Transcript Computational Biology

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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A biological cell: a large construction site?
In a biological cell there are many tasks that
need to be executed in a timely and precise
manner.
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).
All steps depend on interaction of proteins with DNA or with other proteins!
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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.
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Examples of Stable Protein Complexes: Ribosome
The ribosome is a complex
subcellular particle composed of
protein and RNA. It is the site of
protein synthesis,
Model of a ribosome with a
newly manufactured protein
(multicolored beads) exiting
on the right.
http://www.millerandlevine.com/chapter/12
/cryo-em.html
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Examples of Stable Protein Complexes: Proteasome
The proteasome is the central
enzyme of non-lysosomal protein
degradation. It is involved in the
degradation of misfolded proteins
as well as in the degradation and
processing of short lived regulatory
proteins.The 20S Proteasome
degrades completely unfoleded
proteins into peptides with a
narrow length distribution of 7 to
13 amino acids.
http://www.biochem.mpg.de/xray/projects/hu
bome/images/rpr.gif
Löwe, 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.
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Stable Protein Complex: Nuclear Pore Complex
A three-dimensional image of the
nuclear pore complex (NPC),
revealed by electron microscopy.
A-B The NPC in yeast.
Figure A shows the NPC seen
from the cytoplasm while figure B
displays a side view.
C-D The NPC in vertebrate
(Xenopus).
http://www.nobel.se/medicine/educational/dn
a/a/transport/ncp_em1.html
Three-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
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Stable Protein Complex: Photosynthetic Unit
Other large complexes:
Structure suggested by
force field based
molecular docking.
- Apoptosome
-Thermosome
- Transcriptome
http://www.ks.uiuc.edu/Research/vmd/gallery
Other large complexes:
- Apoptosome 7-fold symmetry
- Chaperone (GroEL/GroES)
7-fold symmetry
- Thermosome
- Transcriptome
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2 Protein-protein networks
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2. Yeast 2-Hybrid Screen
Data on protein-protein
interactions from
Yeast 2-Hybrid Screen.
One role of bioinformatics is to
sort the data.
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Protein cluster in yeast
Cluster-algorithm
generates one large
cluster for proteins
interacting with each
other based on
binding data of
yeast proteins.
Schwikowski, Uetz, Fields, Nature Biotech. 18, 1257 (2001)
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Annotation of function
After functional annotation:
connect clusters of
interacting proteins.
Schwikowski, Uetz, Fields, Nature Biotech. 18, 1257 (2001)
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Annotation of localization
Schwikowski, Uetz, Fields, Nature Biotech. 18, 1257 (2001)
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Relation between lethality and function as centers
in protein networks
•
study analyzed protein-protein interaction network in yeast S. cerevisae
Yeast two-hybrid screen data identified
2240 direct physical interactions
between 1870 proteins, see
Uetz et al. (1999) und Xenarios et al. (2000).
•
analyze the effects of single gene deletions for lethality:
in proteom data base existed 1572 entries of known phenotypic
profiles.
Jeong, Mason, Barabási, Oltvai, Nature 411, 41 (2001)
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Protein-Protein interactions in yeast
Cluster analysis of 2YHB data.
Shown is largest cluster
containing 78% of all proteins.
The color of each node marks
the phenotypic effect if this
protein is removed from the
cell (gene knockout).
red - lethal
green – no effect
orange – slow growth
gelb - unknown
Jeong, Mason, Barabási, Oltvai, Nature 411, 41 (2001)
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Relation between lethality and function as centers
in protein networks
Likehood p(k) of finding proteins in yeast that interact
with exactly k other proteins.
Probability has power law dependence.
(Similar plot for bacterium Heliobacter pylori.)
 network of protein-protein interactions is a very
inhomogenous scale-free network where a few, highly
connected, proteins play central roles of mediating the
interactions among other, less strongly connected,
proteins.
