Structure-based protein-protein interaction networks and drug design

doi:10.1007/s40484-013-0018-y

Quant. Biol.

2013, Vol. 1

Issue (3) : 183-191 https://doi.org/10.1007/s40484-013-0018-y

REVIEW

Structure-based protein-protein interaction networks and drug design

Hammad Naveed, Jingdong J. Han(

)

Chinese Academy of Sciences Key Laboratory of Computational Biology, Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China

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Abstract

Proteins carry out their functions by interacting with other proteins and small molecules, forming a complex interaction network. In this review, we briefly introduce classical graph theory based protein-protein interaction networks. We also describe the commonly used experimental methods to construct these networks, and the insights that can be gained from these networks. We then discuss the recent transition from graph theory based networks to structure based protein-protein interaction networks and the advantages of the latter over the former, using two networks as examples. We further discuss the usefulness of structure based protein-protein interaction networks for drug discovery, with a special emphasis on drug repositioning.

Keywords protein-protein interaction network structure-based drug design drug reposition

Corresponding Author(s): Han Jingdong J.,Email:jdhan@picb.ac.cn

Issue Date: 05 September 2013

Cite this article:

Hammad Naveed,Jingdong J. Han. Structure-based protein-protein interaction networks and drug design[J]. Quant. Biol., 2013, 1(3): 183-191.

URL:

https://academic.hep.com.cn/qb/EN/10.1007/s40484-013-0018-y
https://academic.hep.com.cn/qb/EN/Y2013/V1/I3/183

Tab.1 A brief description of the databases that integrate protein-protein interaction data from various sources

Fig.1 The nodes represent the proteins and the interactions between them are shown as edges. Such a representation does not allow for the inclusion of the structural information for the interaction interface. (Main) Structure-based representation of the hypothetical protein-protein interaction network shown in the inset. Mechanistic information, such as where the proteins bind and whether they compete for a binding interaction, is incorporated in this representation. The red patches on the proteins show predominantly positive charge in the active site, while the blue patches represent predominantly negative charge. Some interactions occur only in a particular oligomerization state, e.g., Protein D only interacts with the AB protein complex and not with proteins A or B individually. Such scenarios occur when the binding surface for an interaction is dependent on the interaction of two other proteins. Protein C can bind to Protein A regardless of the binding events of Protein A to Protein B and Protein D. The interactions are numbered for clarity and do not represent a chronological order. The two ends of an interaction are numbered the same to specify which proteins/complexes are involved in the interaction. The complex formed as a result of the interaction is shown in the middle of each interaction (edge).

Name	Description	Website
ZDOCK [40]	Performs full rigid-body searches of docking orientations between two proteins	Zlab.bu.edu/zdock/
ClusPro [41,42]	Includes rigid body docking data from PIPER, selection of docked structures with favorable desolvation, and electrostatics, and clustering of the retained complexes using a pairwise RMSD criterion. It reports the centers of the largest clusters	cluspro.bu.edu/
HADDOCK [43]	Is a docking method that utilizes biochemical and/or biophysical interaction data, such as chemical shift perturbation data resulting from NMR titration experiments, mutagenesis data or bioinformatic predictions	www.nmr.chem.uu.nl/haddock2.1/
RosettaDOCK [44]	Identifies low-energy conformations of a protein-protein interaction by optimizing rigid-body orientation and side-chain conformations.	rosettadock.graylab.jhu.edu/
PatchDock [45]	Uses geometric hashing and pose-clustering matching techniques to match surface patches	bioinfo3d.cs.tau.ac.il/PatchDock/
InterPreTS [48]	Uses alignments of homologs of the interacting proteins, to assess the fitness of any possible interacting pair on the complex by using empirical potentials	www.russelllab.org/interprets/
PrePPI [50]	Predicts interactions based on structural, functional, evolutionary and expression information	bhapp.c2b2.columbia.edu/PrePPI/
PRISM [51]	Uses rigid-body structural comparisons of target proteins to known template protein-protein interfaces, and flexible refinement using a docking energy function	prism.ccbb.ku.edu.tr/prism_protocol/
IBIS [68]	Uses conservation of structural location and sequence patterns of protein–protein binding sites	www.ncbi.nlm.nih.gov/Structure/ibis/ibis.cgi
TMBB-Explorer [64]	Predicts the structure, oligomerization state, protein-protein interaction sites, and thermodynamic properties of the transmembrane domains of beta-barrel membrane proteins using a physical interaction model, a simplified conformational space for efficient enumeration, and an empirical potential function from a detailed combinatorial analysis	tanto.bioengr.uic.edu/TMBB-Explorer/
CASTp [66]	Uses weighted Delaunay triangulation and the alpha complex for shape measurements, and provides identification and measurements of surface accessible pockets, as well as interior inaccessible cavities, for proteins	sts.bioengr.uic.edu/castp/
PASS [69]	Uses geometry to characterize regions of buried volume in proteins, and identified positions likely to represent binding sites based upon the size, shape, and burial extent of these volumes	www.ccl.net/cca/software/UNIX/pass/overview.shtml

Tab.2 A brief description of the tools that can be used to predict protein-protein interactions from protein structures

