Protein complex prediction via verifying and reconstructing the topology of domain-domain interactions

Yosuke Ozawa, Rintaro Saito, Shigeo Fujimori, Hisashi Kashima, Masamichi Ishizaka, Hiroshi Yanagawa, Etsuko Miyamoto-Sato, Masaru Tomita

Research output: Contribution to journalArticle

29 Citations (Scopus)

Abstract

Background: High-throughput methods for detecting protein-protein interactions enable us to obtain large interaction networks, and also allow us to computationally identify the associations of proteins as protein complexes. Although there are methods to extract protein complexes as sets of proteins from interaction networks, the extracted complexes may include false positives because they do not account for the structural limitations of the proteins and thus do not check that the proteins in the extracted complex can simultaneously bind to each other. In addition, there have been few searches for deeper insights into the protein complexes, such as of the topology of the protein-protein interactions or into the domain-domain interactions that mediate the protein interactions.Results: Here, we introduce a combinatorial approach for prediction of protein complexes focusing not only on determining member proteins in complexes but also on the DDI/PPI organization of the complexes. Our method analyzes complex candidates predicted by the existing methods. It searches for optimal combinations of domain-domain interactions in the candidates based on an assumption that the proteins in a candidate can form a true protein complex if each of the domains is used by a single protein interaction. This optimization problem was mathematically formulated and solved using binary integer linear programming. By using publicly available sets of yeast protein-protein interactions and domain-domain interactions, we succeeded in extracting protein complex candidates with an accuracy that is twice the average accuracy of the existing methods, MCL, MCODE, or clustering coefficient. Although the configuring parameters for each algorithm resulted in slightly improved precisions, our method always showed better precision for most values of the parameters.Conclusions: Our combinatorial approach can provide better accuracy for prediction of protein complexes and also enables to identify both direct PPIs and DDIs that mediate them in complexes.

Original languageEnglish
Article number350
JournalBMC Bioinformatics
Volume11
DOIs
Publication statusPublished - 2010 Jun 28

Fingerprint

Topology
Proteins
Protein
Prediction
Interaction
Protein-protein Interaction
Protein Interaction Domains and Motifs
Linear Programming
Clustering Coefficient
Protein Interaction Maps
Integer Linear Programming
Fungal Proteins
False Positive
Yeast
High Throughput
Complex Mixtures
Linear programming
Cluster Analysis
Binary
Optimization Problem

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Structural Biology
  • Applied Mathematics

Cite this

Protein complex prediction via verifying and reconstructing the topology of domain-domain interactions. / Ozawa, Yosuke; Saito, Rintaro; Fujimori, Shigeo; Kashima, Hisashi; Ishizaka, Masamichi; Yanagawa, Hiroshi; Miyamoto-Sato, Etsuko; Tomita, Masaru.

In: BMC Bioinformatics, Vol. 11, 350, 28.06.2010.

Research output: Contribution to journalArticle

Ozawa, Yosuke ; Saito, Rintaro ; Fujimori, Shigeo ; Kashima, Hisashi ; Ishizaka, Masamichi ; Yanagawa, Hiroshi ; Miyamoto-Sato, Etsuko ; Tomita, Masaru. / Protein complex prediction via verifying and reconstructing the topology of domain-domain interactions. In: BMC Bioinformatics. 2010 ; Vol. 11.
@article{33cbf22fcd1b4b3da48b8ad64b0db98f,
title = "Protein complex prediction via verifying and reconstructing the topology of domain-domain interactions",
abstract = "Background: High-throughput methods for detecting protein-protein interactions enable us to obtain large interaction networks, and also allow us to computationally identify the associations of proteins as protein complexes. Although there are methods to extract protein complexes as sets of proteins from interaction networks, the extracted complexes may include false positives because they do not account for the structural limitations of the proteins and thus do not check that the proteins in the extracted complex can simultaneously bind to each other. In addition, there have been few searches for deeper insights into the protein complexes, such as of the topology of the protein-protein interactions or into the domain-domain interactions that mediate the protein interactions.Results: Here, we introduce a combinatorial approach for prediction of protein complexes focusing not only on determining member proteins in complexes but also on the DDI/PPI organization of the complexes. Our method analyzes complex candidates predicted by the existing methods. It searches for optimal combinations of domain-domain interactions in the candidates based on an assumption that the proteins in a candidate can form a true protein complex if each of the domains is used by a single protein interaction. This optimization problem was mathematically formulated and solved using binary integer linear programming. By using publicly available sets of yeast protein-protein interactions and domain-domain interactions, we succeeded in extracting protein complex candidates with an accuracy that is twice the average accuracy of the existing methods, MCL, MCODE, or clustering coefficient. Although the configuring parameters for each algorithm resulted in slightly improved precisions, our method always showed better precision for most values of the parameters.Conclusions: Our combinatorial approach can provide better accuracy for prediction of protein complexes and also enables to identify both direct PPIs and DDIs that mediate them in complexes.",
author = "Yosuke Ozawa and Rintaro Saito and Shigeo Fujimori and Hisashi Kashima and Masamichi Ishizaka and Hiroshi Yanagawa and Etsuko Miyamoto-Sato and Masaru Tomita",
year = "2010",
month = "6",
day = "28",
doi = "10.1186/1471-2105-11-350",
language = "English",
volume = "11",
journal = "BMC Bioinformatics",
issn = "1471-2105",
publisher = "BioMed Central",

