Protein–protein interaction

The horseshoe shaped ribonuclease inhibitor (shown as wireframe) forms a protein–protein interaction with the ribonuclease protein. The contacts between the two proteins are shown as coloured patches.

Protein–protein interactions (PPIs) refer to lasting and specific physical contacts established between two or more proteins as a result of biochemical events and/or electrostatic forces. Commonly they are understood as physical contacts with molecular docking between proteins that occur in a cell or in a living organism in specific biomolecular contexts (De Las Rivas J and Fontanillo C, 2010).[1]

Proteins rarely act alone. Many molecular processes within a cell are carried out by molecular machines that are built from a large number of protein components organized by their PPIs. These interactions are important for the interactomics system of the living cell and aberrant PPIs are the bases of multiple diseases, such as Creutzfeld-Jacob, Alzheimer's disease, and cancer.

PPIs have been studied from different perspectives: biochemistry, quantum chemistry, molecular dynamics, signal transduction, among others.[2] All this information enables the creation of large protein interaction networks – similar to metabolic or genetic/epigenetic networks – that empower the current knowledge on biochemical cascades and disease pathogenesis, as well as provide putative new therapeutic targets.

Examples of protein–protein interactions

The activity of the cell is regulated by extracellular signals. Signals propagation to inside and/or along the interior of cells depends on PPIs between the various signaling molecules. This process, called signal transduction, plays a fundamental role in many biological processes and in many diseases (e.g. Parkinson's disease and cancer).
A protein may be carrying another protein (for example, from cytoplasm to nucleus or vice versa in the case of the nuclear pore importins).
In many biosynthetic processes enzymes interact with each other to produce small compounds or other macromolecules.
Physiology of muscle contraction involves several interactions. Myosin filaments act as molecular motors and by binding to actin enables filament sliding.[3] Furthermore, members of the skeletal muscle lipid droplet-associated proteins family associate with other proteins, as activator of adipose triglyceride lipase and its coactivator comparative gene identification-58, to regulate lipolysis in skeletal muscle.[4]

Types of protein–protein interactions

Main article: Multiprotein complex

To describe the types of protein–protein interactions (PPIs) it is important to consider that proteins can interact in a "transient" way (to produce some specific effect in a short time) or to interact with other proteins in a "stable" way to build multiprotein complexes that are molecular machines within the living systems. A protein complex assembly can result in the formation of homo-oligomeric or hetero-oligomeric complexes. In addition to the conventional complexes, as enzyme-inhibitor and antibody-antigen, interactions can also be established between domain-domain and domain-peptide. Another important distinction to identify protein-protein interactions is the way they have been determined, since there are techniques that measure direct physical interactions between protein pairs, named “binary” methods, while there are other techniques that measure physical interactions among groups of proteins, without pairwise determination of protein partners, named “co-complex” methods.[1]

Homo-oligomers vs. hetero-oligomers

Homo-oligomers are macromolecular complexes constituted by only one type of protein subunit. Protein subunits assembly is guided by the establishment of non-covalent interactions in the quaternary structure of the protein. Disruption of homo-oligomers in order to return to the initial individual monomers often requires denaturation of the complex.[5] Several enzymes, carrier proteins, scaffolding proteins, and transcriptional regulatory factors carry out their functions as homo-oligomers. Distinct protein subunits interact in hetero-oligomers, which are essential to control several cellular functions. The importance of the communication between heterologous proteins is even more evident during cell signaling events and such interactions are only possible due to structural domains within the proteins (as described below).

Stable interactions vs. transient interactions

Stable interactions involve proteins that interact for a long time, taking part of permanent complexes as subunits, in order to carry out structural or functional roles. These are usually the case of homo-oligomers (e.g. cytochrome c), and some hetero-oligomeric proteins, as the subunits of ATPase. On the other hand, a protein may interact briefly and in a reversible manner with other proteins in only certain cellular contexts – cell type, cell cycle stage, external factors, presence of other binding proteins, etc. – as it happens with most of the proteins involved in biochemical cascades. These are called transient interactions. For example, some G protein-coupled receptors only transiently bind to Gi/o proteins when they are activated by extracellular ligands,[6] while some Gq-coupled receptors, such as muscarinic receptor M3, pre-couple with Gq proteins prior to the receptor-ligand binding.[7]

