13. Lecture WS 2003/04 Bioinformatics III 1 Protein Networks / Protein Complexes Protein networks could be defined in a number of ways - Co-regulated expression of genes/proteins - Proteins participating in the same metabolic pathways - Proteins sharing substrates - Proteins that are co-localized - Proteins that form permanent supracomplexes = protein machineries - Proteins that bind eachother transiently (signal transduction, bioenergetics ... )
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13. Lecture WS 2003/04Bioinformatics III1 Protein Networks / Protein Complexes Protein networks could be defined in a number of ways - Co-regulated expression.
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13. Lecture WS 2003/04
Bioinformatics III 1
Protein Networks / Protein Complexes
Protein networks could be defined in a number of ways
- Co-regulated expression of genes/proteins
- Proteins participating in the same metabolic pathways
- Proteins sharing substrates
- Proteins that are co-localized
- Proteins that form permanent supracomplexes = protein machineries
- Proteins that bind eachother transiently
(signal transduction, bioenergetics ... )
13. Lecture WS 2003/04
Bioinformatics III 2
A biological cell: a large construction site?
Job office publishes lists (DNA) of people looking for jobs (protein). Managers from the personnel office (DNA-transcription factors) recruit (express) proteins.
Workers (proteins) need to get to their working places (localization).
During work they get energy from drinking beer (ATP).
In a biological cell there are many tasks that need to be executed in a timely and precise manner.
All steps depend on interaction of proteins with DNA or with other proteins!
13. Lecture WS 2003/04
Bioinformatics III 3
1 Protein-Protein Complexes
It has been realized for quite some time that cells don‘t work by random
diffusion of proteins,
but require a delicate structural organization into large protein complexes.
http://www.biochem.mpg.de/xray/projects/hubome/images/rpr.gifLöwe, J., Stock, D., Jap, B., Zwickl, P., Baumeister, W. and Huber, R. (1995). Crystal structure of the 20S proteasome from the archaeon T. acidophilum at 3.4 Å resolution. Science 268, 533-539.
http://www.nobel.se/medicine/educational/dna/a/transport/ncp_em1.htmlThree-Dimensional Architecture of the Isolated Yeast Nuclear Pore Complex: Functional and Evolutionary Implications, Qing Yang, Michael P. Rout and Christopher W. Akey. Molecular Cell, 1:223-234, 1998
Systematic identication of large protein complexesYeast 2-Hybrid-method can only identify binary complexes.
Cellzome company: attach additional protein P to particular protein Pi ,
P binds to matrix of purification column.
yields Pi and proteins Pk bound to Pi .
Gavin et al. Nature 415, 141 (2002)
Identify proteinsby mass spectro-metry (MALDI-TOF).
13. Lecture WS 2003/04
Bioinformatics III 19
Analyis of protein complexes in yeast (S. cerevisae)
Gavin et al. Nature 415, 141 (2002)
Identify proteins by
scanning yeast protein
database for protein
composed of fragments
of suitable mass.
Here, the identified
proteins are listed
according to their
localization (a).
(b) lists the number of
proteins per complex.
13. Lecture WS 2003/04
Bioinformatics III 20
Example of particular complex
Gavin et al. Nature 415, 141 (2002)
Check of the method: can the same complex be obtained for differentchoice of attachment point(tag protein attached to different coponents of complex)? Yes (see gel).
Method allows to identify components of complex, not the binding interfaces.
Better for identification of interfaces:Yeast 2-hybrid screen (binary interactions).
3D models of complexes are importantto develop inhibitors.
- theoretical methods (docking) - electron tomography
13. Lecture WS 2003/04
Bioinformatics III 21
3. Netzwerk aus Proteinkomplexen
Gavin et al. Nature 415, 141 (2002)
Service function of Bioinformatics: catalog such data and prepare for analysis ...
allowing to formulate new models and concepts (biology!).
If results are very important don‘t wait for some biologist to interpret your data. You may want to get the credit yourself.
Collect Cryo-EM pictures of phantom cells for a tilt series from -70º until +70º with 1.5º
increments.
Aim: identify and map the 2 types of proteins in the phantom cell.
This is a problem of matching a template, ideally derived from a high-resolution structure,
to an image feature, the target structure.
