Curso de la Escuela Complutense de Verano 2005

Bioinformática y Biología Computacional

Redes de Interacciones entre Proteínas

Florencio Pazos

http://pdg.cnb.uam.es/pazos/cursos/Verano_UCM_05/

Florencio Pazos CabaleiroProtein Design Group (CNB-CSIC)pazos@cnb.uam.es

Redes de Interacciones entre Proteínas

- Biología de sistemas y redes biológicas- Determinación experimental a gran escala del interactoma- Extracción automática de interacciones descritas en la literatura- Predicción computacional de interacciones- Propiedades de las redes de interacción- Combinación con otra información- Bibliografía general- Práctica

Florencio Pazos CabaleiroProtein Design Group (CNB-CSIC)pazos@cnb.uam.es

Bioinformática orientada al componente individual

MREYKLVVLGSGGVGKSALTVQFVQGIFVDEYDPTIEDSYRKQVEVDCQQCMLEILDTAGTEQFTAMRDLYMKNGQGFALVYSITAQSTFNDLQDLREQILRVKDTEDVPMILVGNKCDLEDERVVGKEQGQNLARQWCNCAFLESSAKSKINVNEIFYDLVRQINR

MLEILDTAGTEQFTAMRDLYMKNGQGFALVYSITAQSTFNDLQDLREQILRVKDTEDVPMILVGNKCDLEDERV

Bioinformática y Sistemas Complejos en BiologíaBiología de Sistemas vs. Biología Molecular

• Visión reduccionista• Objeto de estudio: componentes (genes proteínas).• Biología Molecular• Propiedades de los componentes -> conocimiento biológico• Enfermedades (dianas/marcadores = proteínas/genes).

• Visión desde el punto de vista de sistemas complejos• Objeto de estudio: redes, relaciones, propiedades emergentes

(no propiedades de componentes individuales).• Biología de Sistemas• Propiedades “globales” (emergentes, etc.) -> conocimiento biológico• Enfermedades (dianas/marcadores = redes, patrones complejos).

??

Michael Liebman (www.bioitworld.com)

¿Es suficiente la visión reduccionista?

• Reduccionismo en Biología muy exitoso (Biología Molecular). “The ultimate aim of the modern movement in biology is to explain all biology in terms of physics and chemistry”. F. Crick (1966)

• Sistemas biológicos: prototipo de sistemas complejos. => Muchos fenómenos biológicos nunca podrán explicarsea partir de las propiedades de los componentes (“el todo es mas que la suma de las partes”).

• Determinación de “repertorios de componentes” y sus caracteristicas (secuenciación de genomas, proteómica, genómica estructural ...): Ni el número ni las características de genes y proteínas dan cuenta de muchascaracterísticas de los organismos:

- Similar número de genes en Drosophila y C. elegans.- Alta similaridad de secuencia entre humano y ratón.- ...

•Van Regenmortel, M.H. (2004) Reductionism and complexity in molecular biology. Scientists now have the tools to unravel biological and overcome the limitations of reductionism. EMBO Rep, 5, 1016-1020.

¿Es suficiente la visión reduccionista?

• Fallo en tratamiento de cáncer, ... En parte debido al enfoque reduccionista extremo.

• No vacuna HIV, ... “

• Fracaso vacunas de péptidos. “

• Reducción de fármacos en el mercado a pesar de la creciente inversión. (1 fármaco <–> 1 diana).

• No resultados esperados para técnicas terapéuticas prometedoras de base reduccionista (terapia génica, RNA antisentido, ...).

• No mejora esperada de estas aproximaciones con secuenciación de genomas, etc.

•Van Regenmortel, M.H. (2004) Reductionism and complexity in molecular biology. Scientists now have the tools to unravel biological and overcome the limitations of reductionism. EMBO Rep, 5, 1016-1020.

¿Es suficiente la visión reduccionista?

Fallos en aproximaciones in-vitro e in-silico.