Covalent vs. non-covalent

Covalent interactions are those with the strongest association and are formed by disulphide bonds or electron sharing. Although being rare, these interactions are determinant in some posttranslational modifications, as ubiquitination and SUMOylation. Non-covalent bonds are usually established during transient interactions by the combination of weaker bonds, such as hydrogen bonds, ionic interactions, Van der Waals forces, or hydrophobic bonds.[8]

Techniques to study the molecular structure of protein complexes

Crystal structure of modified Gramicidin S horizontally determined by X-ray crystallography
NMR structure of cytochrome C illustrating its dynamics in solution

The molecular structures of many protein complexes have been unlocked by the technique of X-ray crystallography.[9][10] The first structure to be solved by this method was that of sperm whale myoglobin by Sir John Cowdery Kendrew.[11] In this technique the angles and intensities of a beam of X-rays diffracted by crystalline atoms are detected in a film, thus producing a three-dimensional picture of the density of electrons within the crystal.[12]

Later, nuclear magnetic resonance also started to be applied with the aim of unravelling the molecular structure of protein complexes. One of the first examples was the structure of calmodulin-binding domains bound to calmodulin.[10][13] This technique is based on the study of magnetic properties of atomic nuclei, thus determining physical and chemical properties of the correspondent atoms or the molecules. Nuclear magnetic resonance is advantageous for characterizing weak PPIs.[14]

Properties of protein–protein interface

The study of the molecular structure can give fine details about the interface that enables the interaction between proteins. When characterizing PPI interfaces it is important to take into account the type of complex.[5]

Parameters evaluated include size (measured in absolute dimensions Å2 or in solvent-accessible surface area (SASA)), shape, complementarity between surfaces, residue interface propensities, hydrophobicity, segmentation and secondary structure, and conformational changes on complex formation.[5]

The great majority of PPI interfaces reflects the composition of protein surfaces, rather than the protein cores, in spite of being frequently enriched in hydrophobic residues, particularly in aromatic residues.[15] PPI interfaces are dynamic and frequently planar, although they can be globular and protruding as well.[16] Based on three structures – insulin dimer, trypsin-pancreatic trypsin inhibitor complex, and oxyhaemoglobin – Cyrus Chothia and Joel Janin found that between 1,130 and 1,720 Å2 of surface area was removed from contact with water indicating that hydrophobicity is a major factor of stabilization of PPIs.[17] Later studies refined the buried surface area of the majority of interactions to 1,600±350 Å2. However, much larger interaction interfaces were also observed and were associated with significant changes in conformation of one of the interaction partners.[9] PPIs interfaces exhibit both shape and electrostatic complementarity.[5][18]

Factors that regulate protein–protein interactions

Structural domains involved in protein–protein interactions

Proteins hold structural domains that allow their interaction with and bind to specific sequences on other proteins:

SH2 domains are structurally composed by three-stranded twisted beta sheet sandwiched flanked by two alpha-helices. The existence of a deep binding pocket with high affinity for phosphotyrosine, but not for phosphoserine or phosphothreonine, is essential for the recognition of tyrosine phosphorylated proteins, mainly autophosphorylated growth factor receptors. Growth factor receptor binding proteins and phospholipase Cγ are examples of proteins that have SH2 domains.[19]
Structurally, SH3 domains are constituted by a beta barrel formed by two orthogonal beta sheets and three anti-parallel beta strands. These domains recognize proline enriched sequences, as polyproline type II helical structure (PXXP motifs) in cell signaling proteins like protein tyrosine kinases and the growth factor receptor bound protein 2 (Grb2).[19]
PTB domains interact with sequences that contain a phosphotyrosine group. These domains can be found in the insulin receptor substrate.[19]
LIM domains were initially identified in three homeodomain transcription factors (lin11, is11, and mec3). In addition to this homeodomain proteins and other proteins involved in development, LIM domains have also been identified in non-homeodomain proteins with relevant roles in cellular differentiation, association with cytoskeleton and senescence. These domains contain a tandem cysteine-rich Zn2+-finger motif and embrace the consensus sequence CX2CX16-23HX2CX2CX2CX16-21CX2C/H/D. LIM domains bind to PDZ domains, bHLH transcription factors, and other LIM domains.[19]
SAM domains are composed by five helices forming a compact package with a conserved hydrophobic core. These domains, which can be found in the Eph receptor and the stromal interaction molecule (STIM) for example, bind to non-SAM domain-containing proteins and they also appear to have the ability to bind RNA.[19]
PDZ domains were first identified in three guanylate kinases: PSD-95, DlgA and ZO-1. These domains recognize carboxy-terminal tri-peptide motifs (S/TXV), other PDZ domains or LIM domains and bind them through a short peptide sequence that has a C-terminal hydrophobic residue. Some of the proteins identified as having PDZ domains are scaffolding proteins or seem to be involved in ion receptor assembling and receptor-enzyme complexes formation.[19]
FERM domains contain basic residues capable of binding PtdIns(4,5)P2. Talin and focal adhesion kinase (FAK) are two of the proteins that present FERM domains.[19]
CH domains are mainly present in cytoskeletal proteins as parvin.[19]
Pleckstrin homology domains bind to phosphoinositides and acid domains in signaling proteins.
WW domains bind to proline enriched sequences.
Found in cytokine receptors

