grammatical inference for disulfide bonds prediction within proteins n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Grammatical inference for disulfide bonds prediction within proteins PowerPoint Presentation
Download Presentation
Grammatical inference for disulfide bonds prediction within proteins

Loading in 2 Seconds...

play fullscreen
1 / 1

Grammatical inference for disulfide bonds prediction within proteins - PowerPoint PPT Presentation


  • 84 Views
  • Uploaded on

We work on the amino acid alphabet and we use an universal ambiguous context-free grammar. Grammar rules are: 0 S  cScS  cysteine bond 1 S  aS 2 S  cS 3 S  dS … 6 S  gS … 13 S  pS … 15 S  rS 16 S  sS … 19 S  wS 22 S    numbering of rules.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Grammatical inference for disulfide bonds prediction within proteins' - dulcea


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
grammatical inference for disulfide bonds prediction within proteins

We work on the amino acid alphabet and we use an universal ambiguous context-free grammar. Grammar rules are:0 S  cScS  cysteine bond 1 S  aS2 S  cS3 S  dS …6 S  gS …13 S  pS …15 S  rS16 S  sS …19 S  wS22 S   numbering of rules

The sequence: GCCSDPRCAWRC has a disulfide bond between the cysteine at position 3 and 8. The disulfide bond is detected using the following chaining rules: 6 2 0 16 3 13 15 22 1 19 15 2This chaining is learned to guide future analyzes.The learning language is the regular control set.

1,0,2,3,6,13,15,16,19

1,6,15,19

22

2

22

Grammatical inference for disulfide bonds prediction within proteins

IRISA – INRIA

Symbiose project

Campus de Beaulieu

35042 Rennes cedex

Ingrid Jacquemin

Ingrid.Jacquemin@irisa.fr

Jacques Nicolas

Jacques.Nicolas@irisa.fr

Abstract

Our method is based on the control language framework proposed by Takada[1]. The idea is to modelize the structure of the protein with an universal model (a context-free grammar bonding any cystein pair) and to learn by examples to control the model application (a regular language on allowed sequences of rules)

Keywords

Disulfide bonds, grammatical inference, regular control sets.

From protein to sequence

C

R

TC

D

R

C 54

R 55

Disulfide bond

... CRTDRC ...

T 56

C 59

D 57

R 58

Grammatical inference with control sets

The training set is composed of positive and negative examples (which are “near missed” close to positive). In both sets, sequences are the same but bonds are different. Example:

NEGATIVE:

POSITIVE:

3 6 2 1 19 1 1 6 16 0 15 6 22 22

3 6 0 1 19 1 1 6 16 2 15 6 22 22

6 2 2 16 3 13 15 0 1 19 22 22

6 2 2 16 3 13 15 2 1 19 15 2 22

3 6 0 1 19 1 1 6 16 22 15 6 2 22

6 2 0 16 3 13 15 22 1 19 15 2 22

RESULTING AUTOMATON :

  • References
  • Y.Takada. Learning formal languages based on control sets. Lecture notes in AI, 961, 1995.
  • Y.Takada. Grammatical inference for even linear languages based on control sets. Information processing letters 28, 1988.
  • A.Fiser, I.Simon. Predicting the oxidation state of cysteines by multiple sequence alignment. Bioinformatics, 16(3), p.251-256, 2000.
  • P.Fariselli, P.Riccobelli, R.Casadio. The role of evolutionary information in predicting the disulfide-bonding state of cysteine in proteins. Proteins 36, 340-346, 1999.

For more information contact

Ingrid Jacquemin

Campus de Beaulieu

35042 Rennes France

tel.: +33(0)2 99 84 74 51

ingrid.jacquemin@irisa.fr