Jeong, Mason, Barabási, Oltvai, Nature 411, 41 (2001)
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Relation between lethality and function as centers
in protein networks
Computational analysis of the tolerance of protein
networks for random errors (gene deletions).
Random mutations don’t have an effect on the total
topology of the network.
When “hub” proteins with many interactions are
eliminated, the diameter of the network decreases
quickly.
The degree of proteins being essential (gene knockout is lethal for cell) depends on the connectivity in the
yeast protein network.
Strongly connected proteins with central roles in the
architecture of the network are 3 times as essential as
proteins with few connections.
Jeong, Mason, Barabási, Oltvai, Nature 411, 41 (2001)
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3 Identification of protein complexes
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Systematic identication of large protein complexes
Yeast 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 .
Identify proteins
by mass spectrometry (MALDITOF).
Gavin et al. Nature 415, 141 (2002)
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Analyis of protein complexes in yeast (S. cerevisae)
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.
Gavin et al. Nature 415, 141 (2002)
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Example of particular complex
Check of the method: can the same
complex be obtained for different
choice 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 important
to develop inhibitors.
Gavin et al. Nature 415, 141 (2002)
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- theoretical methods (docking)
- electron tomography
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3. Netzwerk aus Proteinkomplexen
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.
Modularity =
Formation of separated Islands ??
Gavin et al. Nature 415, 141 (2002)
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Structural Proteomics
Sali, Glaeser, Earnest, Baumeister, Nature 422, 216 (2003)
Biological cells are not organized by undirected diffusion of the soluble proteins!
Instead many important cellular functions are carried out by stable
or transiently formed protein complexes.
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known protein structures
Large proteins are underrepresented in the PDB
data base.
Based on the Cellzome results, people estimate
that each protein complex in yeast contains
Sali et al. Nature 422, 216 (2003)
7.5 proteins.
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Single particle analysis with EM
(a) Complexes of 44 tripeptidyl-peptidase II molecules on a surface.
The pictures in each line show different averaged views of complexes possessing
the same orientation  image analysis.
(b) 3D-rekonstruction of the TPP II-complex at 3.3 nm resolution.
Different views. Note the enhanced resolution by combining information of
the different views shown in (a).
Sali et al. Nature 422, 216 (2003)
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Information about macromolecular complexes
‚Subunit structure‘ : atomic
resolution < 3 Å
‘Subunit shape’ : medium
resolution > 3 Å
‘Subunit contact’: Knowledge
about direct spatial contacts
between subunits
‘Subunit proximity’: subunits don’t
need to be in direct contact.
Grey boxes indicate areas with
large experimental difficulties.
Sali et al. Nature 422, 216 (2003)
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Hybrid-methods for macromolecular complexes
Structural Bioinformatics
(a) Integration of varios
protein elements into
one large complex.
(b) Partial atomic model
of the entire yeast
ribosome by fitting
atomic models of rRNA
and proteins into a lowresolution EM map of the
80S ribosome.
Sali et al. Nature 422, 216 (2003)
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Structure of large complexes: combine EM + X-ray
docking of atomic X-ray structure of tubulin (3.5 Å resolution)
into 8Å-EM-structure of microtubuli.
Sali et al. Nature 422, 216 (2003)
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Situs package: Automated low-resolution fitting
Situs was developed for automatic fitting of high-resolution structures from Xray crystallography into low-resolution maps from electron microscopy.
http://biomachina.org
see also database for animations of EM data:
http://emotion.biomachina.org/
Idea:
Create low-resolution image of X-ray structure.
Determine center of mass and moments of inertia.
Model one protein by a few mass centers.
Use neuronal network to best position nodes (mass points)
into EM density map of the molecular complex.
Molecular mass represented by nodes should maximally overlap with EM map.
