The Community for Technology Leaders
RSS Icon
Issue No.04 - July/August (2011 vol.8)
pp: 1004-1016
Ivan Merelli , Italian National Research Council, Milan
Paolo Cozzi , Italian National Research Council, Milan
Daniele D'Agostino , Italian National Research Council, Genoa
Andrea Clematis , Italian National Research Council, Genoa
Luciano Milanesi , Italian National Research Council, Milan
Emerging technologies for structure matching based on surface descriptions have demonstrated their effectiveness in many research fields. In particular, they can be successfully applied to in silico studies of structural biology. Protein activities, in fact, are related to the external characteristics of these macromolecules and the ability to match surfaces can be important to infer information about their possible functions and interactions. In this work, we present a surface-matching algorithm, based on encoding the outer morphology of proteins in images of local description, which allows us to establish point-to-point correlations among macromolecular surfaces using image-processing functions. Discarding methods relying on biological analysis of atomic structures and expensive computational approaches based on energetic studies, this algorithm can successfully be used for macromolecular recognition by employing local surface features. Results demonstrate that the proposed algorithm can be employed both to identify surface similarities in context of macromolecular functional analysis and to screen possible protein interactions to predict pairing capability.
Bioinformatics, surface matching, image representations, pattern recognition, structural biology.
Ivan Merelli, Paolo Cozzi, Daniele D'Agostino, Andrea Clematis, Luciano Milanesi, "Image-Based Surface Matching Algorithm Oriented to Structural Biology", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.8, no. 4, pp. 1004-1016, July/August 2011, doi:10.1109/TCBB.2010.21
[1] H.M. Berman, T.N. Bhat, P.E. Bourne, Z. Feng, G. Gilliland, H. Weissig, and J. Westbrook, “The Protein Data Bank and the Challenge of Structural Genomics,” Nature Structural Biology, vol. 7, no. 11, pp. 957-959, 2000.
[2] I. Friedberg, “Automated Protein Function Prediction—the Genomic Challenge,” Briefings in Bioinformatics, vol. 7, pp. 225-242, 2006.
[3] K. Kinoshita and H. Nakamura, “Identification of the Ligand Binding Sites on the Molecular Surface of Proteins,” Protein Science, vol. 14, pp. 711-718, 2004.
[4] A.E. Todd, C.A. Orengo, and J.M. Thornton, “Evolution of Function in Protein Superfamilies, from a Structural Perspective,” J. Molecular Biology, vol. 307, pp. 1113-1143, 2001.
[5] N. Nagano, C.A. Orengo, and J.M. Thornton, “One Fold with Many Functions: The Evolutionary Relationships between TIM Barrel Families Based on Their Sequences, Structures and Functions,” J. Molecular Biology, vol. 321, pp. 741-765, 2002.
[6] N. Tuncbag, G. Kar, O. Keskin, A. Gursoy, and R. Nussinov, “A Survey of Available Tools and Web Servers for Analysis of Protein-Protein Interactions and Interfaces,” Briefings in Bioinformatics, vol. 10, pp. 217-232, 2009.
[7] I. Ezkurdia, L. Bartoli, P. Fariselli, R. Casadio, A. Valencia, and M.L. Tress, “Progress and Challenges in Predicting Protein-Protein Interaction Sites,” Briefings in Bioinformatics, vol. 10, pp. 233-246, 2009.
[8] A.E. Johnson and M. Hebert, “Using Spin-Images for Efficient Multiple Model Recognition in Cluttered 3-D Scenes,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 21, no. 5, pp. 433-449, May 1999.
[9] I. Merelli, P. Cozzi, D. D'Agostino, A. Cleamatis, and L. Milanesi, “Images Based System for Surface Matching in Macromolecular Screening,” Proc. IEEE Int'l Conf. Bioinformatics and Biomedicine, pp. 397-401, 2008.
[10] H. Alt and L.J. Guibas, “Discrete Geometric Shapes: Matching, Interpolation, and Approximation: A Survey,” Technical Report B 96-1 1, EVL-1996-142, Freie Univ., 1996.
[11] F. Arman and J. Aggarwal, “Model-Based Object Recognition in Dense-Range Images—A Review,” ACM Computing Surveys, vol. 25, no. 1, pp. 5-43, 1993.
[12] P.J. Besl and R.C. Jain, “Three-Dimensional Object Recognition,” ACM Computing Surveys, vol. 17, no. 1, pp. 75-145, 1985.
[13] S. Loncaric, “A Survey of Shape Analysis Techniques,” Pattern Recognition, vol. 31, no. 8, pp. 983-1001, 1998.
[14] A.R. Pope, “Model-Based Object Recognition: A Survey of Recent Research,” Technical Report TR-94-04, Univ. British Columbia, 1994.
[15] R.C. Veltkamp and M. Hagedoom, “State-of-the-Art in Shape Matching,” Technical Report UU-CS-1999-27, Utrecht Univ., 1999.
[16] T. Binford, “Visual Perception by Computer,” Proc. IEEE Conf. Systems Science and Cybernetics, 1971.
