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A computational interactome and functional annotation for the human proteome

  1. José Ignacio Garzón
  2. Lei Deng
  3. Diana Murray
  4. Sagi Shapira
  5. Donald Petrey
  6. Barry Honig  Is a corresponding author
  1. Columbia University, United States
  2. Central South University, China
Tools and Resources
Cite this article as: eLife 2016;5:e18715 doi: 10.7554/eLife.18715
7 figures, 5 tables, 26 data sets and 2 additional files

Figures

Overview of PrePPI prediction evidence and methodology.

Each box represents a type of interaction evidence used in PrePPI. Those titled with a green background use structural information and those with a blue background do not. Contained in each box is a visual outline of how that type of evidence is used (details are provided in the text and Materials and methods). Overall, an interaction between two given proteins A and B is inferred based on some similarity to others that are known to interact (yellow double arrows) or if they have other properties that correlate in some way (orange double arrows). A red 'X' indicates that an interaction between two proteins does not exist.

https://doi.org/10.7554/eLife.18715.002
PrePPI prediction performance.

(a) ROC curves assessing PrePPI prediction performance using different reference sets. The y-axis shows the fraction of interactions recovered from a positive reference set (TPR, true positive rate) vs. the fraction recovered from a corresponding negative reference set (FPR, false positive rate). The solid grey and black lines are based on the human HC and N reference sets defined in Materials and methods and compare performance based only on the sources of evidence used in the previous version of PrePPI (grey) and incorporating the new sources of evidence (black). The dashed lines are based on the PRS/RRS reference sets described in the text. The inset shows recovery in the low FPR region. (b) Fraction of PRS (solid line) and RRS (dashed line) interactions recovered with an LR above the cutoff shown on the x-axis. Solid horizontal lines show the fraction of the PRS sets recovered by the Y2H (dark purple) and BioPlex (light purple) databases.

https://doi.org/10.7554/eLife.18715.003
Figure 2—source data 1

PrePPI LRs for interactions in the HC, PRS and RRS reference interaction sets.

https://doi.org/10.7554/eLife.18715.004
PrePPI performance with and without homology models.

The red line shows PrePPI performance using only crystal structures available in the PDB for individual proteins. The black line shows the performance if both homology models and crystal structures are used.

https://doi.org/10.7554/eLife.18715.006
Predictions between proteins annotated as paralogs of proteins in the template.

Panels (a) and (b) show the numbers of predictions (y-axis) as a function of LR (x-axis). In Panel (a) the LR is based only on the SM term and in Panel (b) is based on all sources of evidence. Panel (c) and (d) show the probability (y-axis) of interactions being in the HC set as a function of LR (x-axis). In Panel (c) the LR is based only the SM score and in Panel (d) it is based on all sources of evidence.

https://doi.org/10.7554/eLife.18715.007
Functional relationships of PrePPI predicted interaction partners.

(a) Percentage of pairs of proteins (y-axis) that are predicted to interact and that share the GO biological process term on the x-axis. Green bars are for PrePPI predictions, pink for the STRING database, purple for the human HC reference set, and black bars are for a random set of protein pairs. (b) The solid orange line shows the fraction of CORUM complexes (y-axis) recovered as a function of LR (x-axis). The dashed line shows the recovery of randomly generated complexes with the same number of subunits as those in CORUM. The horizontal lines show the percentage of CORUM complexes recovered by the Y2H (dark purple) and BioPlex (light purple) interaction sets. The vertical line shows the point on the x-axis with LR=600.

https://doi.org/10.7554/eLife.18715.008
Figure 5—source data 1

List of interactions in the HC reference set that share a GO biological process term at the second level.

https://doi.org/10.7554/eLife.18715.009
Figure 5—source data 2

List of PrePPI predicted interactions that share a GO biological process term at the second level.

https://doi.org/10.7554/eLife.18715.010
Figure 5—source data 3

List of interactions in the STRING database that share a GO biological process term at the second level.

https://doi.org/10.7554/eLife.18715.011
Figure 5—source data 4

List of CORUM complexes recovered by PrePPI.

https://doi.org/10.7554/eLife.18715.012
Gene set enrichment using the PrePPI interactome.