1	Hartwell, L. H.,Hopfield, J. J.,Leibler, S., and Murray, A. W. (1999) From molecular to modular cell biology. Nature , 402, C47–C52 . doi: 10.1038/35011540 pmid:10591225
2	Dunham, I.,Kundaje, A.,Aldred, S. F.,Collins, P. J.,Davis, C. A.,Doyle, F.,Epstein, C. B.,Frietze, S.,Harrow, J.,Kaul, R., and the ENCODE Project Consortium. (2012) An integrated encyclopedia of DNA elements in the human genome. Nature , 489, 57–74 . doi: 10.1038/nature11247 pmid:22955616
3	Whisstock, J. C. and Lesk, A. M. (2003) Prediction of protein function from protein sequence and structure. Q. Rev. Biophys., 36, 307–340 . doi: 10.1017/S0033583503003901 pmid:15029827
4	Fields, S.Uetz, P.Giot, L.Cagney, G.Mansfield, T. A.Judson, R. S.Knight, J. R.Lockshon, D.Narayan, V.Srinivasan, M. (2000) A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae. Nature , 403, 623–627 . doi: 10.1038/35001009 pmid:10688190
5	Ito, T.Chiba, T.Ozawa, R.Yoshida, M.Hattori, M. and Sakaki, Y. (2001) A comprehensive two-hybrid analysis to explore the yeast protein interactome. Proc. Natl. Acad. Sci. U.S.A. , 98, 4569–4574 . doi: 10.1073/pnas.061034498 pmid:11283351
6	Li, S.Armstrong, C. M.Bertin, N.Ge, H.Milstein, S.Boxem, M.Vidalain, P. O.Han, J. D.Chesneau, A.Hao, T. (2004) A map of the interactome network of the metazoan C. elegans. Science , 303, 540–543 . doi: 10.1126/science.1091403 pmid:14704431
7	Rual, J.-F.Venkatesan, K.Hao, T.Hirozane-Kishikawa, T.Dricot, A.Li, N.Berriz, G. F.Gibbons, F. D.Dreze, M.Ayivi-Guedehoussou, N. (2005) Towards a proteome-scale map of the human protein-protein interaction network. Nature , 437, 1173–1178 . doi: 10.1038/nature04209 pmid:16189514
8	Rajagopala, S. V. and Uetz, P. (2011) Analysis of protein-protein interactions using high-throughput yeast two-hybrid screens. Methods Mol. Biol. , 781, 1–29 . doi: 10.1007/978-1-61779-276-2_1 pmid:21877274
9	Gavin, A. C.Aloy, P.Grandi, P.Krause, R.Boesche, M.Marzioch, M.Rau, C.Jensen, L. J.Bastuck, S.Dümpelfeld, B. (2006) Proteome survey reveals modularity of the yeast cell machinery. Nature , 440, 631–636 . doi: 10.1038/nature04532 pmid:16429126
10	Krogan, N. J.Cagney, G.Yu, H.Zhong, G.Guo, X.Ignatchenko, A.Li, J.Pu, S.Datta, N.Tikuisis, A. P. (2006) Global landscape of protein complexes in the yeast Saccharomyces cerevisiae. Nature , 440, 637–643 . doi: 10.1038/nature04670 pmid:16554755
11	Soong, T. T.Wrzeszczynski, K. O. and Rost, B. (2008) Physical protein-protein interactions predicted from microarrays. Bioinformatics , 24, 2608–2614 . doi: 10.1093/bioinformatics/btn498 pmid:18829707
12	Liu, C. T., Yuan, S. and Li, K. C. (2009) Patterns of co-expression for protein complexes by size in Saccharomyces cerevisiae. Nucleic Acids Res. , 37, 526–532 . doi: 10.1093/nar/gkn972 pmid:19056822
13	Salwinski, L.Miller, C. S.Smith, A. J.Pettit, F. K.Bowie, J. U. and Eisenberg, D. (2004) The database of interacting Proteins: 2004 update. Nucleic Acids Res. , 32, D449–D451 . doi: 10.1093/nar/gkh086 pmid:14681454
14	Licata, L.Briganti, L.Peluso, D.Perfetto, L.Iannuccelli, M.Galeota, E.Sacco, F.Palma, A.Nardozza, A. P.Santonico, E. (2012) MINT the molecular interaction database: 2012 update. Nucleic Acids Res. , 40, D857–D861 . doi: 10.1093/nar/gkr930 pmid:22096227
15	Keshava Prasad, T. S.Goel, R.Kandasamy, K.Keerthikumar, S.Kumar, S.Mathivanan, S.Telikicherla, D.Raju, R.Shafreen, B.Venugopal, A. (2009) Human Protein Reference Database--2009 update. Nucleic Acids Res. , 37, D767–D772 . doi: 10.1093/nar/gkn892 pmid:18988627
16	Stark, C.Breitkreutz, B. J.Chatr-Aryamontri, A.Boucher, L.Oughtred, R.Livstone, M. S.Nixon, J.Van Auken, K.Wang, X.Shi, X. (2011) The BioGRID Interaction Database: 2011 update. Nucleic Acids Res. , 39, D698–D704 . doi: 10.1093/nar/gkq1116 pmid:21071413
17	Willis, R. C. and Hogue, C. W. (2006) Searching viewing and visualizing data in the Biomolecular Interaction Network Database (BIND) Curr. Protoc. Bioinformatics , 8.9.1–8.9.30 .