Methods to investigate protein–protein interactions

There are a multitude of methods to detect them.[20] Each of the approaches has its own strengths and weaknesses, especially with regard to the sensitivity and specificity of the method. The most conventional and widely used high-throughput methods are yeast two-hybrid screening and affinity purification coupled to mass spectrometry.[1]

Principles of yeast and mammalian two-hybrid systems

Yeast two-hybrid screening

Main article: Two-hybrid screening

This system was firstly described in 1989 by Fields and Song using Saccharomyces cerevisiae as biological model.[21] Yeast two hybrid allows the identification of pairwise PPIs (binary method) in vivo, indicating non-specific tendencies towards sticky interactions.[22]

Yeast cells are transfected with two plasmids: the bait (protein of interest fused with the DNA-binding domain of a yeast transcription factor, like Gal4), and the prey (a library of cDNA fragments linked to the activation domain of the transcription factor. Transcription of reporter genes does not occur unless bait and prey interact with each other and form a functional transcription factor. Thus, the interaction between proteins can be inferred by the presence of the products resultant of the reporter gene expression.[8][23]

Despite its usefulness, the yeast two-hybrid system has limitations: specificity is relatively low; uses yeast as main host system, which can be a problem when studying other biological models; the number of PPIs identified is usually low because some transient PPIs are lost during purification steps;[24] and, understates membrane proteins, for example.[25][26] Limitations have been overcoming by the emergence of yeast two-hybrid variants, such as the membrane yeast two-hybrid (MYTH)[26] and the split-ubiquitin system,[23] which are not limited to interactions that occur in the nucleus; and, the bacterial two-hybrid system, performed in bacteria;[27]

Principle of Tandem Affinity Purification

Affinity purification coupled to mass spectrometry

Main article: Mass spectrometry

Affinity purification coupled to mass spectrometry mostly detects stable interactions and thus better indicates functional in vivo PPIs.[22][23] This method starts by purification of the tagged protein, which is expressed in the cell usually at in vivo concentrations, and its interacting proteins (affinity purification). One of the most advantageous and widely used method to purify proteins with very low contaminating background is the tandem affinity purification, developed by Bertrand Seraphin and Mathias Mann and respective colleagues. PPIs can then be quantitatively and qualitatively analysed by mass spectrometry using different methods: chemical incorporation, biological or metabolic incorporation (SILAC), and label-free methods.[5]

Other potential methods

Diverse techniques to identify PPIs have been emerging along with technology progression. These include co-immunoprecipitation, protein microarrays, analytical ultracentrifugation, light scattering, fluorescence spectroscopy, luminescence-based mammalian interactome mapping (LUMIER), resonance-energy transfer systems, mammalian protein–protein interaction trap, electro-switchable biosurfaces, surface plasmon resonance, protein-fragment complementation assay, and calorimetry.[25][26]

Text mining methods

Text mining protocol.