Fallos en técnicas experimentales de base reduccionista (delecciones, knockout, ...).Knockout: no efecto, efecto distinto al esperado, o efecto “inespecífico” (cambio expresión 100’s genes).

“Some mice should, by rights, be dead. At the very least, Teyumuras Kurzchaliaexpected his to be critically ill. But the most prominent symptom of his genetically engineered mice was a persistent erection”

Pearson, H. (2002) Surviving a knockout blow. Nature, 415, 8-9.

Biología de componentes vs. Biología de sistemas

In vivo In vivo + in vitro In vivo + in vitro + in silicoCaracterísticas del

sistema desde el pto.vista de los componentes y sus

relacionesPropiedades emergentes, ...

Características de los componentes (moléculas)

Características delsistema

“genomics” / “post-genomics”Multi-level high-throughput characterization of components

- Full-genome sequencing (“genome”).- Characterization of transcripts (mRNA) (“transcriptome”)- Characterization of the protein repertory (“proteome”)- Cellular localization of the components (“localizome”)- Genetic regulation networks (“regulome”)- Protein interaction networks (“interactome”)- High throughput characterization of gene-phenotype relationships (“phenome”)- Metabolic networks (“metabolome”)- ......

cd

ej

a gb f

klh

i

jk

lhi

ei

gf

c

i

=

+

+ .....

The interactome

Walhout, A. J. & Vidal, M. (2001). Protein interaction maps for model organisms. Nat Rev Mol Cell Biol 2(1), 55-62.

Experimental determination of the interactome

Y2H TAP/MShttp://pubs.acs.org/hotartcl/mdd/00/sep/edwards.html

Experimental determination of the interactome

TAP/MS Y2H

A.Valencia

High-throughput experimentally determined interactomes

A.Valencia

•Rain, J.C., Selig, L., De Reuse, H., et al. (2001) The protein-protein interaction map of Helicobacter pylori. Nature, 409, 211-215.•Gavin, A.C., et al. (2002) Functional organisation of the yeast proteome by systematic analysis of protein complexes. Nature, 415, 141-147.•Ho, Y., et al. (2002) Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature, 415, 180-183.•Ito, T., et al. (2000) Toward a protein-protein interaction map of the budding yeast: A comprehensive system to examine two-hybrid interactions in all possible combinations between the yeast proteins. Proc Natl Acad Sci USA, 97, 1143-1147.•Uetz, P., et al. (2000) A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae. Nature, 403, 623-631.•Giot, L., Bader, J.S., Brouwer, et al. (2003) A protein interaction map of Drosophila melanogaster. Science, 302, 1727-1736.•Li, S., Armstrong, C.M., Bertin, N., et al. (2004) A map of the interactome network of the metazoan C. elegans. Science, 303, 540-543.•Butland, G., Peregrin-Alvarez, J.M., Li, J., et al. (2005) Interaction network containing conserved and essential protein complexes in Escherichia coli. Nature, 433, 531-537.

Quality of the high-throughput interaction data

Overlap:6 int !

Estimation (yeast): 12.000-40000 (6000)

Uetz, P. and Finley, R.L., Jr. (2005) From protein networks to biological systems. FEBS Lett, 579, 1821-1827.

von Mering, C., Krause, R., Snel, B., Cornell, M., Oliver, S.G., Fields, S. and Bork, P. (2002) Comparative assessment of large scale data sets of protein-protein interactions. Nature, 417, 399-403.

Quality of the high-throughput interaction data

Quality of the high-throughput interaction data

Hoffmann R, Valencia A. (2003). Protein interaction: same network, different hubs. Trends Genet. 19(12):681-683.

Retrieving protein relationships from the literature(text mining)

Extraction of interactions.

