Protein interactions and their importance
Published on EMBL-EBI Train online (http://www.ebi.ac.uk/training/online)
Protein interactions and their importance
Pablo Porras Millan [1]
Systems
Proteins
Beginner
1 hour
This course will provide an introduction to molecular interactions, their importance and the
methodologies use to generate and capture interaction data.
Learning objectives:
Understanding the importance of molecular interaction information and be able to provide
examples of different types of molecular interaction
Be able to list the main experimental methodologies used to study protein-protein
interactions
Be aware of the limitations that these methodologies have
Knowing where to find molecular interaction data at EMBL-EBI
Protein-protein Interactions
Understanding physical and functional interactions between molecules in living systems is of vital
importance in biology. Several powerful methodologies and techniques have been developed to
generate molecular interaction data, concentrating mainly on protein–protein interactions (Figure 1)
10
[2].
Given the importance of protein-protein interactions and their vast numbers in comparison with
datasets involving other types of molecules, we focus on them in this course.
Molecular interaction data can be generated using many different techniques, all of which have their
strengths and weaknesses. However, it is important to stress that all molecular interaction data
is to some degree artifactual. No single method can accurately reproduce a true binary
interaction observed under physiological conditions.
The "boom" in molecular interaction research that we have experienced in the past few years has
been caused by the increasingly wide availability of high throughput technologies that can
potentially provide information on several thousand pairwise interactions at a time 11 [2]. Such highthroughput studies can provide a global 'snapshot' of the molecular interactions that take place in a
cell, an organism or as particular physiological context. This is known as the interactome [3].
Understanding the cellular machinery and identifying interactions that underpin particular
physiological processes relies on the retrieval, organisation and analysis of these valuable data
[2]. Efforts have been made in the protein interaction field towards addressing this challenge.
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Figure 1. Data obtained by interaction detection methods are stored and represented in databases.
This figure is adapted from Koh et al.5 [2]
This course will review some of the main techniques used to produce protein interaction data and
discuss their respective advantages and disadvantages. We will discuss how you should regard the
reliability of each of the methods. We will also explain how the experimental data are captured
electronically.
In nucleotide sequence databases, sequence data is represented simply as a string of letters.
Representing protein interaction data is somewhat more complex. We need to use the correct
identifiers for the molecules reported to interact; we also need to record the method used to detect
the interaction, among other relevant information. Although there are ongoing significant
international efforts to standardise how such information is reported and described and enable
exchange of data among different public repositories, this field is not as mature as the nucleotide
sequence-data field.
The importance of molecular interactions
Molecular interactions are important to molecular biologists because:
1. They help us to understand a protein's function and behaviour (Figure 2).
2. They can help us to predict the biological processes that a protein of unknown function is involved
in:
We may assume “Guilt by association” if a protein of unknown function associates with one
of known function
Proteins involved in the same process should cluster together in network maps
3. They can help us to characterise protein complexes and pathways; interaction networks can be
used as a draft 'map' to add detail to biological processes and pathways.
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Figure 2. Contrary to the original belief that one protein had a single function, proteins have
different functions and cellular roles depending on their immediate environment, which has an
impact on their position in protein networks.
Where do the data come from?
Methods for molecular interaction identification
There are two approaches to gaining information about molecular interactions:
computational
experimental
In this course we will concentrate on the experimental methods, but there is an increasing variety of
computational methods that can predict protein–protein interactions. Because only a small
proportion of all the molecular interactions in an organism are currently covered by the experimental
data, these methods provide a meaningful resource that can help use to analyse under-represented
regions of the interactome [3]. You can find out more about the different methods used to predict
protein-protein interactions in a comprehensive Wikipedia entry entitled protein-protein interaction
prediction [4].
A wide variety of experimental methods can be used to detect protein-protein interactions. It is
important to realise that there is no perfect approach. Each method has its limitations and is to an
extent potentially artifactual. Therefore, it is advisable to check interactions using more than one
approach: interactions detected by more than one method are more likely to be "real"1, [2]2,3 [2].
Next, we will have a look at some ways to experimentally identify protein-protein interactions.
