Semi-isometric Registration of Line Features for Flexible Fitting of Protein Structures
In this paper, we study a registration problem that is motivated by a practical biology problem – fitting protein structures to low‐re solution density maps. We consider registration between two sets of lines features (e.g., helices in the proteins) that have undergone not a single, but multiple iso...
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| Published in | Computer graphics forum Vol. 29; no. 7; pp. 2243 - 2252 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Oxford, UK
Blackwell Publishing Ltd
01.01.2010
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0167-7055 1467-8659 1467-8659 |
| DOI | 10.1111/j.1467-8659.2010.01813.x |
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| Abstract | In this paper, we study a registration problem that is motivated by a practical biology problem – fitting protein structures to low‐re solution density maps. We consider registration between two sets of lines features (e.g., helices in the proteins) that have undergone not a single, but multiple isometric transformations (e.g., hinge‐motions). The problem is further complicated by the presence of symmetry in each set. We formulate the problem as a clique‐finding problem in a product graph, and propose a heuristic solution that includes a fast clique‐finding algorithm unique to the structure of this graph. When tested on a suite of real protein structures, the algorithm achieved high accuracy even for very large inputs containing hundreds of helices. |
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| AbstractList | In this paper, we study a registration problem that is motivated by a practical biology problem - fitting protein structures to low-resolution density maps. We consider registration between two sets of lines features (e.g., helices in the proteins) that have undergone not a single, but multiple isometric transformations (e.g., hinge-motions). The problem is further complicated by the presence of symmetry in each set. We formulate the problem as a clique-finding problem in a product graph, and propose a heuristic solution that includes a fast clique-finding algorithm unique to the structure of this graph. When tested on a suite of real protein structures, the algorithm achieved high accuracy even for very large inputs containing hundreds of helices.In this paper, we study a registration problem that is motivated by a practical biology problem - fitting protein structures to low-resolution density maps. We consider registration between two sets of lines features (e.g., helices in the proteins) that have undergone not a single, but multiple isometric transformations (e.g., hinge-motions). The problem is further complicated by the presence of symmetry in each set. We formulate the problem as a clique-finding problem in a product graph, and propose a heuristic solution that includes a fast clique-finding algorithm unique to the structure of this graph. When tested on a suite of real protein structures, the algorithm achieved high accuracy even for very large inputs containing hundreds of helices. In this paper, we study a registration problem that is motivated by a practical biology problem – fitting protein structures to low‐re solution density maps. We consider registration between two sets of lines features (e.g., helices in the proteins) that have undergone not a single, but multiple isometric transformations (e.g., hinge‐motions). The problem is further complicated by the presence of symmetry in each set. We formulate the problem as a clique‐finding problem in a product graph, and propose a heuristic solution that includes a fast clique‐finding algorithm unique to the structure of this graph. When tested on a suite of real protein structures, the algorithm achieved high accuracy even for very large inputs containing hundreds of helices. In this paper, we study a registration problem that is motivated by a practical biology problem - fitting protein structures to low-resolution density maps. We consider registration between two sets of lines features (e.g., helices in the proteins) that have undergone not a single, but multiple isometric transformations (e.g., hinge-motions). The problem is further complicated by the presence of symmetry in each set. We formulate the problem as a clique-finding problem in a product graph, and propose a heuristic solution that includes a fast clique-finding algorithm unique to the structure of this graph. When tested on a suite of real protein structures, the algorithm achieved high accuracy even for very large inputs containing hundreds of helices. |
| Author | Baker, M. L. Ju, T. Chiu, W. Abeysinghe, S.S. |
| AuthorAffiliation | 1 Washington University in St. Louis, USA 2 Baylor College of Medicine, USA |
| AuthorAffiliation_xml | – name: 2 Baylor College of Medicine, USA – name: 1 Washington University in St. Louis, USA |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/21124809$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1016/j.cad.2007.02.009 10.1007/11802914_10 10.1145/1122501.1122507 10.1109/34.765655 10.1016/j.str.2008.03.005 10.1016/j.str.2007.11.016 10.1038/nsb1203-980 10.1111/j.1467-8659.2008.01283.x 10.1006/jsbi.2000.4350 10.1109/99.641604 10.1145/1360612.1360684 10.1145/358669.358692 10.1016/j.str.2006.11.008 10.1109/34.121791 10.1007/s00371-009-0324-6 10.1109/TPAMI.2006.81 10.1016/j.cad.2007.06.007 10.1109/ICCV.1999.790410 10.1016/j.str.2004.12.016 10.1145/1531326.1531378 |
