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Rebecca Alford Commack High School

A Novel Approach to Modeling Genetic Sensory Impairments through De Novo Prediction of Mutant Protein Structure. Rebecca Alford Commack High School. Genetic Sensory Impairments. Group of inherited visual and hearing impairments Affects 1 in 2,000 per year

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Rebecca Alford Commack High School

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  1. A Novel Approach to Modeling Genetic Sensory Impairments through De Novo Prediction of Mutant Protein Structure Rebecca Alford Commack High School

  2. Genetic Sensory Impairments • Group of inherited visual and hearing impairments • Affects 1 in 2,000 per year • Limited diagnostic systems and treatments • Varied Symptoms make impairments hard to characterize (Zietz, 2007) Zeitz et al/ 2009

  3. Mutations and Disease • Existing systems look at destabilizing mutations (Nagy, 2004) • Goal: Develop a system that identifies severity of a mutation to predict functional effects Non-Native Conformation Native Conformation Functional Changes Destabilizing More Severe Symptoms No Symptoms

  4. Hypothesis Predicting changes in 3D protein structure and folding patterns can be used to predict functional consequences of mutations causing genetic sensory impairments

  5. Methods Build De Novo Structure Prediction System Compare folding patterns and resulting structures between variants Identify impact of mutation based on wild type structural domains Predict degree of functional impact

  6. Knowledge-Based De Novo Prediction Algorithm Input: Protein Sequences • Prediction Components: • Hydrophobic effect • Polarity driven residue distribution • Surface Area • Primary folds/characteristics • Secondary Structure Output: Data for Each Component

  7. Recursive Analysis for Prediction and Comparison

  8. Interpreting Comparisons • Input secondary prediction data • Differences can be aligned with known domains on Wild Type proteins

  9. Example Output Project Title: Examplerun-rhodopsin Sequence Size: 348 Structure Change Type: Insertion Size: 4 Position: 75 Composition: HHHN Domains Affected Type: 7 Transmembrane Segment 4 Priority: 1 Loss/Gain: HHH Position: 75-78 Percent Preserved: 85% Alignment Score:90.9% Sequence Data Effect of Mutation on Structure Effect of Mutation on Domain Alignment Score

  10. Testing • Independent Variable: Natural/Computer Integrated Variations • Dependent Variable: Resultant Structure and Folding Patterns • Controls: Null Mutations, No Mutations (Wild-Type), Working Prediction Algorithm

  11. Results: Comparing Folding Patterns

  12. Results: Chromodomain Helicase DNA Binding Protein 7 Testing • Test- Missense Mutation D4 becomes Q4 in BRK domain • CHD7 associated with CHARGE Syndrome Helicase BRK BRK Chromodomain N’ C’ Wild Type- N’- N NH HE H E N NNH HHH-C’ Mutant- N’- N NNNE H E N NNH HHH-C’

  13. Conclusions and Discussion • Mutations produced recognizable and measurable differences • Changes can be predicted by comparing secondary structures using this model • Range of function can be inferred from this information

  14. Future Directions • Develop and adapt prediction algorithm for tertiary and quaternary folding • Add specific parameters/functions for membrane protein folding • Test sequence data with clinical information • Link to Biotechnology Databases

  15. Applications • Genetic Based Diagnosis • Diagnostic System • Early Detection/Preventing Progression • Design and optimization of Specific Treatments Chuang et al. 2010

  16. Selected References Banks, J. L., Beard, H. S., Cao, Y., Cho, A. E., Damm, W., Farid, R., . . . Halgren, T. A. (2005, April). Integrated Modeling Program: Applied Chemical Theory (IMPACT). Journal of Computational Chemistry, 26, 1752-1780. Bolon, D. N., Marcus, J. S., Ross, S. A., & Mayo, S. A. (2003). Prudent modeling of core polar residues in computational protein design. Journal of Molecular Biology, (329), 611-622. Dahiyat, B. I., & Mayo, S. L. (1997, October). De Novo protein design: Fully Automated Sequence Selection. Science, 278, 82-88. Retieved from http://www.sciencemag.org Haspel, N., Tsai, C. J., Wolfson, H., & Nussinov, R. (2003, February). Reducing the complexity of computational protein folding via fragment folding and assembly. Protein Science, 12, 1177-1187. Hellinga, H. W. (1997, September). Rational protein design: combining theory and experiment. Proceedings from the National Academy of Sciences, 94, 10015-10017. Johnson, C. G., Goldiman, J. P., & Gullick, W. J. (2004). Simulating complex intracellular processes using object oriented computational modeling. Progress in aBiophysics and Molecular Biology, 86, 379-406. Kortemme, T., Ramirez-Alvarado, M., & Serrano, L. (1998, July). Design of a 20-amino acid, three-stranded beta sheet protein. Science, 281, 253-257. Lippow, S. M., & Tidor, B. (2007). Progress in computational protein design. Elsevier: Current Opinion in Biotechnology, 18(1), 1-7. Mandell, D. J., & Kortemme, T. (2009, October). Computer-aided design of functional protein interactions [Review of the biological process Computational Protein Design]. Nature Chemical Biology, 5(11), 797-808. Suarez, M., Tortosa, P., Garcia-Mira, M. M., Rodriguez-Larrea, D., Godoy-Ruiz, R., Ibarra-Molero, B., . . . Jaramillo, A. (2010, January). Using multi-objective computational design to extend protein promiscuity. Biophysical Chemistry, 147, 13-19.

  17. Acknowledgements • Research teachers Ms. Collette, Mr. Kurtz and Dr. Solomon for all of their support!

  18. Thank You Any Questions?

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