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GreedyPlus: An Algorithm for the Alignment of Interface Interaction Networks.

Law B, Bader GD - Sci Rep (2015)

Bottom Line: The increasing ease and accuracy of protein-protein interaction detection has resulted in the ability to map the interactomes of multiple species.We now have an opportunity to compare species to better understand how interactomes evolve.GreedyPlus is fast and simple, allowing for easy customization of behaviour, yet still capable of generating biologically meaningful network alignments.

View Article: PubMed Central - PubMed

Affiliation: 1] Department of Computer Science, University of Toronto, Toronto, ON, Canada [2] The Donnelly Centre, University of Toronto, Toronto, ON, Canada.

ABSTRACT
The increasing ease and accuracy of protein-protein interaction detection has resulted in the ability to map the interactomes of multiple species. We now have an opportunity to compare species to better understand how interactomes evolve. As DNA and protein sequence alignment algorithms were required for comparative genomics, network alignment algorithms are required for comparative interactomics. A number of network alignment methods have been developed for protein-protein interaction networks, where proteins are represented as vertices linked by edges if they interact. Recently, protein interactions have been mapped at the level of amino acid positions, which can be represented as an interface-interaction network (IIN), where vertices represent binding sites, such as protein domains and short sequence motifs. However, current algorithms are not designed to align these networks and generally fail to do so in practice. We present a greedy algorithm, GreedyPlus, for IIN alignment, combining data from diverse sources, including network, protein and binding site properties, to identify putative orthologous relationships between interfaces in available worm and yeast data. GreedyPlus is fast and simple, allowing for easy customization of behaviour, yet still capable of generating biologically meaningful network alignments.

No MeSH data available.


Related in: MedlinePlus

Percent RPO and EA achieved for pairwise yeast species alignments.Using the full set of similarity features and no optimization, GreedyPlus was run on each pair of yeast networks (see Methods). The percent of represented protein orthologies and edges aligned for each alignment was retrieved and plotted on the same scale.
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f8: Percent RPO and EA achieved for pairwise yeast species alignments.Using the full set of similarity features and no optimization, GreedyPlus was run on each pair of yeast networks (see Methods). The percent of represented protein orthologies and edges aligned for each alignment was retrieved and plotted on the same scale.

Mentions: As these species are more closely related than C. elegans and S. cerevisiae, we found, as expected, that GreedyPlus is able to recover more orthologous pairs in these pairwise alignments. When using a minimal set of similarity features with optimized weights (see Table 4), GreedyPlus alignments almost always recovered more than 70% of the known orthologous protein pairs while still maintaining a high percentage of edges aligned (mean 50.6% of maximum possible, see Fig. 7). Using all the gathered similarity features, except TCSS (26 features), GreedyPlus still performed well, aligning an average of 56% of orthologous protein pairs (see Fig. 8).


GreedyPlus: An Algorithm for the Alignment of Interface Interaction Networks.

Law B, Bader GD - Sci Rep (2015)

Percent RPO and EA achieved for pairwise yeast species alignments.Using the full set of similarity features and no optimization, GreedyPlus was run on each pair of yeast networks (see Methods). The percent of represented protein orthologies and edges aligned for each alignment was retrieved and plotted on the same scale.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4499810&req=5

f8: Percent RPO and EA achieved for pairwise yeast species alignments.Using the full set of similarity features and no optimization, GreedyPlus was run on each pair of yeast networks (see Methods). The percent of represented protein orthologies and edges aligned for each alignment was retrieved and plotted on the same scale.
Mentions: As these species are more closely related than C. elegans and S. cerevisiae, we found, as expected, that GreedyPlus is able to recover more orthologous pairs in these pairwise alignments. When using a minimal set of similarity features with optimized weights (see Table 4), GreedyPlus alignments almost always recovered more than 70% of the known orthologous protein pairs while still maintaining a high percentage of edges aligned (mean 50.6% of maximum possible, see Fig. 7). Using all the gathered similarity features, except TCSS (26 features), GreedyPlus still performed well, aligning an average of 56% of orthologous protein pairs (see Fig. 8).

Bottom Line: The increasing ease and accuracy of protein-protein interaction detection has resulted in the ability to map the interactomes of multiple species.We now have an opportunity to compare species to better understand how interactomes evolve.GreedyPlus is fast and simple, allowing for easy customization of behaviour, yet still capable of generating biologically meaningful network alignments.

View Article: PubMed Central - PubMed

Affiliation: 1] Department of Computer Science, University of Toronto, Toronto, ON, Canada [2] The Donnelly Centre, University of Toronto, Toronto, ON, Canada.

ABSTRACT
The increasing ease and accuracy of protein-protein interaction detection has resulted in the ability to map the interactomes of multiple species. We now have an opportunity to compare species to better understand how interactomes evolve. As DNA and protein sequence alignment algorithms were required for comparative genomics, network alignment algorithms are required for comparative interactomics. A number of network alignment methods have been developed for protein-protein interaction networks, where proteins are represented as vertices linked by edges if they interact. Recently, protein interactions have been mapped at the level of amino acid positions, which can be represented as an interface-interaction network (IIN), where vertices represent binding sites, such as protein domains and short sequence motifs. However, current algorithms are not designed to align these networks and generally fail to do so in practice. We present a greedy algorithm, GreedyPlus, for IIN alignment, combining data from diverse sources, including network, protein and binding site properties, to identify putative orthologous relationships between interfaces in available worm and yeast data. GreedyPlus is fast and simple, allowing for easy customization of behaviour, yet still capable of generating biologically meaningful network alignments.

No MeSH data available.


Related in: MedlinePlus