(A) To infer a function for a given protein, Q, all proteins in the human proteome, Ii, are placed in a list and sorted according to the interaction LR between Ii and Q. This list is then searched for gene sets associated with a given GO annotation enriched among the high-scoring interactors of Q. In the example in panel A, Gene Set 1 would be enriched whereas Gene Sets 2 and 3 would not be, since the proteins in those sets are either evenly distributed throughout the ranked list or clustered with proteins that are unlikely to interact with Q. (B) Histogram showing the position, in the list of enriched gene sets, of the first set associated with a known GO annotation for Q. Gene sets are ranked according to their enrichment at the top of LR-ranked list of interactors {Ii} of Q. (C) Top ranked gene sets found for two examples, BRCA2 and PEX1.

https://doi.org/10.7554/eLife.18715.013
ROC plots comparing interaction prediction performance based on orthology for individual databases indicated in the legend.

Three bins are defined for any given database, representing whether there 0, 1 or >1 orthologs of two given proteins reported to interact in some other species. LR’s for each bin are trained using the yeast HC and N reference sets of interactions. ROC curves are plotted for the human HC and, for this analysis only, a negative reference set consisting of ~200M pairs of proteins for which there is no literature evidence of an interaction.

https://doi.org/10.7554/eLife.18715.016

Tables

Table 1

Contributions of different sources of evidence to PrePPI performance. The second column shows the number of reliable predictions that can be made using only the evidence identified in the first column. The third column shows the number of predictions that can be made without the evidence shown in the first column, but including all other types of evidence. Note that excluding the OR evidence results in more interactions than using all evidence. This is because there are many interactions for which the LR based on orthology is less than 1. Rows with a green background indicate evidence types that use structural information and rows with a blue background evidence types that are based on non-structural information.

https://doi.org/10.7554/eLife.18715.005

Evidence

Number of interactions from this source alone

Number of interactions from all other sources

All

1,354,008

0

SM

48,090

675,965

PrP

1396

1,041,951

SM,PrP

49,457

192,315

PR

0

1,022,829

SM,PrP,PR

127,140

114,970

OR

624

1,388,341

PP

0

802,762

CE

0

726,592

GO

0

732,198

OR,PP,CE,GO

114,970

127,140

Table 2

Interaction database overlap. Each cell shows the number of interactions shared by the databases indicated in blue at the top and right. Green boxes along the diagonal show the total number of interactions in a single database.

https://doi.org/10.7554/eLife.18715.014

PrePPI

Y2H

BioPlex

PIP

I2D Ophid

HumanNet

String

Comp. All

Hum.Exp.

1,354,007

972

5364

17,639

67,556

76,905

123,457

212,463

44,864

PrePPI

13,584

425

140

13,470

804

918

13,584

1777

Y2H

56,553

918

5689

4361

5549

56,553

4763

BioPlex

44,148

6253

10,324

703

44,148

5154

PIP

296,008

56,584

53,178

296,008

160,581

I2D Ophid

458,518

58,512

458,518

44,047

HumanNet

311,635

311,635

45,890

String

1,004,622

162,065

Comp. All

169,368

Hum. Exp.

Table 3

Unique database interactions. Each cell shows the number of interactions contained in the database indicated on the left in blue, but not in the database indicated at the top in blue. Green boxes show the total number of interactions in a single database

https://doi.org/10.7554/eLife.18715.015

PrePPI

Y2H

BioPlex

PIP

I2D Ophid

HumanNet

String

Comp.All

Human Exp.

PrePPI

1,354,007

1,353,035

1,348,643

1,336,368

1,286,451

1,277,102

1,230,550

1,141,544

1,309,143

Y2H

12,612

13,584

13,159

13,444

114

12,780

12,666

0

11,807

BioPlex

51,189

56,128

56,553

55,635

50,864

52,192

51,004

0

51,790

PIP

26,509

44,008

43,230

44,148

37,895

33,824

36,245

0

38,994

I2d Ophid

228,452

282,538

290,319

289,755

296,008

239,424

242,830

0

135,427

HumanNet

381,613

457,714

454,157

448,194

401,934

458,518

400,006

0

414,471

String

188,178

310,717

306,086

303,732

258,457

253,123

311,635

0

265,745

Comp. All

792,159

991,038

948,069

960,474

708,614

546,104

692,987

1,004,622

842,557

Human Exp.