18	Kerrien, S.Aranda, B.Breuza, L.Bridge, A.Broackes-Carter, F.Chen, C.Duesbury, M.Dumousseau, M.Feuermann, M.Hinz, U. (2012) The IntAct molecular interaction database in 2012. Nucleic Acids Res. , 40, D841–D846 . doi: 10.1093/nar/gkr1088 pmid:22121220
19	Jeong, H.Mason, S. P.Barabási, A.-L. and Oltvai, Z. N. (2001) Lethality and centrality in protein networks. Nature , 411, 41–42 . doi: 10.1038/35075138 pmid:11333967
20	Barabási, A.-L. and Bonabeau, E. (2003) Scale-free networks. Sci. Am. , 288, 60–69 . doi: 10.1038/scientificamerican0503-60 pmid:12701331
21	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. (2004) Evidence for dynamically organized modularity in the yeast protein-protein interaction network. Nature , 430, 88–93 . doi: 10.1038/nature02555 pmid:15190252
22	Yook, S. H.Oltvai, Z. N. and Barabási, A. L. (2004) Functional and topological characterization of protein interaction networks. Proteomics , 4, 928–942 . doi: 10.1002/pmic.200300636 pmid:15048975
23	Lim, J.Hao, T.Shaw, C.Patel, A. J.Szabá, G.Rual, J. F.Fisk, C. J.Li, N.Smolyar, A.Hill, D. E. (2006) A protein-protein interaction network for human inherited ataxias and disorders of Purkinje cell degeneration. Cell , 125, 801–814 . doi: 10.1016/j.cell.2006.03.032 pmid:16713569
24	Huang, H.Jedynak, B. M. and Bader, J. S. (2007) Where have all the interactions gone? Estimating the coverage of two-hybrid protein interaction maps. PLoS Comput. Biol. , 3, e214. doi: 10.1371/journal.pcbi.0030214 pmid:18039026
25	Shen-Orr, S. S.Milo, R.Mangan, S. and Alon, U. (2002) Network motifs in the transcriptional regulation network of Escherichia coli. Nat. Genet. , 31, 64–68 . doi: 10.1038/ng881 pmid:11967538
26	Milo, R.Shen-Orr, S.Itzkovitz, S.Kashtan, N.Chklovskii, D. and Alon, U. (2002) Network motifs: simple building blocks of complex networks. Science , 298, 824–827 . doi: 10.1126/science.298.5594.824 pmid:12399590
27	Said, M. R.Begley, T. J.Oppenheim, A. V.Lauffenburger, D. A. and Samson, L. D. (2004) Global network analysis of phenotypic effects: protein networks and toxicity modulation in Saccharomyces cerevisiae. Proc. Natl. Acad. Sci. U.S.A. , 101, 18006–18011 doi: 10.1073/pnas.0405996101 pmid:. 15608068
28	Shachar, R.Unger, L.Kupiec, M.Ruppin, R. and Sharan, R. (2008) A systems-level approach to mapping the telomere-length maintenance gene circuitryMol. Syst. Biol. , 4, 172.
29	Wachi, S.Yoneda, K. and Wu, R. (2005) Interactome-transcriptome analysis reveals the high centrality of genes differentially expressed in lung cancer tissues. Bioinformatics , 21, 4205–4208 . doi: 10.1093/bioinformatics/bti688 pmid:16188928
30	Jonsson, P. F. and Bates, P. A. (2006) Global topological features of cancer proteins in the human interactome. Bioinformatics , 22, 2291–2297 . doi: 10.1093/bioinformatics/btl390 pmid:16844706
31	Goh, K. I.Cusick, M. E.Valle, D.Childs, B.Vidal, M. and Barabási, A. L. (2007) The human disease network. Proc. Natl. Acad. Sci. U.S.A. , 104, 8685–8690 doi: 10.1073/pnas.0701361104 pmid:. 17502601
32	Nguyen, T. N. and Goodrich, J. A. (2006) Protein-protein interaction assays: eliminating false positive interactions. Nat. Methods , 3, 135–139 . doi: 10.1038/nmeth0206-135 pmid:16432524
33	Fullwood, M. J. and Ruan, Y. (2009) ChIP-based methods for the identification of long-range chromatin interactions. J. Cell. Biochem., 107, 30–39 doi: 10.1002/jcb.22116 pmid:.19247990
34	Cusick, M. E.Yu, H.Smolyar, A.Venkatesan, K.Carvunis, A. R.Simonis, N.Rual, J. F.Borick, H.Braun, P.Dreze, M. (2009) Literature-curated protein interaction datasets. Nat. Methods 6, 39–46 . doi: 10.1038/nmeth.1284 pmid:19116613
35	Ennifar, E. (2012) X-ray crystallography as a tool for mechanism-of-action studies and drug discovery. Curr. Pharm. Biotechnol. , PMID: 22429136
36	Guerry, P. and Herrmann, T. (2011) Advances in automated NMR protein structure determination. Q. Rev. Biophys. , 44, 257–309 doi: 10.1017/S0033583510000326 pmid:. 21411039
37	Glaeser, R. M. and Hall, R. J. (2011) Reaching the information limit in cryo-EM of biological macromolecules: experimental aspects. Biophys. J. , 100, 2331–2337 . doi: 10.1016/j.bpj.2011.04.018 pmid:21575566
