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Genetic Algorithm Based Beta-Barrel Detection for Medium Resolution Cryo-EM Density Maps

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Bioinformatics Research and Applications (ISBRA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 10330))

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Abstract

Cryo-electron microscopy (Cryo-EM) is a technique that produces three-dimensional density maps of large protein complexes and enables the study of the interactions and structures of those molecules. Identifying the secondary structures (α-helices and β-sheets) located in proteins using density maps is vital in identifying and matching the backbone of the protein with the cryo-EM density map. The β-barrel is a unique β-sheet structure commonly found in proteins, such as membranes and lipocalins. We present a new approach utilizing a genetic algorithm and ray tracing to automatically identify and extract β-barrels from cryo-EM density maps. This approach was tested using ten simulated density maps at 9 Å resolution and six experimental density maps at various resolutions. The results suggest that our approach is capable of performing automatic detection and extraction of the β-barrels from medium resolution cryo-EM density maps.

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References

  1. Adrian, M., Dubochet, J., Lepault, J., McDowall, A.W.: Cryo-electron microscopy of viruses. Nature 308, 32–36 (1984)

    Article  Google Scholar 

  2. Kühlbrandt, W.: Cryo-EM enters a new era. eLife 3, e03678 (2014)

    Google Scholar 

  3. Zhou, Z.H.: Atomic resolution cryo electron microscopy of macromolecular complexes. Adv. Protein Chem. Struct. Biol. 82, 1–35 (2011)

    Article  Google Scholar 

  4. Lawson, C.L., Baker, M.L., Best, C., Bi, C., Dougherty, M., Feng, P., van Ginkel, G., Devkota, B., Lagerstedt, I., Ludtke, S.J., Newman, R.H., Oldfield, T.J., Rees, I., Sahni, G., Sala, R., Velankar, S., Warren, J., Westbrook, J.D., Henrick, K., Kleywegt, G.J., Berman, H.M., Chiu, W.: EMDataBank.org: unified data resource for CryoEM. Nucleic Acids Res. 39, D456–D464 (2011)

    Article  Google Scholar 

  5. Al Nasr, K., Sun, W., He, J.: Structure prediction for the helical skeletons detected from the low resolution protein density map. BMC Bioinf. 11, S44 (2010)

    Article  Google Scholar 

  6. Baker, M.L., Ju, T., Chiu, W.: Identification of secondary structure elements in intermediate-resolution density maps. Struct. Lond. Engl. 1993(15), 7–19 (2007)

    Google Scholar 

  7. Si, D., Ji, S., Nasr, K.A., He, J.: A machine learning approach for the identification of protein secondary structure elements from electron cryo-microscopy density maps. Biopolymers 97, 698–708 (2012)

    Article  Google Scholar 

  8. Si, D., He, J.: Combining image processing and modeling to generate traces of beta-strands from cryo-EM density images of beta-barrels. In: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3941–3944 (2014)

    Google Scholar 

  9. Rusu, M., Wriggers, W.: Evolutionary bidirectional expansion for the tracing of alpha helices in cryo-electron microscopy reconstructions. J. Struct. Biol. 177, 410–419 (2012)

    Article  Google Scholar 

  10. Jiang, W., Baker, M.L., Ludtke, S.J., Chiu, W.: Bridging the information gap: computational tools for intermediate resolution structure interpretation. J. Mol. Biol. 308, 1033–1044 (2001)

    Article  Google Scholar 

  11. Si, D., He, J.: Tracing beta strands using StrandTwister from cryo-EM density maps at medium resolutions. Struct. Lond. Engl. 1993(22), 1665–1676 (2014)

    Google Scholar 

  12. Li, R., Si, D., Zeng, T., Ji, S., He, J.: Deep convolutional neural networks for detecting secondary structures in protein density maps from cryo-electron microscopy. In: 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 41–46 (2016)