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High-Throughput: Yeast two hybrid
The most frequently used laboratory method for experimentally determining molecular interactions
is yeast two-hybrid (Y2H) screening4 [2].
Y2H is a complementation assay [5]. The readout mechanism is based on a transcription factor,
which is split into two independent parts, the DNA-binding domain [6] (BD) and the DNA-activation
domain (AD). The BD and AD domains are fused to two proteins of interest, the bait (X) and the prey
(Y). This ensures that the readout can only take place when the two halves are brought into close
proximity. If the bait and prey proteins bind to each other when expressed in a yeast cell, the
transcription machinery becomes activated and a reporter gene is turned on (Figure 3).
Figure 3. The yeast two hybrid (Y2H) concept and a typical readout. [1] The BD domain fused to the
bait protein (X) and the AD domain fused to prey protein (Y) are expressed in yeast cells. [2] If
proteins X and Y interact, BD binds DNA and AD activates RNA polymerase.
An example readout [3] of a Y2H assay with two bait proteins (Bait 1 and Bait 2) and five prey
proteins (1 to 5). In this example, positive interactions are shown by colony growth. Readouts for a
Y2H assay can also be detected by DNA sequencing or colorimetric methods such as the betagalactosidase assay (Figure from Koh et. al.5 [2]).
Advantages:
Fast
Inexpensive
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Scalable
An in vivo system in which binding sites can be accurately mapped
Disadvantages:
False positives occur when a yeast protein acts as a bridge for the interaction.
Interactions occur between proteins that would not normally be present in the same cellular
compartment, in the same cell type, or at the same time.
Both bait and prey proteins can fail to be expressed or might be toxic to the cell.
High-Throughput: Affinity Purification Mass Spectrometry
The second high-throughput method significantly contributing to the growth in published proteinprotein interaction data is affinity purification mass spectometry (AP-MS, Figure 4).
AP-MS is an affinity-based assay [5]. Its specificity [7] and sensitivity [8] depend greatly on the
strength and stability of the interaction between the proteins involved 6 [2].
In AP-MS, a single protein or molecule of interest is immobilised in a matrix as a bait. Then a protein
mixture is passed through the matrix and interacting partners (prey) are captured by the bait
protein. Any form of technique relying on mass spectrometry [9] (MALDI, LC-MS/MS, etc...) is then
used to identify the captured proteins.
The affinity purification step and type of mass-spectrometry-based identification can be
modified. For example, you can perform an immunoprecipitation and then identify the captured
proteins using LC-MS/MS or you can perform a pull-down of epitope-tagged molecules and then
identify the proteins using MALDI-MS 7,8 [2].
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Figure 4. Affinity purification and mass spectometry (AP-MS). The bait protein (yellow) is
immobilised on a matrix [1]. A protein mixture is passed through and only the interacting partners
(prey) are retained [2]. In the following step the prey proteins are removed, digested with a protease
and the resulting peptides are analysed by MS [3] 5 [2].
Advantages:
Potentially, depending on the sensitivity of your MS-approach and the affinity of the
interacting partners, this method has the ability to examine interactions among multiple
proteins at subpicomole concentrations.
The prey proteins are present in their native state (so long as they are not affected by the
sample lysis process) and concentration.
Disadvantages:
Prey proteins without a peptide signature recognisable by MS (owing to obscure posttranslational modifications) or present in very low amounts will not be identified.
Biologically relevant transient interactions and weak interactions may be missed.
Mixing of compartments during cell lysis/purification is a potential source of false positives.
For example, interactions between proteins that would not normally be in the same cellular
compartment may confound your results.
There is an overwhelming variety of techniques that can be used to detect protein-protein
interactions using low- or medium-throughput setups. Next we summarise some methods that are
often used to improve the confidence of an interaction detected by high-throughput methods or on
their own merits in small-scale experiments.
Co-immunoprecipitation
Co-immunoprecipitation (Co-IP) is the immunoprecipitation of intact protein complexes (i.e. antigen
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along with any proteins or ligands that are bound to it); see Figure 5. Co-IP works by selecting an
antibody that targets a known protein that is believed to be a member of a larger complex of
proteins. By targeting this known member of a complex with an antibody, you might be able to pull
the entire protein complex out of solution and thereby identify unknown members of the complex.
