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Efficient Algorithms Group Prof. Ernst W. Mayr Technical University of Munich. Fast Approximate Database Searching of Polypeptide Structures. Hanjo Taeubig Arno Buchner Jan Griebsch. German Conference on Bioinformatics October 4th, 2004. Structure. motivation & problem definition - PowerPoint PPT Presentation
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Fast Approximate Database Searchingof Polypeptide Structures
Fast Approximate Database Searchingof Polypeptide Structures
Hanjo Taeubig Arno Buchner Jan Griebsch
Efficient Algorithms GroupProf. Ernst W. Mayr
Technical University of Munich
German Conference on Bioinformatics
October 4th, 2004
www14.informatik.tu-münchen.de/PAST {taeubig|buchner|griebsch}@in.tum.de
Structure
I. motivation & problem definition
II. suffix trees
III. polypeptide angles suffix trees
IV. application & future work
www14.informatik.tu-münchen.de/PAST {taeubig|buchner|griebsch}@in.tum.de
I. Motivation
• the function of a protein is largely determined by it’s structure and geometric shape
• How to find similar structures in a database ?
• related work
– DALI, VAST, CE
– TopScan, ProtDex2
• existing methods are mostly based on the principlefilter heuristics + exhaustive search/pairwise comparison and scale at least linearly
www14.informatik.tu-münchen.de/PAST {taeubig|buchner|griebsch}@in.tum.de
I. Motivation
• PDB – Protein Data Bank
– ca. 3.5GB compressed, 14GB decompressed
– > 23.000 entries
– 90% Proteins, 5% Nucleotidesequences, 4% Nucleotide-Protein complexes
– 85% x-ray cristalography, 15% NMR
• protein structure databases grow almost exponentially
• search methods with time complexity at most O(n) required
www14.informatik.tu-münchen.de/PAST {taeubig|buchner|griebsch}@in.tum.de
I. Problem Definition
• search a given polypeptide structure in a protein database
• search the longest common substructure in the database
• identify frequent substructures (motifs) in the database
www14.informatik.tu-münchen.de/PAST {taeubig|buchner|griebsch}@in.tum.de
II. Suffix Trees
Tries
• tree with a root node
• every edge is labeled with a letter
• labels of all edges to the child nodes of one node are pairwise distinct
www14.informatik.tu-münchen.de/PAST {taeubig|buchner|griebsch}@in.tum.de
II. Suffix Trees
Suffixtries
• stores all suffixes of a string
• the sentinel $ ensures that every suffix is represented by a leaf
Suffixtree for the word aaabbb$
www14.informatik.tu-münchen.de/PAST {taeubig|buchner|griebsch}@in.tum.de
II. Suffix Trees
Compressed Suffixtries
• collapse linear paths in the tree
• store only start- and end-index
• linear number of inner nodes
www14.informatik.tu-münchen.de/PAST {taeubig|buchner|griebsch}@in.tum.de
II. Suffix Trees
Further Extensions
• generalized suffix trees
– stores suffixes of multiple strings in one tree
• online linear time construction
Time Complexity
• Finding an occurrence of the search pattern does not depend on the size of the searched database, but linearly on the length m of the pattern
• Finding all k occurrences of a pattern takes time proprtional to m+k
www14.informatik.tu-münchen.de/PAST {taeubig|buchner|griebsch}@in.tum.de
III. Polypeptide Angles Suffix Tree
Idea
I. encode the geometry of the database proteins in a translation and rotation invariant linear description (“structural text”)
– torsion angle encoding of the protein backbone
II. adapt efficient text mining methods to the error tolerant substructure searching problem
– generalized suffix trees with fault tolerant search strategies
www14.informatik.tu-münchen.de/PAST {taeubig|buchner|griebsch}@in.tum.de
III. Polypeptide Angles Suffix Tree
… (22,93), (112, 4) …
Discretization
… a b b a …
… (22,93), (112, 4) …
Discretization
… a b b a …
1a1f
www14.informatik.tu-münchen.de/PAST {taeubig|buchner|griebsch}@in.tum.de
III. Polypeptide Angles Suffix Tree
… (22,93), (112, 4) …
Discretization
… a b b a …
… (22,93), (112, 4) …
Discretization
… a b b a …
1a1f
www14.informatik.tu-münchen.de/PAST {taeubig|buchner|griebsch}@in.tum.de
III. Polypeptide Angles Suffix Tree
Fault Tolerant Searching
• accept a “neighborhood range” of intervals left and right
• worst case time complexity: exponential (!)