38	Berman, H. M.Westbrook, J.Feng, Z.Gilliland, G.Bhat, T. N.Weissig, H.Shindyalov, I. N. and Bourne, P. E. (2000) The protein data bank. Nucleic Acids Res. , 28, 235–242 . doi: 10.1093/nar/28.1.235 pmid:10592235
39	Dunitz, J. D. and Gavezzotti, A. (2005) Molecular recognition in organic crystals: directed intermolecular bonds or nonlocalized bonding? Angew. Chem. Int. Ed. Engl. , 44, 1766–1787 . doi: 10.1002/anie.200460157 pmid:15685679
40	Chen, R., Li, L. and Weng, Z. (2003) ZDOCK: an initial-stage protein-docking algorithm. Proteins , 52, 80–87 . doi: 10.1002/prot.10389 pmid:12784371
41	Kozakov, D.Brenke, R.Comeau, S. R. and Vajda, S. (2006) PIPER: an FFT-based protein docking program with pairwise potentials. Proteins , 65, 392–406 . doi: 10.1002/prot.21117 pmid:16933295
42	Kozakov, D.Hall, D. R.Beglov, D.Brenke, R.Comeau, S. R.Shen, Y.Li, K.Zheng, J.Vakili, P.Paschalidis, I. C. (2010) Achieving reliability and high accuracy in automated protein docking: Cluspro PIPER SDU and stability analysis in CAPRI rounds 13–19, Proteins. Proteins , 78, 3124–3130 . doi: 10.1002/prot.22835
43	de Vries, S. J.van Dijk, M. and Bonvin, A. M. (2010) The HADDOCK web server for data-driven biomolecular docking. Nat. Protoc. , 5, 883–897 . doi: 10.1038/nprot.2010.32 pmid:20431534
44	Lyskov, S. and Gray, J. J. (2008) The RosettaDock server for local protein-protein docking. Nucleic Acids Res. , 36, W233-8. doi: 10.1093/nar/gkn216 pmid:18442991
45	Schneidman-Duhovny, D.Inbar, Y.Nussinov, R. and Wolfson, H. J. (2005) PatchDock and SymmDock: servers for rigid and symmetric docking. Nucleic Acids Res. , 33, W363-7. doi: 10.1093/nar/gki481 pmid:15980490
46	Fernández-Recio, J. and Sternberg, M. J. E. (2010) The 4th meeting on the Critical Assessment of Predicted Interaction (CAPRI) held at the Mare NostrumBarcelona Proteins , 78, 3065–3066 .
47	Aloy, P. and Russell, R. B. (2002) Interrogating protein interaction networks through structural biology. Proc. Natl. Acad. Sci. U.S.A. , 99, 5896–5901 . doi: 10.1073/pnas.092147999 pmid:11972061
48	Aloy, P. and Russell, R. B. (2003) InterPreTS: protein interaction prediction through tertiary structure. Bioinformatics , 19, 161–162 . doi: 10.1093/bioinformatics/19.1.161 pmid:12499311
49	Kundrotas, P. J.Lensink, M. F. and Alexov, E. (2008) Homology-based modeling of 3D structures of protein-protein complexes using alignments of modified sequence profiles. Int. J. Biol. Macromol. , 43, 198–208 . doi: 10.1016/j.ijbiomac.2008.05.004 pmid:18572239
50	Zhang, Q. C.Petrey, D.Deng, L.Qiang, L.Shi, Y.Thu, C. A.Bisikirska, B.Lefebvre, C.Accili, D.Hunter, T. (2012) Structure-based prediction of protein-protein interactions on a genome-wide scale. Nature , 490, 556–560 doi: 10.1038/nature11503 pmid:.23023127
51	Ogmen, U.Keskin, O.Aytuna, A. S.Nussinov, R. and Gursoy, A. (2005) PRISM: protein interactions by structural matching. Nucleic Acids Res. , 33, W331-6. doi: 10.1093/nar/gki585 pmid:15991339
52	Gunther, S.May, P.Hoppe, A.Frommel, C. and Preissner, R. (2007) Docking without docking: ISEARCH-prediction of interactions using known interfaces. Proteins , 69, 839–844 . doi: 10.1002/prot.21746
53	Sinha, R.Kundrotas, P. J. and Vakser, I. A. (2010) Docking by structural similarity at protein-protein interfaces. Proteins , 78, 3235–3241 . doi: 10.1002/prot.22812 pmid:20715056
54	Tyagi, M.Thangudu, R. R.Zhang, D.Bryant, S. H.Madej, T. and Panchenko, A. R. (2012) Homology inference of protein-protein interactions via conserved binding sites. PLoS ONE , 7, e28896 doi: 10.1371/journal.pone.0028896 pmid:. 22303436
55	Fraser, H. B.Wall, D. P. and Hirsh, A. E. (2003) A simple dependence between protein evolution rate and the number of protein-protein interactions. BMC Evol. Biol. , 3, 11. doi: 10.1186/1471-2148-3-11 pmid:12769820
56	Fraser, H. B. (2005) Modularity and evolutionary constraint on proteins. Nat. Genet. , 37, 351–352 . doi: 10.1038/ng1530 pmid:15750592