This technique works when the proteins involved in the complex bind to each other tightly, making it
possible to pull multiple members of the complex out of solution by latching onto one member with
an antibody.
The concept of pulling protein complexes out of solution is sometimes referred to as a "pull-down".
Co-IP has been traditionally considered as the "gold standard" assay [5] for protein-protein
interactions, but its caveats are very similar to those of AP-MS [10] as it is also an affinity purification
method.
Figure 5. Protein
complex immunoprecipitation (Co-IP) method. [1] Addition of antibody to protein extract [11]. [2]
Target proteins are immunoprecipitated with the antibody. [3] Coupling of antibody to beads. [4]
Isolation of protein complexes.
X-ray crystallography
X-ray crystallography [12] is a method of determining the arrangement of atoms within a crystal, in
which a beam of X-rays strikes a crystal and causes the beam of light to spread into many specific
directions. From the angles and intensities of these diffracted beams, a crystallographer can produce
a three-dimensional picture of the density of electrons within the crystal (Figure 6). From this
electron density, the mean positions of the atoms in the crystal can be determined, as well as their
chemical bonds and other types of information.
This method is considered to be another "gold standard" because it provides an extremely deep
level of detail about interacting surfaces and residues (at a level of atoms and chemical bonds) and
high quality data.
However, it is extremely challenging technically, is very low-throughput and is not free from false
negatives or false positives; not every protein is amenable to co-crystallization and some proteins
that co-crystallise in vitro do not interact in a physiological context.
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Figure 6. X-ray crystallography is used to to obtain detailed structural and chemical insights for
selected interactions. The figure shows a model of the cullin complex 13 [2].
Other methods
Sometimes it is necessary to use methods that can be performed in mammalian cell lines, providing
a more physiological environment for studies using mammalian proteins.The following techniques
can be applied in medium- or high-throughput setups and have become widely used in the past few
years:
LUMIER [13]: luminescence-based mammalian interactome [3] mapping
MAPPIT [14]: mammalian protein-protein interaction trap
FRET [15]/BRET [16]: fluorescence/bioluminescence-resonance energy transfer
For more information on these techniques, see Reference 9 [2].
Finally, one of the few ways of identifying transient interactions missed by other methods is the
enzyme assay [5]. These assays are based on taking enzyme-catalysed reactions as evidence that
an enzyme interacts with its substrate, for example. However, these assays can only use in vitro
data, requiring purified proteins, as there are too many unknowns if they are performed with a whole
cell lysates. Moreover, many enzymes are promiscuous in vitro – most prominently kinases. This can
lead to a large number of false positives.
Interaction databases
Given the variety of techniques available to produce protein-protein interaction data and the large
number of studies that are published every day, an enormous effort is required to store this
information in a way that is both accessible and intelligible to the user (Figure 7).
Molecular interaction databases aim to fulfil this need by extracting information from scientific
publications or, in some cases, by including protein-protein interaction predictions found using
computational methods 14 [2].
The storage of interactions in publicly available databases allows access to a large volume of
interaction data and subsequent analysis of the interactome [3].
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Figure 7. Database storage and analysis of interaction data. Data obtained by interaction detection
methods is stored in databases and available for analysis; here we show a molecular interaction
network as one means of visualising such an analysis. Figure adapted from Koh et al. 5 [2]
There are several issues when representing interaction data, including use of nomenclature,
representing complex data and cross-referencing to other resources. Different databases have
different approaches to solving these issues, and database teams are constantly developing new
strategies to improve their representation of the data. They also have different levels of curation
[17], depending on how much detail they capture about each interaction.
We will now discuss how heterogeneous database representation of interactions is, and describe
some of the initiatives that aim to reduce this heterogeneity.
Different types of molecular-interaction databases
There are a large number of publicly available molecular interaction databases. These can be
classified into three main types, according to their data-acquisition policies 14 [2].
