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 HSCH 2 CCOOH H NH 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 HSCH 2 CCOOH H NH 2

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

Oxidation of Cysteines Sanchez (2007)

Cysteine Prediction Two types of cysteine oxidation: 1.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) 2.Reactive surface cysteine thiols: cysteines that become oxidized under certain conditions, most reversibly –No prediction programs exist  COPP

Biological Significance: Oxidation and Enzyme Function The active sites of glutaredoxin and thioredoxin cycle between reduced and oxidized states

Enzyme Inactivation via Oxidation H 2 O 2 inactivates PTEN tumor suppressor protein by causing the formation of a disulfide bond Lee et al. JBC (2002)

Summer 2007 Goals 1.Increase the size of the COPP database 2.Test new parameters to determine if they affect the rules and accuracy of COPP HSCH 2 CCOOH H NH 2

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

Methods: Increase Database Size 1.Search Entrez for keywords i.e. cysteine and oxidation, sulfenic acid, etc. 2.Look for proteins in Protein Data Bank Potential Proteins

Increase Database Size 3.Do BLASTALL – eliminate proteins with: Identity > 35% E value < 1 Conserved cys Potential Proteins CysteinesOxidize

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

Results: New Proteins 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) 6Å6Å + 5.2Å - Sanchez (2007) Increased database size caused reduction in rules Accuracy decreased Old Rules New Rules 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% Accuracy79.1% Accuracy

Methods: Parameters already used by COPP S1 DISTANCEdistance to nearest sulfur atom S1 ASAarea exposed to the surface N1 DISTANCEdistance to the nearest +nitrogen atom N1 DONORnitrogen’s parent side chain N1 ASAarea exposed to the surface O1 DISTANCEdistance to the nearest -oxygen O1 DONORoxygen’s parent side chain O1 ASAarea exposed to the surface ASAexposed surface of S in question CLASSclass: 0 if reduced; 1 otherwise

Methods: Parameters already used by COPP S1 DISTANCEdistance to nearest sulfur atom S1 ASAarea exposed to the surface N1 DISTANCEdistance to the nearest +N atom N1 DONORnitrogen’s parent side chain N1 ASAarea exposed to the surface O1 DISTANCEdistance to the nearest -oxygen O1 DONORoxygen’s parent side chain O1 ASAarea exposed to the surface ASAexposed surface of S in question CLASSclass: 0 if reduced; 1 otherwise

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? - SCH 2 CCOOH H NH 2 H

Methods: New Parameters PCE: Protein Continuum Electrostatics –Calculates Electrostatic Potential Coordinates Electrostatic Potential Miteva et al. NAR (2005)

New Parameters PROPKA –Calculates pKa Li et al. Proteins (2005)

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

Results: New Parameters New parameters caused an alteration in the final rule The accuracy is similar Old 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) % Accuracy 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) New Parameters % Accuracy

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

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

Results: New Proteins Increased database size caused reduction in rules Accuracy decreased 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) 81.8% Accuracy79.1% Accuracy Old RulesNew Rules 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-SHHS-Cys