Complementarity of network and sequence information in homologous proteins
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Transcript of Complementarity of network and sequence information in homologous proteins
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Complementarity of network and sequence
information in homologous proteins
March, 2010
1Department of Computing, Imperial College London, London, UK2Department of Computer Science, University of California, Irvine, USA
International Symposium on Integrative Bioinformatics
Vesna Memišević2, Tijana Milenković2, and Nataša Pržulj1
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Motivation
• Genetic sequences – revolutionized understanding of biology• Non-sequence based data of importance, e.g.:
– secondary & tertiary structure of RNA have the dominant role in RNA function (tRNA: Gautheret et al., Comput. Appl. Biosci., 1990)(rRNA: Woese et al., Microbiological Reviews, 1983)
– Secondary structure-based approach – more effective at finding new functional RNAs than sequence-based alignments(Webb et al., Science, 2009)
• What about patterns of interconnections in PPI networks?– Can they complement the knowledge learned from genomic sequence?– Wiring patterns of duplicated proteins in PPI net – insights into evol. dist.?
– Does the information about homologues captured by PPI network topology differ from that captured by their sequence?
Nataša Prž[email protected]
.uk
2
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Background
• Homologs – descend from a common ancestor:
1. Paralogs: in the same species, evolve through gene duplication events
2. Orthologs: in different species, evolve through speciation events
3
Nataša Prž[email protected]
.uk
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Background
• Sequence-based homology data from: 1. Clusters of Orthologous Groups – COG[1]
2. KEGG Orthology System[2]
4
Nataša Prž[email protected]
.uk
[1] Tatusov et al., BMC Bioinformatics, 4(41), 2003.[2] Kanehisa et al., Nucleic Acids Res., 28:27–30, 2000.
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• Sequence-based homology data from: 1. Clusters of Orthologous Groups – COG[1]
• Proteins in different genomes – sequence compared for the best hits (BeTs)
• The graph of BeTs constructed
2. KEGG Orthology System[2]
5
Nataša Prž[email protected]
.uk
[1] Tatusov et al., BMC Bioinformatics, 4(41), 2003.[2] Kanehisa et al., Nucleic Acids Res., 28:27–30, 2000.
Background
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Background
• Sequence-based homology data from : 1. Clusters of Orthologous Groups – COG[1]
• Proteins in different genomes – sequence compared for the best hits (BeTs)
• The graph of BeTs constructed
2. KEGG Orthology System[2]
6
Nataša Prž[email protected]
.uk
[1] Tatusov et al., BMC Bioinformatics, 4(41), 2003.[2] Kanehisa et al., Nucleic Acids Res., 28:27–30, 2000.
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Background
• Sequence-based homology data from : 1. Clusters of Orthologous Groups – COG[1]
• Proteins in different genomes – sequence compared for the best hits (BeTs)
• The graph of BeTs constructed
• Triangles in it found
2. KEGG Orthology System[2]
7
Nataša Prž[email protected]
.uk
[1] Tatusov et al., BMC Bioinformatics, 4(41), 2003.[2] Kanehisa et al., Nucleic Acids Res., 28:27–30, 2000.
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Background
• Sequence-based homology data from : 1. Clusters of Orthologous Groups – COG[1]
• Proteins in different genomes – sequence compared for the best hits (BeTs)
• The graph of BeTs constructed
• Triangles in it found
2. KEGG Orthology System[2]
8
Nataša Prž[email protected]
.uk
[1] Tatusov et al., BMC Bioinformatics, 4(41), 2003.[2] Kanehisa et al., Nucleic Acids Res., 28:27–30, 2000.
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2 3
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Background
• Sequence-based homology data from : 1. Clusters of Orthologous Groups – COG[1]
• Proteins in different genomes – sequence compared for the best hits (BeTs)
• The graph of BeTs constructed
• Triangles in it found
• Triangles sharing a side merged into the groups of orthologs and paralogs
2. KEGG Orthology System[2]
9
Nataša Prž[email protected]
.uk
[1] Tatusov et al., BMC Bioinformatics, 4(41), 2003.[2] Kanehisa et al., Nucleic Acids Res., 28:27–30, 2000.