Primary databases are those that collect experimental
molecular interaction data exclusively from peer-reviewed scientific publications. IntAct [18] 11 [2],
MINT 10 [2] and MatrixDB 16 [2] are examples of this type of database. They can be further classified
by the level of detail that they use to represent the information and the depth of their curation [17]
policies.
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Secondary databases, also known as meta-databases, seek to integrate
the data curated by several primary databases in one, integrated repository. APID 17 [2] and PINA 18
[2] are examples of this type.
Finally, predictive databases combine the experimentally inferred
data taken from primary databases with computational predictions of molecular interactions.
Examples include STRING 19 [2] and UniHI 20 [2].
Now, let's have a look at the different types of curation that primary databases use to register
molecular interaction data.
Curation levels
As explained before, different databases use varying levels of annotation [19] or curation [17].
Figure 8 organises some molecular interactions and pathways databases, including members of the
IMEx consortium [20], according to the different levels of detail in their curation procedures, ranging
from light curation to the very detailed level of description that the IMEx guidelines [21] require.
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Figure 8. Some molecular interactions and pathways databases organised according to their level of
curation.
Light curation is used where the main goal is to re-publish maximum content with minimum effort sufficient to give identifiers to the interacting partners and state the interaction detection method
used. The MIMIx (Minimum Information required for reporting a Molecular Interaction
eXperiment) standard goes one step beyond this, providing a lightweight version of the IMEx
guidelines 12,15 [2].
Deep curation requires a detailed description of all the features involved in the interaction and
interacting partners, and complies with the full version of the IMEx guidelines; this requires more
time and resources. IMEx members including IntAct [18], MINT, DIP and MatrixDB are required to
register their data complying with these guidelines. You can see a full list of IMEx-compliant
databases on the IMEx Consortium [22] website.
Now, let's have a look at the molecular interactions database hosted in EMBL-EBI.
Molecular interaction databases at EMBL-EBI
IntAct [23] is a central, public repository where molecular interactions data can be stored and
accessed. It is hosted by the European Bioinformatics Institute (EMBL-EBI) in Hinxton, UK, where it is
maintained by a group of curators and developers.
We populate IntAct [24] with interaction data from literature curation [17] or direct user submissions.
Most of the data refer to protein-protein interactions, but interactions involving other types of
molecules, such as small chemical compounds or nucleic acids, can also be found in IntAct.
At EMBL-EBI IntAct is the main database for molecular interactions. There are other EMBL-EBI
databases that also capture interaction information, the most prominent one being ChEMBL [25],
which hosts a large collection of small molecule-protein/drug-target interactions.
IntAct is a member of the International Molecular Exchange (IMEx [22]) Consortium - a group of
major public interaction data providers whose goal is to share curation effort and exchange
completed records on molecular interaction data. When you query data in IntAct you also access
over 150 million interactions in a further 31 data resources via our PSICQUIC [26] (Proteomics
Standard Initiative Common QUery InterfaCe) service or a consistently annotated, non-redundant,
experimentally determined subset from the IMEx Consortium [27].
You can learn more about IntAct in a separate course: IntAct: Molecular Interactions at the EBI [28].
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Summary
Knowledge of molecular interactions helps us to assign new functions to molecules with
unknown roles and to draw interaction maps that can improve our knowledge of biological
pathways.
The largest amount of interaction data available in the public domain [6] is for protein-protein
interactions; a wide variety of experimental approaches can be used to detect these.
There is no perfect detection method for molecular interactions. Each method has its
limitations and is to an extent potentialy artefactual. Therefore, it is advisable to take
complementary approaches to gain confidence that a particular interaction exists in a
physiological context.
The two most frequently used high-throughput methods for detecting protein-protein
interactions are yeast two-hybrid (Y2H) screening and affinity purification mass spectometry
(AP-MS). There is a great variety of other methods, generally used for smaller-scale setups.
At EMBL-EBI, IntAct [24] is the main database storing molecular interactions. IntAct is a
member of the International Molecular Exchange (IMEx [22]) Consortium, an effort to
standardise curation [17] procedures for molecular interactions and combine the work of
different databases.
