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Protein Structure Prediction with Visuospatial Analogy

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Spatial Cognition V Reasoning, Action, Interaction (Spatial Cognition 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4387))

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Abstract

We show that visuospatial representations and reasoning techniques can be used as a similarity metric for analogical protein structure prediction. Our system retrieves pairs of α-helices based on contact map similarity, then transfers and adapts the structure information to an unknown helix pair, showing that similar protein contact maps predict similar 3D protein structure. The success of this method provides support for the notion that changing representations can enable similarity metrics in analogy.

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References

  1. Aaronson, J.S., Juergen, H., Overton, G.C.: Knowledge discovery in genbank. In: Hunter, L., Searls, D., Shavlik, J. (eds.) Proceedings of the First International Conference on Intelligent Systems for Molecular Biology, pp. 3–11. AAAI Press, Stanford, California, USA (1993)

    Google Scholar 

  2. Amarel, S.: On representations of problems of reasoning about actions. In: Michie, D. (ed.) Machine Intelligence 3, vol. 3, pp. 131–171. Elsevier/North-Holland, Amsterdam, London, New York (1968)

    Google Scholar 

  3. Berman, H.M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T.N., Weissig, H., Shindyalov, I.N., Bourne, P.E.: Protein data bank. Nucleic Acids Research 28, 235–242 (2000)

    Article  Google Scholar 

  4. Biname, J., Meurice, N., Leherte, L., Glasgow, J., Fortier, S., Vercauteren, D.P.: Use of electron density critical points as chemical function-based reduced representations of pharmacological ligands. Journal of Chemical Information and Computer Science 44, 1394–1401 (2004)

    Article  Google Scholar 

  5. Canny, J.: A computational approach to edge detection. IEEE Transactions on Pattern Alanysis and Machine Intelligence 8(6), 769–798 (1986)

    Google Scholar 

  6. Casakin, H., Goldschmidt, G.: Expertise and the use of visual analogy: Implications for design education. Design Studies 20, 153–175 (1999)

    Article  Google Scholar 

  7. Croft, D., Thagard, P.: Dynamic imagery: A computational model of motion and visual analogy. In: Magnani, L., Nersessian, N.J. (eds.) Model-Based Reasoning: Science, Technology, & Values, pp. 259–274. Kluwer Academic: Plenum Publishers, New York (2002)

    Chapter  Google Scholar 

  8. Davies, J., Goel, A.K.: Visual analogy in problem solving. In: Nebel, B. (ed.) Proceedings of the International Joint Conference for Artificial Intelligence 2001, pp. 377–382. Morgan Kaufmann Publishers, Seattle, WA (2001)

    Google Scholar 

  9. Falkenhainer, B., Forbus, K.D., Gentner, D.: The structure-mapping engine: Algorithm and examples. Artificial Intelligence 41, 1–63 (1990)

    Article  MATH  Google Scholar 

  10. Faltings, B., Sun, K.: FAMING: supporting innovative mechanism shape design. Computer-aided Design 28(3), 207–216 (1996)

    Article  Google Scholar 

  11. Farah, M.J.: The neuropsychology of mental imagery: Converging evidence from brain-damaged and normal subjects. In: Stiles-Davis, J., Kritchevsky, M., Bellugi, U. (eds.) Spatial Cognition– Brain bases and development, pp. 33–59. Erlbaum, Hillsdale, New Jersey (1988)

    Google Scholar 

  12. Fariselli, P., Olmea, O., Valencia, A., Casadio, R.: Prediction of contact maps with neural networks and correlated mutations. Protein Engineering 14(11), 835–843 (2001)

    Article  Google Scholar 

  13. Ferguson, R.W.: Magi: Analogy-based encoding using regularity and symmetry. In: Ram, A., Eiselt, K. (eds.) Proceedings of the Sixteenth Annual Conference of the Cognitive Science Society, Atlanta, GA, pp. 283–288. Lawrence Erlbaum Associates, Mahwah (1994)

    Google Scholar 

  14. Gebhardt, F., Voss, A., Grather, W., Schmidt-Belz, B.: Reasoning with Complex Cases. Kluwer Academic Publishers, Dordrecht (1997)