124,504

167,591

164,605

164,214

8787

125,321

123,478

7303

169,368

Table 4

Accession numbers, species, Affymetrix ID and source database for the expression profile datasets used for the expression profile evidence.

https://doi.org/10.7554/eLife.18715.017

Accession

Species

Affymetrix ID

Source

Hsa.c4-0

Human

HG-U133_Plus_2

Coxpressdb

Hsa2.c1-0

Human

HuGene-1_0-st-v1

Coxpressdb

E-MTAB-62

Human

HG-U133A

ArrayExpress

Mmu.c3-0

Mouse

Mouse430_2

Coxpressdb

Rno.c2-0

Norway rat

Rat230_2

Coxpressdb

Gga.c2-0

Chicken

Chicken

Coxpressdb

Dre.c2-0

Zebrafish

Zebrafish

Coxpressdb

Dme.c2-0

Fruit fly

Drosophila_2

Coxpressdb

Cel.c2-0

Nematoda

Celegans

Coxpressdb

Mcc.c1-0

Rhesus monkey

Rhesus

Coxpressdb

Cfa.c1-0

Dog

Canine_2

Coxpressdb

Sce.c1-0

Budding yeast

Yeast_2

Coxpressdb

Spo.c1-0

Fission yeast

Yeast_2

Coxpressdb

Table 5

Contingency tables for analyzing enrichment of SNPs in modeled interfaces. For both the disease-associated and benign sets, the numbers in parentheses are as follows: n11 is the number of SNPs that are interfacial, n10 is the number that are not interfacial, n01 is the number of interfacial unmutated residues and n00 is the number of non-interfacial, unmutated residues. These numbers are used in the odds-ratio and Z-score calculation as described in the 'SNP Analysis' section.

https://doi.org/10.7554/eLife.18715.018

Disease

Interfacial

Non-interfacial

Mutated

12,151 (n11)

61,401 (n10)

Non-mutated

298,442 (n01)

2,387,146 (n00)

Benign

Mutated

3554 (n11)

64,656 (n10)

Non-mutated

845,876 (n01)

8,221,708 (n00)