Your feedback
Please tell us what you thought about this course. Your feedback is invaluable and helps us to
improve our courses and thus enhance your learning experience.
Learn more
Find out more
Proteomics: The interaction map. Baker, M. Nature. (2012) 484(7393): 271-275,
[PMID:22498631 [29]] - A short report about protein-protein interactions research, with
comments from some of the most prominent researchers in the field.
Protein–Protein interactions essentials: Key Concepts to Building and Analyzing
Interactome Networks. De Las Rivas, J., & Fontanillo, C. PLoS Comput Biol. (2010) 6(6):
e1000807, [PMID:20589078 [30]] - General review about the basic concepts required to
understand protein-protein interactions.
Molecular interaction databases. Orchard, S. Proteomics. (2012) 12(10): 1656-1662,
[PMID:22611057] [31] - A comprehensive review about the main molecular interaction
databases that are available at present.
An experimentally derived confidence score for binary protein-protein
interactions. Braun, P., Tasan, M., Dreze, M., Barrios-Rodiles, M., Lemmens, I., Yu, H.,
Sahalie, J. M., et al. Nat Methods. (2009) 6(1): 91-97, [PMID:19060903 [32]] - The
assessment of confidence values to molecular interactions requires the use of
several, complementary approaches. In this study, the performance of different protein
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interaction detection methods with respect to a golden standard set is evaluated.
Analyzing protein-protein interaction networks. Koh, G. C., Porras, P., Aranda, B.,
Hermjakob, H., & Orchard, S. E. Journal of Proteome Research. (2012). 11(4): 2014-31,
[PMID:22385417] [33] - The IntAct group has produced a tutorial with HUPO discussing the
importance of molecular interactions network analysis and applying a similar approach to the
one presented here, using BiNGO in combination with the topological cluster analysis plugin
clusterMaker.
PSICQUIC and PSISCORE - accessing and scoring molecular interactions. Aranda, B.
et al. Nat. Methods. (2011). 8(7): 528-529, [PMID:21716279 [34]] - The paper in which
PSICQUIC, a query interface that allows access to data from multiple molecular interactions
and pathways databases, is presented.
Recommended courses
IntAct: Quick tour [35]
IntAct: Molecular Interactions at the EBI [28] - online course
Proteomics: An introduction to the EBI resources [36] - online course
Networks and Pathways for Biologists [37] - held at EMBL-EBI
References
1. Venkatesan K. et al., An empirical framework for binary interactome mapping. [38] Nat Methods.
2009 Jan; 6 (1): 83-90.
2. Phizicky E.M. Fields S., Protein-protein interactions: methods for detection and analysis. [39]
Microbiol Rev. 1995 Mar; 59 (1): 94-123. Review.
3. Lalonde S. et al., Molecular and cellular approaches for the detection of protein-protein
interactions: latest techniques and current limitations. [40] Plant J. Feb; 53 (4): 610–635