13. Lecture WS 2003/04
Bioinformatics III 35
Detection and idenfication strategy
Frangakis et al., PNAS 99, 14153 (2002)
13. Lecture WS 2003/04
Bioinformatics III 36
Search strategy
Frangakis et al., PNAS 99, 14153 (2002)
Adjust pixel size of templates to the pixel size of the EM 3D reconstruction.
The gray value of a voxel (volume element) containing ca. 30 atoms is obtained by
summation of the atomic number of all atoms positioned in it.
Possible search strategies:
(i) Scan reconstructed volume by using small boxes of the size of the target structure
(real space method)
(ii) Paste template into a box of the size of the reconstructed volume (Fourier space
method). This method is much more efficient.
13. Lecture WS 2003/04
Bioinformatics III 37
Correlation with Nonlinear Weighting
R
nn
R
nn
R
nnn
rRrxRx
rxRrxCC
1
22
1
22
1
Frangakis et al., PNAS 99, 14153 (2002)
The correlation coefficient CC is a measure of similarity of two features e.g. a signal x
(image) and a template r both with the same size R.
Expressed in one dimension:
are the mean values of the subimage and the template.
The denominators are the variances rx and
To derive the local-normalized cross correlation function or, equivalently, the
correlation coefficients in a defined region R around each voxel k, which belongs to a
large volume N (whereby N >> R), nonlinear filtering has to be applied.
This filtering is done in the form of nonlinear weighting.
13. Lecture WS 2003/04
Bioinformatics III 38
Raw data
Frangakis et al., PNAS 99, 14153 (2002)
Central x-y slices through the 3D reconstructions of ice-embedded phantom cells filled with
(a) 20S proteasomes, (b) thermosomes, (c) and a mixture of both particles.
At low magnification, the macromolecules appear as small dots.
13. Lecture WS 2003/04
Bioinformatics III 39
Correlation coefficients
Frangakis et al., PNAS 99, 14153 (2002)
(a) Histogram of the correlation coefficients of the particles found in the proteasome-containing phantom cell scanned with the "correct" proteasome and the "false" thermosome template. Of the 104 detected particles, 100 were identified correctly. The most probable correlation coefficient is 0.21 for the proteasome template and 0.12 for the thermosome template.
(b) Histogram of the correlation coefficients of the particles found in the thermosome-containing phantom cell. Of the 88 detected particles, 77 were identified correctly. The most probable correlation value is 0.21 for the thermosome template and 0.16 for the proteasome template.
Detection in (a) works well, but is somehow problematic in (b) because (correct) thermosome and proteasome are not well separated.
13. Lecture WS 2003/04
Bioinformatics III 40
Reconstruction of phantom cell
Frangakis et al., PNAS 99, 14153 (2002)
Volume-rendered representation of
a reconstructed ice-embedded
phantom cell containing a mixture
of thermosomes and 20S
proteasomes. After applying the
template-matching algorithm, the
protein species were identified
according to the maximal
correlation coefficient. The
molecules are represented by their
averages; thermosomes are shown
in blue, the 20S proteasomes in
yellow. The phantom cell contained a 1:1
ratio of both proteins. The algorithm
identifies 52% as thermosomes and
48% as 20S proteasomes.
13. Lecture WS 2003/04
Bioinformatics III 41
Electron tomography
Frangakis et al., PNAS 99, 14153 (2002)
- Method has very high computational cost.
- Observation: biological cells are not packed so densely as expected, allowing the
identification of single proteins and protein complexes
- Problem for real cells: molecular crowding.
Potential difficulties to identify spots.
- need to increase spatial resolution of tomograms
13. Lecture WS 2003/04
Bioinformatics III 42
Reconstruction of endoplasmatic reticulum
http://science.orf.at/science/news/61666Dept. of Structural Biology, Martinsried
The structural characterization of large multi-protein complexes and the
resolution of cellular architectures will likely be achieved by a combination of
methods in structural biology:-X-ray crystallography and NMR for high-resolution structures of single proteins
and pieces of protein complexes- (Cryo) Electron Microscopy to determine medium-resolution structures of
entire protein complexes- Stained EM for still pictures at medium-resolution of cellular organells- (Cryo) Electron Tomography to for 3-dimensional reconstructions of biological
cells and for identification of the individual components.
Mapping and idenfication steps require heavy computation.
Employ protein-protein docking as a help to identify complexes?