    Book  MATH  Google Scholar 

  15. Glasgow, J., Epstein, S.L., Meurice, N., Vercauteren, D.P.: Spatial motifs in design. In: Proceedings of the Third International Conference on Visual and Spatial Reasoning in Design (2004)

    Google Scholar 

  16. Glasgow, J.I., Conklin, D., Fortier, S.: Case-based reasoning for molecular scene analysis. In: Janice, I. (ed.) Working Notes of the AAAI Spring Symposium on Case-Based Reasoning and Information Retrieval, pp. 53–62. AAAI Press, Menlo Park, California (1993)

    Google Scholar 

  17. Janice, I., Glasgow, S., Fortier, D., Allen, F.: Knowledge representation tools for molecular scene analysis. In: Proceedings of the 28th Annual Hawaii International Conference on System Biotechnology Computing Track (January 1995)

    Google Scholar 

  18. Gomes, P., Seco, N., Pereira, F.C., Paiva, P., Carreiro, P., Ferreira, J.L., Bento, C.: The importance of retrieval in creative design analogies. In: Bento, C., Cardoso, A., Gero, J. (eds.) Creative Systems: Approaches to Creativity in AI and Cognitive Science. Workshop program in the Eighteenth International Joint Conference on Artificial Intelligence, Acapulco, Mexico, pp. 37–45 (August 2003)

    Google Scholar 

  19. Rafael, C.G., Richard, E.W.: Digital Image Processing. Addison-Wesley, New York (1992)

    Google Scholar 

  20. Hammond, K.J.: Case-based planning: A framework for planning from experience. Cognitive Science 14(4), 385–443 (1990)

    Article  Google Scholar 

  21. Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Transactions on Systems, Man and Cybernetics SMC-3(6), 610–621 (1973)

    Article  Google Scholar 

  22. Hennessy, D., Buchanan, B., Subramanian, D., Wilkosz, P.A., Rosenberg, J.M.: Statistical methods for the objective design of screening procedures for macromolecular crystallization. Acta Crystallogr. D Biol. Crystallogr. 56(Pt 7), 817–827 (2000)

    Article  Google Scholar 

  23. Holyoak, K.J., Thagard, P.: The analogical mind. American Psychologist 52(1), 35–44 (1997)

    Article  Google Scholar 

  24. Hunter, L.: Life and its molecules. AI Magazine 25(1), 9–22 (2004)

    Google Scholar 

  25. Jaccard, P.: Nouvelles recherches sur la distribution florale. Bulletin de la Société Vaudoise des Sciences Naturelles 44, 223–270 (1908)

    Google Scholar 

  26. Jurisica, I., Glasgow, J.I.: Extending case-based reasoning by discovering and using image features in in-vitro fertilization. In: SAC 2000. ACM Symposium on Application Computing, Biomedical Computing- special session on biomedical applications of knowledge discovery and data mining, CITO. Villa Olmo, Italy (March 2000)

    Google Scholar 

  27. Jurisica, I., Glasgow, J.I.: Applications of case-based reasoning in molecular biology. AI Magazine 25(1) (2004)

    Google Scholar 

  28. Jurisica, I., Rogers, P., Glasgow, J.I., Collins, R.J., Wolfley, J.R., Luft, J.R., DeTitta, G.T.: Improving objectivity and scalability in protein crystallization: Integrating image analysis with knowledge discovery. Intelligent Systems in Biology, Special Issue of IEEE Intelligent Systems, 26–34 (2001)

    Google Scholar 

  29. Jurisica, I., Rogers, P., Glasgow, J., Fortier, S., Collins, R., Wolfley, J., Luft, J., DeTitta, G.T.: Integrating case-based reasoning and image analysis: High-throughput protein crystallization domain. In: IAAI 2001. Proceedings of the Innovative Applications of Artificial Intelligence, Seattle, pp. 73–80. IRIS, CITO (August 2001)

    Google Scholar 

  30. McCarthy, J., Minsky, M., Sloman, A., Gong, L., Lau, T., Morgenstern, L., Mueller, E.T., Riecken, D., Singh, M., Singh, P.: An architecture of diversity for commonsense reasoning. IBM Systems Journal 41(3), 530–539 (2002)