4. Brucker A. et al. Yeast two-two hybrid, a powerful tool for systems biology. [41] Int. J. Mol. Sci.
2009 Jun; 10 (6): 2763-88
5. Koh G.C. et al. Analyzing protein-protein interaction networks. [42] J. Proteome [43] Res. 2012 Apr;
11 (4): 2014-31
6. Rigaut G. et al., A generic protein purification method for protein complex characterization and
proteome exploration. [44] Nat. Biotechnol. 1999, Oct; 17 (10): 1030-1032
7. Bauer A. at al., Affinity purification-mass spectrometry. Powerful tools for the characterization of
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protein complexes. [45] Eur. J. Biochem. 2003, Feb; 270 (4): 570-578
8. Pflieger D. et al., Linking the proteins--elucidation of proteome-scale networks using mass
spectrometry. [46] Mass Spec. Rev. 2011, Mar-Apr; 30 (2): 268-297
9. Petschnigg J. et al., Interactive proteomics research technologies: recent applications and
advances. [47] Curr. Opin. Biotechnol. 2011, Feb; 22 (1): 50-58
10. Licata L. et al., Mint [48], the molecular interaction database: 2012 update. [48] Nucleic Acids
Res. 2012, Jan; 40: D857-61
11. Kerrien S. et al., The IntAct molecular interaction database in 2012. [49] Nucl. Acids Res. 2012,
Jan; 40: D841-46
12. Orchard S. et al., The minimum information required for reporting a molecular interaction
experiment (MIMIx). [50] Nature Biotechnol. 2007, Aug; 25 (8): 894–898
13. Kleiger G. et al., Rapid E2-E3 assembly and disassembly enable processive ubiquitylation of cullinRING ubiquitin ligase substrates. [51] Cell. 2009, Nov; 139 (5): 957-68
14. De Las Rivas J. et al., Protein-protein interactions essentials: key concepts to building and
analyzing interactome networks. [52] PLoS Comput. Biol. 2010, Jun; 6 (6): e1000807
15. Orchard S. et al., Protein interaction data curation: the International Molecular Exchange (IMEx)
consortium. [52] Nat. Methods. 2012, Apr; 9 (4): 345–50
16. Chautard E. et al., MatrixDB, a database focused on extracellular protein-protein and proteincarbohydrate interactions. [53] Bioinformatics. 2009, Mar; 25 (5): 690–91
17. Hernandez-Toro J. et al., APID2NET: unified interactome graphic analyzer. [54]Bioinformatics.
2007, Sep; 23 (18): 2495-7
18. Wu J. et al., Integrated network analysis platform for protein-protein interactions. [55] Nat.
Methods. 2009, Jan; 6 (1): 75-7
19. Szklarczyk D. et al., The STRING database in 2011: functional interaction networks of proteins,
globally integrated and scored. [56] Nucl. Acids Res. 2011, Jan; 39: D561-8
20. Brown K.R. and Jurisica I., Online predicted human interaction database. [57]Bioinformatics.
2005, May; 21 (9): 2076-82
Need some help?
For support, submission and related enquiries, email the intact [18]-help [at] ebi.ac.uk (IntAct help
desk)
.
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Contributors
[1]
Pablo Porras Millan [1]
EMBL-EBI
Scientific curator in IntAct
Pablo Porras got his PhD in 2006 in the University of Córdoba, Spain, having done research about
trans-membrane protein translocation and redox homeostasis. After that, he moved to Berlin to work
in the Neuroproteomics group of the Max Delbrueck Center, getting involved in projects dealing with
interactomics, neurodegenerative diseases and the ubiquitin-proteasome system. During this
postdoc, he faced the problem of how to represent and analyze molecular interactions data. This
experience proved to be of great value once he joined the EBI to work as a scientific curator in the
molecular interactions database IntAct [58] in 2011.
[59]
Mindi Sehra [59]
EMBL-EBI
Scientific Training Officer - eLearning Content Developer
Mindi Sehra is the Scientific Training Officer (eLearning) for the Outreach and Training Team at EMBLEBI. Mindi is responsible for expanding and consolidating the EBI’s range of online training materials
and monitoring and maintaining the portal, which includes investigating ways to exploit electronic
technologies. Mindi completed a Genetics Degree at Sheffield University before moving into genome
analysis at the Wellcome Trust Sanger Institute. She completed a MSc in Medical Genetics and
Immunology at Brunel University, her thesis on the Swine Leukocyte Antigen secured her a position
in the Human and Vertebrate Annotation [60] and Analysis group as a computer biologist. She then
joined the UniProt [61] team at the EBI as a protein curator [62] working on automatic and manual
annotation [63].
