1. Integrating Bioinformatics and Biochemistry Research into Middle School Physical Sciences Lessons Ricardo Sanchez 1, Brianna Rojas 2, and Dr. Jamil A Momand 1 1 Chemistry and Biochemistry, California State University, Los Angeles 2 Robert Louis Stevenson Middle School, Los Angeles Unified School District Introduction Bioinformatics Approach Classroom Integration Results Oxidative stress has been linked with aging and disease in the human body. For many proteins, cysteine oxidation is the means for the regulation of protein activity, thus, serving as a molecular nanoswitch. In this research, our goal is to identify protein structural features that will allow us to predict the oxidation propensity of cysteine amino acids in cellular proteins, and create a web interface program that will allow researchers to use this information Cysteine Oxidation Prediction Algorithm (COPA) S1 ≤ 6.2Å ASA ≥ 1.3Å 2 pKa ≤ 9.05 ? Oxidation Susceptible Not Oxidation Susceptible YES NO YES C 4.5 COPA Protein Properties Unknown Protein Data Prediction Rules Known Protein Data Prediction 80% Prediction Accuracy Oxidative Stress Diabetes Cancer Aging Alzheimer Physics in Chemistry: Separating Proteins by Surface Area In this activity, students slide erasers down a ramp with different size index cards. students learn that the force of friction is proportional to the area of the surfaces making contact. Similarly, in gel electrophoresis, proteins with higher surface area provide higher resistance and thus travel slower down the gel. In my research, I selectively increase the surface area in proteins to observe if the proteins are exposed to oxidation. Li, H., Robertson, A.D., and Jensen, J.H. 2005. Very fast empirical prediction and rationalization of protein pKa values. Proteins 61: 704–721. Miteva, M.A., Tuffe´ry, P., and Villoutreix, B.O. 2005. PCE: Web tools to compute protein continuum electrostatics. Nucleic Acids Res. 33: W372–W375. doi: 10.1093/nar/gki365. Parente, A., Merrifield, B., Geracy, G., and D’Alessio, G.1985. Molecular basis of superreactivity of cysteiene residues 31 and 32 of seminal ribonuclease. Biochemistry 24, 1098-1104. Quinlan, J.R. 1993. C4.5: Programs for machine learning. Morgan Kaufmann Publishers, San Francisco,CA. Sanchez, R., Riddle, M., Woo, J., Momand, J. 2008. Prediction of reversibly Oxidized Protein Cysteine Thiols Using Protein Structure Properties. Protein Science. Volume 17, 473-481. A web program called Cysteine Oxidation Prediction Program (COPP) is currently available at copa.calstatela.edu. When two new parameters were added to the existing dataset used to create COPP, and the resulting dataset was run with the C4.5 classifier, the output model was identical to the current model of COPP. Acknowledgements This work is supported by the NSF GK-12 Program (Award No. 072426). References To Sink or NOT to Sink: A Pattern Discovery Exercise In this laboratory students are given the mass and volume of various materials. The students look for a buoyancy pattern by sorting the data by mass and then by volume to no avail. However when they calculate the density and sort the data again, a pattern emerges. Similarly, my research involves pattern discovery. I analyze proteins’ properties to elucidate an oxidation prediction pattern. Diffraction: A Way to Tell What Stars Are Made of and How Atoms Are Arranged in a Molecule In this activity students get to make a spectroscope, learning about diffraction, in order to answer questions like: “What are stars of?” and “ What is the structure of proteins?” They get to see that light from different sources have a different spectrum. They also get to measure the spacing between the tracks in a CD and a DVD. This allows the students to understand how chemists can measure the very small distances between atoms by using x-ray diffraction. 6Q: A Classification Game that Predicts your Element in Six Questions or Less For this activity, I created a program that is able to predict the name of a student chosen element, given that the student answers some questions about the properties of that element. The students were very engaged with this program and at the same time they got to review the properties of the elements. After explaining how the program was able to guess the name of the element chosen by the students, I was able to explain how the program I created for my research works.