Indexing and Retrieval of Multiple 3D Protein Structure Representations for the Protein Data Bank

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Indexing and Retrieval of Multiple 3D Protein Structure Representations for the Protein Data Bank

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Indexing and Retrieval of Multiple 3D

Protein Structure Representations for

the Protein Data Bank

Eric Paquet,

Senior Research Officer1_,

Adjunct Professor2

Herna L. Viktor,

Associate Professor2

1_{National Research Council of Canada} 2_{University of Ottawa, Canada}

(3)

P

bl

Problem statement

 _{Research suggests that a protein’s function often}

depends on the shape and physical properties of the active sites

 _{Estimates indicate that the number of newly discovered}

protein structures will grow “exponentially”

– (PDB ~ 60,000)

O

 _{Our solution}_:

– 2D and 3D Indexing and Similarity Search System

which eliminates need for prior structure alignment

O l

 _{Our goal}_:

– to aid molecular biologics to label new structures, find

family members, docking problem, etc.

(4)

O

i

Overview

 _{We developed a new indexing and similarity search}

system to retrieve protein structures, based on their 3D shape and 2D appearance

 _{Our system:}

– translation, scale and rotation invariant, which

eliminates the need for prior structure alignment eliminates the need for prior structure alignment

– handles various representations

 Tested against 45.000 protein structures from the

PDB (real-time retrieval)

 _{Suitable for the docking problem}

(5)

R

i

Representations

 _Shape

– Backbone: structural – Van der Waal

– Envelope: interaction

 Encoding of physicochemical properties (Color code) – Secondary structures: structural

Residue types: interaction

– Residue types: interaction

(6)

O

S

A O

i

Our System: An Overview

Application Queries Results Indexing Capri/MR System Representation Generation 2D Indexing 3D Indexing Si m il a ri ty S e ar ch g Protein Data 2D 3D Database 5 Bank (PDB) 2D Indexes 3D Indexes

(7)

Retrieval System: Query by Example

(8)

C

i

f I d

Comparison of Indexes



Comparison of the

indexes with

various metrics



Geometry of the

f

t

feature space:

Riemannian:

geodesic distance

[3DIP 2010]

7

(9)

E

i

l d

i

Experimental design



Tested against 45.000 protein structures

from the Protein Data Bank - PDB

I

l

t d

i

J

d J

3D



Implemented using Java and Java 3D



Experiments on workstations with two dual

core Xeon processors and 32 GB memory



Search engine run on a tablet or a laptop:

retrieval in real time



Evaluation:

–

Precision/recall of nearest structures

(10)

3D I d

i

3D Indexing



Tensor of inertial of the distribution

F

Ei

t



_{Frame: Eigen vectors}



Labeling of the axis: Eigen value

W i ht d

l

d

di l di t ib ti

f



Weighted angular and radial distribution of

surface elements relative to the frame



Translation scale (if needed) and rotation



Translation, scale (if needed) and rotation

invariant



120 bytes

9

(11)

Homo Sapiens Hemoglobin: Query with the lrly structure

(12)

Homo Sapiens Hemoglobin: Query with the 1flq structure

(13)

2D I d

i

f P

i

2D Indexing of Proteins



Color encoding

of physicochemical

properties: secondary structures, residue

d

id

t

(

h d

h bi

names and residue types (e.g. hydrophobic

which is important for docking)



Characteristic views: efficient with only

four



Characteristic views: efficient with only

four

(

4 ) views



Each view is indexed or described according



Each view is indexed or described according

to its visual appearance



~1000 bytes per index (for 4 views)

12 

_{1000 bytes per index (for 4 views)}

(14)

2D I d

i

2D Indexing



Tensor of inertial of the distribution



Frame: Eigen vectors and values

I

t

i

li

4 i



_{Isotropic sampling: 4 views}



For each view:

–

Structuring element

–

Random motion

–

Joint distribution: color and proportion within

the element

–

Accumulation



250 bytes

13

y

(15)

3D

2D R

i

l

3D versus 2D Retrieval

 _{2D indexing approach utilizes the colour, texture and}

composition of the images

 _{3D indexing method is based on shape: more}  _{3D indexing method is based on shape: more}

precise; no ambiguity

 _{Research question:}_q

– The colours of protein structures provide us with a

semantic key to the functionality thereof:

Th f th 2D i t i l h ld id

– Therefore, the 2D image retrieval should provide

us with a complementary view, in contrast to when we apply a shape-based description.

14

(16)

3D and 2D Query results for

Glutamyl-tRNA (GluRS) family members

(17)

Envelope Representation and 3D

Indexing



Important

f

D

ki

for

Docking



Complex

h

shape

Phage T4 lysozyme from Bacteriophage T4 142l

16

(18)

Retrieval of all members of the Phage T4 lysozyme from Bacteriophage T4 conformation, using the

142l t t ith i i 100% d

142l structure as a query, with precision 100% and recall 100% (80 out of 80)

(19)

Envelope: P22 tailspike protein from

Salmonella phage P22 - 1tyv

(20)

Retrieval of all members from the P22 tailspike

protein from Salmonella phage P22 conformation,

i th 1t t t ith

using the 1tyv structure as a query, with a

precision 100% and recall 100% (9 out of 9)

(21)

D

ki

Goal of protein-ligand docking:

To predict the

position

and

Docking

To predict the position

and

orientation

of a ligand when

bound to a protein receptor

p

(22)

Docking through virtual fragmentation



_{Random, or constrained, fragmentation of the}

proteins



3D indexing of each fragment (as previously

described)



For a given fragment or receptor, retrieval of the

closest fragments or ligands from a 3D shape

point-of-view:

query by example

point of view: query by example



Flexibility taken into account by using various

conformations

of the same protein, when

21

p

,

(23)

Docking

through virtual

fragmentation…

(24)

C

l

i

Conclusions

 3D and 2D indexing

 Exhaustive search in real-time  Exhaustive search in real time

 Can describe various representations  _{Similarity search, query by example}

V d i i d ll

 _{Very good precision and recall}  Very Large Databases

 Address the docking problem: virtual fragmentation  _{Help to reduce the number of clinical trials}

Eric: eric paquet@nrc-cnrc gc ca

23

Eric: eric.paquet@nrc cnrc.gc.ca Herna: hlviktor@site.uottawa.ca