    Article  Google Scholar 

  31. Monaghan, J.M., Clement, J.: Use of computer simulation to develop mental simulations for understanding relative motion concepts. International Journal of Science Education 21(9), 921–944 (1999)

    Article  Google Scholar 

  32. Nilges, M., Clore, G.M., Gronenborn, A.M.: Determination of the three-dimensional structures of proteins from interproton distance data by dynamical simulated annealing from a random array of atoms. FEBS Lett. 229, 129–136 (1988)

    Article  Google Scholar 

  33. Perner, P.: Image analysis and classification of hep-2 cells in flourescent images. In: Fourteenth International Conference on Pattern Recognition, pp. 1677–1679. IEEE Computer Society Press, Los Alamitos (1998)

    Google Scholar 

  34. Perner, P.: An architecture for a cbr image segmentation system. Journal of Engineering Application in Artificial Intelligence 12(6), 749–759 (1999)

    Article  Google Scholar 

  35. Perner, P.: Why case-based reasoning is attractive for image interpretation. In: Case-Based Reasoning Research and Development, pp. 27–44. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  36. Perner, P., Gunther, T., Perner, H.: Airborne fungi identification by case-based reasoning. In: Ashley, K.D., Bridge, D.G. (eds.) ICCBR 2003. LNCS, vol. 2689, pp. 73–79. Springer, Heidelberg (2003)

    Google Scholar 

  37. Smith, J.R., Chang, S.F.: Quad-tree segmentation for texture-based image query. In: Proceedings of the second ACM international conference on Multimedia, pp. 279–286 (1994)

    Google Scholar 

  38. Sullivan, G.J., Baker, R.L.: Efficient quadtree coding of images and video. IEEE Transactions on Image Processing 3(3), 327–331 (1994)

    Article  Google Scholar 

  39. Veloso, M.M.: Prodigy/analogy: Analogical reasoning in general problem solving. In: Wess, S., Richter, M., Althoff, K.-D. (eds.) Topics in Case-Based Reasoning. LNCS, vol. 837, pp. 33–52. Springer, Heidelberg (1994)

    Chapter  Google Scholar 

  40. Vendruscolo, M., Kussell, E., Domany, E.: Recovery of protein structure from contact maps. Folding and Design 2, 295–306 (1997)

    Article  Google Scholar 

  41. Wang, J., Stieglitz, K.A., Kantrowitz, E.R.: Metal specificity is correlated with two crucial active site residues in escherichia coli alkaline phosphatase. Biochemistry 44(23), 8378–8386 (2005)

    Article  Google Scholar 

  42. Won, C.S., Park, D.K., Park, S.J.: Efficient use of mpeg-7 edge histogram descriptor. Electronics and Telecommunications Research Institute Journal 24, 23 (2002)

    Google Scholar 

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Authors and Affiliations

  1. School of Computing, Queen’s University, Kingston, ON, K7L 3N6, Canada

    Jim Davies, Janice Glasgow & Tony Kuo

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  1. Jim Davies
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  2. Janice Glasgow
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  3. Tony Kuo
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Editor information

Editors and Affiliations

  1. Department of Computer Sciences, University of Bremen, Enrique-Schmidt-Str. 5, 28359, Bremen, Germany

    Thomas Barkowsky

  2. Department of Psychology, Justus-Liebig University Gießen, Otto-Behaghel-Strasse 10F, 35394, Giessen, Germany

    Markus Knauff

  3. LIMSI-CNRS, Université de Paris-Sud, 91403, Orsay, France

    Gérard Ligozat

  4. Department of Geography, University of California, Santa Barbara, CA, USA

    Daniel R. Montello

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Davies, J., Glasgow, J., Kuo, T. (2007). Protein Structure Prediction with Visuospatial Analogy. In: Barkowsky, T., Knauff, M., Ligozat, G., Montello, D.R. (eds) Spatial Cognition V Reasoning, Action, Interaction. Spatial Cognition 2006. Lecture Notes in Computer Science(), vol 4387. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75666-8_8

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  • DOI: https://doi.org/10.1007/978-3-540-75666-8_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-75665-1

  • Online ISBN: 978-3-540-75666-8

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