Transcript ppt - Chair of Computational Biology

Was kann ich per Knopfdruck über eine
PDB-Struktur lernen?
PdbSum webseite:
http://www.ebi.ac.uk/thornton-srv/databases/cgi-bin/pdbsum/
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Klassifizierung in CATH
http://www.ebi.ac.uk/thornton-srv/databases/cgi-bin/pdbsum/
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Darstellung der Sekundärstruktur
http://www.ebi.ac.uk/thornton-srv/databases/cgi-bin/pdbsum/
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Konservierung innerhalb Proteinfamilie
Oberfläche entsprechend Konservierung
eingefärbt.
http://www.ebi.ac.uk/thornton-srv/databases/cgi-bin/pdbsum/
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Multiples Sequenzalignment
http://www.ebi.ac.uk/thornton-srv/databases/cgi-bin/pdbsum/
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Ramachandran-Diagramm
http://www.ebi.ac.uk/thornton-srv/databases/cgi-bin/pdbsum/
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Oberfläche
Spalten (clefts) auf Oberfläche sind
mögliche Bindungstaschen!
http://www.ebi.ac.uk/thornton-srv/databases/cgi-bin/pdbsum/
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Sekundärstrukturvorhersage: PSIPRED
Enge, sehr polare Bindungstasche auf Proteinoberfläche.
D.T. Jones, J Mol Biol 292, 195 (1999); http://bioinf.cs.ucl.ac.uk/psipred/
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Qualität von PSIRED-Vorhersagen
Ergebnis für 187 Testproteine mit unterschiedlichen Faltungen.
Genauigkeit von PSIPRED:
Ca. 75%
D.T. Jones, J Mol Biol 292, 195 (1999); http://bioinf.cs.ucl.ac.uk/psipred/
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Vorhersage von TM-Helices
Residuen in Transmembranhelices
sind fast ausschließlich hydrophob.
Länge einer TM-Helix ≥ 20 Residuen.
HMMs sind sehr erfolgreich um TMHelices vorherzusagen (>90%
Genauigkeit).
http://darwin.nmsu.edu/~molb470/
fall2003/Projects/koul/tmhmm.html
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Analyse der Oberfläche: elektrostatisches Potential
Proteinoberflächen an ProteinProtein-Bindungsstellen sind
häufig elektrostatisch
komplementär.
Surface representation of the electrostatic
potential of unbound monomers of 4
protein-protein complexes. Open book
view of the protein–protein interfaces is
shown. Color range from deep red to
deep blue corresponds to the range in the
values of electrostatic potential from −10
to +10kT/e, where k is the Boltzmann
constant, T is the absolute temperature
and e is a proton's charge.
Sheinerman, Honig,
J Mol Biol 318, 161 (2002)
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PROCHECK: Qualitätscheck für Proteinstrukturen
The Ramachandran plot shows the phi-psi torsion angles for all
residues in the structure (except those at the chain termini). Glycines are
separately identified by triangles as these are not restricted to the
regions of the plot appropriate to the other sidechain types.
Colouring/shading scheme:
the darkest areas (here shown in red) correspond to the "core" regions
representing the most favourable combinations of phi-psi values.
The regions are labelled as follows:
A - Core alpha
L - Core left-handed alpha
a - Allowed alpha
l - Allowed left-handed alpha
~a - Generous alpha
~l - Generous left-handed alpha
B - Core beta
p - Allowed epsilon
b - Allowed beta
~p - Generous epsilon
~b - Generous beta
The different regions were taken from the observed phi-psi distribution
for 121,870 residues from 463 known X-ray protein structures. The two
most favoured regions are the "core" and "allowed" regions which
correspond to 10° x 10° pixels having more than 100 and 8 residues in
them, respectively. The "generous" regions were defined by Morris et al.
(1992) by extending out by 20° (two pixels) all round the "allowed"
regions. In fact, the authors found very few residues in these
"generous" regions, so they can probably be treated much like the
"disallowed" region and any residues in them investigated more closely.
Ideally, one would hope to have over 90% of the residues in the "core"
regions. The percentage of residues in the "core" regions is one of the
better guides to the stereochemical quality of a protein structure.
http://www.biochem.ucl.ac.uk/~roman/procheck
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PROCHECK
The plot shows separate Ramachandran plots
are shown for each of the 20 different amino
acid types.