Interface for the human intervention

Pubmed10M entries

*[proteinA]...verbindicatinganaction...[proteinB]“After extensive purification, Cdk2 was still bound to cyclin D1”

Rules about interactoins

Terms that indicate interaction

activate,associatedwith,bind,interact,phosphorylate,regulateAction words are for example:

Selection of the text corpus

A. Valencia

C. Blaschke, M. A. Andrade, C. Ouzounis and A. Valencia. (1999). ISMB99. 60-67.

Retrieving protein relationships from the literature. iHop

Robert Hoffmann & Alfonso Valencia. (2004). A gene network for navigating the literature. Nature Genetics 36, 664.

In-silico prediction of protein interactionsc) gene fusion

d) similarity of phylogenetic trees

proteindistancematrices

d1

d2

a) phylogenetic profiles

org. 1org. 2org. 3

prot. a prot. b prot. c prot. d

b) conservation of gene neighbouring

org. 4prot. a prot. cprot. a prot. c

1 1 1 10 1 0 11 0 1 01 0 1 1

org. 1org. 2org. 3org. 4

prot. a

prot. b

prot. c

org. 1

org. 2

prot. a prot. b

prot. ab

prot. a prot. borg. 1

org. 1org. 2 org. 2org. 3 org. 3org. 4

org. 4org. 5

org. 5

r: similaritybetweena and b trees

multiple sequence alignments(MSA)

reducedMSAs& implicittrees

Caa Cbb Cab

0.0

+1.0

correlation values distributions

e) correlated mutations

intra-protein inter-protein

intra- and inter-protein correlatedmutations

interaction index between a and b

reducedMSAs

prot. a prot. b prot. a prot. bprot. a prot. b

•Huynen, M., Snel, B., Lathe, W. & Bork, P. (2000) Predicting protein function by genomic context: quantitative evaluation and qualitative inferences. Genome Res, 10, 1204-1210.•Valencia, A. & Pazos, F. (2002) Computational methods for the prediction of protein interactions. Curr Opin Struct Biol, 12, 368-373.•Salwinski, L. & Eisenberg, D. (2003). Computational methods of analysis of protein-protein interactions. Curr Opin Struct Biol. 13, 377-382.

Conservation of gene neighboring

Dandekar, T., Snel, B., Huynen, M. & Bork, P. (1998). Conservation of gene order: a fingerprint of proteins thatphysicaly interact. Trends Biochem Sci. 23, 324-328.

Overbeek, R., Fonstein, M., D'Souza, M., Pusch, G. D. &Maltsev, N. (1999). Use of contiguity on the chromosome to predict functional coupling. In Silico Biol. 1, 93-108.

Enright, A. J., Iliopoulos, I., Kyrpides, N. C. & Ouzounis, C. A. (1999). Protein interaction maps for complete genomes based on gene fusion events. Nature. 402, 86-90.

Gene Fusion

Marcotte, E. M., Pellegrini, M., Ho-Leung, N., Rice, D. W.,Yeates, T. O. & Eisenberg, D. (1999). Detecting protein function and protein-protein interactions from genome sequences. Science. 285, 751-753.

Phylogenetic Profiles

•Pellegrini, M., Marcotte, E. M., Thompson, M. J., Eisenberg, D. & Yeates, T. O. (1999). Assigning protein functions by comparative genome analysis: Protein pylogenetic profiles. Proc Natl Acad Sci USA. 96, 4285-4288.

•Date, S. V. & Marcotte, E. M. (2003). Discovery of uncharacterized cellular systems by genome-wide analysis of functional linkages. NatBiotechnol. 21, 1055-1062.

H(A) = - Σp(a) ln p(a)

pij = -1/logEij

Phylogenetic Profiles

•Bowers, P.M., Cokus, S.J., Eisenberg, D. and Yeates, T.O. (2004) Use of logic relationships to decipher protein network organization. Science, 306, 2246-2249.

Based on correlated mutations (i2h)

1

......

