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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

GreedyPlus alignment of worm and yeast SH3-mediated IINs, using only protein BLAST, EAW = 0.5.Domain interfaces are represented by triangular vertices, ligand interfaces by circular vertices. Yellow vertices are aligned and from orthologous proteins (OVPs), green vertices and edges are aligned but not orthologous, red are unaligned from worm, blue are unaligned from yeast. Vertex size indicates score. The GreedyPlus algorithm aligns many more edges than IsoRank, resulting in many fewer blue and red edges, as they are replaced by half as many green edges. However, there are still no large clusters of green, with red and blue edges dispersed throughout the alignment, indicating that interaction rewiring is both common and distributed.
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f3: GreedyPlus alignment of worm and yeast SH3-mediated IINs, using only protein BLAST, EAW = 0.5.Domain interfaces are represented by triangular vertices, ligand interfaces by circular vertices. Yellow vertices are aligned and from orthologous proteins (OVPs), green vertices and edges are aligned but not orthologous, red are unaligned from worm, blue are unaligned from yeast. Vertex size indicates score. The GreedyPlus algorithm aligns many more edges than IsoRank, resulting in many fewer blue and red edges, as they are replaced by half as many green edges. However, there are still no large clusters of green, with red and blue edges dispersed throughout the alignment, indicating that interaction rewiring is both common and distributed.

Mentions: Finally, GreedyPlus performs best on RPO and second-best on both OVP and EA (14 of 16, 18 of 22, and 291 of 466 respectively). It is also the only algorithm that performs evenly across the three metrics, with performance each at >60% of max, and thus generally performs the best in this comparison (see Fig. 3).


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

Law B, Bader GD - Sci Rep (2015)

GreedyPlus alignment of worm and yeast SH3-mediated IINs, using only protein BLAST, EAW = 0.5.Domain interfaces are represented by triangular vertices, ligand interfaces by circular vertices. Yellow vertices are aligned and from orthologous proteins (OVPs), green vertices and edges are aligned but not orthologous, red are unaligned from worm, blue are unaligned from yeast. Vertex size indicates score. The GreedyPlus algorithm aligns many more edges than IsoRank, resulting in many fewer blue and red edges, as they are replaced by half as many green edges. However, there are still no large clusters of green, with red and blue edges dispersed throughout the alignment, indicating that interaction rewiring is both common and distributed.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f3: GreedyPlus alignment of worm and yeast SH3-mediated IINs, using only protein BLAST, EAW = 0.5.Domain interfaces are represented by triangular vertices, ligand interfaces by circular vertices. Yellow vertices are aligned and from orthologous proteins (OVPs), green vertices and edges are aligned but not orthologous, red are unaligned from worm, blue are unaligned from yeast. Vertex size indicates score. The GreedyPlus algorithm aligns many more edges than IsoRank, resulting in many fewer blue and red edges, as they are replaced by half as many green edges. However, there are still no large clusters of green, with red and blue edges dispersed throughout the alignment, indicating that interaction rewiring is both common and distributed.
Mentions: Finally, GreedyPlus performs best on RPO and second-best on both OVP and EA (14 of 16, 18 of 22, and 291 of 466 respectively). It is also the only algorithm that performs evenly across the three metrics, with performance each at >60% of max, and thus generally performs the best in this comparison (see Fig. 3).

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