The darker the shaded area on each plot, the
more favourable the region. The data on
which the shading is based has come from a
data set of 163 non-homologous, highresolution protein chains chosen from
structures solved by X-ray crystallography to a
resolution of 2.0Å or better and an R-factor no
greater than 20%.
The numbers in brackets, following each
residue name, show the total number of data
points on that graph. The red numbers above
the data points are the reside-numbers of the
residues in question (ie showing those
residues lying in unfavourable regions of the
plot).
http://www.biochem.ucl.ac.uk/~roman/procheck
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PROCHECK: analysis of side chain angles
http://www.biochem.ucl.ac.uk/~roman/procheck
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PROCHECK
The 6 graphs show how the structure (represented by the solid square)
compares with well-refined structures at a similar resolution. The dark
band in each graph represents the results from the well-refined structures;
the central line is a least-squares fit to the mean trend as a function of
resolution, while the width of the band on either side of it corresponds to a
variation of one standard deviation about the mean. In some cases, the
trend is dependent on the resolution, and in other cases it is not.
The 6 properties plotted are:
a. Ramachandran plot quality. This property is measured by the
percentage of the protein's residues that are in the most favoured, or
core, regions of the Ramachandran plot. For a good model structure,
obtained at high resolution, one would expect this percentage to be over
90%. However, as the resolution gets poorer, so this figure decreases - as
might be expected. The shaded region reflects this expected decrease
with worsening resolution.
b. Peptide bond planarity. This property is measured by calculating the
standard deviation of the protein structure's omega torsion angles. The
smaller the value the tighter the clustering around the ideal of 180 degrees
(which represents a perfectly planar peptide bond).
c. Bad non-bonded interactions. This property is measured by the
number of bad contacts per 100 residues. Bad contacts are selected
from the list of non-bonded interactions and are defined as contacts where
the distance of closest approach is less than or equal to 2.6Å.
d. Calpha tetrahedral distortion. This property is measured by
calculating the standard deviation of the zeta torsion angle. This is a
notional torsion angle in that it is not defined about any actual bond in the
structure. Rather, it is defined by the following four atoms within a given
residue: Calpha, N, C, and Cbeta.
e. Main-chain hydrogen bond energy. This property is measured by the
standard deviation of the hydrogen bond energies for main-chain
hydrogen bonds. The energies are calculated using the method of
Kabsch & Sander (1983).
f. Overall G-factor. The overall G-factor is a measure of the overall
normality of the structure. The overall value is obtained from an average
of all the different G-factors for each residue in the structure.
http://www.biochem.ucl.ac.uk/~roman/procheck
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PROCHECK
The 5 properties plotted are:
a. Standard deviation of the chi-1
gauche minus torsion angles.
b. Standard deviation of the chi-1
trans torsion angles.
c. Standard deviation of the chi-1
gauche plus torsion angles.
d. Pooled standard deviation of all
chi-1 torsion angles.
e. Standard deviation of the chi-2
trans torsion angles.
http://www.biochem.ucl.ac.uk/~roman/procheck
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PROCHECK
Distributions of each of the different
main-chain bond lengths in the structure.
The solid line in the centre of each plot
corresponds to the small-molecule mean
value, while the dashed lines either side
show the small-molecule standard
deviation, the data coming from Engh &
Huber (1991).
Highlighted bars correspond to values
more than 2.0 standard deviations from
the mean, though the value of 2.0 can be
changed by editing the procheck.prm file.
http://www.biochem.ucl.ac.uk/~roman/procheck
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PROCHECK
Distributions of each of the different
main-chain bond angles in the
structure. The solid line in the centre
of each plot corresponds to the smallmolecule mean value, while the
dashed lines either side show the
small-molecule standard deviation,
the data coming from Engh & Huber
(1991).
If any of the histogram bars lie off the
graph, to the left or to the right, a large
arrow indicates the number of these
outliers (as in the CA-C-O and CB-CAC plots above).
http://www.biochem.ucl.ac.uk/~roman/procheck
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PROCHECK
RMS distances from planarity for the
different planar groups in the structure.
The dashed lines indicate different ideal
values for aromatic rings (Phe, Tyr, Trp,
His) and for planar end-groups (Arg, Asn,
Asp, Gln, Glu).