4bcabc

bca

......

bc

3bcabc

2bcabc

1 2

......

bca

......

bc

-1.0

+1.0

P11 P22 P12

-1.0 0.0

+1.0

cP

P Pii

i ii incorr12

12

11 22

1

=+

⋅=∑

.0

Pazos, F. & Valencia, A. (2002). In silico two-hybrid system for the selection of physically interacting protein pairs. Proteins. 47, 219-227.

1alc_1-1rnd_1 0,9683adk_2-4tnc_2 0,9611alc_1-1rnd_2 0,9571sgt_1-2c2c_2 0,8892c2c_2-3pgk_1 0,8783trx_1-9pap_2 0,8574tnc_1-4mt2_2 0,8534tnc_2-4mt2_2 0,8363trx_1-3pgk_2 0,8293trx_1-9pap_1 0,8142c2c_2-1rnd_2 0,8134tms_2-3dfr_2 0,8099pap_2-3adk_2 0,8054tms_1-3dfr_2 0,8041sgt_2-1alc_1 0,7999pap_1-3adk_2 0,7903trx_2-9pap_2 0,7614tnc_2-4mt2_1 0,7473adk_2-3pgk_2 0,7264tnc_1-4mt2_1 0,7189pap_2-4tnc_2 0,7023trx_1-3adk_1 0,6733dfr_1-3dfr_2 * 0,6572pf2_2-1alc_2 0,6283adk_1-4tnc_1 0,6173adk_1-4tnc_2 0,6142pf2_2-1alc_1 0,5953adk_2-4tnc_1 0,5394tms_1-3dfr_1 0,5073trx_2-3pgk_1 0,4893trx_2-3pgk_2 0,4713trx_1-3adk_2 0,4711sgt_1-1alc_1 0,4553trx_1-2c2c_2 0,4533trx_1-2c2c_1 0,4464tms_2-4tnc_2 0,4442c2c_1-1rnd_2 0,4421sgt_2-1alc_2 0,4353trx_2-3adk_1 0,4274tms_1-4tnc_2 0,4131sgt_1-1rnd_1 0,4034tms_1-4tnc_1 0,4014tms_2-3dfr_1 0,3981alc_2-4mt2_2 0,3621sgt_1-1rnd_2 0,3581sgt_1-4mt2_2 0,3561sgt_2-1rnd_1 0,3523trx_1-4tnc_2 0,3162c2c_1-4tnc_1 0,303

2c2c_2-1alc_1 3,5031sgt_2-4mt2_1 3,4489pap_1-9pap_2 * 3,0421alc_1-1alc_2 * 2,8522c2c_1-4mt2_1 2,8254tms_1-4tms_2 * 2,7353trx_1-3trx_2 * 2,5714mt2_1-4mt2_2 * 2,4692c2c_2-4mt2_1 2,3552c2c_2-4mt2_2 2,3314tnc_1-4tnc_2 * 2,2383blm_1-3blm_2 * 2,2063pgk_1-3pgk_2 * 2,1972c2c_1-4mt2_2 2,1391sgt_2-2c2c_1 2,0682c2c_1-1alc_1 2,0112c2c_1-1alc_2 1,8863adk_1-3adk_2 * 1,8621sgt_2-2c2c_2 1,8352c2c_1-2c2c_2 * 1,7873adk_1-3pgk_1 1,6241rnd_1-4mt2_1 1,5302c2c_1-9pap_2 1,5203adk_2-3dfr_2 1,5071sgt_2-2pf2_2 1,4899pap_1-3adk_1 1,4883adk_1-3pgk_2 1,4442c2c_2-1alc_2 1,4152c2c_1-3pgk_2 1,3891sgt_1-4mt2_1 1,3873adk_1-3dfr_1 1,3671rnd_2-4mt2_1 1,3592c2c_2-3adk_1 1,3191rnd_1-1rnd_2 * 1,3143pgk_1-4tms_1 1,2992c2c_1-3adk_1 1,2973pgk_1-4tms_2 1,2923trx_1-3pgk_1 1,2792c2c_1-3pgk_1 1,2781alc_1-4mt2_1 1,2782c2c_2-9pap_2 1,2741rnd_1-4mt2_2 1,2583adk_2-3pgk_1 1,2521rnd_2-4mt2_2 1,2403adk_1-3dfr_2 1,2093trx_2-2c2c_1 1,1963pgk_2-4tms_2 1,1783pgk_2-4tms_1 1,170

