1 / 26

The Effect of New Parameters and Increased Database Size on the Cysteine Oxidation Prediction Program

The Effect of New Parameters and Increased Database Size on the Cysteine Oxidation Prediction Program. Megan Riddle with Ricardo Sanchez and Dr. Jamil Momand California State University, Los Angeles August 23, 2007. NH 2. HS. CH 2. C. COOH. H. Overview.

diamond
Télécharger la présentation

The Effect of New Parameters and Increased Database Size on the Cysteine Oxidation Prediction Program

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. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. The Effect of New Parameters and Increased Database Size on the Cysteine Oxidation Prediction Program Megan Riddle with Ricardo Sanchez and Dr. Jamil Momand California State University, Los Angeles August 23, 2007 NH2 HS CH2 C COOH H

  2. Overview • Cysteine Oxidation Prediction Program (COPP) • Oxidation defined • Biological Significance of Cysteine Oxidation • Effects of oxidation on proteins • Summer 2007 Goals • Increase database size • Add new parameters • Methods and Results NH2 HS CH2 C COOH H

  3. Cysteine Oxidation Prediction Program • Goal: Create a program that will use physicochemical parameters to predict reactive surface cysteine thiols • Methods: • Gather examples of proteins susceptible to cysteine oxidation • Extract parameters from Protein Data Bank • Use computer classifier C4.5 to determine rules that will predict if cysteine can become oxidized

  4. Oxidation of Cysteines Sanchez (2007)

  5. Cysteine Prediction Two types of cysteine oxidation: • Permanent structural oxidation: cysteines that form permanent disulfide bonds or bind to metals shortly after translation • Prediction programs based on sequence already exist  88% accuracy (Martelli et al. 2002) • Reactive surface cysteine thiols: cysteines that become oxidized under certain conditions, most reversibly • No prediction programs exist  COPP

  6. Biological Significance: Oxidation and Enzyme Function • The active sites of glutaredoxin and thioredoxin cycle between reduced and oxidized states http://www.cs.stedwards.edu/chem/Chemistry/CHEM43/CHEM43/Thioredox/RNA2.GIF

  7. Enzyme Inactivation via Oxidation • H2O2 inactivates PTEN tumor suppressor protein by causing the formation of a disulfide bond Lee et al. JBC (2002)

  8. Summer 2007 Goals • Increase the size of the COPP database • Test new parameters to determine if they affect the rules and accuracy of COPP NH2 HS CH2 C COOH H

  9. Increase Database Size • Previously: • 85 proteins that undergo non-structural cysteine oxidation • 135 cysteines that undergo oxidation • 225 cysteines that remain reduced under oxidizing conditions • To create an accurate, general set of rules for cysteine oxidation requires a large, unbiased database

  10. Methods: Increase Database Size • Search Entrez for keywords • i.e. cysteine and oxidation, sulfenic acid, etc. • Look for proteins in Protein Data Bank 60 Potential Proteins

  11. Increase Database Size • Do BLASTALL – eliminate proteins with: • Identity > 35% • E value < 1 • Conserved cys 31 Potential Proteins 40 123 Cysteines Oxidize

  12. Increase Database Size Original Proteins New Proteins C4.5/ J48 Rules to Classify Cysteines

  13. Results: New Proteins • Increased database size caused reduction in rules • Accuracy decreased Old Rules New Rules S1 DISTANCE <= 6: 1 (88.19/17.0) S1 DISTANCE > 6 | ASA (Å2) <= 1: 0 (136.51/6.51) | ASA (Å2) > 1 | | N1 DISTANCE <= 5.2: 1 (32.54/9.0) | | N1 DISTANCE > 5.2 | | | O1 ASA <= 2: 1 (33.0/15.0) | | | O1 ASA > 2: 0 (71.76/15.76) S1 DISTANCE <= 6.1: 1 (115.44/28.0) S1 DISTANCE > 6.1 | ASA (Å2) <= 1.8: 0 (177.51/12.51) | ASA (Å2) > 1.8 | | N1 DISTANCE <= 5.4: 1 (46.54/17.0) | | N1 DISTANCE >5.4: 0 (133.51/39.51) - + Cys-S H Cys-S H O-Q/D N-K/R/H H S-Cys 5.2Å 6Å 81.8% Accuracy 79.1% Accuracy Sanchez (2007)

