Fast Approximate Database Searching of Polypeptide Structures

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


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


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


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


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


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$


II. Suffix Trees

Compressed Suffixtries

• collapse linear paths in the tree

• store only start- and end-index

• linear number of inner nodes


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


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



… (22,93), (112, 4) …

Discretization

… a b b a …

… (22,93), (112, 4) …

Discretization

… a b b a …

1a1f



… (22,93), (112, 4) …

Discretization

… a b b a …

… (22,93), (112, 4) …

Discretization

… a b b a …

1a1f



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


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


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

. . .


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.


IV. Application

Minimum RMSD superposition: 1a1f vs. 1f2i 1a1f vs. 6 other true positives

“False” positives: 1a1f vs. 1vl2


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


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


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, …


Acknowledgements

• Hanjo Taeubig, Arno Buchner

• Volker Heun, Moritz Maass

• BFAM/BMBF

• ALTANA

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Fast Approximate Database Searching of Polypeptide Structures

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