57	Choi, Y. S.Yang, J. S.Choi, Y.Ryu, S. H. and Kim, S. (2009) Evolutionary conservation in multiple faces of protein interaction. Proteins , 77, 14–25 . doi: 10.1002/prot.22410 pmid:19350617
58	Zhao, J.Dundas, J.Kachalo, S.Ouyang, Z. and Liang, J. (2011) Accuracy of functional surfaces on comparatively modeled protein structures. J. Struct. Funct. Genomics , 12, 97–107 . doi: 10.1007/s10969-011-9109-z pmid:21541664
59	Naveed, H.Jackups, R. Jr and Liang, J. (2009) Predicting weakly stable regions oligomerization state and protein-protein interfaces in transmembrane domains of outer membrane proteins. Proc. Natl. Acad. Sci. U. S. A. , 106, 12735–12740 doi: 10.1073/pnas.0902169106.
60	Bordner, A. J. (2009) Predicting protein-protein binding sites in membrane proteins. BMC Bioinformatics , 10, 312. doi: 10.1186/1471-2105-10-312 pmid:19778442
61	Gessmann, D.Mager, F.Naveed, H.Arnold, T.Weirich, S.Linke, D.Liang, J. and Nussberger, S. (2011) Improving the resistance of a eukaryotic β-barrel protein to thermal and chemical perturbations. J. Mol. Biol. , 413, 150–161 . doi: 10.1016/j.jmb.2011.07.054 pmid:21835183
62	Geula, S.Naveed, H.Liang, J. and Shoshan-Barmatz, V. (2012) Structure-based analysis of VDAC1 protein: defining oligomer contact sites. J. Biol. Chem. , 287, 2179–2190 doi: 10.1074/jbc.M111.268920 pmid:. 22117062
63	Naveed, H.Jimenez-Morales, D.Tian, J.Pasupuleti, V.Kenney, L. J. and Liang, J. (2012) Engineered oligomerization state of OmpF protein through computational design decouples oligomer dissociation from unfolding. J. Mol. Biol. , 419, 89–101 . doi: 10.1016/j.jmb.2012.02.043 pmid:22391420
64	Naveed, H. and Liang, J. (2012) TMBB-Explorer: A Webserver to Predict the Structure, Oligomerization State PPI Interface and Thermodynamic Properties of the Transmembrane Domains of Outer Membrane Proteins. Biophys. J. , 102, 469a. doi: 10.1016/j.bpj.2011.11.2573
65	Levitt, D. G. and Banaszak, L. J. (1992) POCKET: a computer graphics method for identifying and displaying protein cavities and their surrounding amino acids. J. Mol. Graph. , 10, 174–177 . doi: 10.1016/0263-7855(92)80074-N pmid:1467333
66	Dundas, J.Ouyang, Z.Tseng, J.Binkowski, A.Turpaz, Y. and Liang, J. (2006) CASTp: computed atlas of surface topography of proteins with structural and topographical mapping of functionally annotated residues. Nucleic Acids Res. , 34, W116-8. doi: 10.1093/nar/gkl282 pmid:16844972
67	Eyrisch, S. and Helms, V. (2007) Transient pockets on protein surfaces involved in protein-protein interaction. J. Med. Chem. , 50, 3457–3464 . doi: 10.1021/jm070095g pmid:17602601
68	Shoemaker, B. A.Zhang, D.Tyagi, M.Thangudu, R. R.Fong, J. H.Marchler-Bauer, A.Bryant, S. H.Madej, T. and Panchenko, A. R. (2012) IBIS (Inferred Biomolecular Interaction Server) reports predicts and integrates multiple types of conserved interactions for proteins. Nucleic Acids Res. , 40, D834–D840 . doi: 10.1093/nar/gkr997 pmid:22102591
69	Brady, G. P. Jr and Stouten, P. F. W. (2000) Fast prediction and visualization of protein binding pockets with PASS. J. Comput. Aided Mol. Des. , 14, 383–401 . doi: 10.1023/A:1008124202956 pmid:10815774
70	Jeong, H.Tombor, B.Albert, R.Oltvai, Z. N. and Barabási, A.-L. (2000) The large-scale organization of metabolic networks. Nature, 407, 651–654 . doi: 10.1038/35036627 pmid:11034217
71	Mirzarezaee, M.Araabi, B. N. and Sadeghi, M. (2010) Features analysis for identification of date and party hubs in protein interaction network of Saccharomyces cerevisiae. BMC Syst. Biol. , 4, 172. doi: 10.1186/1752-0509-4-172 pmid:21167069
72	Dunker, A. K.Cortese, M. S.Romero, P.Iakoucheva, L. M. and Uversky, V. N. (2005) Flexible nets. The roles of intrinsic disorder in protein interaction networks. FEBS J. , 272, 5129–5148 . doi: 10.1111/j.1742-4658.2005.04948.x pmid:16218947
73	Sarmady, M.Dampier, W. and Tozeren, A. (2011) HIV protein sequence hotspots for crosstalk with host hub proteins. PLoS ONE , 6, e23293. doi: 10.1371/journal.pone.0023293 pmid:21858059