• average: O( )
)1*2(log || n
figure: branching with =1
www14.informatik.tu-münchen.de/PAST {taeubig|buchner|griebsch}@in.tum.de
IV. Application
Example
• search occurrences the C2H2 zinc finger in the complete PDB
• discretization: 24 intervals of 15°
• compare with SCOP classification, sequence-based search, SPASM
www14.informatik.tu-münchen.de/PAST {taeubig|buchner|griebsch}@in.tum.de
IV. Application
Score E
Sequences producing significant alignments: (bits) Value
gi|37926551|pdb|1LLM|C Chain C, Crystal Structure Of A Zif2... 47 6e-07
gi|15988358|pdb|1F2I|G Chain G, Cocrystal Structure Of Sele... 42 2e-05
gi|3319019|pdb|1A1H|A Chain A, Qgsr (Zif268 Variant) Zinc F... 42 3e-05
gi|3319013|pdb|1A1F|A Chain A, Dsnr (Zif268 Variant) Zinc F... 41 3e-05
gi|3319022|pdb|1A1I|A Chain A, Radr (Zif268 Variant) Zinc F... 41 3e-05
gi|16975178|pdb|1JK1|A Chain A, Zif268 D20a Mutant Bound To... 41 3e-05
gi|2098365|pdb|1AAY|A Chain A, Zif268 Zinc Finger-Dna Compl... 41 4e-05
gi|33357855|pdb|1P47|A Chain A, Crystal Structure Of Tandem... 41 5e-05
gi|443340|pdb|1ZAA|C Chain C, Zif268 Immediate Early Gene (... 40 8e-05
gi|15988466|pdb|1G2F|C Chain C, Structure Of A Cys2his2 Zin... 33 0.015
gi|15988460|pdb|1G2D|C Chain C, Structure Of A Cys2his2 Zin... 32 0.025
gi|1941952|pdb|1MEY|C Chain C, Crystal Structure Of A Desig... 28 0.44
gi|40889293|pdb|1P7A|A Chain A, Solution Stucture Of The Th... 27 0.64
gi|3318788|pdb|2ADR| Adr1 Dna-Binding Domain From Saccharo... 27 0.78
gi|2094895|pdb|1SP1| Nmr Structure Of A Zinc Finger Domain... 26 1.4
gi|1420993|pdb|1ARD| Yeast Transcription Factor Adr1 (Resi... 23 9.7
. . .
www14.informatik.tu-münchen.de/PAST {taeubig|buchner|griebsch}@in.tum.de
IV. Application
Figure: SearchingPDBentry1a1fwithdifferentneighborhoodsettings
Searchrangein15° ±0 ±1 ±2 ±3 ±4 ±5 ±6 ±7 ±8True positives 11 12 64 05 56 16 26 46 5
1a1f False positives 13333 254Time[s] < 1 < 11 23458 12True positives 113 369 14 15 18
1mfs False positives 49 9Time[s] < 1 < 1 < 1 < 111236True positives 11 78 7 120 132 135 138 144 146
1a3n False positives 0Time[s] < 1 < 11 23468 12
Table 1: The numberof trueandfalsepositivesfort hestructure searches.
www14.informatik.tu-münchen.de/PAST {taeubig|buchner|griebsch}@in.tum.de
IV. Application
Minimum RMSD superposition: 1a1f vs. 1f2i 1a1f vs. 6 other true positives
“False” positives: 1a1f vs. 1vl2
www14.informatik.tu-münchen.de/PAST {taeubig|buchner|griebsch}@in.tum.de
IV. Application
Run Time
• decompression of the packed PDB files
• parsing of the PDB files and calculating the torsion angles
• discretization and building the PAST
• searching a structure
25min
55min
2min
seconds
Pre-processing
Searching
www14.informatik.tu-münchen.de/PAST {taeubig|buchner|griebsch}@in.tum.de
Summary
• suffixtree-based protein (sub-)structure database search method
• preprocessing required
• fast search
• does not rely on heuristics, SSE recognition
• adaptable sensitivity and error models
• until gapped matching is modeled: applicable for shorter peptide chains, motifs
• surprisingly simple
www14.informatik.tu-münchen.de/PAST {taeubig|buchner|griebsch}@in.tum.de
Future Work
• model matching with insertions & deletions
• consensus search pattern
• implementation and practical testing of further error models
and angle encoding
• identification of new motifs
• testing, testing, testing: evaluating the method further with real life problems from pharmaceutical researchers, biologists, patent offices, …
www14.informatik.tu-münchen.de/PAST {taeubig|buchner|griebsch}@in.tum.de
Acknowledgements
• Hanjo Taeubig, Arno Buchner
• Volker Heun, Moritz Maass
• BFAM/BMBF
• ALTANA