[17] F. Solina and R. Bajcsy, “Recovery of Parametric Models from Range Images: The Case for Superquadrics with Global Deformations,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 12, no. 2, pp. 131-147, Feb. 1990.
[18] R. Basri, L. Costa, D. Geiger, and D. Jacobs, “Determining the Similarity of Deformable Shapes,” Vision Research, vol. 382, pp. 365-385, 1998.
[19] K. Siddiqi, A. Shokoufandeh, S.J. Dickinson, and S.W. Zucker, “Shock Graphs and Shape Matching,” Proc. Int'l Conf. Computer Vision (ICCV), pp. 222-229, 1998.
[20] E. Bardinet, S.F. Vidal, S.D. Arroyo, G. Malandain, and N.P. de la Blanca Capilla, “Structural Object Matching,” Technical Report DECSAI-000303, Univ. Granada, 2000.
[21] J. Bloomenthal and C. Lim, “Skeletal Methods of Shape Manipulation,” Proc. Int'l Conf. Shape Modeling and Applications, pp. 44-47, 1999.
[22] D.W. Storti, G.M. Turkiyyah, M.A. Ganter, C.T. Lim, and D.M. Stal, “Skeleton-Based Modeling Operations on Solids,” Proc. ACM Symp. Solid Modeling and Applications, pp. 141-154, 1997.
[23] R.O. Duda, P.E. Hart, and D.G. Stork, Pattern Classication and Scene Analysis. John Wiley and Sons, 1995.
[24] R.J. Prokop and A.P. Reeves, “A Survey of Moment-Based Techniques for Unoccluded Object Representation and Recognition,” Graphics Models and Image Processing, vol. 54, no. 5, pp. 438-460, 1992.
[25] G. Taubin and D. Cooper, “Object Recognition Based on Moment (of Algebraic) Invariants,” Geometric Invariance in Computer Vision, MIT Press, 1992.
[26] M. Ankerst, G. Kastenmuller, H.-P. Kriegel, and T. Seidl, “Nearest Neighbor Classification in 3D Protein Databases,” Proc. Int'l Conf. Intelligent Systems for Molecular Biology, pp. 34-43, 1999.
[27] P. Besl, “Triangles as a Primary Representation,” Lecture Note in Computer Science, vol. 994, pp. 191-206, 1995.
[28] Y. Lamdan and H.J. Wolfson, “Geometric Hashing Method for Model-Based Recognition of an Object,” Proc. Int'l Conf. Computer Vision (ICCV), pp. 238-249, 1988.
[29] A.J. Bordner and A.A. Gorin, “Protein Docking Using Surface Matching and Supervised Machine Learning,” Proteins, vol. 68, no. 2, pp. 488-502, 2007.
[30] D.H. Ballard, “Generalizing the Hough Transform to Detect Arbitrary Shapes,” Pattern Recognition, vol. 13, pp. 111-122, 1981.
[31] K. Arbter, W.E. Snyder, H. Burkhardt, and G. Hirzinger, “Application of Affine-Invariant Fourier Descriptors to Recognition of 3-D Objects,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 12, no. 7, pp. 640-647, July 1990.
[32] E.M. Arkin, L.P. Chew, D.P. Huttenlocher, K. Kedem, and J.S. Mitchell, “An Efficiently Computable Metric for Comparing Polygonal Shapes,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 13, no. 3, pp. 209-216, Mar. 1991.
[33] I. Young, J. Walker, and J. Bowie, “An Analysis Technique for Biological Shape,” Computer Graphics and Image Processing, vol. 25, pp. 357-370, 1974.
[34] Y. Lin, L. Dou, and H. Wang, “Contour Shape Description Based on an Arch Height Function,” Pattern Recognition, vol. 25, pp. 17-23, 1992.
[35] C. Uras and A. Verri, “On the Recognition of the Alphabet of the Sign Language through Size Functions,” Proc. Int'l Conf. Pattern Recognition (IAPR), pp. 334-338, 1994.
[36] C. Uras and A. Verri, “Computing Size Functions from Edge Maps,” Proc. Int'l Conf. Computer Vision (ICCV), vol. 23, no. 2, pp. 169-183, 1997.
[37] B. Hom, “Extended Gaussian Image,” Proc. IEEE, vol. 72, no. 12, pp. 1671-1686, Dec. 1984.
[38] H. Delingette, M. Hebert, and K. Ikeuchi, “Shape Representation and Image Segmentation Using Deformable Surfaces,” Image and Vision Computing, vol. 10, no. 3 pp. 132-144, 1992.
[39] H. Delingette, M. Hebert, and K. Ikeuchi, “A Spherical Representation for the Recognition of Curved Objects,” Proc. Int'l Conf. Computer Vision (ICCV), pp. 103-112, 1993.
[40] D. Zhang and M. Hebert, “Harmonic Maps and Their Applications in Surface Matching,” Proc. Int'l Conf. Computer Vision (ICCV), vol. 2, pp. 663-669, 1999.
[41] Y. Sun and M. Abidi, “Surface Matching by 3D Point's Fingerprint,” Proc. Int'l Conf. Computer Vision (ICCV), pp. 263-269, 2001.