Publicly available information from biomedical research is readily accessible through the internet and is becoming a powerful resource for predictive protein-protein interactions and protein docking. Text mining is much less time costly and consuming compared to other high-throughput techniques. Currently, these methods generally detect binary relations between interacting protein from individual sentences using machine learning and rule/pattern-based information extraction and machine learning approaches.[28] A wide variety of text mining predicting PPIs applications are available for public use, as well as repositories which often stores manually validated and/or computationally predicted PPIs. The principal stages of text mining divides the implementation into two stages: information retrieval, where literature abstracts containing names of either or both proteins complexes are selected and information extraction, where detecting occurrences of residues are retrieved. The extraction is automated by searching for co-existing sentences, abstracts or paragraphs within textual context.

There are also studies using phylogenetic profiling, basing their functionalities on the theory that proteins involved in common pathways co-evovle in a correlated fashion across large number of species. More complex text mining methodologies use advanced dictionaries and generate networks by Natural Language Processing (NLP) of text, considering gene names as nodes and verbs as edges, other developments involve kernel methods to predict protein interactions.[29]

Protein–protein interaction databases

Large scale identification of PPIs generated hundreds of thousands interactions, which were collected together in specialized biological databases that are continuously updated in order to provide complete interactomes. The first of these databases was the Database of Interacting Proteins (DIP).[30] Since that time, the number of public databases has been increasing. Databases can be subdivided into primary databases, meta-databases, and prediction databases.[31]

Protein–protein interaction networks

Main article: Interactome
Network visualisation of the human interactome where each point represents a protein and each blue line between them is an interaction.

Information found in PPIs databases supports the construction of interaction networks. Although the PPI network of a given query protein can be represented in textbooks, diagrams of whole cell PPIs are frankly complex and difficult to generate.

One example of a manually produced molecular interaction map is the Kurt Kohn's 1999 map of cell cycle control.[32] Drawing on Kohn's map, Schwikowski et al. in 2000 published a paper on PPIs in yeast, linking together 1,548 interacting proteins determined by two-hybrid screening. They used a layered graph drawing method to find an initial placement of the nodes and then improved the layout using a force-based algorithm.[33][34]

Bioinformatic tools have been developed to simplify the difficult task of visualizing molecular interaction networks and complement them with other types of data. For instance, Cytoscape is an open-source software widely used and lots of plugins are currently available.[31][35] Pajek software is advantageous for the visualization and analysis of very large networks.[36]

Identification of functional modules in PPI networks is an important challenge in bioinformatics. Functional modules means a set of proteins that are highly connected to each other in PPI network. It is almost similar problem as community detection in social networks. There are some methods such as Jactive [37] modules and MoBaS.[38] Jactive modules integrate PPI network and gene expression data where as MoBaS integrate PPI network and Genome Wide association Studies.

The awareness of the major roles of PPIs in numerous physiological and pathological processes has been driving the challenge of unravel many interactomes. Examples of published interactomes are the thyroid specific DREAM interactome[39] and the PP1α interactome in human brain.[40]

Signed Protein-Protein Interaction Networks

The protein protein interactions are displayed in a signed network that describes what type of interactions that are taking place [41]

Protein–protein interactions often result in one of the interacting proteins either being ‘activated’ or ‘repressed’. Such effects can be indicated in a PPI network by "signs" (e.g. "activation" or "inhibition"). Although such attributes have been added to networks for a long time,[42] Vinayagam et al. (2014) coined the term Signed network for them. Signed networks are often expressed by labeling the interaction as either positive or negative. A positive interaction is one where the interaction results in one of the proteins being activated. Conversely a negative interaction indicates that one of the proteins being inactivated.[43]

Protein-Protein interaction networks are often constructed as a result of lab experiments such as yeast two hybrid screens or ‘affinity purification and subsequent mass spectrometry’ techniques.[44] However these methods do not provide the layer of information needed in order to determine what type of interaction is present in order to be able to attribute signs to the network diagrams.

RNA Interference Screens

RNA Interference (RNAi) screens (repression of individual proteins between transcription and translation) are one method that can be utilized in the process of providing signs to the protein-protein interactions. Individual proteins are repressed and the resulting phenotypes are analyzed. A correlating phenotypic relationship (i.e. where the inhibition of either of two proteins results in the same phenotype) indicates a positive, or activating relationship. Phenotypes that do no correlate (i.e. where the inhibition of either of two proteins results in two different phenotypes) indicate a negative or inactivating relationship. If protein A is dependent on protein B for activation then the inhibition of either protein A or B will result in a cell losing the service that is provided by protein A and the phenotypes will be the same for the inhibition of either A or B. If, however, protein A is inactivated by protein B then the phenotypes will differ depending on which protein is inhibited (inhibit protein B and it can no longer inactivate protein A leaving A active however inactivate A and there is nothing for B to activate since A is inactive and the phenotype changes). Multiple RNAi screens need to be performed in order to reliably appoint a sign to a given protein-protein interaction. Vinayagam et al. who devised this technique state that a minimum of nine RNAi screens are required with confidence increasing as one carries out more screens.[43]