The default values are 0.03Å and 0.02Å,
respectively.
http://www.biochem.ucl.ac.uk/~roman/procheck
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Wie kann man 2 Proteinstrukturen vergleichen?
Paarweise Sequenzvergleiche
Paarweise Strukturvergleiche?
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Partitioning protein space into homologous families
Protein architecture. The tramtrack protein
[Protein Data Bank entry 2drp (30)] is a
small protein (525 heavy atoms,
63 residues, and 6 elements of secondary
structure), yet it exhibits typical modular
protein architecture with two compact
structural domains, the so-called zinc
fingers.
(A) The most detailed description of
atomic positions is required to understand
the function of the tramtrack protein (gray
and black, running left to right), which
involves binding to a specific base
sequence of DNA (white).
Holm, Sander Science 273, 5275 (1996)
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Partitioning protein space into homologous families
(B) The complicated 3D shape of proteins
is encoded in their linear sequence of
amino acids. Side chains stripped off, the
polypeptide backbone (thick) can be seen
meandering from the bottom left to the
upper right. Regular patterns of hydrogen
bonding (thin lines) between amide and
carbonyl groups of the polypeptide
backbone give rise to secondary structure,
shown schematically in (C) as arrows for
 strands and cylinders for  helices (with
zinc atoms as spheres).
Holm, Sander Science 273, 5275 (1996)
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Meaning of structural equivalence
Shape comparison aims at the 1:1 enumeration of
equivalent polymer units in 2 protein molecules.
The problem and solution can be represented in
3D, as a rigid-body superimposition; in 2D, as
similar patterns in distance matrices; or in 1D, as
an alignment of amino acid sequences.
Here, the comparison of the tramtrack protein
with another zinc finger protein, the human
enhancer-binding protein MBP-1 [PDB entry
1bbo], is used as an example.
(A) In the 3D comparison, the problem is to find a
translation and rotation of one molecule (red:
1bbo) onto the other (blue: 2drpA). The 3D
superimposition (residue centers only, green lines
join equivalenced residue centers, zinc atoms as
spheres) is not exact because of an internal
rotation of the two zinc finger domains relative to
one another.
Holm, Sander Science 273, 5275 (1996)
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Ranges of similarity between proteins
Holm et al. Prot Sci 1, 1691 (1992)
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Surprising similarities
Holm et al. Prot Sci 1, 1691 (1992)
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Surprising similarities
Holm et al. Prot Sci 1, 1691 (1992)
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Surprising similarities
Holm et al. Prot Sci 1, 1691 (1992)
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Partitioning protein space into homologous families
(B) The 2D distance matrices reveal
the conserved structure of the zinc
fingers (left: distance matrices of the
whole structures; black dots are
intramolecular distances less than
12 Å, 1bbo at bottom and 2drpA on
top; right: distance matrices brought
into register by keeping only rows or
columns corresponding to
structurally equivalent residues).
(C) One-dimensional alignment of
amino acid strings. Evolutionary
comparison aligns the histidine (H)
residues involved in zinc binding
(bold; helices and strands of
secondary structure are underlined).
Holm, Sander Science 273, 5275 (1996)
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2 Algorithms for structural alignment
(A) The 3D lookup is a fast heuristic algorithm that catches easy-to-find
structural similarities and is part of the Dali 3D search server. The idea is
that in favorable cases, 3D superimposition of only a pair of secondary
structure elements (SSEs) leads to superimposition of the entire
structures.
Top: Structure comparison of an SH3 domain of c-Src kinase [1cskA,
query structure] with the enzyme papain [1ppn, target structure] reveals
similar domain folds, although there is no sequence relation between the
proteins and one is much larger. The appropriate orientation of the
molecules is found by exhaustive comparison of internal coordinate
frames of each protein. An internal coordinate frame is defined by an
ordered pair of SSEs (centering one SSE at the origin, aligning it with the
y axis, and rotating the molecule around this axis so that the center of a
second SSE is in the positive x-y plane).
Bottom left: Target structure, papain, loaded onto the SSE lookup grid.
Each pair of SSEs where the segment midpoints are within 12 Å defines a
coordinate frame relative to the grid axes. The figure shows the
transformed positions of the 12 SSEs of papain (dotted lines) in each of
the 100 different coordinate frames defined by different pairs of SSEs.