74	Matthews, L. R.Vaglio, P.Reboul, J.Ge, H.Davis, B. P.Garrels, J.Vincent, S. and Vidal, M. (2001) Identification of potential interaction networks using sequence-based searches for conserved protein-protein interactions or “interologs”. Genome Res. , 11, 2120–2126 . doi: 10.1101/gr.205301 pmid:11731503
75	Wang, X.Wei, X.Thijssen, B.Das, J.Lipkin, S. M. and Yu, H. (2012) Three-dimensional reconstruction of protein networks provides insight into human genetic disease. Nat. Biotechnol. , 30, 159–164 . doi: 10.1038/nbt.2106 pmid:22252508
76	Tuncbag, N.Gursoy, A.Nussinov, R. and Keskin, O. (2011) Predicting protein-protein interactions on a proteome scale by matching evolutionary and structural similarities at interfaces using PRISM. Nat. Protoc. , 6, 1341–1354 . doi: 10.1038/nprot.2011.367 pmid:21886100
77	Kuzu, G.Keskin, O.Gursoy, A. and Nussinov, R. (2012) Constructing structural networks of signaling pathways on the proteome scaleCurr. Opin. Struct. Biol. , 22, 367–377 .
78	Shin, C. J.Wong, S.Davis, M. J. and Ragan, M. A. (2009) Protein-protein interaction as a predictor of subcellular location. BMC Syst. Biol. , 3, 28. doi: 10.1186/1752-0509-3-28 pmid:19243629
79	Jiang, J. Q. and Wu, M. (2012) Predicting multiplex subcellular localization of proteins using protein-protein interaction network: a comparative study. BMC Bioinformatics , 13, S20. doi: 10.1186/1471-2105-13-S10-S20 pmid:22759426
80	Liu, J.Zhao, H.Tan, J.Luo, D.Yu, W.Harner, E. J. and Shih, W. J. (2008) Is subcellular localization informative for modeling protein-protein interaction signal? Research Letters in Signal Processing , doi: 10.1155/2008/365152
81	Kanehisa, M.Goto, S.Sato, Y.Furumichi, M. and Tanabe, M. (2012) KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. , 40, D109–D114 . doi: 10.1093/nar/gkr988 pmid:22080510
82	Keseler, I. M.Mackie, A.Peralta-Gil, M.Santos-Zavaleta, A.Gama-Castro, S.Bonavides-Martínez, C.Fulcher, C.Huerta, A. M.Kothari, A.Krummenacker, M. (2013) EcoCyc: fusing model organism databases with systems biology. Nucleic Acids Res. , 41, D605–D612 . doi: 10.1093/nar/gks1027 pmid:23143106
83	Caspi, R.Altman, T.Dale, J. M.Dreher, K.Fulcher, C. A.Gilham, F.Kaipa, P.Karthikeyan, A. S.Kothari, A.Krummenacker, M. (2010) The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res., 38, D473–D479 . doi: 10.1093/nar/gkp875 pmid:19850718
84	Whitaker, J. W.Letunic, I.McConkey, G. A. and Westhead, D. R. (2009) MetaTIGER: a metabolic evolution resource. Nucleic Acids Res. , 37, D531–D538 . doi: 10.1093/nar/gkn826 pmid:18953037
85	Ideker, T. and Sharan, R. (2008) Protein networks in disease. Genome Res. , 18, 644–652 . doi: 10.1101/gr.071852.107 pmid:18381899
86	Wong, J. M.Ionescu, D. and Ingles, C. J. (2003) Interaction between BRCA2 and replication protein A is compromised by a cancer-predisposing mutation in BRCA2. Oncogene , 22, 28–33 . doi: 10.1038/sj.onc.1206071 pmid:12527904
87	Bruncko, M.Oost, T. K.Belli, B. A.Ding, H.Joseph, M. K.Kunzer, A.Martineau, D.McClellan, W. J.Mitten, M.Ng, S. C. (2007) Studies leading to potent dual inhibitors of Bcl-2 and Bcl-xL. J. Med. Chem. , 50, 641–662 . doi: 10.1021/jm061152t pmid:17256834
88	Schuster-B?ckler, B. and Bateman, A. (2008) Protein interactions in human genetic diseases. Genome Biol. , 9, R9. doi: 10.1186/gb-2008-9-1-r9 pmid:18199329
89	London, N.Raveh, B.Movshovitz-Attias, D. and Schueler-Furman, O. (2010) Can self-inhibitory peptides be derived from the interface of globular protein-protein interactions? Proteins , 78, 3140–3149 . doi: 10.1002/prot.22785
90	Eldar-Finkelman, H. and Eisenstein, M. (2009) Peptide inhibitors targeting protein kinases. Curr. Pharm. Des. , 15, 2463–2470 . doi: 10.2174/138161209788682253 pmid:19601843
91	Xie, L., Xie, L. and Bourne, P. E. (2011) Structure-based systems biology for analyzing off-target binding. Curr. Opin. Struct. Biol. , 21, 189–199 . doi: 10.1016/j.sbi.2011.01.004 pmid:21292475
92	Nwaka, S. and Hudson, A. (2006) Innovative lead discovery strategies for tropical diseases. Nat. Rev. Drug Discov. , 5, 941–955 . doi: 10.1038/nrd2144 pmid:17080030
