Download Dali: A Protein Structural Comparison Algorithm

Survey
yes no Was this document useful for you?
   Thank you for your participation!

* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project

Document related concepts

Artificial gene synthesis wikipedia , lookup

Expression vector wikipedia , lookup

Gene expression wikipedia , lookup

Biochemistry wikipedia , lookup

Magnesium transporter wikipedia , lookup

G protein–coupled receptor wikipedia , lookup

Ribosomally synthesized and post-translationally modified peptides wikipedia , lookup

Ancestral sequence reconstruction wikipedia , lookup

Protein purification wikipedia , lookup

Protein wikipedia , lookup

Interactome wikipedia , lookup

Western blot wikipedia , lookup

Proteolysis wikipedia , lookup

Metalloprotein wikipedia , lookup

Two-hybrid screening wikipedia , lookup

Protein–protein interaction wikipedia , lookup

Transcript
Dali: A Protein Structural
Comparison Algorithm Using 2D
Distance Matrices
Main Points for Discussion
• Overview of why structural comparison can
be a useful mode of analysis.
• Using a 2-D distance matrix to represent a
3-D protein structure.
• Specific computer algorithms that have
been used to accomplish this analysis,
including Monte Carlo optimization.
• Further applications of Dali.
Why consider structural comparison?
• 1D sequence comparisons has traditionally been
(and still is) used to determine degree of
relatedness, although a low degree of sequence
homology may yield surprisingly similar
structures.
• 3D structural alignment is aimed at providing
more information about the structure-function
similarities between proteins with nondetectable evolutionary relationships.
The Distance Matrix and How It’s Read
1
2
3
Assignment of Equivalent Residue Pairs
Additive Similarity Score (general)
L
L
i=1
j=1
S = S S f(i,j)
• i and j are labeled pairs of equivalent (matched)
residues (i.e. i = iA,iB).
 f = similarity measure based on Ca-Ca distances
dAij and dBij
• Largest S corresponds to optimal set of
equivalencies.
Rigid Similarity Score
R
f (i,j)
=q –|
R
A
d ij
–
B
d ij
|
•dAij and dBij are equivalenced residues
in proteins A and B.
q R = zero level of similarity
Elastic Similarity Score
E
f (i,j)
=
A – dB |
|
d
ij
ij
* )
qEw(d
ij
d*ij
qE
• d*ij = the average of dAij and dBij
 q E = tolerance of 20% deviation
• w(r) = envelope function = exp(-r2/a2)
Robustness of Dali
Quality of Generated Alignments
• Accuracy was verified by examining
conserved functional residues in seeemingly
divergent structures.
• The elasticity score is useful in that it
captures relative movements of structural
elements (e.g. ATP binding site in hsp70)
and leaves only extremely non-homologous
loops unaligned.
Quality of Generated Alignments (cont.)
• Detection of inter-domain motion brings
functionally important residues into focus
(e.g. ATP binding site in hsp70).
• Manipulation of the elastic similarity score
determines the stringency of the alignment.
Dendrogram
Examination of Relatedness
Using a Dendrogram
Further Applications of Dali
• Continuing further in an attempt to map the
entire protein space using quantitative
comparisons between structures
(correspondence analysis on p. 133)
• Applications to residue-residue energy
interactions to create a more accurate
biochemical representation of the protein.
Also able to yield more useful information
to predict 3D structure from amino acid
sequence due to the energies of interacting
residues.