Bottom right: The target lookup grid is probed with the SH3 domain, which
has four SSEs (thick continuous lines). The coordinate frames shown are
the ones yielding the best 3D match of four segments. Iterative extension
of a residue-wise alignment starting from the preorientation defined by the
SSE match shown here leads to the equivalence of 43 C atoms with
1.7 Å root-mean-square positional deviation on an optimal least-squares
superimposition.
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Branch-and-bound algorithm
(B) A branch-and-bound algorithm is guaranteed to yield the global optimum but may, in the
worst case, need an exponential number of steps to do so. An implementation of this
algorithm is an essential part of the Dali 3D search server.
First, protein structures A and B are represented by distance matrices (bottom left and right;
each point in a matrix is a residue-residue distance; an internal square is a set of contacts
made by two segments; the secondary structure segments are ,, and ). The problem of
shape comparison becomes one of finding a best subset of residues in each matrix (subsets
of rows and columns) such that the set of residues in protein A has a similar pattern of
intramolecular distances as the set in protein A, as in Fig. 2B. A single solution to the
problem is given in terms of the two sets of equivalent residues (an alignment), as shown in
Fig. 2C. The solution space consists of all possible placements of residues in protein B
relative to the segments of residues of protein A. The key algorithmic idea is to recursively
split the solution subspace (schematically shown as a circle at upper left, in which each point
is a solution to the problem and the lines divide subsets of solutions) that yields the highest
upper bound until there is a single alignment trace left: start with the entire circle; calculate
the upper bound for the left (9) and right (17) half; choose the right half and split it into top
(upper bound 10) and bottom (upper bound 16) quarters; choose the bottom part and split it
(left: 14; right: 12); choose the right part; and so on until the area of solution space has
shrunk to a single solution (shown as the residue-residue alignment matrix enlarged at right).
The upper bound for each part of the solution space is estimated in terms of a simplified
subproblem that asks for the best match of residues in protein B onto a predefined set of
residues in protein A (the match is illustrated by the circle-ended line connecting the single
square in matrix A with a set of candidate squares in matrix B). The best match is the one
with the maximal pair score (sum of similarities of distances between the square in A and the
square in B). The predefined set corresponds to residues in secondary structure elements ( ,
). The upper bound for each of the segment-segment submatrices of matrix A is found by
calculating the similarity scores between the submatrix in A and all accessible submatrices in
B. An upper bound of the total similarity score (sum over all segment-segment submatrices in
A) for one set of solutions is given by the sum of separately calculated upper bounds for each
segment-segment pair of matrix A. The method for choosing constraints that define a set of
solutions works in terms of defining allowed residue ranges at each stage of the iteration and
is not illustrated.
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Recurrent folds
(A) A small number of frequently occurring
domains (folds) covers a large fraction of all
known protein structures. The 287 structurally
unique protein domains (folds) are ranked in
descending order of occurrence in the
representative set of 740 proteins. Domains
ranked 1 through 16 occur 10 or more times
each. Domains ranked 1 through 26 cover 50%
of all known structures that is, the essential parts
of these structures can be constructed from
these domains or described in terms of these
domains (within the limits of similarity within a
domain class). Domains ranked about 170 or
higher occur only once in the current database
(singlets).
Holm, Sander Science 273, 5275 (1996)
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Partitioning protein space into homologous families
(B) Examples of frequently observed fold classes, with one class
from each of the attractor regions in Fig. 5 (each attractor region
contains several classes, where the term "class" is defined in the
text). Color coding indicates which parts of the fold are present in
more or fewer members of the class. The color changes from
light blue (regions present in 100% of members of the fold class)
to red (0% occupancy). The representative classes are defined
as follows (attractor, class name, and number of recurrences in
sequence-unique set of 740 structures): attractor I: parallel :
COOH-terminal domain of succinyl-CoA synthetase  chain
(126); attractor II: -meander: mouse opg2 immunoglobulin
heavy chain variable domain (52); attractor III: -helical:
myoglobin; attractor IV: -zigzag: COOH-terminal domain of
pertussis toxin; and attractor V:  meander: COOH-terminal
domain of phosphoglycerate dehydrogenase. Note that other fold
classes in the same attractor region are not shown, but the most
frequently occurring are shown in Fig. 5B.