Source URL: http://www.ebi.ac.uk/training/online/course/protein-interactions-and-their-importance
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Links
[1] http://www.ebi.ac.uk/training/online/trainers/pporras
[2] http://www.ebi.ac.uk/training/online/course/protein-interactions-and-their-importance/references
[3] http://www.ebi.ac.uk/training/online/glossary/interactome
[4] http://en.wikipedia.org/wiki/Protein%E2%80%93protein_interaction_prediction
[5] http://www.ebi.ac.uk/training/online/glossary/assay
[6] http://www.ebi.ac.uk/training/online/glossary/domain
[7] http://www.ebi.ac.uk/training/online/glossary/specificity
[8] http://www.ebi.ac.uk/training/online/glossary/sensitivity
[9] http://en.wikipedia.org/wiki/Mass_spectrometry
[10] http://www.ebi.ac.uk/training/online/glossary/term/118
[11] http://www.ebi.ac.uk/training/online/glossary/extract
[12] http://www.ebi.ac.uk/training/online/glossary/x-ray-crystallography
[13] http://ophid.utoronto.ca/LUMIER/
[14] http://www.crl-mappit.be/mappit_toolbox/mappit_concept/
[15] http://en.wikipedia.org/wiki/Fluorescence_resonance_energy_transfer
[16] http://en.wikipedia.org/wiki/F%C3%B6rster_resonance_energy_transfer#BRET
[17] http://www.ebi.ac.uk/training/online/glossary/curation
[18] http://www.ebi.ac.uk/training/online/glossary/intact
[19] http://www.ebi.ac.uk/training/online/glossary/term/260
[20] http://www.imexconsortium.org
[21] http://www.imexconsortium.org/sites/imexconsortium.org/files/documents/imex_curation_rules_
01_12.pdf
[22] http://www.imexconsortium.org/about-imex
[23] http://www.ebi.ac.uk/intact/main.xhtml
[24] http://www.ebi.ac.uk/training/online/glossary/term/353
[25] https://www.ebi.ac.uk/chembl/
[26] http://www.ebi.ac.uk/training/online/glossary/term/367
[27] http://www.ebi.ac.uk/training/online/glossary/term/368
[28] http://www.ebi.ac.uk/training/online/course/intact-molecular-interactions-ebi
[29] http://europepmc.org/abstract/MED/22498631
[30] http://europepmc.org/search/?page=1&query=20589078
[31] http://europepmc.org/abstract/MED/22611057
[32] http://europepmc.org/abstract/MED/19060903
[33] http://europepmc.org/search/?page=1&query=22385417
[34] http://europepmc.org/abstract/MED/21716279
[35] http://www.ebi.ac.uk/training/online/course/intact-quick-tour
[36] http://www.ebi.ac.uk/training/online/course/proteomics-introduction-ebi-resources
[37] http://www.ebi.ac.uk/training/course/networks-and-pathways-bioinformatics-biologists-0
[38] http://europepmc.org/abstract/MED/19060904
[39] http://europepmc.org/abstract/MED/7708014
[40] http://europepmc.org/abstract/MED/18269572
[41] http://europepmc.org/abstract/MED/19582228
[42] http://europepmc.org/abstract/MED/22385417
[43] http://www.ebi.ac.uk/training/online/glossary/proteome
[44] http://europepmc.org/abstract/MED/10504710
[45] http://europepmc.org/abstract/MED/12581197
[46] http://europepmc.org/abstract/MED/21337599
[47] http://europepmc.org/abstract/MED/20884196
[48] http://europepmc.org/abstract/MED/22096227
[49] http://europepmc.org/abstract/MED/22121220
[50] http://europepmc.org/abstract/MED/17687370
[51] http://europepmc.org/abstract/MED/19945379
[52] http://europepmc.org/abstract/MED/22453911
[53] http://europepmc.org/abstract/MED/19147664
[54] http://europepmc.org/abstract/MED/17644818
[55] http://europepmc.org/abstract/MED/19079255
[56] http://europepmc.org/abstract/MED/21045058
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Protein interactions and their importance
Published on EMBL-EBI Train online (http://www.ebi.ac.uk/training/online)
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http://europepmc.org/abstract/MED/15657099
http://www.ebi.ac.uk/intact/
http://www.ebi.ac.uk/training/online/trainers/mindi
http://www.ebi.ac.uk/training/online/glossary/annotation
http://www.ebi.ac.uk/training/online/glossary/uniprot
http://www.ebi.ac.uk/training/online/glossary/curator
http://www.ebi.ac.uk/training/online/glossary/manual-annotation
Page 17 of 17