Protein–protein interaction as therapeutic targets

Modulation of PPI is challenging and is receiving increasing attention by the scientific community.[45] Several properties of PPI such as allosteric sites and hotspots, have been incorporated into drug-design strategies.[46][47] The relevance of PPI as putative therapeutic targets for the development of new treatments is particularly evident in cancer, with several ongoing clinical trials within this area. The consensus among these promising targets is, nonetheless, denoted in the already available drugs on the market to treat a multitude of diseases. Examples are Titrobifan, inhibitor of the glycoprotein IIb/IIIa, used as a cardiovascular drug, and Maraviroc, inhibitor of the CCR5-gp120 interaction, used as anti-HIV drug.[48] Recently, Amit Jaiswal and others were able to develop 30 peptides using protein–protein interaction studies to inhibit telomerase recruitment towards telomeres.[49][50]

See also

References

  1. 1 2 3 De Las Rivas, J; Fotanillo, C. (2010). "Protein-protein interactions essentials: key concepts to building and analyzing interactome networks.". PLoS Comput Biol 6 (6): e1000807. doi:10.1371/journal.pcbi.1000807. PMC 2891586. PMID 20589078.
  2. ↑ Herce, Henry D.; Deng, Wen; Helma, Jonas; Leonhardt, Heinrich; Cardosoa, M. Cristina (2013). "Visualization and targeted disruption of protein interactions in living cells". Nature Communications 4: 2660. doi:10.1038/ncomms3660. PMC 3826628. PMID 24154492.
  3. ↑ Cooper, G.M. (2000). The cell : a molecular approach (2nd ed.). Washington DC: ASM Press. ISBN 0-87893-106-6.
  4. ↑ MacPherson, R.E.; Ramos, S.V.; Vandenboom, R.; Roy, B.D.; Peters, S.J. (2013). "Skeletal muscle PLIN proteins, ATGL and CGI-58, interactions at rest and following stimulated contraction.". American Journal of Physiology. Regulatory, Integrative and Comparative Physiology 304 (8): R644–50. doi:10.1152/ajpregu.00418.2012. PMC 3627954. PMID 23408028.
  5. 1 2 3 4 5 Jones, S.; Thornton, J.M. (1996). "Principles of protein–protein interactions.". Proceedings of the National Academy of Sciences of the United States of America 93 (1): 13–20. doi:10.1073/pnas.93.1.13. PMC 40170. PMID 8552589.
  6. ↑ Kou Qin, Pooja R. Sethi and Nevin A. Lambert (August 2008). "Abundance and stability of complexes containing inactive G protein-coupled receptors and G proteins". The FASEB Journal 22 (8): 2920–2927. doi:10.1096/fj.08-105775. PMC 2493464. PMID 18434433.
  7. ↑ Kou Qin, Chunmin Dong, Guangyu Wu & Nevin A Lambert (August 2011). "Inactive-state preassembly of Gq-coupled receptors and Gq heterotrimers". Nature Chemical Biology 7 (11): 740–747. doi:10.1038/nchembio.642. PMC 3177959. PMID 21873996.
  8. 1 2 Westermarck, J.; Ivaska, J.; Corthals, G.L. (2013). "Identification of protein interactions involved in cellular signaling.". Molecular & cellular proteomics : MCP 12 (7): 1752–63. doi:10.1074/mcp.R113.027771. PMC 3708163. PMID 23481661.
  9. 1 2 Janin J, Chothia C (September 1990). "The structure of protein-protein recognition sites". The Journal of Biological Chemistry 265 (27): 16027–30. PMID 2204619.
  10. 1 2 Bruce, A.; Johnson, A.; Lewis, J.; Raff, M.; Roberts, K.; Walter, P. (2002). Molecular biology of the cell (4th ed.). New York: Garland Science. ISBN 0-8153-3218-1.