Holm, Sander Science 273, 5275 (1996)
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Partitioning protein space into homologous families
(C) Growth and redundancy of protein 3D structures in the
Protein Data Bank.
Entry: one of currently more than 4000 sets of protein
coordinates in the PDB.
Family: collection of proteins set as equivalent if pairwise
sequence identity exceeds 25%.
Fold: fold class as defined above.
The number of new structure entries grows rapidly in time (note
logarithmic scale). Redundancy is defined in terms of sequence
similarity (sequence families) or structure similarity (fold
classes). Currently, there are about 6.4 entries per sequence
family and 2.4 families per fold class, for a total of 15 entries per
fold. One may expect that in the near future a new fold will
appear for about every 15 new entries. The curve of new folds
lags behind the curve of sequence-unique families, which
indicates the increasing frequency of recurrent folds in newly
solved structures (although this may be the result of bias in
experimental work). There is no indication that the growth in
new fold classes is slowing down at present.
Holm, Sander Science 273, 5275 (1996)
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Partitioning protein space into homologous families
(B) 40% of all known domains (protein substructures) are covered by 16 fold
classes (shown as topology diagrams; , -helix segment; , -strand segment;
thick bar, parallel chain connection between segments; thin bars, antiparallel
connection; arc,  helices crossing at roughly right angles). Although each fold
class has individual features, most fold classes map to five attractor regions
(peaks I through V).
All folds with sheets of mainly parallel  strands map to attractor I. The parallel
 folds contain a  x  unit, where the intervening segment (x) is required to
reverse chain direction so that the strands are parallel. The    unit has a
preferred handedness determined by polymer physics and the natural twist of 
strands. Attractor II contains a variety of helical folds. The connectivity of
elements in the folds of attractors III and IV contains meander motifs suggestive
of the collapse of a long hairpin, either of  strands only or of  strands
alternating with a helical pair, ()2. The  zigzag motif of attractor V is simply
a series of antiparallel hairpin connections between sequentially adjacent
strands. Elementary polymer physics indicates that interactions in space
between regions of the chain that are close in sequence are much more
probable than those between sequence-distant regions. The  zigzag motif
occurs both in flat sheets and barrels, and there is considerable variation in the
length of strands (about 4 residues in propeller blades, about 13 in porin
barrels).
Holm, Sander Science 273, 5275 (1996)
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Evolutionary adaptation of enzyme function
(A) Discovery of an essential structure-function feature
by shape comparison. A structure database search with
DNA polymerase detects  kanamycin
nucleotidyltransferase (rather than other known DNA or
RNA polymerases) as the nearest neighbor in fold space
and reveals conserved residues and structural features
supporting the active site.
Following up the lead provided by structure database
searching with profile searches in sequence databases
resulted in the identification of the same characteristics
in a large superfamily of nucleotidyltransferases.
The biological functions of member families range from
DNA repair to regulation of biosynthetic pathways and
antibiotic resistance.
Holm, Sander Science 273, 5275 (1996)
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Partitioning protein space into homologous families
(B) Variety of substrate specificity of a
common chemical reaction on an essential
protein substructure is the remarkable result
of biological evolution. All member enzymes
of this extended family unified as a result of
shape comparison catalyze a common
chemical reaction, the coupling of nucleoside
triphosphates (black squares and dots) to a
free hydroxyl group by means of elimination
of pyrophosphate [top row: DNA polymerase
, DNA nucleotidyl exotransferase; middle
row: polyadenylate polymerase, (2‘-5‘)
oligoadenylate synthetase, kanamycin
nucleotidyltransferase; bottom row: protein
PII uridylyltransferase, glutamine synthetase
adenylyltransferase, and streptomycin 3‘adenylyltransferase].
