sw-v6-prot-structure

8/7/2019 SW-V6-Prot-Structure

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4. Vorlesung WS 2004/05 Softwarewerkzeuge 1

Was kann ich per Knopfdruck ber einePDB-Struktur lernen?

PdbSum webseite:

http://www.ebi.ac.uk/thornton-srv/databases/cgi-bin/pdbsum/


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Klassifizierung in CATH



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Darstellung der Sekundrstruktur



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Konservierung innerhalb Proteinfamilie

Oberflche entsprechend Konservierung

eingefrbt.



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Multiples Sequenzalignment



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Ramachandran-Diagramm



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Sekundrstrukturvorhersage: PSIPRED

D.T. Jones, J Mol Biol 292, 195 (1999); http://bioinf.cs.ucl.ac.uk/psipred/

Enge, sehr polare Bindungstasche auf Proteinoberflche.


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Qualitt von PSIRED-Vorhersagen

D.T. Jones, J Mol Biol 292, 195 (1999); http://bioinf.cs.ucl.ac.uk/psipred/

Ergebnis fr 187 Testproteine mit unterschiedlichen Faltungen.

Genauigkeit von PSIPRED:Ca. 75%


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Vorhersage von TM-Helices

http://darwin.nmsu.edu/~molb470/fall2003/Projects/koul/tmhmm.html

Residuen in Transmembranhelices

sind fast ausschlielich hydrophob.

Lnge einer TM-Helix 20 Residuen.

HMMs sind sehr erfolgreich um TM-

Helices vorherzusagen (>90%Genauigkeit).


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Analyse derOberflche: elektrostatisches Potential

Sheinerman, Honig,J Mol Biol 318, 161 (2002)

Proteinoberflchen an Protein-

Protein-Bindungsstellen sind

hufig elektrostatisch

komplementr.

Surface representation of the electrostatic

potential of unbound monomers of 4

protein-protein complexes. Open bookview of the proteinprotein 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 kis the Boltzmann

constant, Tis the absolute temperature

and e is a proton's charge.


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PROCHECK: Qualittscheck fr ProteinstrukturenTheRamachandran plot shows the phi-psi torsion angles for allresidues in the structure (except those at the chain termini). Glycines areseparately identified by triangles as these are not restricted to theregions of the plot appropriate to the other sidechain types.Colouring/shading scheme:the darkest areas (here shown in red) correspond to the "core" regionsrepresenting the most favourable combinations ofphi-psi values.The regions are labelled as follows:

A - Core alpha B - Core betaL - Core left-handed alpha p - Allowed epsilona - Allowed alpha b - Allowed betal - Allowed left-handed alpha

~a - Generous alpha ~p - Generous epsilon~l - Generous left-handed alpha ~b - Generous beta

The different regions were taken from the observed phi-psi distributionfor121,870 residues from 463 known X-ray protein structures. The twomost favoured regions are the "core" and "allowed" regions whichcorrespond to 10 x 10 pixels having more than 100 and 8 residues inthem, 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 over90% 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 darkerthe shaded area on each plot, the

more favourable the region. The data on

which the shading is based has come from a

data set of163 non-homologous, high-

resolution protein chains chosen from

structures solved by X-ray crystallography to aresolution of2.0 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).



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PROCHECKThe 6 graphs show how the structure (represented by the solid square)compares with well-refined structures at a similar resolution. The darkband 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 ofresolution, while the width of the band on either side of i t corresponds to a

variation of one standard deviation about the mean. In some cases, thetrend 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 thepercentage of the protein's residues that are in the most favoured, orcore, regions of the Ramachandran plot. For a good model structure,obtained at high resolution, one would expect this percentage to be over90%. However, as the resolution gets poorer, so this figure decreases - asmight be expected. The shaded region reflects this expected decreasewith worsening resolution.b. Peptide bond planarity. This property is measured by calculating the

standard deviation of the protein structure's omega torsion angles. Thesmaller the value the tighter the clustering around the ideal of180 degrees(which represents a perfectly planar peptide bond).c. Bad non-bonded interactions. This property is measured by thenumber of bad contacts per100 residues. Bad contacts are selectedfrom the list of non-bonded interactions and are defined as contacts wherethe distance of closest approach is less than or equal to 2.6.d. Calpha tetrahedral distortion. This property is measured bycalculating the standard deviation of the zeta torsion angle. This is anotional torsion angle in that it is not defined about any actual bond in thestructure. 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 thestandard deviation of the hydrogen bond energies formain-chainhydrogen bonds. The energies are calculated using the method ofKabsch & Sander (1983).f. Overall G-factor. The overall G-factoris a measure of the overallnormality of the structure. The overall value is obtained from an averageof all the different G-factors for each residue in the structure.



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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 allchi-1 torsion angles.

e. Standard deviation of the chi-2

trans torsion angles.

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 plotcorresponds 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.


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PROCHECK


Distributions of each of the different

main-chain bond angles in the

structure. The solid line in the centreof 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).

If any of the histogram bars lie off the

graph, to the left or to the right, a large

arrow indicates the number of theseoutliers (as in the CA-C-O and CB-CA-

C plots above).