Data availability

The following previously published data sets were used
  1. 1
    The Database of Interacting Proteins:
    1. Salwinski L
    2. Miller CS
    3. Smith AJ
    4. Pettit FK
    5. Bowie JU
    6. Eisenberg D
    (2004)
    Available at the Database of Interacting Proteins (http://dip.mbi.ucla.edu/dip/).
  2. 2
    MINT: the Molecular INTeraction database
    1. Chatr-Aryamontri A
    2. Ceol A
    3. Palazzi LM
    4. Nardelli G
    5. Schneider MV
    6. Castagnoli L
    7. Cesareni G
    (2007)
    Available at the IntAct website (http://www.ebi.ac.uk/intact/).
  3. 3
  4. 4
    MIPS
    1. Mewes HW
    2. Albermann K
    3. Heumann K
    4. Liebl S
    5. Pfeiffer F
    (1997)
    Available at the MIPS Mammalian Protein-Protein Interaction Database (http://mips.helmholtz-muenchen.de/proj/ppi/).
  5. 5
  6. 6
    HumanNet
    1. Franceschini A
    2. Szklarczyk D
    3. Frankild S
    4. Kuhn M
    5. Simonovic M
    6. Roth A
    7. Lin J
    8. Minguez P
    9. Bork P
    10. von Mering C
    11. Jensen LJ
    (2013)
    Available at HumanNet (http://www.functionalnet.org/humannet/about.html).
  7. 7
    PIPs
    1. McDowall MD
    2. Scott MS
    3. Barton GJ
    (2009)
    Available at Human Protein-Protein Interaction Prediction website (http://www.compbio.dundee.ac.uk/www-pips/).
  8. 8
    OPHID
    1. Brown KR1
    2. Jurisica I
    (2007)
    Available at I2D Interologous Interaction Database (http://ophid.utoronto.ca/ophidv2.204/index.jsp).
  9. 9
    KEGG
    1. Kanehisa M
    2. Goto S
    (2000)
    Available at KEGG PATHWAY Database (http://www.genome.jp/kegg/).
  10. 10
    eggNOG
    1. Huerta-Cepas J
    2. Szklarczyk D
    3. Forslund K
    4. Cook H
    5. Heller D
    6. Walter MC
    7. Rattei T
    8. Mende DR
    9. Sunagawa S
    10. Kuhn M
    11. Jensen LJ
    12. von Mering C
    13. Bork P
    (2016)
    Available at eggNOG database (http://eggnogdb.embl.de/#/app/home).
  11. 11
    Hogenom
    1. Penel S
    2. Arigon AM
    3. Dufayard JF
    4. Sertier AS
    5. Daubin V
    6. Duret L
    7. Gouy M
    8. Perrière G
    (2009)
    Available at Hogenom database (ftp://pbil.univ-lyon1.fr/pub/hogenom/).
  12. 12
    OMA
    1. Altenhoff AM
    2. Škunca N
    3. Glover N
    4. Train CM
    5. Sueki A
    6. Piližota I
    7. Gori K
    8. Tomiczek B
    9. Müller S
    10. Redestig H
    11. Gonnet GH
    12. Dessimoz C
    (2015)
    Available at OMA Orthology database (http://omabrowser.org/oma/home/).
  13. 13
    OrthoDB
    1. Kriventseva EV
    2. Tegenfeldt F
    3. Petty TJ
    4. Waterhouse RM
    5. Simão FA
    6. Pozdnyakov IA
    7. Ioannidis P
    8. Zdobnov EM
    (2015)
    Available at OrthoDB database (http://www.orthodb.org/).
  14. 14
    Coexpression data for C. elegans
    1. Okamura Y
    2. Aoki Y
    3. Obayashi T
    4. Tadaka S
    5. Ito S
    6. Narise T
    7. Kinoshita K
    (2015)
    Publicly available at Coexpressdb (accession no: Cel.c2-0).
  15. 15
    Coexpression data for dog
    1. Okamura Y
    2. Aoki Y
    3. Obayashi T
    4. Tadaka S
    5. Ito S
    6. Narise T
    7. Kinoshita K
    (2015)
    Publicly available at Coexpressdb (accession no: Cfa.c1-0).
  16. 16
    Coexpression data for fruit fly
    1. Okamura Y
    2. Aoki Y
    3. Obayashi T
    4. Tadaka S
    5. Ito S
    6. Narise T
    7. Kinoshita K
    (2015)
    Publicly available at Coexpressdb (accession no: Dme.c2-0).
  17. 17
    Coexpression data for zebrafish
    1. Okamura Y
    2. Aoki Y
    3. Obayashi T
    4. Tadaka S
    5. Ito S
    6. Narise T
    7. Kinoshita K
    (2015)
    Publicly available at Coexpressdb (accession no: Dre.c2-0).
  18. 18
    Coexpression data for chicken
    1. Okamura Y
    2. Aoki Y
    3. Obayashi T
    4. Tadaka S
    5. Ito S
    6. Narise T
    7. Kinoshita K
    (2015)
    Publicly available at Coexpressdb (accession no: Gga.c2-0).
  19. 19
    Coexpression data for human
    1. Okamura Y
    2. Aoki Y
    3. Obayashi T
    4. Tadaka S
    5. Ito S
    6. Narise T
    7. Kinoshita K
    (2015)
    Publicly available at Coexpressdb (accession no: Hsa.c4-0).
  20. 20
    Coexpression data for human
    1. Okamura Y
    2. Aoki Y
    3. Obayashi T
    4. Tadaka S
    5. Ito S
    6. Narise T
    7. Kinoshita K
    (2015)
    Publicly available at Coexpressdb (accession no: Hsa2.c1-0).
  21. 21
    Coexpression data for rhesus monkey
    1. Okamura Y
    2. Aoki Y
    3. Obayashi T
    4. Tadaka S
    5. Ito S
    6. Narise T
    7. Kinoshita K
    (2015)
    Publicly available at Coexpressdb (accession no: Mcc.c1-0).
  22. 22
    Coexpression data for mouse
    1. Okamura Y
    2. Aoki Y
    3. Obayashi T
    4. Tadaka S
    5. Ito S
    6. Narise T
    7. Kinoshita K
    (2015)
    Publicly available at Coexpressdb (accession no: Mmu.c3-0).
  23. 23
    Coexpression data for Norway rat
    1. Okamura Y
    2. Aoki Y
    3. Obayashi T
    4. Tadaka S
    5. Ito S
    6. Narise T
    7. Kinoshita K
    (2015)
    Publicly available at Coexpressdb (accession no: Rno.c2-0).
  24. 24
    Coexpression data for budding yeast
    1. Okamura Y
    2. Aoki Y
    3. Obayashi T
    4. Tadaka S
    5. Ito S
    6. Narise T
    7. Kinoshita K
    (2015)
    Publicly available at Coexpressdb (accession no: Sce.c1-0).
  25. 25
    Coexpression data for fission yeast
    1. Okamura Y
    2. Aoki Y
    3. Obayashi T
    4. Tadaka S
    5. Ito S
    6. Narise T
    7. Kinoshita K
    (2015)
    Publicly available at Coexpressdb (accession no: Spo.c1-0).
  26. 26

Additional files

Supplementary file 1

UniProt codes for interacting proteins that are related to the same disease.

Each line shows a pair of proteins annotated as associated with the same disease in the ClinVar database.

https://doi.org/10.7554/eLife.18715.019
Supplementary file 2

LRs for orthology, partner redundancy and expression profile interaction evidence.

https://doi.org/10.7554/eLife.18715.020

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