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| References_xml | – reference: Hou F., Qi Y., Shen X., Yang S., Zhao Q.: Automatic registration of multiple range images based on cycle space. The Visual Computer: International Journal of Computer Graphics 25 (2009), 657-665. – reference: Wriggers W., Birmanns S.: Using situs for flexible and rigid-body fitting of multiresolution single-molecule data. Journal of Structural Biology 133, 2-3 (2001), 193-202. – reference: Topf M., Lasker K., Webb B., Wolfson H., Chiu W., Sali A.: Protein structure fitting and refinement guided by cryo-em density. Structure 16 (2008), 295-307. – reference: Lowe D. G.: Object recognition from local scale-invariant features. In Proc. Seventh IEEE International Conference on Computer Vision The (1999), vol. 2, pp. 1150-1157. – reference: Baker M. L., Ju T., Chiu W.: Identification of secondary structure elements in intermediate-resolution density maps. Structure 15, 1 (January 2007), 7-19. – reference: Li M., Langbein F. C., Martin R. R.: Detecting approximate symmetries of discrete point subsets. Comput. Aided Des. 40, 1 (2008), 76-93. – reference: Gal R., Cohen-Or D.: Salient geometric features for partial shape matching and similarity. ACM Trans. Graph. 25, 1 (2006), 130-150. – reference: Johnson A. E., Hebert M.: Using spin images for efficient object recognition in cluttered 3d scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence 21, 5 (May 1999), 433-449. – reference: Fischler M. A., Bolles R. C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM 24, 6 (June 1981), 381-395. – reference: Jain V., Zhang H.: A spectral approach to shape-based retrieval of articulated 3d models. Computer-Aided Design 39, 5 (May 2007), 398-407. – reference: Wang Y., Guibas L. J.: Toward unsupervised segmentation of semi-rigid low-resolution molecular surfaces. Geometric Modeling and Processing - GMP 2006 4077, 2006 (July 2006), 129-142. – reference: Wolfson H., Rigoutsos I.: Geometric hashing: An overview. IEEE Computational Science and Engineering 4, 4 (1997), 10-21. – reference: Aiger D., Mitra N. J., Cohen-Or D.: 4-points congruent sets for robust pairwise surface registration. ACM Transactions on Graphics (TOG) 27, 3 (August 2008), 1-10. – reference: Besl P. J., McKay N. D.: A method for registration of 3-d shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence 14, 2 (February 1992), 239-256. – reference: Zhang H., Sheffer A., Cohen-Or D., Zhou Q., Van Kaick O., Tagliasacchi A.: Deformation-driven shape correspondence. Computer Graphics Forum 27, 5 (2008), 1431-1439. – reference: Berman H., Henrick K., Nakamura H.: Announcing the worldwide protein data bank. Nature Structural Biology 10, 12 (2003), 980-980. – reference: Lipman Y., Funkhouser T.: Möbius voting for surface correspondence. ACM Transactions on Graphics 28, 3 (August 2009), 1-12. – reference: Chiu W., Baker M. L., Jiang W., Dougherty M., Schmid M. F.: Electron cryomicroscopy of biological machines at subnanometer resolution. Structure 13, 3 (March 2005), 363-372. – reference: Zheng Y., Doermann D.: Robust point matching for nonrigid shapes by preserving local neighborhood structures. IEEE Transactions on Pattern Analysis and Machine Intelligence 28 (2006), 643-649. – reference: Trabuco L. G., Villa E., Mitra K., Frank J., Schulten K.: Flexible fitting of atomic structures into electron microscopy maps using molecular dynamics. 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Aided Des. – volume: 21 start-page: 433 issue: 5 year: May 1999 end-page: 449 article-title: Using spin images for efficient object recognition in cluttered 3d scenes publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence – volume: 10 start-page: 980 issue: 12 year: 2003 end-page: 980 article-title: Announcing the worldwide protein data bank publication-title: Nature Structural Biology – volume: 39 start-page: 398 issue: 5 year: May 2007 end-page: 407 article-title: A spectral approach to shape‐based retrieval of articulated 3d models publication-title: Computer-Aided Design – volume: 15 start-page: 7 issue: 1 year: January 2007 end-page: 19 article-title: Identification of secondary structure elements in intermediate‐resolution density maps publication-title: Structure – volume: 24 start-page: 381 issue: 6 year: June 1981 end-page: 395 article-title: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography publication-title: Communications of the ACM – year: 2001 – start-page: 131 year: June 2006 end-page: 142 – volume: 25 start-page: 130 issue: 1 year: 2006 end-page: 150 article-title: Salient geometric features for partial shape matching and similarity publication-title: ACM Trans. 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| Snippet | In this paper, we study a registration problem that is motivated by a practical biology problem – fitting protein structures to low‐re solution density maps.... In this paper, we study a registration problem that is motivated by a practical biology problem - fitting protein structures to low-resolution density maps. We... |
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| SubjectTerms | and systems Computer graphics I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling-Geometric algorithms I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling—Geometric algorithms, languages, and systems I.4.7 [Image Processing and Computer Vision]: Feature Measurement-Invariants I.5.3 [Pattern Recognition]: Clustering-Similarity measures Image coding languages Mapping Optimization algorithms Proteins Registration Studies |
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| Title | Semi-isometric Registration of Line Features for Flexible Fitting of Protein Structures |
| URI | https://api.istex.fr/ark:/67375/WNG-V01RMZDG-4/fulltext.pdf https://onlinelibrary.wiley.com/doi/abs/10.1111%2Fj.1467-8659.2010.01813.x https://www.ncbi.nlm.nih.gov/pubmed/21124809 https://www.proquest.com/docview/758864947 https://www.proquest.com/docview/1826161261 https://pubmed.ncbi.nlm.nih.gov/PMC2993115 http://doi.org/10.1111/j.1467-8659.2010.01813.x |
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