Through Proteome-Wide Simulations. PL o S Computational Biology. 2015 Oct 22;11(10):e1004424. https://doi.org/10.1371/journal.pcbi.1004424

Author

Rizzetto, Simone ; Priami, Corrado ; Csikász-Nagy, Attila. / Qualitative and Quantitative Protein Complex Prediction Through Proteome-Wide Simulations. In: PL o S Computational Biology. 2015 ; Vol. 11, No. 10. pp. e1004424.

Bibtex Download

@article{a959444c3df54af7b0c11e83d8340047,
title = "Qualitative and Quantitative Protein Complex Prediction Through Proteome-Wide Simulations",
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.",
author = "Simone Rizzetto and Corrado Priami and Attila Csik{\'a}sz-Nagy",
year = "2015",
month = "10",
day = "22",
doi = "10.1371/journal.pcbi.1004424",
language = "English",
volume = "11",
pages = "e1004424",
journal = "PL o S Computational Biology",
issn = "1553-734X",
number = "10",

}

RIS (suitable for import to EndNote) Download

TY - JOUR

T1 - Qualitative and Quantitative Protein Complex Prediction Through Proteome-Wide Simulations

AU - Rizzetto, Simone

AU - Priami, Corrado

AU - Csikász-Nagy, Attila

PY - 2015/10/22

Y1 - 2015/10/22

N2 - 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.

AB - 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.

U2 - 10.1371/journal.pcbi.1004424

DO - 10.1371/journal.pcbi.1004424

M3 - Article

C2 - 26492574

VL - 11

SP - e1004424

JO - PL o S Computational Biology

JF - PL o S Computational Biology

SN - 1553-734X

IS - 10

ER -

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