Wriggers et al. J. Mol. Biol. 284, 1247 (1998)
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Discretization of proteins by few mass points
Wriggers et al. J. Mol. Biol. 284, 1247 (1998)
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Reconstruction of actin filament using Situs
Wriggers et al. J. Mol. Biol. 284, 1247 (1998)
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Reconstruction of actin filament using Situs
Wriggers et al. J. Mol. Biol. 284, 1247 (1998)
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Situs package: Conformational Dynamics
In the mean time, the Situs
developers have also
switched to using FFT
techniques to match images
and real data.
Chacon et al. Acta Cryst D 59, 1371 (2003)
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Electron Tomography
a) The electron beam of the EM
microscope is scattered by the central
object and the scattered electrons are
detected on the black plate.
By tilting the object in small steps, we
collect electrons scattered at different
angles.
b) reconstruction in the computer.
Back-projection (Fourier method) of the
scatter-information at different angles.
The superposition generates a threedimensional tomogrom.
Sali et al. Nature 422, 216 (2003)
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Identification of macromolecular complexes in
cryoelectron tomograms of phantom cells
Prepare „phantom cells“ (ca. 400 nm diameter) with well-defined contents:
Liposomes filled with thermosomes and 20S proteasomes.
Thermosome: 933 kD, 16 nm diameter, 15 nm height, subunits assemble into toroidal
structure with 8-fold symmetry.
20S proteasome: 721 kD, 11.5 nm diameter, 15 nm height, subunits assemble into
toroidal structure with 7-fold symmetry.
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.
Frangakis et al., PNAS 99, 14153 (2002)
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Detection and idenfication strategy
Frangakis et al., PNAS 99, 14153 (2002)
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Search strategy
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.
Frangakis et al., PNAS 99, 14153 (2002)
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Correlation with Nonlinear Weighting
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:
R
CC 
 x
n 1
 rn   R  x  r
 x   R  x  r  R  r
R
n 1
x and r
n
R
2
n
2
n 1
2
n
2
are the mean values of the subimage and the template.
The denominators are the variances
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.
Frangakis et al., PNAS 99, 14153 (2002)
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Raw data
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.
Frangakis et al., PNAS 99, 14153 (2002)
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Correlation coefficients
(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.
Frangakis et al., PNAS 99, 14153 (2002)
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Reconstruction of phantom cell
The phantom cell contained a 1:1
ratio of both proteins. The algorithm
identifies 52% as thermosomes and
48% as 20S proteasomes.
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.
Frangakis et al., PNAS 99, 14153 (2002)
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Electron tomography
- 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
Frangakis et al., PNAS 99, 14153 (2002)
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Reconstruction of endoplasmatic reticulum
Picture rights shows rough
endoplasmatic reticulum (membrane
network in eukaryotic cells that
generates proteins and new
membranes) coated with ribosomes.
The picture is taken from an intact
cell.
Membranes are shown in blue, the
ribosomes in green-yellow.
http://science.orf.at/science/news/61666
Dept. of Structural Biology, Martinsried
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Reconstruction of actin filaments
Actin filaments are structural proteins – they form filaments which span the entire cell.
They stabilize the cellular shape, are required for motion, and are involved in important
cellular transport processes (molecular motors like kinesin walk along these filaments).
Shown is the cytoskeleton of Dictyostelium. Apparently, filaments cross and bridge each other
at different angles, and are connected to the cell membrane (right picture).
Actin filaments are shown in brown. The cell segment left has a size of 815 x 870 x 97 nm 3.
Middle: single actin filaments connected at different angles.
Right: actin filaments (brown) binding to the cell membrane (blue).
http://science.orf.at/science/news/61666
Dept. of Structural Biology, Martinsried
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Science fiction
Reconstruct proteom of real biological cells.
Required steps:
(1) obtain EM maps of isolated (e.g. 6000 yeast) proteins
(2) enhance resolution of tomography
(3) speed up detection algorithm
http://science.orf.at/science/news/61666
Dept. of Structural Biology, Martinsried
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Summary
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?
Botstein & Risch, Nature Gen. 33, 228 (2003)
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