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A domain-based approach to predict protein-protein interactions.

Singhal M, Resat H - BMC Bioinformatics (2007)

Bottom Line: Knowing which proteins exist in a certain organism or cell type and how these proteins interact with each other are necessary for the understanding of biological processes at the whole cell level.Obtained domain interaction scores are then used to predict whether a pair of proteins interacts.We envision the DomainGA as a first step of a multiple tier approach to constructing organism specific PPIs.

View Article: PubMed Central - HTML - PubMed

Affiliation: Computational Biology and Bioinformatics Group, Pacific Northwest National Laboratory, Richland, WA 99352, USA. mudita.singhal@pnl.gov <mudita.singhal@pnl.gov>

ABSTRACT

Background: Knowing which proteins exist in a certain organism or cell type and how these proteins interact with each other are necessary for the understanding of biological processes at the whole cell level. The determination of the protein-protein interaction (PPI) networks has been the subject of extensive research. Despite the development of reasonably successful methods, serious technical difficulties still exist. In this paper we present DomainGA, a quantitative computational approach that uses the information about the domain-domain interactions to predict the interactions between proteins.

Results: DomainGA is a multi-parameter optimization method in which the available PPI information is used to derive a quantitative scoring scheme for the domain-domain pairs. Obtained domain interaction scores are then used to predict whether a pair of proteins interacts. Using the yeast PPI data and a series of tests, we show the robustness and insensitivity of the DomainGA method to the selection of the parameter sets, score ranges, and detection rules. Our DomainGA method achieves very high explanation ratios for the positive and negative PPIs in yeast. Based on our cross-verification tests on human PPIs, comparison of the optimized scores with the structurally observed domain interactions obtained from the iPFAM database, and sensitivity and specificity analysis; we conclude that our DomainGA method shows great promise to be applicable across multiple organisms.

Conclusion: We envision the DomainGA as a first step of a multiple tier approach to constructing organism specific PPIs. As it is based on fundamental structural information, the DomainGA approach can be used to create potential PPIs and the accuracy of the constructed interaction template can be further improved using complementary methods. Explanation ratios obtained in the reported test case studies clearly show that the false prediction rates of the template networks constructed using the DomainGA scores are reasonably low, and the erroneous predictions can be filtered further using supplementary approaches such as those based on literature search or other prediction methods.

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Comparison of the strengths of the MIPS positive (red line with squares) and negative (blue line with circles) protein-protein interactions computed using the DomainGA optimized domain-domain interaction scores. Vertical axis shows the percentage of the PPIs with interaction scores that were calculated by binning the total protein-protein interaction scores using unit bin sizes. Top: Inclusive set yeast PPI; Bottom: Closed set human PPI.
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Figure 6: Comparison of the strengths of the MIPS positive (red line with squares) and negative (blue line with circles) protein-protein interactions computed using the DomainGA optimized domain-domain interaction scores. Vertical axis shows the percentage of the PPIs with interaction scores that were calculated by binning the total protein-protein interaction scores using unit bin sizes. Top: Inclusive set yeast PPI; Bottom: Closed set human PPI.

Mentions: As discussed in Part A above, a rationale behind the presented research was the lack of discriminatory power of the InterDom domain-domain interaction scores. To further evaluate the DomainGA method's performance, we have performed a similar analysis using our interaction scores. Figure 6 reports the distributions of the predicted yeast PPI scores obtained using the domain-domain interaction scores obtained in the inclusive 867 parameter study. Using the same optimized parameter values, as in the cross-verification study reported above, Figure 6 also reports the predicted score distribution for the human interactome for the closed PPI dataset. For both cases, distributions for the positive and negative PPI scores are clearly well separated indicating that, in terms of having discriminatory power, our DomainGA method significantly improves on the InterDom scores.


A domain-based approach to predict protein-protein interactions.

Singhal M, Resat H - BMC Bioinformatics (2007)

Comparison of the strengths of the MIPS positive (red line with squares) and negative (blue line with circles) protein-protein interactions computed using the DomainGA optimized domain-domain interaction scores. Vertical axis shows the percentage of the PPIs with interaction scores that were calculated by binning the total protein-protein interaction scores using unit bin sizes. Top: Inclusive set yeast PPI; Bottom: Closed set human PPI.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC1919395&req=5

Figure 6: Comparison of the strengths of the MIPS positive (red line with squares) and negative (blue line with circles) protein-protein interactions computed using the DomainGA optimized domain-domain interaction scores. Vertical axis shows the percentage of the PPIs with interaction scores that were calculated by binning the total protein-protein interaction scores using unit bin sizes. Top: Inclusive set yeast PPI; Bottom: Closed set human PPI.
Mentions: As discussed in Part A above, a rationale behind the presented research was the lack of discriminatory power of the InterDom domain-domain interaction scores. To further evaluate the DomainGA method's performance, we have performed a similar analysis using our interaction scores. Figure 6 reports the distributions of the predicted yeast PPI scores obtained using the domain-domain interaction scores obtained in the inclusive 867 parameter study. Using the same optimized parameter values, as in the cross-verification study reported above, Figure 6 also reports the predicted score distribution for the human interactome for the closed PPI dataset. For both cases, distributions for the positive and negative PPI scores are clearly well separated indicating that, in terms of having discriminatory power, our DomainGA method significantly improves on the InterDom scores.

Bottom Line: Knowing which proteins exist in a certain organism or cell type and how these proteins interact with each other are necessary for the understanding of biological processes at the whole cell level.Obtained domain interaction scores are then used to predict whether a pair of proteins interacts.We envision the DomainGA as a first step of a multiple tier approach to constructing organism specific PPIs.

View Article: PubMed Central - HTML - PubMed

Affiliation: Computational Biology and Bioinformatics Group, Pacific Northwest National Laboratory, Richland, WA 99352, USA. mudita.singhal@pnl.gov <mudita.singhal@pnl.gov>

ABSTRACT

Background: Knowing which proteins exist in a certain organism or cell type and how these proteins interact with each other are necessary for the understanding of biological processes at the whole cell level. The determination of the protein-protein interaction (PPI) networks has been the subject of extensive research. Despite the development of reasonably successful methods, serious technical difficulties still exist. In this paper we present DomainGA, a quantitative computational approach that uses the information about the domain-domain interactions to predict the interactions between proteins.

Results: DomainGA is a multi-parameter optimization method in which the available PPI information is used to derive a quantitative scoring scheme for the domain-domain pairs. Obtained domain interaction scores are then used to predict whether a pair of proteins interacts. Using the yeast PPI data and a series of tests, we show the robustness and insensitivity of the DomainGA method to the selection of the parameter sets, score ranges, and detection rules. Our DomainGA method achieves very high explanation ratios for the positive and negative PPIs in yeast. Based on our cross-verification tests on human PPIs, comparison of the optimized scores with the structurally observed domain interactions obtained from the iPFAM database, and sensitivity and specificity analysis; we conclude that our DomainGA method shows great promise to be applicable across multiple organisms.

Conclusion: We envision the DomainGA as a first step of a multiple tier approach to constructing organism specific PPIs. As it is based on fundamental structural information, the DomainGA approach can be used to create potential PPIs and the accuracy of the constructed interaction template can be further improved using complementary methods. Explanation ratios obtained in the reported test case studies clearly show that the false prediction rates of the template networks constructed using the DomainGA scores are reasonably low, and the erroneous predictions can be filtered further using supplementary approaches such as those based on literature search or other prediction methods.

Show MeSH
Related in: MedlinePlus