Based on correlated mutations

(i2h)

Based on correlated mutations (i2h)

Based on correlated mutations (i2h)

Interaction-based function prediction

• Pazos, F. & Valencia, A. (2002). In silico two-hybrid system for the selection of physically interacting protein pairs. Proteins. 47, 219-227.•Vazquez, A., Flammini, A., Maritan, A. & Vespignani, A. (2003). Global protein function prediction from protein-protein interaction networks. Nat Biotechnol. 21, 697-700.•Samanta, M. P. & Liang, S. (2003). Predicting protein functions from redundancies in large-scale protein interaction networks. Proc Natl Acad Sci U S A. 100, 12579-12583.

MirrorTree

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)()(

∑∑

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n

iii

SSRR

SSRRr

Goh, C.-S., Bogan, A.A., Joachimiak, M., Walther, D. and Cohen, F.E. (2000) Co-evolution of Proteins with their Interaction Partners.J Mol Biol, 299, 283-293.

Pazos, F. and Valencia, A. (2001) Similarity of phylogenetic trees as indicator of protein-protein interaction. Protein Eng, 14, 609-614.

2c2c_2-4mt2_2 0,9599pap_1-9pap_2 * 0,9072c2c_1-4mt2_2 0,9013pgk_1-3pgk_2 * 0,9014mt2_1-4mt2_2 * 0,8983trx_1-3trx_2 * 0,8944tms_1-4tms_2 * 0,8542c2c_2-4mt2_1 0,8491rnd_1-1rnd_2 * 0,8172c2c_1-4mt2_1 0,8131alc_1-1alc_2 * 0,8014tnc_1-4tnc_2 * 0,7942c2c_1-2c2c_2 * 0,7733pgk_1-4tms_1 0,7563pgk_1-4tms_2 0,7312c2c_1-3adk_1 0,7263pgk_2-4tms_1 0,7232c2c_2-3pgk_1 0,7151alc_1-1rnd_1 0,7122c2c_2-3pgk_2 0,6981alc_2-1rnd_1 0,6971sgt_1-1sgt_2 * 0,6933pgk_2-4tms_2 0,6913adk_2-3dfr_2 0,6751sgt_2-2pf2_2 0,6733dfr_1-3dfr_2 * 0,6722c2c_2-9pap_1 0,6582c2c_1-3pgk_1 0,6483trx_2-9pap_1 0,6461sgt_1-2pf2_2 0,6462c2c_2-3adk_1 0,6313trx_1-9pap_1 0,6272c2c_2-1alc_2 0,6262c2c_1-3pgk_2 0,6203trx_2-9pap_2 0,6201rnd_2-4mt2_1 0,6191alc_2-1rnd_2 0,6071rnd_2-4mt2_2 0,6063blm_1-3blm_2 * 0,6031alc_1-1rnd_2 0,5993trx_1-3pgk_1 0,5953trx_1-9pap_2 0,5891alc_2-4mt2_1 0,5882c2c_1-1alc_2 0,5872c2c_1-9pap_1 0,5813trx_1-3pgk_2 0,5774tnc_1-4mt2_1 0,5563adk_1-3pgk_1 0,554