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10101010
Background
• Sequence-based homology data from : 1. Clusters of Orthologous Groups – COG[1]
• Proteins in different genomes – sequence compared for the best hits (BeTs)
• The graph of BeTs constructed
• Triangles in it found
• Triangles sharing a side merged into the groups of orthologs and paralogs
2. KEGG Orthology System[2]
10
Nataša Prž[email protected]
.uk
[1] Tatusov et al., BMC Bioinformatics, 4(41), 2003.[2] Kanehisa et al., Nucleic Acids Res., 28:27–30, 2000.
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Background
• Sequence-based homology data from : 1. Clusters of Orthologous Groups – COG[1]
• Proteins in different genomes – sequence compared for the best hits (BeTs)
• The graph of BeTs constructed
• Triangles in it found
• Triangles sharing a side merged into the groups of orthologs and paralogs
• No dependence on the absolute level of similarity between compared proteins
2. KEGG Orthology System[2]
11
Nataša Prž[email protected]
.uk
[1] Tatusov et al., BMC Bioinformatics, 4(41), 2003.[2] Kanehisa et al., Nucleic Acids Res., 28:27–30, 2000.
1 1’
2 3
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Background
• Sequence-based homology data from : 1. Clusters of Orthologous Groups – COG[1]
2. KEGG Orthology System[2]
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Nataša Prž[email protected]
.uk
[1] Tatusov et al., BMC Bioinformatics, 4(41), 2003.[2] Kanehisa et al., Nucleic Acids Res., 28:27–30, 2000.
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Background
• Sequence-based homology data from : 1. Clusters of Orthologous Groups – COG[1]
2. KEGG Orthology System[2]
• Sequences aligned
• If alignment score < 10-8 then 1 assigned as “similarity bit”
• Otherwise, 0 assigned as “similarity bit”
• “Bit vectors” constructed for a protein, over all proteins
• Graph constructed with nodes protein sequences and edges correlation coefficients of bit vectors of nodes
• Cliques found in the graph = orthology groups
[1] Tatusov et al., BMC Bioinformatics, 4(41), 2003.[2] Kanehisa et al., Nucleic Acids Res., 28:27–30, 2000. Nataša Pržulj
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Nataša Prž[email protected]
.uk
[1] Tatusov et al., BMC Bioinformatics, 4(41), 2003.[2] Kanehisa et al., Nucleic Acids Res., 28:27–30, 2000.
1 1’
2 3
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Background
• Sequence-based homology data from : 1. Clusters of Orthologous Groups – COG[1]
2. KEGG Orthology System[2]
• Sequences aligned
• If alignment score < 10-8 then 1 assigned as “similarity bit”
• Otherwise, 0 assigned as “similarity bit”
• “Bit vectors” constructed for a protein, over all proteins
• Graph constructed with nodes protein sequences and edges correlation coefficients of bit vectors of nodes
• Cliques found in the graph = orthology groups
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Nataša Prž[email protected]
.uk
[1] Tatusov et al., BMC Bioinformatics, 4(41), 2003.[2] Kanehisa et al., Nucleic Acids Res., 28:27–30, 2000.
1 1’
2 3
4
5
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Background
• Sequence-based homology data from : 1. Clusters of Orthologous Groups – COG[1]
2. KEGG Orthology System[2]
• Sequences aligned
• If alignment score < 10-8 then 1 assigned as “similarity bit”
• Otherwise, 0 assigned as “similarity bit”
• “Bit vectors” constructed for a protein, over all proteins
• Graph constructed with nodes protein sequences and edges correlation coefficients of bit vectors of nodes
• Cliques found in the graph = orthology groups
• Again, no dependence on absolute level of similarity
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Background
• Sequence-based homology data from :1. Clusters of Orthologous Groups – COG[1]
2. KEGG Orthology System[2]
• We examine yeast proteins only:• Extract all possible pairs of them in COG and
KEGG groups = “orthologous pairs” • There are 9,643 of unique such pairs
• What are their topological similarities within the PPI network?
