Protein structure prediction
Sponsored Links
This presentation is the property of its rightful owner.
1 / 24

Protein structure prediction PowerPoint PPT Presentation


  • 80 Views
  • Uploaded on
  • Presentation posted in: General

Protein data bank (PDB) : 46818 structures (oct 2007) SCOP (Structural Classification Of Proteins): • 971 folds (major structural similarity) • 1586 super-families (probable common evolutionary origin) • 3004 families (clear evolutionary relationship, ~ 30% identity).

Download Presentation

Protein structure prediction

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Protein data bank (PDB) : 46818 structures (oct 2007)SCOP (Structural Classification Of Proteins): • 971 folds (major structural similarity)• 1586 super-families (probable common evolutionary origin)• 3004 families (clear evolutionary relationship, ~ 30% identity)

Protein structure prediction

Nearly all folds are known (?)

But 5 millions known protein sequences (trEMBL) 

-> needs for structure prediction


  • Usually, structure-activity relationships : site-directed mutagenesis, pharmacologic studies, drug design,…

  • But also:

  • • genomic studies : recognizing orphan genes

  • • distant evolution studies

Structure prediction: what for ?

  • Sequences diverge more than structures


Methods for protein structural studies

Known structures :

Simulations at the atom level:

molecular modelling (enthalpic energy) / molecular dynamics /normal modes


Methods for protein structural studies

Unknown structures :

Before using molecular mechanics, one must have a « realistic » structure.

3D structure prediction :

1) homology modelling

2) ab initio folding

3) threading


Homology modelling

Needs to know a 3D structure that is homolog to the query sequence

AGLNVIAGSILQNS

GGINVLAASLLNNS

e.g.: Modeller web server (http://www.salilab.org/modeller)


Homology modelling

AGLNVIAGSILQNS

GGINVLAASLLNNS

e.g.: Modeller web server (http://www.salilab.org/modeller)


Ab initio folding

Protein Data Bank (PDB)

AGVLVAGHM

Target sequence:

generation

AGV

AGV

LVA

GHM

AGV

LVA

AGV

GHM

LVA

AGV

LVA

GHM

LVA

. . .

GHM

Minimisation - energy evaluation

Baker et al.


Threading (1)

Protein Data Bank (PDB)

families


Threading (1)

--------------

family core + interactions

family

Protein Data Bank -> library of cores


Threading (2)

Protein Data Bank (PDB)

Statistics for 3D neighboring residue pairs -> Energy

A

L = -1.2

A

I = -2.2

...

Other characteristics:

residue accessibility, secondary structure,…


Threading (3)

core

--------------


Threading (3)

Thread the sequence onto the core

V

I = -2.3

L

N = -4.2

L

G = -5.1

GGINVLAGSLLNNS


Threading (3)

Thread the sequence onto the core

N

G = -1.3

V

I = -2.2

S

A = -4.2

AGGINVLAGSLLNN


Threading (3)

Thread the sequence onto the core

I

G = -3.3

N

G = -3.0

G

L = -2.1

LAGGINVLAGSLLN

Compute energy for every alignment of the sequence onto the core (many alignments, gaps…)

Thread the sequence onto all cores

-> choose the best core (low energy)


Threading

Can be used when sequence tools (BLAST or PSIBLAST) cannot find simlarities

Threading methods are under developments :- optimisation of 3D alignments- better core definition- statistical assessment for results


Threading

Robetta : http://robetta.bakerlab.org/3DPSSM : http://www.sbg.bio.ic.ac.uk/∼3dpssm/bioinbgu : http://www.cs.bgu.ac.il/∼bioinbgu/form.htmlGenTHREADER : http://bioinf.cs.ucl.ac.uk/psipred/psiform.htmlFROST :http://genome.jouy.inra.fr/frost/


The end…


A) La quantification des similarités des paires de structures (comparaison «~tout contre tout~») donne la position d'une structure dans un espace abstrait de hautes dimensions. La hauteur des pics reflète la densité de population de repliements, les axes horizontaux sont les axes des deux premiers vecteurs propres (i.e. associés aux deux plus grandes valeurs propres), l'axe vertical donne le nombre de repliements. La distribution des architectures est donnée par la projection sur le plan (la proximité sur ce plan donne une indication sur la similarité structurale entre 2 protéines)

B) 40% de tous les domaines connus sont couverts par 16 classes de repliements. Ces 16 repliements sont montrés ici sous forme de diagrammes topologiques de structures secondaires dans la classe de leur attracteur (le numéro d'attracteur est le même que dans la figure A).

Figures tirées de Holm et Sander (1996) "Mapping the protein universe"


Threading: fonction d’évaluation


Méthode d’alignement séquence/structure


Méthode d’alignement séquence/structure (2)


Normalisation des scores


  • Login