Holm, Sander Science 273, 5275 (1996)
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Partitioning protein space into homologous families
a, All-against-all structure alignment by DALI reveals a hierarchical
organization of fold space. The method is sensitive enough to
recognize similarities of general folding pattern — e.g., the -sandwich
topology of superoxide dismutase and immunoglobulin domains — and
selective enough to give higher scores to pairs of structures with more
closely superimposable C traces — e.g., any two globins score
higher than any globin–phycocyanin pair. Structure similarity alone
yields an operational definition of 'folds'. The thick circles denoting
folds (left) are defined using a uniform radius for clusters of structural
neighbors. The vertical bar (right) denotes cutting the fold dendrogram
at a uniform value of structural similarity. However, the level of
structural similarity, or degree of structural divergence, varies between
different families, and we need other criteria to delineate superfamilies.
b, Divergent evolution from a common ancestor retains not only the
fold but also many functional features. This means that homologs
remain in a structural neighborhood and can be delineated by similar
functional attributes (marked here by similar color) in the map of fold
space. Functional convergence (from independent evolutionary origins)
would appear as blotches of similar color in disconnected regions of
the map of fold space and in disjoint branches of the fold dendrogram.
Partitioning the fold dendrogram in terms of functional similarities
yields family-specific thresholds in terms of structural similarity (nodes
that partition the fold dendrogram into functionally conserved
superfamilies are circled on the right). This combination of structural
and functional similarity measures results in an automatically
Dietmann & Holm, Nat Struct Biol 8, 953 (2001)
generated hierarchical classification m_n at the fold (m) and
superfamily
(n) levels.
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Proteinstruktur-Analyse
c, The principles are illustrated on a branch of the fold dendrogram
consisting of aminopeptidases (1xjo and 1amp), carboxypeptidase
(1aye), purine nucleoside phosphorylases (1b8oA, 1cb0A and
1ecpA), pyrrolidone carboxyl peptidase (1a2zA), peptidyl–tRNA
hydrolase (2pth) and hydrogenase maturating endopeptidase
(1cfzA). The functional similarity between all pairs of structures is
evaluated using a neural network with output  in the range 0
(analogous)-1 (homologous) — for example, (1cb0A, 1b8oA) =
0.91, (1amp, 1aye) = 0.74, (1cfzA, 2pth) = 0.59, (1xjo, 1amp) =
0.30 and (1a2zA, 2pth) = 0.13. Here, line thickness indicates the
magnitude of the term (i,j) -  (Eq. 1) with color-coding for positive
(red) or negative (blue) values. The threshold parameter  was
arbitrarily set to 0.30 in this numerical example.
d, The protein set is partitioned into superfamilies in the context of
the fold dendrogram. Node scores s(C) are computed for each node,
with = 0.30. For example, each structure is homologous to itself;
therefore, leaf nodes get a score s(leaf) = 1.00 -  = 0.70, whereas
s(1cfzA, 2pth) = (1.00 + 1.00 + (2 0.59)) / 4 -  = 1.98. The optimal
partition (circled nodes) maximizes the sum of node scores over
selected nodes (underlined scores). This optimal partition is stable
for threshold values 0.09 <  < 0.53.
Dietmann & Holm, Nat Struct Biol 8, 953 (2001)
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Partitioning protein space into homologous families
Dietmann & Holm, Nat Struct Biol 8, 953 (2001)
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Proteinstruktur-Vergleich durch Feature-Vector
Input für Neuronales Netzwerk ist ein Feature-Vector.
„Keyword similarity“: Vektorprodukt für Häufigkeiten von Swissprot-Keywörter innerhalb der beiden
Sequenzfamilien.
„Functional preference“ is pro Aminosäure definiert und wird über alle Residuen in einem 3D-Cluster von
konservierten Residuen summiert.
Dietmann & Holm, Nat Struct Biol 8, 953 (2001)
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Funktionszuordnung per Strukturvergleich
Dietmann & Holm, Nat Struct Biol 8, 953 (2001)
4. Vorlesung WS 2004/05
Softwarewerkzeuge
41
Zusammenfassung
Viele, sehr bequeme Tools verfügbar, mit denen man schnell einen guten
Überblick über bestimmte Proteinstrukturen erhalten kann.
Proteinstruktur ist evolutionär wesentlich länger konserviert als Sequenz
 Strukturvergleiche erlauben es, wesentlich entferntere Verwandtschaften
aufzudecken.
Numerische Klassifizierung erlaubt (nun erstmals) eine robuste, automatische
evolutionäre Klassifikation von Proteinstrukturen.
Dietmann & Holm, Nat Struct Biol 8, 953 (2001)
4. Vorlesung WS 2004/05
Softwarewerkzeuge
42