16
Nataša Prž[email protected]
.uk
[1] Tatusov et al., BMC Bioinformatics, 4(41), 2003.[2] Kanehisa et al., Nucleic Acids Res., 28:27–30, 2000.
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Background
• Sequence-based homology data from :1. Clusters of Orthologous Groups – COG[1]
2. KEGG Orthology System[2]
• We examine yeast proteins only:• Extract all possible pairs of them in COG and
KEGG groups = “orthologous pairs” • There are 9,643 of unique such pairs
• What are their topological similarities within the PPI network?
17
Nataša Prž[email protected]
.uk
[1] Tatusov et al., BMC Bioinformatics, 4(41), 2003.[2] Kanehisa et al., Nucleic Acids Res., 28:27–30, 2000.
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Background
• Sequence-based homology data from :1. Clusters of Orthologous Groups – COG[1]
2. KEGG Orthology System[2]
• We examine yeast proteins only:• Extract all possible pairs of them in COG and
KEGG groups = “orthologous pairs” • There are 9,643 of unique such pairs
• What are their topological similarities within the PPI network?
18
Nataša Prž[email protected]
.uk
[1] Tatusov et al., BMC Bioinformatics, 4(41), 2003.[2] Kanehisa et al., Nucleic Acids Res., 28:27–30, 2000.
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Background
• Sequence-based homology data from :1. Clusters of Orthologous Groups – COG[1]
2. KEGG Orthology System[2]
• Previous network-topology assisted approaches:
• Network-alignment-based (ISORank)• Yosef, Sharan & Noble, Bioinformatics, 2008
(hybrid Rankprop) Rely heavily on sequence information Use only limited amount of network topology
19
Nataša Prž[email protected]
.uk
[1] Tatusov et al., BMC Bioinformatics, 4(41), 2003.[2] Kanehisa et al., Nucleic Acids Res., 28:27–30, 2000.
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Our Method
• We examine yeast proteins only:• Extract all possible pairs of them in COG and
KEGG groups = “orthologous pairs” • There are 9,643 of unique such pairs
• What are their topological similarities within the PPI network?
• PPI networks are noisy• We analyze the high-confidence part of yeast PPI
network by Collins et al.[3]: 9,074 edges amongst 1,621 proteins
• Focus on proteins with degree > 3 to avoid noisy PPIs• There are 175 orthologous pairs amongst 181
proteins
20
Nataša Prž[email protected]
.uk
[3] Collins et al., Molecular and Cellular Proteomics, 6(3):439–450, 2008
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Our Method
Nataša Prž[email protected]
.uk
• Does PPI network topology contain homology information? Are similarly wired proteins homologous?
• Does homology information obtained from network topology differ from that obtained from sequence?
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Our Method
Nataša Prž[email protected]
.uk
N. Przulj, D. G. Corneil, and I. Jurisica, “Modeling Interactome: Scale Free or Geometric?,” Bioinformatics, vol. 20, num. 18, pg. 3508-3515, 2004.
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232323
Our Method
Nataša Prž[email protected]
.uk
N. Przulj, D. G. Corneil, and I. Jurisica, “Modeling Interactome: Scale Free or Geometric?,” Bioinformatics, vol. 20, num. 18, pg. 3508-3515, 2004.
Induced Of any frequency
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Our Method
Nataša Prž[email protected]
.uk
Generalize node degree
N. Przulj, “Biological Network Comparison Using Graphlet Degree Distribution,” ECCB, Bioinformatics, vol. 23, pg. e177-e183, 2007.
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Our Method
Nataša Prž[email protected]
.uk
N. Przulj, “Biological Network Comparison Using Graphlet Degree Distribution,” ECCB, Bioinformatics, vol. 23, pg. e177-e183, 2007.
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Our Method
Nataša Prž[email protected]
.uk
N. Przulj, “Biological Network Comparison Using Graphlet Degree Distribution,” ECCB, Bioinformatics, vol. 23, pg. e177-e183, 2007.
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T. Milenkovic and N. Przulj, “Uncovering Biological Network Function via Graphlet Degree Signatures”, Cancer Informatics, vol. 4, pg. 257-273, 2008.