    Google Scholar 

  13. Dal Palù, A., He, J., Pontelli, E., Lu, Y.: Identification of alpha-helices from low resolution protein density maps. In: Computational Systems Bioinformatics Conference, pp. 89–98 (2006)

    Google Scholar 

  14. Si, D., He, J.: Modeling beta-traces for beta-barrels from cryo-EM density maps. Biomed. Res. Int. 2017, 1793213 (2017)

    Article  Google Scholar 

  15. Si, D., He, J.: Beta-sheet detection and representation from medium resolution cryo-EM density maps. In: Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics, pp. 764:764–764:770. ACM (2013)

    Google Scholar 

  16. Si, D.: Automatic detection of beta-barrel from medium resolution Cryo-Em density maps. In: Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, pp. 156–164. ACM, New York (2016)

    Google Scholar 

  17. Wimley, W.C.: The versatile β-barrel membrane protein. Curr. Opin. Struct. Biol. 13, 404–411 (2003)

    Article  Google Scholar 

  18. Flower, D.R.: The lipocalin protein family: structure and function. Biochem. J. 318, 1–14 (1996)

    Article  Google Scholar 

  19. Pettersen, E.F., Goddard, T.D., Huang, C.C., Couch, G.S., Greenblatt, D.M., Meng, E.C., Ferrin, T.E.: UCSF Chimera–a visualization system for exploratory research and analysis. J. Comput. Chem. 25, 1605–1612 (2004)

    Article  Google Scholar 

  20. Baker, M.L., Baker, M.R., Hryc, C.F., Ju, T., Chiu, W.: Gorgon and pathwalking: macromolecular modeling tools for subnanometer resolution density maps. Biopolymers 97, 655–668 (2012)

    Article  Google Scholar 

  21. Fraser, A.S., Fraser, A.S.: Simulation of genetic systems by automatic digital computers I. Introduction. Aust. J. Biol. Sci. 10, 484–491 (1957)

    Article  Google Scholar 

  22. Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. MIT Press, Cambridge (1992)

    Google Scholar 

  23. Zhao, X., Gao, X.-S., Hu, Z.-C.: Evolutionary programming based on non-uniform mutation. Appl. Math. Comput. 192, 1–11 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  24. Goldstein, R.A., Nagel, R.: 3-D visual simulation. Trans. Soc. Comput. Simul. 16, 25–31 (1971)

    Google Scholar 

  25. Altman, D.G., Bland, J.M.: Statistics notes: diagnostic tests 1: sensitivity and specificity. BMJ 308, 1552 (1994)

    Article  Google Scholar 

  26. Ludtke, S.J., Baldwin, P.R., Chiu, W.: EMAN: semiautomated software for high-resolution single-particle reconstructions. J. Struct. Biol. 128, 82–97 (1999)

    Article  Google Scholar 

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Acknowledgement

This work was supported by the Graduate Research Award from the Computing and Software Systems division of University of Washington Bothell and the startup fund 74-0525.

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Authors and Affiliations

  1. University of Washington, Bothell, 18115 Campus Way NE, Bothell, WA, 98011, USA

    Albert Ng & Dong Si

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  1. Albert Ng
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  2. Dong Si
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Correspondence to Dong Si .

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Editors and Affiliations

  1. Georgia State University, Atlanta, Georgia, USA

    Zhipeng Cai

  2. University of Texas at Dallas, Richardson, Texas, USA

    Ovidiu Daescu

  3. Central South University, Changsha, China

    Min Li

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Ng, A., Si, D. (2017). Genetic Algorithm Based Beta-Barrel Detection for Medium Resolution Cryo-EM Density Maps. In: Cai, Z., Daescu, O., Li, M. (eds) Bioinformatics Research and Applications. ISBRA 2017. Lecture Notes in Computer Science(), vol 10330. Springer, Cham. https://doi.org/10.1007/978-3-319-59575-7_16

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  • DOI: https://doi.org/10.1007/978-3-319-59575-7_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59574-0

  • Online ISBN: 978-3-319-59575-7

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