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PROCHECK

RMS distances from planarity for the

different planar groups in the structure.

The dashed lines indicate different idealvalues for aromatic rings (Phe, Tyr, Trp,

His) and for planar end-groups (Arg, Asn,

Asp, Gln, Glu).

The default values are 0.03 and 0.02,

respectively.



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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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(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 polypeptidebackbone give rise to secondary structure,

shown schematically in (C) as arrows for

F strands and cylinders forE helices (with

zinc atoms as spheres).



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Meaning of structural equivalenceShape 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 entry1bbo], 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.



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Ranges of similarity between proteins

Holm et al. Prot Sci 1, 1691 (1992)


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Surprising similarities



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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 , 1bbo at bottom and 2drpA on

top; right: distance matrices brought

into register by keeping only rows or

columns corresponding tostructurally 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 ofsecondary structure are underlined).



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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 theyaxis, and rotating the molecule around this axis so that the center of a

second SSE is in the positive x-yplane).

Bottom left: Target structure, papain, loaded onto the SSE lookup grid.

Each pair of SSEs where the segment midpoints are within 12 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 CE atoms with

1.7 root-mean-square positional deviation on an optimal least-squares

superimposition.Holm, Sander Science 273, 5275 (1996)


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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 F,F, and E). 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; calculatethe upper bound for the left (9) and right (17) half; choose the r ight 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 thesquare in B). The predef ined 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 structurallyunique 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 partsof 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).



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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 F:

COOH-terminal domain of succinyl-CoA synthetase F chain(126); attractor II: F-meander: mouse opg2immunoglobulin

heavy chain variable domain (52); attractor III: E-helical:

myoglobin; attractor IV: F-zigzag: COOH-terminal domain of

pertussis toxin; and attractor V: F 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.



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(C) Growth and redundancy of protein 3D structures in the

Protein Data Bank.

Entry: one of currently more than 4000 sets of proteincoordinates 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 sequencesimilarity (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 newlysolved 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.




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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; E, E-helix segment; F, F-strand segment;

thick bar, parallel chain connection between segments; thin bars, antiparallel

connection; arc, E helices crossing at roughly right angles). Although each foldclass has individual features, most fold classes map to five attractor regions

(peaks I through V).

All folds with sheets of mainly parallel F strands map to attractor I. The parallel

F folds contain a F x F unit, where the intervening segment (x) is required to

reverse chain direction so that the strands are parallel. The F E F unit has a

preferred handedness determined by polymer physics and the natural twist ofFstrands. 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 ofF strands only or ofF strands

alternating with a helical pair, (FEF)2. The F 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 moreprobable than those between sequence-distant regions. The F 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 F kanamycinnucleotidyltransferase (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 andantibiotic resistance.



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(B) Variety of substrate specificity of a

common chemical reaction on an essential

protein substructure is the remarkable resultof 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 eliminationof pyrophosphate [top row: DNA polymerase

F, 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].



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Partitioning protein space into homologous familiesa, 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 CE traces e.g., any two globins score

higher than any globinphycocyanin 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 betweendifferent 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

generated hierarchical classification m_n at the fold (m) and

superfamily (n) levels.

Dietmann & Holm, Nat Struct Biol 8, 953 (2001)


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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), peptidyltRNAhydrolase (2pth) and hydrogenase maturating endopeptidase

(1cfzA). The functional similarity between all pairs of structures is

evaluated using a neural network with output J in the range 0

(analogous)-1 (homologous) for example, J(1cb0A, 1b8oA) =

0.91, J(1amp, 1aye) = 0.74, J(1cfzA, 2pth) = 0.59, J(1xjo, 1amp) =

0.30 and J(1a2zA, 2pth) = 0.13. Here, line thickness indicates the

magnitude of the term J(i,j) - U (Eq. 1) with color-coding for positive

(red) or negative (blue) values. The threshold parameterU 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 - U = 0.70, whereas

s(1cfzA, 2pth) = (1.00 + 1.00 + (2 0.59)) / 4 -U = 1.98. The optimal

partition (circled nodes) maximizes the sum of node scores overselected nodes (underlined scores). This optimal partition is stable

for threshold values 0.09 < U < 0.53.



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Proteinstruktur-Vergleich durch Feature-Vector

Input fr Neuronales Netzwerk ist ein Feature-Vector.


Keyword similarity: Vektorprodukt fr Hufigkeiten von Swissprot-Keywrter innerhalb der beiden

Sequenzfamilien.

Functional preference is pro Aminosure definiert und wird ber alle Residuen in einem 3D-Cluster von

konservierten Residuen summiert.


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Funktionszuordnung per Strukturvergleich



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Zusammenfassung

Viele, sehr bequeme Tools verfgbar, mit denen man schnell einen guten

berblick ber bestimmte Proteinstrukturen erhalten kann.

Proteinstruktur ist evolutionr wesentlich lnger konserviert als Sequenz

p Strukturvergleiche erlauben es, wesentlich entferntere Verwandtschaften

aufzudecken.

Numerische Klassifizierung erlaubt (nun erstmals) eine robuste, automatische

evolutionre Klassifikation von Proteinstrukturen.


sw-v6-prot-structure

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