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MP-PIPE: a massively parallel protein-protein interaction prediction engine

Authors:

Sylvain PitreAuthors Info & Claims

ICS '11: Proceedings of the international conference on Supercomputing

Pages 327 - 337

https://doi.org/10.1145/1995896.1995946

Published: 31 May 2011 Publication History

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Abstract

Interactions among proteins are essential to many biological functions in living cells but experimentally detected interactions represent only a small fraction of the real interaction network. Computational protein interaction prediction methods have become important to augment the experimental methods; in particular sequence based prediction methods that do not require additional data such as homologous sequences or 3D structure information which are often not available. Our Protein Interaction Prediction Engine (PIPE) method falls into this category. Park has recently compared PIPE with the other competing methods and concluded that our method "significantly outperforms the others in terms of recall-precision across both the yeast and human data". Here, we present MP-PIPE, a new massively parallel PIPE implementation for large scale, high throughput protein interaction prediction. MP-PIPE enabled us to perform the first ever complete scan of the entire human protein interaction network; a massively parallel computational experiment which took three months of full time 24/7 computation on a dedicated SUN UltraSparc T2+ based cluster with 50 nodes, 800 processor cores and 6,400 hardware supported threads. The implications for the understanding of human cell function will be significant as biologists are starting to analyze the 130,470 new protein interactions and possible new pathways in Human cells predicted by MP-PIPE.

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Cited By

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Dick KPattang AHooker JNissan NSadowski MBarnes BTan LBurnside DPhanse SAoki HBabu MDehne FGolshani ACober EGreen JSamanfar B(2021)Human–Soybean Allergies: Elucidation of the Seed Proteome and Comprehensive Protein–Protein Interaction PredictionJournal of Proteome Research10.1021/acs.jproteome.1c0013820:11(4925-4947)Online publication date: 28-Sep-2021
https://doi.org/10.1021/acs.jproteome.1c00138
Dick KSamanfar BBarnes BCober EMimee BTan LMolnar SBiggar KGolshani ADehne FGreen J(2020)PIPE4: Fast PPI Predictor for Comprehensive Inter- and Cross-Species InteractomesScientific Reports10.1038/s41598-019-56895-w10:1Online publication date: 29-Jan-2020
https://doi.org/10.1038/s41598-019-56895-w
Li YIlie L(2019)Predicting Protein–Protein Interactions Using SPRINTProtein-Protein Interaction Networks10.1007/978-1-4939-9873-9_1(1-11)Online publication date: 4-Oct-2019
https://doi.org/10.1007/978-1-4939-9873-9_1
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Index Terms

MP-PIPE: a massively parallel protein-protein interaction prediction engine
1. Applied computing
  1. Life and medical sciences

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Pipe: a protein-protein interaction prediction engine based on the re-occurring short polypeptide sequences between known interacting protein pairs

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Published In

ICS '11: Proceedings of the international conference on Supercomputing

May 2011

398 pages

ISBN:9781450301022

DOI:10.1145/1995896

General Chair:
David K. Lowenthal
University of Arizona
,
Program Chairs:
Bronis R. de Supinski
Lawrence Livermore National Laboratory
,
Sally A. McKee
Chalmers University of Technology

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Published: 31 May 2011

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ICS '11: International Conference on Supercomputing

May 31 - June 4, 2011

Arizona, Tucson, USA

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Dick KPattang AHooker JNissan NSadowski MBarnes BTan LBurnside DPhanse SAoki HBabu MDehne FGolshani ACober EGreen JSamanfar B(2021)Human–Soybean Allergies: Elucidation of the Seed Proteome and Comprehensive Protein–Protein Interaction PredictionJournal of Proteome Research10.1021/acs.jproteome.1c0013820:11(4925-4947)Online publication date: 28-Sep-2021
https://doi.org/10.1021/acs.jproteome.1c00138
Dick KSamanfar BBarnes BCober EMimee BTan LMolnar SBiggar KGolshani ADehne FGreen J(2020)PIPE4: Fast PPI Predictor for Comprehensive Inter- and Cross-Species InteractomesScientific Reports10.1038/s41598-019-56895-w10:1Online publication date: 29-Jan-2020
https://doi.org/10.1038/s41598-019-56895-w
Li YIlie L(2019)Predicting Protein–Protein Interactions Using SPRINTProtein-Protein Interaction Networks10.1007/978-1-4939-9873-9_1(1-11)Online publication date: 4-Oct-2019
https://doi.org/10.1007/978-1-4939-9873-9_1
Dick KGreen J(2018)Fitting Rank Order Data in the Age of Context2018 IEEE Life Sciences Conference (LSC)10.1109/LSC.2018.8572090(142-146)Online publication date: Oct-2018
https://doi.org/10.1109/LSC.2018.8572090
Dick KGreen J(2018)Reciprocal Perspective for Improved Protein-Protein Interaction PredictionScientific Reports10.1038/s41598-018-30044-18:1Online publication date: 3-Aug-2018
https://doi.org/10.1038/s41598-018-30044-1
Schoenrock ABurnside DMoteshareie HPitre SHooshyar MGreen JGolshani ADehne FWong A(2017)Evolution of protein-protein interaction networks in yeastPLOS ONE10.1371/journal.pone.017192012:3(e0171920)Online publication date: 1-Mar-2017
https://doi.org/10.1371/journal.pone.0171920
Dick KDehne FGolshani AGreen J(2017)Positome: A method for improving protein-protein interaction quality and prediction accuracy2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)10.1109/CIBCB.2017.8058545(1-8)Online publication date: Aug-2017
https://doi.org/10.1109/CIBCB.2017.8058545
Kazmirchuk TDick KBurnside DBarnes BMoteshareie HHajikarimlou MOmidi KAhmed DLow ALettl CHooshyar MSchoenrock APitre SBabu MCassol ESamanfar BWong ADehne FGreen JGolshani A(2017)Designing anti-Zika virus peptides derived from predicted human-Zika virus protein-protein interactionsComputational Biology and Chemistry10.1016/j.compbiolchem.2017.10.01171:C(180-187)Online publication date: 1-Dec-2017
https://dl.acm.org/doi/10.1016/j.compbiolchem.2017.10.011
Dick KGreen J(2016)Comparison of sequence- and structure-based protein-protein interaction sites2016 IEEE EMBS International Student Conference (ISC)10.1109/EMBSISC.2016.7508605(1-4)Online publication date: May-2016
https://doi.org/10.1109/EMBSISC.2016.7508605
Schoenrock ABurnside DMoteshareie HWong AGolshani ADehne FGreen JKern JVetter J(2015)Engineering inhibitory proteins with InSiPS: the in-silico protein synthesizerProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.1145/2807591.2807630(1-11)Online publication date: 15-Nov-2015
https://dl.acm.org/doi/10.1145/2807591.2807630

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Integrative approach for computationally inferring protein domain interactions

Novel Generated Peptides for COVID-19 Targets

Pipe: a protein-protein interaction prediction engine based on the re-occurring short polypeptide sequences between known interacting protein pairs

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