}

TY - JOUR

T1 - Protein complex prediction via verifying and reconstructing the topology of domain-domain interactions

AU - Ozawa, Yosuke

AU - Saito, Rintaro

AU - Fujimori, Shigeo

AU - Kashima, Hisashi

AU - Ishizaka, Masamichi

AU - Yanagawa, Hiroshi

AU - Miyamoto-Sato, Etsuko

AU - Tomita, Masaru

PY - 2010/6/28

Y1 - 2010/6/28

N2 - Background: High-throughput methods for detecting protein-protein interactions enable us to obtain large interaction networks, and also allow us to computationally identify the associations of proteins as protein complexes. Although there are methods to extract protein complexes as sets of proteins from interaction networks, the extracted complexes may include false positives because they do not account for the structural limitations of the proteins and thus do not check that the proteins in the extracted complex can simultaneously bind to each other. In addition, there have been few searches for deeper insights into the protein complexes, such as of the topology of the protein-protein interactions or into the domain-domain interactions that mediate the protein interactions.Results: Here, we introduce a combinatorial approach for prediction of protein complexes focusing not only on determining member proteins in complexes but also on the DDI/PPI organization of the complexes. Our method analyzes complex candidates predicted by the existing methods. It searches for optimal combinations of domain-domain interactions in the candidates based on an assumption that the proteins in a candidate can form a true protein complex if each of the domains is used by a single protein interaction. This optimization problem was mathematically formulated and solved using binary integer linear programming. By using publicly available sets of yeast protein-protein interactions and domain-domain interactions, we succeeded in extracting protein complex candidates with an accuracy that is twice the average accuracy of the existing methods, MCL, MCODE, or clustering coefficient. Although the configuring parameters for each algorithm resulted in slightly improved precisions, our method always showed better precision for most values of the parameters.Conclusions: Our combinatorial approach can provide better accuracy for prediction of protein complexes and also enables to identify both direct PPIs and DDIs that mediate them in complexes.

AB - Background: High-throughput methods for detecting protein-protein interactions enable us to obtain large interaction networks, and also allow us to computationally identify the associations of proteins as protein complexes. Although there are methods to extract protein complexes as sets of proteins from interaction networks, the extracted complexes may include false positives because they do not account for the structural limitations of the proteins and thus do not check that the proteins in the extracted complex can simultaneously bind to each other. In addition, there have been few searches for deeper insights into the protein complexes, such as of the topology of the protein-protein interactions or into the domain-domain interactions that mediate the protein interactions.Results: Here, we introduce a combinatorial approach for prediction of protein complexes focusing not only on determining member proteins in complexes but also on the DDI/PPI organization of the complexes. Our method analyzes complex candidates predicted by the existing methods. It searches for optimal combinations of domain-domain interactions in the candidates based on an assumption that the proteins in a candidate can form a true protein complex if each of the domains is used by a single protein interaction. This optimization problem was mathematically formulated and solved using binary integer linear programming. By using publicly available sets of yeast protein-protein interactions and domain-domain interactions, we succeeded in extracting protein complex candidates with an accuracy that is twice the average accuracy of the existing methods, MCL, MCODE, or clustering coefficient. Although the configuring parameters for each algorithm resulted in slightly improved precisions, our method always showed better precision for most values of the parameters.Conclusions: Our combinatorial approach can provide better accuracy for prediction of protein complexes and also enables to identify both direct PPIs and DDIs that mediate them in complexes.

UR - http://www.scopus.com/inward/record.url?scp=77953922689&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77953922689&partnerID=8YFLogxK

U2 - 10.1186/1471-2105-11-350

DO - 10.1186/1471-2105-11-350

M3 - Article

VL - 11

JO - BMC Bioinformatics

JF - BMC Bioinformatics

SN - 1471-2105

M1 - 350

ER -