  11. ↑ Kendrew, J. C.; Bodo, G.; Dintzis, H. M.; Parrish, R. G.; Wyckoff, H.; Phillips, D. C. (1958). "A Three-Dimensional Model of the Myoglobin Molecule Obtained by X-Ray Analysis". Nature 181 (4610): 662–6. Bibcode:1958Natur.181..662K. doi:10.1038/181662a0. PMID 13517261.
  12. ↑ Cooper, D.R.; Porebski, P.J.; Chruszcz, M.; Minor, W. (2011). "X-ray crystallography: Assessment and validation of protein–small molecule complexes for drug discovery.". Expert opinion on drug discovery 6 (8): 771–782. doi:10.1517/17460441.2011.585154. PMC 3138648. PMID 21779303.
  13. ↑ Wand, A.J.; Englander, SW (1996). "Protein complexes studied by NMR spectroscopy.". Current opinion in biotechnology 7 (4): 403–8. doi:10.1016/s0958-1669(96)80115-7. PMC 3442359. PMID 8768898.
  14. ↑ Vinogradova, O.; Qin, J. (2012). "NMR as a unique tool in assessment and complex determination of weak protein-protein interactions.". Topics in current chemistry 326: 35–45. doi:10.1007/128_2011_216. PMC 3676910. PMID 21809187.
  15. ↑ Yan, C.; Wu, F.; Jernigan, R.L.; Dobbs, D.; Honavar, V. (2008). "Characterization of protein–protein interfaces.". The protein journal 27 (1): 59–70. doi:10.1007/s10930-007-9108-x. PMC 2566606. PMID 17851740.
  16. ↑ Jones, S.; Thornton, J.M. (1997). "Analysis of protein–protein interaction sites using surface patches.". Journal of Molecular Biology 272 (1): 121–32. doi:10.1006/jmbi.1997.1234. PMID 9299342.
  17. ↑ Chothia, C.; Janin, J. (1975). "Principles of protein-protein recognition". Nature 256 (5520): 705–708. doi:10.1038/256705a0. PMID 1153006.
  18. ↑ Kou Qin, Chunmin Dong, Guangyu Wu & Nevin A Lambert (August 2011). "Inactive-state preassembly of Gq-coupled receptors and Gq heterotrimers". Nature Chemical Biology 7 (11): 740–747. doi:10.1038/nchembio.642. PMC 3177959. PMID 21873996.
  19. 1 2 3 4 5 6 7 8 Berridge, M.J. (2012). "Cell Signalling Biology: Module 6 – Spatial and Temporal Aspects of Signalling". Biochemical Journal. doi:10.1042/csb0001006.
  20. ↑ Phizicky, E. M.; Fields, S. (1995). "Protein-protein interactions: Methods for detection and analysis". Microbiological reviews 59 (1): 94–123. PMC 239356. PMID 7708014.
  21. ↑ Terentiev, A.A.; Moldogazieva, N.T.; Shaitan, K.V. (2009). "Dynamic proteomics in modeling of the living cell. Protein–protein interactions.". Biochemistry. Biokhimiia 74 (13): 1586–607. doi:10.1134/s0006297909130112. PMID 20210711.
  22. 1 2 Brettner, L. M.; Masel, J. (2012). "Protein stickiness, rather than number of functional protein-protein interactions, predicts expression noise and plasticity in yeast". BMC Systems Biology 6: 128. doi:10.1186/1752-0509-6-128. PMC 3527306. PMID 23017156.
  23. 1 2 3 Wodak, S.J.; Vlasblom, J.; Turinsky, A.L.; Pu, S. (2013). "Protein–protein interaction networks: the puzzling riches.". Current Opinion in Structural Biology 23 (6): 941–53. doi:10.1016/j.sbi.2013.08.002. PMID 24007795.
  24. ↑ Rajagopala, S.V.; Sikorski, P.; Caufield, J.H.; Tovchigrechko, A.; Uetz, P. (2012). "Studying protein complexes by the yeast two-hybrid system.". Methods 58 (4): 392–9. doi:10.1016/j.ymeth.2012.07.015. PMC 3517932. PMID 22841565.
  25. 1 2 Stelzl, U.; Wanker, E.E. (2006). "The value of high quality protein–protein interaction networks for systems biology.". Current Opinion in Chemical Biology 10 (6): 551–8. doi:10.1016/j.cbpa.2006.10.005. PMID 17055769.