93	Arrowsmith, J. (2011) Trial watch: phase III and submission failures: 2007–2010. Nat. Rev. Drug Discov. , 10, 87. doi: 10.1038/nrd3375 pmid:21283095
94	Arrowsmith, J. (2011) Trial watch: Phase II failures: 2008–2010. Nat. Rev. Drug Discov. , 10, 328–329 doi: 10.1038/nrd3439 pmid:. 21532551
95	Major, E. O. (2010) Progressive multifocal leukoencephalopathy in patients on immunomodulatory therapies. Annu. Rev. Med. , 61, 35–47 . doi: 10.1146/annurev.med.080708.082655 pmid:19719397
96	Booth, B. and Zemmel, R. (2003) Quest for the best. Nat. Rev. Drug Discov. , 2, 838–841 . doi: 10.1038/nrd1203 pmid:14526387
97	Mestres, J.Gregori-Puigjané, E.Valverde, S. and Solé, R. V. (2009) The topology of drug-target interaction networks: implicit dependence on drug properties and target families. Mol. Biosyst. , 5, 1051–1057 . doi: 10.1039/b905821b pmid:19668871
98	Müller, G. (2003) Medicinal chemistry of target family-directed masterkeys. Drug Discov. Today , 8, 681–691 . doi: 10.1016/S1359-6446(03)02781-8 pmid:12927511
99	Hopkins, A. L. and Groom, C. R. (2002) The druggable genome. Nat. Rev. Drug Discov. , 1, 727–730 . doi: 10.1038/nrd892 pmid:12209152
100	Bisson, W. H.Cheltsov, A. V.Bruey-Sedano, N.Lin, B.Chen, J.Goldberger, N.May, L. T.Christopoulos, A.Dalton, J. T.Sexton, P. M. (2007) Discovery of antiandrogen activity of nonsteroidal scaffolds of marketed drugs. Proc. Natl. Acad. Sci. U.S.A. , 104, 11927–11932 . doi: 10.1073/pnas.0609752104 pmid:17606915
101	Liu, C. I.Liu, G. Y.Song, Y.Yin, F.Hensler, M. E.Jeng, W. Y.Nizet, V.Wang, A. H. and Oldfield, E. (2008) A cholesterol biosynthesis inhibitor blocks Staphylococcus aureus virulence. Science , 319, 1391–1394 . doi: 10.1126/science.1153018 pmid:18276850
102	Specker, E.B?ttcher, J.Lilie, H.Heine, A.Schoop, A.Müller, G.Griebenow, N. and Klebe, G. (2005) An old target revisited: two new privileged skeletons and an unexpected binding mode for HIV-protease inhibitors. Angew. Chem. Int. Ed. Engl. , 44, 3140–3144 . doi: 10.1002/anie.200462643 pmid:15822136
103	Weber, A.Casini, A.Heine, A.Kuhn, D.Supuran, C. T.Scozzafava, A. and Klebe, G. (2004) Unexpected nanomolar inhibition of carbonic anhydrase by COX-2-selective celecoxib: new pharmacological opportunities due to related binding site recognition. J. Med. Chem., 47, 550–557 . doi: 10.1021/jm030912m pmid:14736236
104	Stauch, B.Hofmann, H.Perkovic, M.Weisel, M.Kopietz, F.Cichutek, K.Münk, C. and Schneider, G. (2009) Model structure of APOBEC3C reveals a binding pocket modulating ribonucleic acid interaction required for encapsidation. Proc. Natl. Acad. Sci. U.S.A., 106, 12079–12084 . doi: 10.1073/pnas.0900979106 pmid:19581596
105	Amaro, R. E.Schnaufer, A.Interthal, H.Hol, W.Stuart, K. D. and McCammon, J. A. (2008) Discovery of drug-like inhibitors of an essential RNA-editing ligase in Trypanosoma brucei. Proc. Natl. Acad. Sci. U.S.A. , 105, 17278–17283 . doi: 10.1073/pnas.0805820105 pmid:18981420
106	Lounkine, E.Keiser, M. J.Whitebread, S.Mikhailov, D.Hamon, J.Jenkins, J. L.Lavan, P.Weber, E.Doak, A. K.C?té, S. (2012) Large-scale prediction and testing of drug activity on side-effect targets. Nature 486, 361–367 . pmid:22722194
107	Butcher, E. C.Berg, E. L. and Kunkel, E. J. (2004) Systems biology in drug discovery. Nat. Biotechnol. , 22, 1253–1259 doi: 10.1038/nbt1017 pmid:.15470465
108	Yildirim, M. A.Goh, K. I.Cusick, M. E.Barabási, A. L. and Vidal, M. (2007) Drug-target network. Nat. Biotechnol. , 25, 1119–1126 . doi: 10.1038/nbt1338 pmid:17921997
109	Taylor, I. W.Linding, R.Warde-Farley, D.Liu, Y.Pesquita, C.Faria, D.Bull, S.Pawson, T.Morris, Q. and Wrana, J. L. (2009) Dynamic modularity in protein interaction networks predicts breast cancer outcome. Nat. Biotechnol. , 27, 199–204 . doi: 10.1038/nbt.1522 pmid:19182785
110	Chang, R. L.Xie, L.Xie, L.Bourne, P. E. and Palsson, B. ?. (2010) Drug off-target effects predicted using structural analysis in the context of a metabolic network model. PLOS Comput. Biol. , 6, e1000938. doi: 10.1371/journal.pcbi.1000938 pmid:20957118