4tnc_1-4mt2_2 0,4489pap_2-4tnc_1 0,4461alc_2-4mt2_2 0,4461sgt_2-4mt2_1 0,4333adk_1-4tnc_2 0,4211rnd_1-4mt2_2 0,4054tnc_2-4mt2_2 0,4052c2c_1-3adk_2 0,4011sgt_2-2c2c_1 0,3994tms_2-3dfr_2 0,3943adk_1-3dfr_1 0,3901sgt_2-2c2c_2 0,3813adk_2-3dfr_1 0,3721sgt_2-1alc_1 0,3714tms_1-3dfr_2 0,3581sgt_1-4mt2_1 0,3431sgt_1-4mt2_2 0,3369pap_2-4tnc_2 0,3314tms_1-3dfr_1 0,3273trx_1-2c2c_2 0,3193trx_1-2c2c_1 0,3121sgt_1-1alc_1 0,3123trx_2-2c2c_2 0,2873trx_2-2c2c_1 0,2811sgt_1-2c2c_2 0,2701alc_1-4mt2_2 0,2681sgt_1-2c2c_1 0,2682c2c_1-1rnd_1 0,2639pap_1-3adk_2 0,2542c2c_2-3adk_2 0,2543adk_2-3pgk_1 0,2511sgt_1-1rnd_1 0,2383adk_2-3pgk_2 0,2389pap_2-3adk_2 0,2211sgt_2-1alc_2 0,2192c2c_2-1alc_1 0,2039pap_1-4tnc_1 0,2021sgt_2-1rnd_1 0,1911sgt_1-1alc_2 0,1783trx_2-3adk_2 0,1751sgt_1-1rnd_2 0,1682pf2_2-1alc_1 0,1602c2c_1-1alc_1 0,1559pap_1-4tnc_2 0,1492c2c_2-1rnd_2 0,1464tms_2-3dfr_1 0,1303trx_1-3adk_2 0,1282c2c_2-1rnd_1 0,1252c2c_1-1rnd_2 0,113

Pazos, F. and Valencia, A. (2001) Similarity of phylogenetic trees as indicator of protein-protein interaction. Protein Eng, 14, 609-614.

Pazos, F. and Valencia, A. (2001) Similarity of phylogenetic trees as indicator of protein-protein interaction. Protein Eng, 14, 609-614.

MirrorTreeVariations

Gertz, J., Elfond, G., Shustrova, A., Weisinger, M., Pellegrini, M., Cokus, S. and Rothschild, B. (2003) Inferring protein interactions from phylogenetic distance matrices. Bioinformatics, 19, 2039-2045.

33. Goh, C.S. and Cohen, F.E. (2002) Co-evolutionary analysis reveals insights into protein-protein interactions. J Mol Biol, 324, 177-192.

34. Ramani, A.K. and Marcotte, E.M. (2003) Exploiding the co-evolution of interacting proteins to discover interaction specificity. J Mol Biol, 327, 273-284.

35. Sato, T., Yamanishi, Y., Horimoto, K., Toh, H. and Kanehisa, M. (2003) Prediction of protein-protein interactions from phylogenetictrees using partial correlation coefficient. Genome Informatics, 14, 496-497.

36. Kim, W.K., Bolser, D.M. and Park, J.H. (2004) Large-scale co-evolution analysis of protein structural interlogues using the global protein structural interactome map (PSIMAP). Bioinformatics, 20, 1138-1150. Epub 2004 Feb 1135.

37. Tan, S., Zhang, Z. and Ng, S. (2004) ADVICE: Automated Detection and Validation of Interaction by Co-Evolution. Nucl. Acids. Res., 32, W69-W72.

MirrorTree. Variations

Ramani, A.K. & Marcotte, E.M. (2003) Exploiding the co-evolution of interacting proteins to discover interaction specificity. J Mol Biol, 327, 273-284.

?

Protein family A(i.e. ligands)

Protein family B(i.e. receptors)

HGT?.....?

HGT?.....?

