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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

A zoom-in of the “optimal” GreedyPlus alignment of worm and yeast SH3-mediated IINs, consisting of the three worm CYK1 vertices and all their neighbours.Domain interfaces are represented by triangular vertices, ligand interfaces by circular vertices. Yellow vertices are aligned and orthologous; green vertices and edges are aligned, red are unaligned from worm, blue are unaligned from yeast. Vertex size indicates score.
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f6: A zoom-in of the “optimal” GreedyPlus alignment of worm and yeast SH3-mediated IINs, consisting of the three worm CYK1 vertices and all their neighbours.Domain interfaces are represented by triangular vertices, ligand interfaces by circular vertices. Yellow vertices are aligned and orthologous; green vertices and edges are aligned, red are unaligned from worm, blue are unaligned from yeast. Vertex size indicates score.

Mentions: We also observe that while BNI1 is an interaction partner with BZZ1, with two sites targeted by the two BZZ1 SH3 domains, its worm ortholog CYK-1 does not interact with either EPHX-1 or SDPN-1. This non-interaction is also supported by iRefIndex. In our worm network, the respective CYK-1 sites are targeted only by Y106G6H.14 and TOCA-1, neither of which have functional annotations in GO, though TOCA-1 is indicated to be involved in endocytosis as well3637 (see Fig. 6). This extensive interaction rewiring suggests that IIN alignment approaches based on maximizing topological overlap may not be appropriate in identifying orthologs.


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

Law B, Bader GD - Sci Rep (2015)

A zoom-in of the “optimal” GreedyPlus alignment of worm and yeast SH3-mediated IINs, consisting of the three worm CYK1 vertices and all their neighbours.Domain interfaces are represented by triangular vertices, ligand interfaces by circular vertices. Yellow vertices are aligned and orthologous; green vertices and edges are aligned, red are unaligned from worm, blue are unaligned from yeast. Vertex size indicates score.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f6: A zoom-in of the “optimal” GreedyPlus alignment of worm and yeast SH3-mediated IINs, consisting of the three worm CYK1 vertices and all their neighbours.Domain interfaces are represented by triangular vertices, ligand interfaces by circular vertices. Yellow vertices are aligned and orthologous; green vertices and edges are aligned, red are unaligned from worm, blue are unaligned from yeast. Vertex size indicates score.
Mentions: We also observe that while BNI1 is an interaction partner with BZZ1, with two sites targeted by the two BZZ1 SH3 domains, its worm ortholog CYK-1 does not interact with either EPHX-1 or SDPN-1. This non-interaction is also supported by iRefIndex. In our worm network, the respective CYK-1 sites are targeted only by Y106G6H.14 and TOCA-1, neither of which have functional annotations in GO, though TOCA-1 is indicated to be involved in endocytosis as well3637 (see Fig. 6). This extensive interaction rewiring suggests that IIN alignment approaches based on maximizing topological overlap may not be appropriate in identifying orthologs.

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