Cysteine Oxidation Prediction Program (COPP): A New Software Program That Predicts Reversible Protein Cysteine Thiol Oxidation Reactions Ricardo Sanchez, Larry Grant, and Jamil A. Momand Chemistry and Biochemistry, California State University, Los Angeles Abstract References Li, H., Robertson, A.D., and Jensen, J.H Very fast empirical prediction and rationalization of protein pKa values. Proteins 61, 704–721. Miteva, M.A., Tuffery, P., and Villoutreix, B.O PCE: Web tools to compute protein continuum electrostatics. Nucleic Acids Res. 33: W372–W375. doi: /nar/gki365. Parente, A., Merrifield, B., Geracy, G., and D’Alessio, G Molecular basis of superreactivity of cysteiene residues 31 and 32 of seminal ribonuclease. Biochemistry 24, Quinlan, J.R C4.5: Programs for machine learning. Morgan Kaufmann Publishers, San Francisco,CA. Richmond, T. J Solvent accessible surface area and excluded volume in proteins. Analytical equations for overlapping spheres and implications for the hydrophobic effect. J. Mol. Biol. 178, Sanchez, R., Riddle, M., Woo, J., Momand, J Prediction of reversibly Oxidized Protein Cysteine Thiols Using Protein Structure Properties. Protein Sci. 17, Schreiter, E.R., Rodríguez, M.M., Weichsel, A., Montfort, W.R., and Bonaventura, J S-nitrosylation- induced conformational change in blackfin tuna myoglobin. J. Biol. Chem. 282, A web interface implementation of the COPA algorithm is presented here and it is available at copa.calstatela.edu. COPP is able to predict S-nitrosylation sites at 66.7% accuracy on this dataset. COPP is able to predict disulfide bond formation with 91.8% accuracy on this dataset. COPP is an exceptional tool for predicting reversible disulfide bond formation. Acknowledgements This work is supported by the NSF GK-12 Program (Award No ) and NSF-NIH Award number Travel funds provided by the FASEB MARC Program. A software program called the Cysteine Oxidation Prediction Program (COPP) was created and is accessible by internet ( through a Graphical User Interface. COPP uses molecular structure data from the Protein Data Bank to predict reversible cysteine thiol oxidation susceptibility in non- membrane proteins. COPP was created from an algorithm generated via the decision tree output of the J48 machine learning program. COPP was shown to be 80% accurate by jackknife analysis of a database that contained 161 oxidation susceptible cysteines and 161 oxidation non-susceptible cysteines. Of the cysteine thiols in the oxidation-susceptible portion of the database, 61% formed intramolecular disulfides and 39% formed ligands with oxygen, glutathione, or intermolecular disulfides to cysteine residue thiols. Protein S- nitrosylation is a common event but predicting the target cysteine thiols that become S-nitrosylated remains problematic. We used 29 known sites of S- nitrosylation to test the accuracy of our COPP program and found that 20 were predicted correctly, giving an accuracy of 69%. Out of the 20 that were predicted accurately, 19 were predicted to occur because the calculated target cysteine thiol pKa was equal to or less than 9.05 and there was greater than 1.3 A2 calculated surface exposure of the thiol group. Cysteine Oxidation Prediction Algorithm (COPA) Distance to next thiol ≤ 6.2Å Solvent accessibility of thiol ≥ 1.3Å 2 Thiol pKa ≤ 9.05 Oxidation Susceptible Not Oxidation Susceptible YES NO YES C 4.5/J48COPA Protein Properties Unknown Protein Data Prediction Rules Known Protein Data Prediction 80% Prediction Accuracy Methods Oxidative stress has been linked to aging and disease in the human body. For many proteins, cysteine oxidation is the means for regulation of protein activity, thus, serving as an important molecular nanoswitch. In this research, we identified protein structural features that allow us to predict the oxidation propensity of cysteine amino acids in proteins, and created COPP, a web interface program. Anyone with a PDB file can quickly predict the oxidation susceptibilities of cysteine thiols in their proteins of interest. Introduction Figure 1. COPA was converted into a Java computer program that was integrated into the COPP Web Interface. COPP Interface PDB OR WEB DATABASE PROpKaNACCESS2.1.1 Internet COPP Program Remote User COPP Web Interface Scheme Methods Cont… Figure 2. 1) The remote user issues a request for PDB file. 2) The COPP Interface retrieves a PDB file from the users machine or from a specified website. 3) The COPP Interface runs the COPP Program. 4)The COPP Program calculates atomic distances, retrieves the calculated thiol pKa from PROpKa, retrieves the calculated ASA from Naccess2.1.1., and produces a results file. 5) The COPP Interface returns a results page to the remote user. Results Figure 3. COPP Web Interface request page. Figure 4. COPP Prediction Results page. Results Cont… Figure 5. COPP predicts that myoglobin from blackfin tuna (PDB 2NRL) is susceptible to nitrosylation at CYS10 due to its low pKa and exposure to solvent (see Figure 4). Structure B (PDB 2NRM) shows that CYS10 is indeed S-nitrosylated. We used 29 known sites of S-nitrosylation to test the accuracy of the COPP program and found that 20 were predicted correctly, giving an accuracy of 69%. On this nitrosylation database, COPP achieved an overall 66.7% accuracy, with a precision of 40.6, a specificity of 64.2 %, and sensitivity of 74.3%. Observed OxidizedObserved Reduced Predicted Oxidized404 Predicted Reduced227 Conclusion Table 2. Disulfide Bond Prediction Results. Future Work Collect more oxidation susceptible protein data to create a new model to improve COPP’s prediction accuracy. Incorporate new physicochemical parameters that may improve COPP’s prediction accuracy-especially for non-disulfide bond forming oxidations. Incorporate visual features to the COPP web interface. Observed OxidizedObserved Reduced Predicted Oxidized2638 Predicted Reduced968 Table 1. Nitrosylation Prediction Results. COPP was tested on 42 protein thiol groups that can form intramolecular disulfide bonds and 31 thiols that remain reduced within the oxidized protein. COPP achieved an overall accuracy of 91.8%, a precision of 90.9%, a specificity of 87.1%, and a sensitivity of 95.2%. reduced thiol oxidized to S-nitrosothiol AB CYS10 Abstract # 274