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Model Database

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Model Database. Scene. Recognition. Lamdan, Schwartz, Wolfson, “Geometric Hashing”,1988. Geometric Matching task = Geometric Pattern Discovery. T. Inexact Alignment. Simple case – two closely related proteins with the same number of amino acids. - PowerPoint PPT Presentation

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Page 1: Model  Database

Model Database

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Scene

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Recognition

Lamdan, Schwartz, Wolfson, “Geometric Hashing”,1988.

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Geometric Matching task = Geometric Pattern Discovery

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Inexact Alignment.

Simple case – two closely related proteins with the same number of amino acids.

T

Question: how to measure alignment error?

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Superposition - best least squares(RMSD – Root Mean Square Deviation)

Given two sets of 3-D points :P={pi}, Q={qi} , i=1,…,n;

rmsd(P,Q) = √ i|pi - qi |2 /n

Find a 3-D rigid transformation T* such that:

rmsd( T*(P), Q ) = minT √ i|pi - qi |2 /n

A closed form solution exists for this task.It can be computed in O(n) time.

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Structure Alignment (Straightforward Algorithm)

• For each pair of triplets, one from each molecule which define ‘almost’ congruent triangles compute the rigid transformation that superimposes them.

• Count the number of point pairs, which are ‘almost’ superimposed and sort the hypotheses by this number.

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• For the highest ranking hypotheses improve the transformation by replacing it by the best RMSD transformation for all the matching pairs.

• Complexity : assuming order of n points in both molecules - O(n8) .

O(n4) if one exploits protein backbone geometry.

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Accuracy improvement during detection of 3D transformation.

Instead of 3 points use more. How many?

Align any possible pair of fragments - Fij(k)

i

j

i+k-1

j+k-1

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Accept Fij(k) if rmsd(Fij

(k)) <

Complexity O(n3 n).

(For each Fij(k) we need compute its rmsd)

can be reduced to O(n3)

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Improvement : BLAST idea - detect short similar fragments, then extend as much as possible.

j

i+1

j+1

i

j-1

i-1

ai-1 ai ai+1

bj-1 bj bj+1

k

t

k+l-1

t+l-1

Complexity: O(n2)

Extend while: rmsd(Fij(k)) <

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Protein Structural Alignment based on

Geometric Hashing

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Sequence Based Structure Alignment

•Run pairwise sequence alignment.

•Based on sequence correspondence compute 3D transformation (least square fit can be applied).

•Iteratively improve structural superposition.

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Alignment of Flexible Molecular Structures

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Motivation

• Proteins are flexible. One would like to align proteins modulo the flexibility.

• Hinge and shear protein domain motions (Gerstein, Lesk , Chotia).

• Conformational flexibility in drugs.

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Motivation

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Flexible protein alignment without prior hinge knowledge

FlexProt - algorithm

– detects automatically flexibility regions

– exploits amino acid sequence order

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Examples

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Experimental Results

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• Task: largest flexible alignment by largest flexible alignment by decomposing the two molecules into a decomposing the two molecules into a minimalminimal number of rigid fragment pairs number of rigid fragment pairs having similar 3-D structure.having similar 3-D structure.