  14. Methods: Parameters already used by COPP • S1 DISTANCE distance to nearest sulfur atom • S1 ASA area exposed to the surface • N1 DISTANCE distance to the nearest +nitrogen atom • N1 DONOR nitrogen’s parent side chain • N1 ASA area exposed to the surface • O1 DISTANCE distance to the nearest -oxygen • O1 DONOR oxygen’s parent side chain • O1 ASA area exposed to the surface • ASA exposed surface of S in question • CLASS class: 0 if reduced; 1 otherwise

  15. Methods: Parameters already used by COPP • S1 DISTANCE distance to nearest sulfur atom • S1 ASA area exposed to the surface • N1 DISTANCE distance to the nearest +N atom • N1 DONOR nitrogen’s parent side chain • N1 ASA area exposed to the surface • O1 DISTANCE distance to the nearest -oxygen • O1 DONOR oxygen’s parent side chain • O1 ASA area exposed to the surface • ASA exposed surface of S in question • CLASS class: 0 if reduced; 1 otherwise

  16. New Parameters • pKa: acid dissociation constant • How easily can the S lose a proton? • Electrostatic Potential: potential energy per unit charge • How well stabilized is the charged S after the proton is lost? NH2 - H S CH2 C COOH H

  17. Methods: New Parameters • PCE: Protein Continuum Electrostatics • Calculates Electrostatic Potential CoordinatesElectrostatic Potential 7.854 0.668 -0.602 -10.725 4.683 1.223 3.305 -25.413 3.330 8.072 3.708 -19.335 2.256 -11.243 9.879 -21.887 14.014 7.907 3.298 -13.670 http://bioserv.rpbs.jussieu.fr/cgi-bin/PCE-Pot Miteva et al. NAR (2005)

  18. New Parameters • PROPKA • Calculates pKa http://propka.ki.ku.dk/ Li et al. Proteins (2005)

  19. New Parameters Electrostatic Potential and pKa Data Original Data C4.5/ J48 Rules to Classify Cysteines

  20. Results: New Parameters • New parameters caused an alteration in the final rule • The accuracy is similar Old Parameters New Parameters S1 DISTANCE <= 6.1: 1 (115.44/28.0) S1 DISTANCE > 6.1 | ASA (Å2) <= 1.8: 0 (177.51/12.51) | ASA (Å2) > 1.8 | | N1 DISTANCE <= 5.4: 1 (46.54/17.0) | | N1 DISTANCE >5.4: 0 (133.51/39.51) S1 DISTANCE <= 6.1: 1 (115.44/28.0) S1 DISTANCE > 6.1 | ASA (Å2) <= 1.8: 0 (177.51/12.51) | ASA (Å2) > 1.8 | | pKa of S0 <= 8.75: 1 (74.29/32.0) | | pKa of S0 > 8.75: 0 (105.76/26.76) 79.0698% Accuracy 78.6469% Accuracy

  21. Conclusions • New Proteins: • A larger database results in a more general, but less accurate, set of rules • New Parameters: • A low pKa value correlates with oxidation, but does not improve the accuracy of COPP • Future Goals: • Make COPP publicly available • Modify COPP to predict type of oxidation

  22. With many thanks to. . . Dr. Jamil Momand, Ricardo Sanchez, and the rest of the Momand lab SoCalBSI fellow students and mentors California State University at Los Angeles Funding from: LA Orange County Biotechnology Center

  23. Results: New Proteins • Increased database size caused reduction in rules • Accuracy decreased Cys-SH HS-Cys Old Rules New Rules S1 DISTANCE <= 6: 1 (88.19/17.0) S1 DISTANCE > 6 | ASA (Å2) <= 1: 0 (136.51/6.51) | ASA (Å2) > 1 | | N1 DISTANCE <= 5.2: 1 (32.54/9.0) | | N1 DISTANCE > 5.2 | | | O1 ASA <= 2: 1 (33.0/15.0) | | | O1 ASA > 2: 0 (71.76/15.76) S1 DISTANCE <= 6.1: 1 (115.44/28.0) S1 DISTANCE > 6.1 | ASA (Å2) <= 1.8: 0 (177.51/12.51) | ASA (Å2) > 1.8 | | N1 DISTANCE <= 5.4: 1 (46.54/17.0) | | N1 DISTANCE >5.4: 0 (133.51/39.51) 81.8% Accuracy 79.1% Accuracy

More Related