Graphlet Degree (GD) vectors, or “node signatures”
Nataša Prž[email protected]
.uk
Our Method
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Nataša Prž[email protected]
.uk
Our Method
Similarity measure between nodes’ Graphlet Degree vectors
T. Milenkovic and N. Przulj, “Uncovering Biological Network Function via Graphlet Degree Signatures”, Cancer Informatics, vol. 4, pg. 257-273, 2008.
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Nataša Prž[email protected]
.uk
Our Method
T. Milenkovic and N. Przulj, “Uncovering Biological Network Function via Graphlet Degree Signatures”, Cancer Informatics, vol. 4, pg. 257-273, 2008.
Signature Similarity Measure
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Results
Nataša Prž[email protected]
.uk
• Orthologous pairs often perform the same or similar function.
• Does GD vector similarity (GDS) imply shared biological function?
• Note: most GO annotations were obtained from sequences Similar topology ~ similar sequence ~ similar function
Network Topology
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Results
Nataša Prž[email protected]
.uk
• Orthologous proteins have high GD vector similarities Network Topology
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Results
Nataša Prž[email protected]
.uk
• Orthologous proteins have high GD vector similarities
p-value < 0.05
85%
Network Topology
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Results
Nataša Prž[email protected]
.uk
• Orthologous proteins have high GD vector similarities
p-value < 0.05
85%
> 20% of orthologous pairs have GDS > 85%
Network Topology
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Results
Nataša Prž[email protected]
.uk
• PPI networks are noisy• Random edge additions, deletions and rewirings in the PPI
net
Network Topology – Robustness
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Results
Nataša Prž[email protected]
.uk
• PPI networks are noisy• Random edge additions, deletions and rewirings in the PPI
net
Network Topology – Robustness
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Results
Nataša Prž[email protected]
.uk
• PPI networks are noisy• Random edge additions, deletions and rewirings in the PPI
net
Network Topology – Robustness
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Results
Nataša Prž[email protected]
.uk
• Sequence identities for the 175 orthologous pairsSequence
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Results
Nataša Prž[email protected]
.uk
• Sequence identities for the 175 orthologous pairsSequence
~70% orth. pairs have seq. identity < 35%
35%
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Results
Nataša Prž[email protected]
.uk
• Sequence identities for the 175 orthologous pairsSequence
~20% orth. pairs have seq. identity > 90%
90%
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Results
Nataša Prž[email protected]
.uk
• Sequence identities for the 175 orthologous pairsSequence
“Twilight zone” for homology
20-35%
~70% orth. pairs have seq. identity < 35% No dependence on the absolute similarity COG& KEGG, but triangles in the graph of best matches
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85%
20% 35%
~20% of orthologous pairs have signature similarities
above 85% (35 pairs)
~30% of orthologous pairs have sequence identities above 35% (53 pairs)
Overlap: 22 pairs (~60% of the smaller set) Sequence and network topology somewhat complementary slices of homology information
Nataša Prž[email protected]
.uk
ResultsComparison:
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Results
Nataša Prž[email protected]
.uk
• 59 of the yeast ribosomal proteins – retained two genomic copies
• Are duplicated proteins functionally redundant?• No: have different genetic requirements for their
assembly and localization so are functionally distinct• Also note: avg sequence identity of struct. similar prots
~8-10%• Two pairs with identical sequence:
Examples
100% sequence identity 50% signature similarity
Degrees 25 and 5
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Results
Nataša Prž[email protected]
.uk
• 59 of the yeast ribosomal proteins – retained two genomic copies
• Are duplicated proteins functionally redundant?• No: have different genetic requirements for their
assembly and localization so are functionally distinct• Also note: avg sequence identity of struct. similar prots
~8-10%• Two pairs with identical sequence:
Examples
100% sequence identity 65% signature similarity
Degrees 54 and 9
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Conclusions
• Homology information captured by PPI network topology differs from that captured by sequence
• Complementary sources for identifying homologs
Future work:• Could topological similarity be used to
identify orthologs from best-hits graph analysis as done for sequences?
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Acknowledgements
This project was supported by the NSF CAREER
IIS-0644424 grant
Nataša Prž[email protected]
.uk