Pair-wise Structural Comparison using DALILite Software of DALI
Rajalekshmy Usha
Overview
History Protein Structure Comparison Comparison Algorithm Input and Output Interface Demo on the software Analysis of the Result References
History
Earliest resources(1970s) were sequence data Pioneered by Dayhoff
Structural database appeared in mid-1990s Structural data is sparse PDB (protein Data Bank) has 39,464 structural entries to date NCBI (National Center for Biotechnology Information)has over 12 million entries on sequence data
Popular Structural classifications of proteins in: Structural Classification of Proteins (SCOP) Distance Matrix Alignment (DALI) CATH Others are DDBase, 3Dee and DaliDD (Dali Domain Database)
Protein Structure Comparison
Popularized by Liisa Holm and Chris Sander (1993) DALI
Created by Liisa Holm Completely automated Too large and complex to be installed in external sites Use distance matrices Standalone version of search engine of Dali server
Why use structural data? 3D structure of the proteins have been conserved over time Leads to interesting evolutionary observations, prediction of
structure and functions
Comparison Algorithm
Exhaustive, all-against-all 3D structure comparison Helps to understand the distribution of known structure in shape
space Use protein structures from PDB
Use distance matrix three dimensional coordinates of each protein residues (i.e., C-α
atoms) pair-wise distance between the residue centers (a 2D
representation of 3D structure) each structure’s contact map are overlaid move them horizontally and vertically overlap along the diagonal represent similar backbone
confirmations (secondary structure) off-diagonal similarity tertiary structure similarity
Underlying Algorithms
Branch and Bound Search to find the optimal alignment Uses distance matrices
Collapsed into regions of overlap (sub-matrices) of fixed size
The sub-matrices are stitched together if there is an overlap with the neighboring fragments
Uses similarity score Monte Carlo Optimization Algorithm
To optimize the alignment
Understanding the Formula Used Similarity Score
core is the set of structurally equivalent residue pairs between proteins A and B
Δ is the deviation of the intermolecular Cα-Cα intermolecular distance between (iA,jA) and (iB,jB), relative to their arithmetic mean d.
θ is the similarity threshold, set empirically to 0.2 ω is the envelope function and ω = exp(-d2/r2), where r =
20ºA High score means good fit
Branch and Bound Search Consider only nongapped segment pairs
This reduces the complexity of structure alignment Natural segmentation uses the secondary structures of the
query structure E.g. α helices and β strands
Diagonal lines represent the nongapped segment pairings Pairing between segments of query structure (horizontal)
and the proteins being aligned to it (vertical). Do an alignment score (similarity score) within the segments and
between the segments Split the search space into smaller subset of candidate pairings
(matrices) Chose the upper bound on the sum-of-pairs score Subset with the highest bound contains the optimal alignment
Branch and Bound Search
Image source: Holm L., Park J (2000)DaliLite workbench for protein structure comparison. Bioinformatics 16, 567
Monte Carlo Optimization Algorithm A basic move is made
The move is random Probability of accepting a move is p = e beta*(s’-s), where S’ = new score,
S= old score and beta is a parameter Involves addition or deletion of residue equivalence assignment
Two basic modes of operation Expansion mode
Alignment is incremented by using overlapping contact patterns Extend the alignment by including all pairs of matching contact
patterns with the same residue pairs (iA ,iB) Adding new fragment requires tentative removal of inconsistent
previous equivalent assignment The removal is permanent
Trimming mode Removal of fragment that give a net negative contribution to the
similarity score Done after the 1st and every 5 subsequent expansion cycles
The Monte Carlo Optimization
Image source: Holm L., Park J (2000)DaliLite workbench for protein structure comparison. Bioinformatics 16, 567
Thick black line indicates the optimum found after branch and bound algorithm
Red dashed line indicates final alignment after Monte Carlo Optimization
DaliLite Database Search Input Interface
DaliLite Database Server Output
DaliLite Database Server Output : 2
DaliLite Pair wise Comparison Input Interface
Statistical Analysis of the Result
Z- score: X is the raw score to be standardized σ is the standard deviation μ is the mean Score < 2.0 are structurally dissimilar
RMSD (Root Mean Square Deviation) Average distance between the backbones of the
superimposed proteins δ = distance between N pairs of equivalent Cα atoms
Sequence Identity percentage of identical amino acids over all structurally
equivalent residues
DaliLite Output
DaliLite Output : 2 – cont’d
DaliLite Output : 3 – cont’d
Demo on Using DaliLite
http://www.ebi.ac.uk/dali/index.html
1CDK and 1CJA 1CDK:A 1CJA:A
1CDK is a cAMP-dependent protein kinase and 1CJA is an actin-fragmin kinase
Image source : PDB.org
1CPC and 1KTP1CPC:A 1KTP:A
1CPC and 1KTP belong to the same phycocyanin family (light harvesting protein complex); both have six helices sequentially aligned.
Image source : PDB.org
References
Holm L., Sander C(1993 a) Protein Structure Comparison by Alignment of Distance Matrices. Journal of Molecular Biol. 233(1): 123-138
Holm L., Park J(2000) DaliLite workbench for protein structure comparison. Bioinformatics 16, 566-567
Holm L., Sander C(1996) Mapping the protein universe. Science 273: 595-602
Bourne P.E., Weissig H. Structural Bioinformatics. Wiley-Liss, Hoboken, New Jersey
http://wikipedia.org