1. Modeling Protein Secondary Structures from Three Dimensional Cryo-EM Density Images Dong Si June,30 th 2014

2. OUTLINE Protein Structure & Cryo-EM Density Image Secondary Structure Detection from 3D Images β-sheet & β-barrel Modeling

3. 1. Protein Essential nutrients for human body: Horrible viruses that attack human body: Dengue virus

4. 1.2 Protein Structure Large biological molecules consisting of amino acids Secondary Structure Element (SSE) --- α-helix & β-sheet

5. 1.3 Protein Cryo-EM Density Image Cryo-electron microscopy Location of α-helices & β-sheets ? Dengue virus Why we do this ?

6. 2. Our SSE Detection Methods Machine learning method (SSElearner, 2012 - Dong & etc.) Spheres Cylinders Planes SVM Tensor features Classification result Geometry & Voting method (SSEtracer, 2013 - Dong & Dr. Jing)

7. 2.2 Beta-sheet Modeling from Density Images at 5-10 Å Task: 1. Orientation of the β-strands? 2. Number of the β-strands? 3. Location of the β-strands? ?

8. 2.3 Previous Method Gorgon & Pathwalking (integrated with SSEhunter, 2012) Can only work on high resolution (3.5 - 6 Å) image for β-contained proteins 3.88 Å 7.9 Å -- “Gorgon and pathwalking: macromolecular modeling tools for subnanometer resolution density maps”, Baker ML and etc.

9. 3. β-sheet & β-barrel Modeling Two main forms of β-sheet: Single-layer β-sheet β-barrel

10. 3.1 For Single-layer β-sheet Protein - 1FX2, simulated to 10Å

11. 3.2 Overall β-sheet Detection Procedure Input density Least-square 3D surface fitting β-strand detection

12. 3.3 Advantage of β-sheet Surface Modeling Reveal the 3D surface feature even from the noisy density: Help detect the β-strand from the error-contained density:

13. β-sheet: 3.4 Beta-sheet Modeling Result (Under revision)

14. 4. For β-barrel

15. 4.1 β-barrel Modeling Procedure Generate β-strand on the barrel surface:

16. 4.2 β-barrel Modeling Result β-barrel: (Accept)

17. 5. Summary Secondary Structure (helix & β-sheet) Detection:  Machine learning method (SSElearner, 2012 - Dong & etc.)  Geometry & Voting method (SSEtracer, 2013 - Dong & Dr. Jing) β-sheet Modeling (StrandTwister, 2014 - Dong & Dr. Jing) β-Barrel Modeling (StrandRoller, 2014 - Dong & Dr. Jing)

18. Publications to the Related Work D. Si, J. He, "Tracing Beta-strands using StrandTwister from Cryo-EM Density Maps at Medium Resolutions", under revision, Structure - Cell Press. D. Si, J. He, “Combining Image Processing and Modeling to Generate Traces of Beta-strands from Cryo-EM Density Images of Beta-barrel”, Accepted, International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE-EMBC’14). J. He, D. Si, “Towards De Novo Folding of Protein Structures from Cryo-EM 3D Images at Medium Resolutions”, Accepted, RSS (Robotics: Science and Systems Conference) 2014 Workshop on Robotics Methods for Structural and Dynamic Modeling of Molecular Systems. D. Si, J. He, “Modeling Protein Structure Features from Three Dimensional Cryo-EM Images”, Modeling, Simulation, and Visualization Student Capstone Conference, Suffolk VA, April 17, 2014. A. McKnight, D. Si, K. Al Nasr, A. Chernikov, N. Chrisochoides, J. He, "Estimating loop length from CryoEM images at medium resolutions", BMC Structural Biology CSBW Special Issue, Volume 13, Suppl 1:S5, 2013. D. Si and J. He, "Beta-sheet Detection and Representation from Medium Resolution Cryo-EM Density Maps", Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics, p764-770, Washington, D.C., September 22-25, 2013. K. Al Nasr, L. Chen, D. Ranjan, M. Zubair, D. Si, J. He, "A Constrained K-shortest Path Algorithm to Rank the Topologies of the Protein Secondary Structure Elements Detected in CryoEM Volume Maps", Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics, p479-455, Washington, D.C., September 22-25, 2013. D. Si, H. E. Elsayed-Ali, W. Cao, O. Pakhomova, H. White, J. He, "Simulation of the Localized 3-dimensional Reconstruction for Electron Cryo- tomography", Modeling, Simulation, and Visualization Student Capstone Conference, Suffolk VA, April 11, 2013. D. Si, S. Ji, K. Al Nasr, J. He, "A Machine Learning Approach for the Identification of Protein Secondary Structure Elements from Electron Cryo-Microscopy Density Maps", Biopolymers, Special Issue: The 2010 Cryo-EM Modeling Challenge, Volume 97, Issue 9, pages 698-708, 2012. A. Biswas, D. Si, K. Al Nasr, D. Ranjan, M. Zubair, J. He, "Improved Effeciency in CryoEM Secondary Structure Topology Determination from Inaccurate Data", Journal of Bioinfrmatics Computational Biology (JBCB), Volume 10, Issue 3, (16 pages), 2012. A. McKnight, K. Al Nasr, D. Si, A. Chernikov, N. Chrisochoides, J. He, "CryoEM Skeleton Length Estimation using a Decimate Curve", Computational Structural Bioinformatics Workshop (in conjunction with The IEEE International Conference on Bioinformatics and Biomedicine), 2012. K. Al Nasr, L. Chen, D. Si, D. Ranjan, M. Zubair, J. He, "Building the Initial Chain of the Proteins through De Novo Modeling of the Cryo-Electron Microscopy Volume Data at the Medium Resolutions", ACM Conference on Bioinformatics, Computational Biology and Biomedicine, 2012. A. Biswas, D. Si, K. Al Nasr, D. Ranjan, M. Zubair, J. He, "A constraint dynamic graph approach to identify the secondary structure topology from cryoEM density data in presence of errors", The Proceeding of the IEEE International Conference of Bioinformatics and Biomedicine, p160-3, 2011.

19. THANK YOU!