111	Raman, K. and Chandra, N. (2008) Mycobacterium tuberculosis interactome analysis unravels potential pathways to drug resistance. BMC Microbiol. , 8, 234. doi: 10.1186/1471-2180-8-234 pmid:19105810
112	DiMasi, J. A.Hansen, R. W. and Grabowski, H. G. (2003) The price of innovation: new estimates of drug development costs. J. Health Econ., 22, 151–185 . doi: 10.1016/S0167-6296(02)00126-1 pmid:12606142
113	Hughes, J. P.Rees, S.Kalindjian, S. B. and Philpott, K. L. (2011) Principles of early drug discovery. Br. J. Pharmacol. , 162, 1239–1249 . doi: 10.1111/j.1476-5381.2010.01127.x pmid:21091654
114	Ashburn, T. T. and Thor, K. B. (2004) Drug repositioning: identifying and developing new uses for existing drugs. Nat. Rev. Drug Discov. , 3, 673–683 . doi: 10.1038/nrd1468 pmid:15286734
115	Dudley, J. T.Deshpande, T. and Butte, A. J. (2011) Exploiting drug-disease relationships for computational drug repositioning. Brief. Bioinform. , 12, 303–311 . doi: 10.1093/bib/bbr013 pmid:21690101
116	von Eichborn, J.Murgueitio, M. S.Dunkel, M.Koerner, S.Bourne, P. E. and Preissner, R. (2011) PROMISCUOUS: a database for network-based drug-repositioning. Nucleic Acids Res. , 39, D1060–D1066 . doi: 10.1093/nar/gkq1037 pmid:21071407
117	Chen, B.Dong, X.Jiao, D.Wang, H.Zhu, Q.Ding, Y. and Wild, D. J. (2010) Chem2Bio2RDF: a semantic framework for linking and data mining chemogenomic and systems chemical biology data. BMC Bioinformatics , 11, 255. doi: 10.1186/1471-2105-11-255 pmid:20478034
118	Ekins, S.Williams, A. J.Krasowski, M. D. and Freundlich, J. S. (2011) In silico repositioning of approved drugs for rare and neglected diseases. Drug Discov. Today , 16, 298–310 . doi: 10.1016/j.drudis.2011.02.016 pmid:21376136
119	Sardana, D.Zhu, C.Zhang, M.Gudivada, R. C.Yang, L. and Jegga, A. G. (2011) Drug repositioning for orphan diseases. Brief. Bioinform., 12, 346–356 doi: 10.1093/bib/bbr021 pmid:.21504985
120	Campillos, M.Kuhn, M.Gavin, A. C.Jensen, L. J. and Bork, P. (2008) Drug target identification using side-effect similarity. Science 321, 263–266 . doi: 10.1126/science.1158140 pmid:18621671
121	Duran-Frigola, M. and Aloy, P. (2012) Recycling side-effects into clinical markers for drug repositioning. Genome Med., 4 3. doi: 10.1186/gm302 pmid:22283977
122	Rajkumar, S. V. (2004) Thalidomide: tragic past and promising future. Mayo Clin. Proc. , 79, 899–903 . doi: 10.4065/79.7.899 pmid:15244387
123	Tatro, D. S. (1992) Drug Interaction Facts 1992. St Louis: Facts and Comparisons.
124	Huang, J.Niu, C.Green, C. D.Yang, L.Mei, H. and Han, J.-D. (2013) Systematic prediction of pharmacodynamic drug-drug interactions through protein-protein-interaction network. PLoS Comput. Biol. , 9, e1002998. doi: 10.1371/journal.pcbi.1002998 pmid:23555229
125	Greco, F. and Vicent, M. J. (2009) Combination therapy: opportunities and challenges for polymer-drug conjugates as anticancer nanomedicines. Adv. Drug Deliv. Rev. , 61, 1203–1213 . doi: 10.1016/j.addr.2009.05.006 pmid:19699247
126	Breen, E. C. and Walsh, J. J. (2010) Tubulin-targeting agents in hybrid drugs. Curr. Med. Chem. , 17, 609–639 . doi: 10.2174/092986710790416254 pmid:20088764
127	Altundag, O.Dursun, P. and Ayhan, A. (2010) Emerging drugs in endometrial cancers. Expert Opin. Emerg. Drugs , 15, 557–568 . doi: 10.1517/14728214.2010.517521 pmid:20828226
128	De Clercq, E. (2012) Where rilpivirine meets with tenofovir, the start of a new anti-HIV drug combination era. Biochem. Pharmacol., 84, 241–248 . doi: 10.1016/j.bcp.2012.03.024 pmid:22504027
129	Schuelter, E.Luebke, N.Jensen, B.Zazzi, M.S?nnerborg, A.Lengauer, T.Incardona, F.Camacho, R.Schmit, J.Clotet, B. (2012) Etravirine in protease inhibitor-free antiretroviral combination therapies. J. Int. AIDS Soc. , 15, 18260. doi: 10.7448/IAS.15.6.18260
130	Bogojeska, J. and Lengauer, T. (2012) Hierarchical Bayes Model for predicting effectiveness of HIV combination therapies. Stat. Appl. Genet. Mol. Biol. , 11, 11. doi: 10.1515/1544-6115.1769 pmid:22611598

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