  26. 1 2 3 Petschnigg, J.; Snider, J.; Stagljar, I. (2011). "Interactive proteomics research technologies: recent applications and advances.". Current opinion in biotechnology 22 (1): 50–8. doi:10.1016/j.copbio.2010.09.001. PMID 20884196.
  27. ↑ Battesti, A; Bouveret, E (2012). "The bacterial two-hybrid system based on adenylate cyclase reconstitution in Escherichia coli.". Methods 58 (4): 325–34. doi:10.1016/j.ymeth.2012.07.018. PMID 22841567.
  28. ↑ Badal, Varsha D.; Kundrotas, Petras J.; Vakser, Ilya A. (2015-12-09). "Text Mining for Protein Docking". PLoS Comput Biol 11 (12): e1004630. doi:10.1371/journal.pcbi.1004630.
  29. ↑ Papanikolaou, Nikolas; Pavlopoulos, Georgios A.; Theodosiou, Theodosios; Iliopoulos, Ioannis (2015-03-01). "Protein–protein interaction predictions using text mining methods". Methods. Text mining of biomedical literature 74: 47–53. doi:10.1016/j.ymeth.2014.10.026.
  30. ↑ Xenarios, I.; Rice, D. W.; Salwinski, L.; Baron, M. K.; Marcotte, E. M.; Eisenberg, D. (2000). "DIP: The database of interacting proteins". Nucleic Acids Research 28 (1): 289–291. doi:10.1093/nar/28.1.289. PMC 102387. PMID 10592249.
  31. 1 2 3 4 5 De Las Rivas, J.; Fontanillo, C. (2010). "Protein–protein interactions essentials: key concepts to building and analyzing interactome networks.". PLOS Computational Biology 6 (6): e1000807. doi:10.1371/journal.pcbi.1000807. PMC 2891586. PMID 20589078.
  32. ↑ Schwikowski, B.; Uetz, P.; Fields, S. (2000). "A network of protein-protein interactions in yeast". Nature Biotechnology 18 (12): 1257–1261. doi:10.1038/82360. PMID 11101803.
  33. ↑ Rigaut, G.; Shevchenko, A.; Rutz, B.; Wilm, M.; Mann, M.; Séraphin, B. (1999). "A generic protein purification method for protein complex characterization and proteome exploration". Nature Biotechnology 17 (10): 1030–1032. doi:10.1038/13732. PMID 10504710.
  34. ↑ Prieto, C.; De Las Rivas, J. (2006). "APID: Agile Protein Interaction DataAnalyzer". Nucleic Acids Research 34 (Web Server issue): W298–W302. doi:10.1093/nar/gkl128. PMC 1538863. PMID 16845013.
  35. ↑ Kohl, Michael; Wiese, Sebastian; Warscheid, Bettina (2011). "Cytoscape: Software for Visualization and Analysis of Biological Networks". Data Mining in Proteomics. Methods in Molecular Biology 696. pp. 291–303. doi:10.1007/978-1-60761-987-1_18. ISBN 978-1-60761-986-4.
  36. ↑ Raman, K. (2010). "Construction and analysis of protein–protein interaction networks.". Automated experimentation 2 (1): 2. doi:10.1186/1759-4499-2-2. PMC 2834675. PMID 20334628.
  37. ↑ Ideker, Trey; Ozier, Owen; Schwikowski, Benno; Siegel, Andrew F. (2002-01-01). "Discovering regulatory and signalling circuits in molecular interaction networks". Bioinformatics (Oxford, England). 18 Suppl 1: S233–240. doi:10.1093/bioinformatics/18.suppl_1.s233. ISSN 1367-4803. PMID 12169552.
  38. ↑ Ayati, Marzieh; Erten, Sinan; Chance, Mark R.; Koyutürk, Mehmet (2015-06-30). "MOBAS: identification of disease-associated protein subnetworks using modularity-based scoring". EURASIP Journal on Bioinformatics and Systems Biology 2015 (1): 1–14. doi:10.1186/s13637-015-0025-6. ISSN 1687-4153.
  39. ↑ Rivas, M.; Villar, D.; González, P.; Dopazo, X.M.; Mellstrom, B.; Naranjo, J.R. (2011). "Building the DREAM interactome.". Science China. Life sciences 54 (8): 786–92. doi:10.1007/s11427-011-4196-4. PMID 21786202.