[42] M.E. Bock, C. Garutti, and C. Guerra, “Discovery of Similar Regions on Protein Surfaces,” J. Computational Biology, vol. 14, no. 3, pp. 285-299, 2007.
[43] D. Schneidman-Duhovny, Y. Inbar, R. Nussinov, and H.J. Wolfson, “PatchDock and SymmDock: Servers for Rigid and Symmetric Docking,” Nucleic Acids Research, vol. 33, pp. W363-W367, 2005.
[44] R. Norel, S.L. Lin, H.J. Wolfson, and R. Nussinov, “Molecular Surface Complementarity at Protein-Protein Interfaces: The Critical Role Played by Surface Normals at Well Placed, Sparse, Points in Docking,” J. Molecular Biology, vol. 252, no. 2, pp. 263-273, 1995.
[45] D. D'Agostino, A. Cleamatis, I. Merelli, P. Cozzi, and L. Milanesi, “Parallel Decomposition of 3D Surfaces in Images of Local Descriptors for Molecular Screening,” Proc. Int'l Conf. Parallel, Distributed, and Network-Based Processing (PDP '09), pp. 261-267, 2009.
[46] M. Nayal and B. Honig, “On the Nature of Cavities on Protein Surfaces: Application to the Identification of Drug-Binding Sites,” Proteins: Structure, Function, and Bioinformatics, vol. 63, no. 4, pp. 892-906, 2006.
[47] T. Gatzke, C. Grimm, M. Garland, and S. Zelinka, “Curvature Maps for Local Shape Comparison,” Proc. Int'l Conf. Shape Modeling and Applications, pp. 244-256, 2005.
[48] Levenberg-Marquardt Nonlinear Least Squares Algorithms in C/C++, http:/, 2010.
[49] The Object File Format, , 2010.
[50] M.L. Connolly, “Molecular Surfaces: A Review,” Network Science, , 1996.
[51] M. Sanner, A.J. Olson, and J.C. Spehner, “Fast and Robust Computation of Molecular Surfaces,” Proc. ACM Symp. Computational Geometry (SCG '95), pp. C6-C7, 1995.
[52] I. Merelli, A. Orro, D. D'Agostino, A. Clematis, and L. Milanesi, “A Parallel Protein Surface Reconstruction System,” Int'l J. Bioinformatics Research and Applications, vol. 4, no. 3, pp. 221-239, 2008.
[53] A. Shulman-Peleg, R. Nussinov, and H.J. Wolfson, “Recognition of Functional Sites in Protein Structures,” J. Molecular Biology, vol. 339, pp. 607-633, 2004.
[54] S.Y. Yin, E.A. Proctor, A.A. Lugovskoy, and N.V. Dokholyan, “Fast Screening of Protein Surfaces Using Geometric Invariant Fingerprints,” Proc. Nat'l Academy of Sciences USA, vol. 106, pp. 16622-16626, 2009.
[55] E. Katchalski-Katzir, I. Shariv, M. Eisenstein, A. Friesem, C. Aflalo, and I. Vakser, “Molecular Surface Recognition: Determination of Geometric Fit between Proteins and Their Ligands by Correlation Techniques,” Proc. Nat'l Academy of Sciences USA, vol. 89, pp. 2195-2199, 1992.
[56] J.J. Gray, S. Moughon, C. Wang, O. Schueler-Furman, B. Kuhlman, C.A. Rohl, and D. Baker, “Protein-Protein Docking with Simultaneous Optimization of Rigid-Body Displacement and Side-Chain Conformations,” J. Molecular Biology, vol. 331, pp. 281-299, 2003.
[57] R. Chen, J. Mintseris, J. Janin, and Z. Weng, “A Protein-Protein Docking Benchmark,” Proteins: Structure, Function, and Genetics, vol. 52, pp. 88-91, 2003.
[58] R. Mendez, R. Leplae, L. De Maria, and S.J. Wodak, “Assessment of Blind Predictions of Protein-Protein Interactions: Current Status of Docking Methods,” Proteins: Structure, Function, and Genetics, vol. 52, pp. 51-67, 2003.
[59] Non Redundant Chain Set from the Protein Data Bank, nrpdb.html, 2010.
[60] C.J.A. Sigrist, L. Cerutti, N. Hulo, A. Gattiker, L. Falquet, M. Pagni, A. Bairoch, and P. Bucher, “PROSITE: A Documented Database Using Patterns and Profiles as Motif Descriptors,” Briefings in Bioinformatics, vol. 3, pp. 265-274, 2002.
[61] E. Quevillon, V. Silventoinen, S. Pillai, N. Harte, N. Mulder, R. Apweiler, and R. Lopez, “InterProScan: Protein Domains Identifier,” Nucleic Acids Research, vol. 33, pp. W116-W120, 2005.
[62] S. Schmitt, D. Kuhn, and G. Klebe, “A New Method to Detect Related Function among Proteins Independent of Sequence and Fold Homology,” J. Molecular Biology, vol. 323, no. 2, pp. 387-406, 2002.
20 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool