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In Silico Protein Behavior

In Silico Protein Behavior. Predicting the Activity of p53 Tumor Suppressor Protein Mutants Using Features Derived From Homology Modeling. Sam Danziger Department of Biomedical Engineering University of California, Irvine. Dr. Rainer Brachmann School of Medicine. Dr. Richard Lathrop

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In Silico Protein Behavior

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  1. In SilicoProtein Behavior Predicting the Activity of p53 Tumor Suppressor Protein Mutants Using Features Derived From Homology Modeling Sam Danziger Department of Biomedical Engineering University of California, Irvine Dr. Rainer Brachmann School of Medicine Dr. Richard Lathrop School of Information and Computer Science Jue Zeng School of Medicine

  2. Machine Learning Use Homology Modeling to guide biological research What Am I Doing: Big Picture Homology Modeling Biology Predict a protein structure using a template PRO: Fast CON: Inaccurate, so what does it tell us? Make a protein and test it in-vitro PRO: Real CON: Slow

  3. What is p53? • Tumor Suppressor Protein • p53 Mutations are present in ~50% of human cancers. • Receives upstream signals in response to cellular stress. • Arrests cell growth if there is repairable DNA damage. • Triggers apoptosis if DNA damage is irreparable. • Second site mutations can rescue cancer mutants. p53 core domain bound to DNA

  4. P53 Cancer Rescue Mutants Cancer Mutation Rescue Mutation 1) Healthy DNA | Healthy P53 2a) Damaged DNA | Inactive P53 3a) Damaged DNA | Healthy P53 www.vh.org/adult/provider/pathology/OBGYNOncology/Images/Endo1.jpg 2b) Cancer http://www.barrettsinfo.com/figures/cycle-p53.gif 3b) DNA Repair or Apoptosis

  5. What is Homology Modeling?Modeling done using Amber™ with zinc ion characteristics tuned by Dr. Qiang Lu working in Dr. Ray Lui’s lab. 2. Substitute one or more amino acids to mutate the protein. 1. Use a wild type crystal structure of the protein in question. 4. Minimize the energy of the new mutant protein. 3. Apply simulated physical laws to determine an energy function.

  6. How Do We Use Homology Models? 3D p53 Molecule 2D Surface Map Features from a grid

  7. What is Machine Learning? Training: Set the parameters with n features. Testing: Use the parameters to predict unknown classes

  8. What do we know about Rescue Mutants? • 261 mutants created in-vitro • 5 sites for mutation = 194*193*192*191*190*195 = 6.46 * 1017 possible mutants • We know 1 in every 2.47 * 1015 • It takes light about 1 month to go 1015 meters. http://www.alcyone.com/max/physics/orders/metre.html

  9. Where to look next? Known Mutants Spiral Galaxy M101 http://hubblesite.org/

  10. Let The Computer Pick The Next Experiments • Find interesting cancer rescue regions by random sampling. • Focus the classifier on these regions for detailed analysis. • Predict the behavior of putative rescue mutants and create them in-vitro. • Improve the classifier with knowledge about these new mutants.

  11. + = Broken p53 Engineered Small Molecule Functional Complex Ultimate Goal Intermediate Goals Specific: Build models to understand how p53 breaks and help guide biological research by mapping the space of p53 mutants. General: Build a generalized toolset to explore any protein with functional mutations.

  12. Acknowledgments • People: Pierre Baldi, Josh Swamidass, Richard Chamberlin, Jonathan Chen, Jianlin Cheng, Melanie Cocco, Richard Colman, John Coroneus, Lawrence Dearth, Vinh Hoang, Qiang Lu, Hartmut Luecke, Ray Luo, Hiroto Saigo, Don Senear, Ying Wang • Funding: NIH, NSF, Harvey Fellowship (JS), UCI Medical Scientist Training Program, UCI Office of Research and Graduate Studies, UCI Institute for Genomics and Bioinformatics

  13. Questions? Thanks to Rainer Brachmann, Jue Zeng, Richard Lathrop, and everyone else who contributed

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