CS365 PROJECT

Abstract: Study of protein folding has been a highly studied problem for quite a while. Past years have seen a lot of work from the Amato Group from the Texas A & M University. We present an attempt to implement their work on finding the sequence of intermediate protein conformations a protein molecule may adapt to fold into the most stable state also known as its native state. We implement the calculation of energy and other useful parameters for a protein conformation as well as the sampling technique used to generate nodes for a PRM based RoadMap. We also verify the work of N. S. Bogatyreva and D. N. Ivankov by observing the behaviour of protein energy with variation in its native contact number. Future work involves creating the map and extracting the path of folding from a conformation to the native state. Proposal Slides Code and Data Report References: Nancy M. Amato, Ken A. Dill, and Guang Song. Using Motion Planning to Map Protein Folding Landscapes and Analyze Folding Kinetics of Known Native Structures. In 6th International Conference on Computational Molecular Biology (RECOMB 2002), 2002. H. M. Berman, K. Henrick, and H. Nakamura. World Wide Protein Data Bank, 2003. N. S. Bogatyreva and D. N. Ivankov. The Relationship Between the Solvent-Accessible Surface Area of a Protein and the Number of Native Contacts in its Structure. Molecular Biology, 2008, 42(6):932-938, 2008. Dr. Somenath Biswas. Protein Folding Challenge and Theoretical Computer Science. September 2011. Amato Group. The Protein Folding Server. Mike Harms and Marcin Cielik. pdb-tools, 2007. John Hunter, Darren Dale, and Michael Droettboom. matplotlib, October 2011. Rufei Lu, Lauren Yarholar, Warren Yates, and Dr. Miguel Bagajewicz. Protein Folding Prediction. March 2008. Jerod Parsons, J. Bradley Holmes, J. Maurice Rojas, Jerry Tsai, and Charlie E. M. Strauss. Practical Con- version from Torsion Space to Cartesian Space for in-silico Protein Synthesis. Journal of Computational Chemistry, 26(10):1063-8, 2005. Rutgers and UCSD. An Information Portal to Biological Macromolecular Structures, 2003. L. Tapia. Intelligent Motion Planning and Analysis With Probabilistic Roadmap Methods for the Study of Complex and High-Dimensional Motions. PhD thesis, Texas AM, 2009. Lydia Tapia, Shawna Thomas, and Nancy M. Amato. A Motion Planning Approach To Studying Molec- ular Motions. Communications In Information And Systems, 10(1):53–68, 2010. Texas AM University. The Protein Folding Server.