Protein A 16SrRNA Protein B

Mul

tiple

seq

uenc

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ents

Phy

loge

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trees

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dist

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pred

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Florencio Pazos, Juan A. G. Ranea, David Juan & Michael J. E. Sternberg. (2005). Assessing Protein Co-evolution in the Context of the Tree of Life Assists in the Prediction of the Interactome. (Submited).

MirrorTreeVariations

tol-mirrortree

tol-mirrortree

P00000List of pairs sorted by score

DIP:516 interactions (E coli)

20,087 pairs calculated(115 true)118 proteins with>=1 calculated true interactor

fraction of false positives0%: perfect50%: random

(1 int.)

sen

1-esp

ROC area1.0: perfect0.5: random

0.9

2

0.7

35

0.5

69

0.0

66

0.9

22

0.7

96

0.5

78

0.0

97

0.9

7

0.8

29

0.6

62

0.1

06

0

0.2

0.4

0.6

0.8

1

25 50 75 100

2.8

16.5

38.7

93.1

2.9

12

35.7

89.8

1.1 6

.4

23.2

88.9

0

20

40

60

80

100

25 50 75 100

% f

alse

pos

itive

s

RO

C a

rea

% cases % cases

10 20 30 40 50 60 70 80 90 1000

10

20

30

40

50

60

70

80

90

100

0

25

50

75

100

125

150

175

200

225

250

% fa

lse

posi

tives

#pro

tein

s in

list

% cases

mirrrotree

mirrortree(tree dist.)

tol-mirrortree

tol-mirrortree

tol-mirrortreeComparison with old methods

P(N) values (sign test):

tol-mirrortreePrediction of HGT events

00.10.20.30.40.50.60.70.80.9

1

0 0.5 1 1.5 2 2.5

a) Prolyl-tRNA synthetase

16SrRNA distances (substitutions/site)

prot

ein

dist

ance

s (s

ubst

itutio

ns/s

ite)

0

0.1

0.2

0.3

0.4

0.5

0.6

0 0.5 1 1.5 2 2.5

b) Ribosomal protein L36

16SrRNA distances (substitutions/site)pr

otei

n di

stan

ces

(sub

stitu

tions

/site

)

r= 0.72r= 0.53

r<=0.5 25% false pos (vs. 15%)Excluding them: 13.7% false pos (vs. 15%)

A B

rRNA

tol-mirrortreeCo-HGT events

Lawrence, J.G. (1997) Selfish operons and speciation by gene transfer. Trends Microbiol, 5, 355-359.

Public repositories of protein interactions

Xenarios, I., Salwinski, L., Duan, X.J., Higney, P., Kim, S.M. & Eisenberg, D. (2002) DIP, the Database of Interacting Proteins: a research tool for studying cellular networks of protein interactions. Nucleic Acids Res, 30, 303-305.

von Mering, C., Huynen, M., Jaeggi, D., Schmidt, S., Bork, P. and Snel, B. (2003) STRING: a database of predicted functional associations between proteins. Nucleic Acids Res, 31, 258-261.

Bioinformatics for designing high-troughput experiments

Lappe, M. and Holm, L. (2004) Unraveling protein interaction networks with near-optimal efficiency. Nat Biotechnol, 22, 98-103.

Topological Characteristics of Biological Networks

Barabasi, A.L. and Oltvai, Z.N. (2004) Network biology: understanding the cell's functional organization. Nat Rev Genet, 5, 101-113.

Study of the Interactome

Jeong, H., Mason, S. P., Barabasi, A. L. & Oltvai, Z. N. (2001). Lethality and centrality in protein networks. Nature411, 41-42.

Schwikowski, B., Uetz, P. & Fields, S. (2002). A network of protein-protein interactions in yeast. Nature Biotech 18, 1257-1261.

Fraser, H.B., Hirsh, A.E., Steinmetz, L.M., Scharfe, C. and Feldman, M.W. (2002) Evolutionary rate in the protein interaction network. Science, 296, 750-752.