  40. ↑ Esteves, S.L.; Domingues, S.C.; da Cruz e Silva, O.A.; Fardilha, M.; da Cruz e Silva, E.F. (2012). "Protein phosphatase 1α interacting proteins in the human brain.". Omics : a journal of integrative biology 16 (1–2): 3–17. doi:10.1089/omi.2011.0041. PMC 3275796. PMID 22321011.
  41. ↑ Fischer, Bernd; Sandmann, Thomas; Horn, Thomas; Billmann, Maximilian; Chaudhary, Varun; Huber, Wolfgang; Boutros, Michael (2015-04-02). "A map of directional genetic interactions in a metazoan cell". eLife 4: e05464. doi:10.7554/eLife.05464. ISSN 2050-084X. PMC 4384530. PMID 25748138.
  42. ↑ Ideker T., Tan K. & Uetz P. (2005) Visualization and integration of protein-protein interactions. In: Golemis,E. (ed.) Protein-Protein Interactions – A Molecular Cloning Manual, 2nd ed. Cold Spring Harbor Laboratory Press.
  43. 1 2 Vinayagam, Arunachalam; Zirin, Jonathan; Roesel, Charles; Hu, Yanhui; Yilmazel, Bahar; Samsonova, Anastasia A.; Neumüller, Ralph A.; Mohr, Stephanie E.; Perrimon, Norbert (2014-01-01). "Integrating protein-protein interaction networks with phenotypes reveals signs of interactions". Nature Methods 11 (1): 94–99. doi:10.1038/nmeth.2733. ISSN 1548-7091. PMC 3877743. PMID 24240319.
  44. ↑ Chen, Ginny I.; Gingras, Anne-Claude. "Affinity-purification mass spectrometry (AP-MS) of serine/threonine phosphatases". Methods 42 (3): 298–305. doi:10.1016/j.ymeth.2007.02.018.
  45. ↑ Laraia, L.; McKenzie, G.; Spring, D.R.; Venkitaraman, A.R.; Huggins, D.J. (June 2015). "Overcoming Chemical, Biological, and Computational Challenges in the Development of Inhibitors Targeting Protein-Protein Interactions". Chemistry & Biology 22 (6): 689–703. doi:10.1016/j.chembiol.2015.04.019. PMC 4518475. PMID 26091166.
  46. ↑ Arkin, M.R.; Wells, J.A. (April 2004). "Small-molecule inhibitors of protein–protein interactions: progressing towards the dream". Nature Reviews Drug Discovery 3 (4): 301–317. doi:10.1038/nrd1343. PMID 15060526.
  47. ↑ Chen, J.; Sawyer, N.; Regan, L. (2013). "Protein–protein interactions: General trends in the relationship between binding affinity and interfacial buried surface area". Protein Science 22 (4): 510–515. doi:10.1002/pro.2230.
  48. ↑ Ivanov, A.A.; Khuri, F.R.; Fu, H. (2013). "Targeting protein–protein interactions as an anticancer strategy". Trends in Pharmacological Sciences 34 (7): 393–400. doi:10.1016/j.tips.2013.04.007.
  49. ↑ Jaiswal, A (9 September 2014). "Molecular inhibition of telomerase recruitment using designer peptides: an in silico approach.". Journal of biomolecular structure & dynamics: 1–18. doi:10.1080/07391102.2014.953207. PMID 25204447.
  50. ↑ Jaiswal, Amit. "AtTRB1–3 Mediates Structural Changes in AtPOT1b to Hold ssDNA". ISRN Structural Biology 2014: 1–16. doi:10.1155/2014/827201.
  51. ↑ Casado-Vela, Juan (2014). "Screening of Protein–Protein and Protein–DNA Interactions Using Microarrays: Applications in Biomedicine". Advances in Protein Chemistry and Structural Biology 95: 231–281. doi:10.1016/B978-0-12-800453-1.00008-7. PMID 24985775.

External links

This article is issued from Wikipedia - version of the Monday, February 15, 2016. The text is available under the Creative Commons Attribution/Share Alike but additional terms may apply for the media files.