Study of the Interactome

•Ravasz, E., Somera, L., Mongru, D.A., Oltvai, Z.N. & Barabási, A.L. (2002) Hierarchical organization of modularity in metabolic networks. Science, 297, 1551-1555.

•Barabasi, A.L. and Oltvai, Z.N. (2004) Network biology: understanding the cell's functional organization. Nat Rev Genet, 5, 101-113.

Artifacts due to the sampling process ?

Stumpf, M.P., Wiuf, C. and May, R.M. (2005) Subnets of scale-free networks are not scale-free: Sampling properties of networks. Proc Natl Acad Sci U S A, 102, 4221-4224.

Study of the Interactome

Wuchty, S., Oltvai, Z.N. & Barabasi, A.L. (2003) Evolutionary conservation of motif constituents in the yeast protein interaction network. Nat Genet, 35, 176-179.

Kelley, B.P., et al. (2003) Conserved pathways within bacteria and yeast as revealed by global protein network alignment. Proc Natl Acad Sci U S A, 100, 11394-11399.

Integration with other “-omics” sources

Ge, H., Walhout, A.J. & Vidal, M. (2003) Integrating 'omic' information: a bridge between genomics and systems biology. Trends Genet, 19, 551-560.

Combination with other sources to increase reliability

Lee, I., Date, S.V., Adai, A.T. and Marcotte, E.M. (2004) A probabilistic functional network of yeast genes. Science, 306, 1555-1558.

Combination of protein interactions with expression arrays

RandomHubsPartyDate

Han, J.D., Bertin, N., Hao, T., Goldberg, D.S., Berriz, G.F., Zhang, L.V., Dupuy, D., Walhout, A.J., Cusick, M.E., Roth, F.P. and Vidal, M. (2004) Evidence for dynamically organized modularity in the yeast protein-protein interaction network. Nature, 430, 88-93. Epub 2004 Jun 2009.

de Lichtenberg U, Jensen LJ, Brunak S, Bork P. (2005). Dynamic complex formation during the yeast cell cycle. Science. 307(5710):724-727.

Going down to the structuresLinking top-down and botton-up approaches

Aloy, P., Bottcher, B., Ceulemans, H., Leutwein, C., Mellwig, C., Fischer, S., Gavin, A.C., Bork, P., Superti-Furga, G., Serrano, L. and Russell, R.B. (2004) Structure-based assembly of protein complexes in yeast. Science, 303, 2026-2029.

Redes de Interacciones entre Proteínas

Bibliografía general (revisiones)

• Van Regenmortel, M.H. (2004) Reductionism and complexity in molecular biology. Scientists now have the tools to unravel biological and overcome the limitations of reductionism. EMBO Rep, 5, 1016-1020.

•Uetz, P. and Finley, R.L., Jr. (2005) From protein networks to biological systems. FEBS Lett, 579, 1821-1827.

• Xia, Y., Yu, H., Jansen, R., Seringhaus, M., Baxter, S., Greenbaum, D., Zhao, H. and Gerstein, M. (2004) Analyzing cellular biochemistry in terms of molecular networks. Annu Rev Biochem, 73, 1051-1087.

• Barabasi, A.L. and Oltvai, Z.N. (2004) Network biology: understanding the cell's functional organization. Nat Rev Genet, 5, 101-113.

• Alm, E. and Arkin, A.P. (2003) Biological networks. Curr Opin Struct Biol, 13, 193-202.

• Salwinski, L. and Eisenberg, D. (2003) Computational methods of analysis of protein-protein interactions. Curr Opin Struct Biol, 13, 377-382.

Redes de Interacciones entre Proteínas

Material de teoría y práctica:

http://pdg.cnb.uam.es/pazos/cursos/Verano_UCM_05/

Contacto:

Dr. Florencio Pazos CabaleiroProtein Design GroupCentro Nacional de Biotecnología (CNB-CSIC). Madridpazos@cnb.uam.eshttp://pdg.cnb.uam.es/pazos