Geometrical comparison of two protein structures using Wigner-D functions
S. M. Saberi Fathi
Department of Physics, Ferdowsi University of Mashhad, Mashhad, Iran
Search for more papers by this authorDiana T. White
Department of Oncology, University of Alberta, Edmonton, Alberta, Canada
Search for more papers by this authorCorresponding Author
Jack A. Tuszynski
Department of Physics, University of Alberta, Edmonton, Alberta, Canada
Correspondence to: Jack A. Tuszynski, Department of Physics, 4–181 CCIS, University of Alberta, Edmonton, Canada AB T6G 2E1. E-mail: [email protected]Search for more papers by this authorS. M. Saberi Fathi
Department of Physics, Ferdowsi University of Mashhad, Mashhad, Iran
Search for more papers by this authorDiana T. White
Department of Oncology, University of Alberta, Edmonton, Alberta, Canada
Search for more papers by this authorCorresponding Author
Jack A. Tuszynski
Department of Physics, University of Alberta, Edmonton, Alberta, Canada
Correspondence to: Jack A. Tuszynski, Department of Physics, 4–181 CCIS, University of Alberta, Edmonton, Canada AB T6G 2E1. E-mail: [email protected]Search for more papers by this authorABSTRACT
In this article, we develop a quantitative comparison method for two arbitrary protein structures. This method uses a root-mean-square deviation characterization and employs a series expansion of the protein's shape function in terms of the Wigner-D functions to define a new criterion, which is called a “similarity value.” We further demonstrate that the expansion coefficients for the shape function obtained with the help of the Wigner-D functions correspond to structure factors. Our method addresses the common problem of comparing two proteins with different numbers of atoms. We illustrate it with a worked example. Proteins 2014; 82:2756–2769. © 2014 Wiley Periodicals, Inc.
Supporting Information
Additional Supporting Information may be found in the online version of this article.
Filename | Description |
---|---|
prot24640-sup-0001-suppinfo.pdf202 KB |
Supplementary Information |
Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
REFERENCES
- 1Li B, Turuvekere S, Agrawal M, La D, Ramani K, Kihara D. Characterization of local geometry of protein surfaces with the visibility criterion. Proteins 2008; 71: 670–683.
- 2Rupp B, Wang J. Predictive models for protein crystallization. Methods (San Diego, Calif.) 2004; 34: 390–407.
- 3Arnold K, Bordoli L, Kopp J, Schwede T. The SWISS-MODEL workspace: a web-based environment for protein structure homology modelling. Bioinformatics (Oxford, England) 2006; 22: 195–201.
- 4Kolodny R, Petrey D, Honig B. Protein structure comparison: implications for the nature of “fold space”, and structure and function prediction. Curr Opin Struct Biol 2006; 16: 393–398.
- 5Carugo O. Recent progress in measuring structural similarity between proteins. Curr Protein Peptide Sci 2007; 8: 219–241.
- 6Wolfson HJ, Rigoutsos I. Geometric hashing: an overview. IEEE Comput Sci Eng 1997; 4: 10–21.
- 7Funkhouser T, Min P, Kazhdan M, Chen J, Halderman A, Dobkin D, Jacobs D. A search engine for 3D models. ACM Trans Graph 2003; 22: 83–105.
- 8Kihara D, Sael L, Chikhi R, Esquivel-Rodriguez J. Molecular surface representation using 3D Zernike descriptors for protein shape comparison and docking. Curr Protein Peptide Sci 2011; 12: 520–530.
- 9Murzin AG, Brenner SE, Hubbard T, Chothia C. SCOP: a structural classification of proteins database for the investigation of sequences and structures. J Mol Biol 1995; 247: 536–540.
- 10Andreeva A, Howorth D, Chandonia J-M, Brenner SE, Hubbard TJP, Chothia C, Murzin AG. Data growth and its impact on the SCOP database: new developments. Nucleic Acids Res 2008; 36: D419–425.
- 11Orengo CA, Michie AD, Jones S, Jones DT, Swindells MB, Thornton JM. CATH—a hierarchic classification of protein domain structures. Structure (London, England: 1993) 1997; 5: 1093–1108.
- 12Holm L, Ouzounis C, Sander C, Tuparev G, Vriend G. A database of protein structure families with common folding motifs. Protein Sci 1992; 1: 1691–1698.
- 13Maiorov VN, Crippen GM. Significance of root-mean-square deviation in comparing three-dimensional structures of globular proteins. J Mol Biol 1994; 235: 625–634.
- 14Carugo O, Pongor S. A normalized root-mean-square distance for comparing protein three-dimensional structures. Protein Sci 2001; 10: 1470–1473.
- 15Levitt M, Gerstein M. STRUCTAL. A structural alignment program. Stanford University; 2005. Available from: http://csb.stanford.edu/levitt/Structal.
- 16Wigner EP. Gruppentheorie und ihre Anwendungen auf die Quantenmechanik der Atomspektren. Braunschweig: Vieweg Verlag; 1931.
10.1007/978-3-663-02555-9 Google Scholar
- 17Potts D, Prestin J, Vollrath A. A fast algorithm for nonequispaced Fourier transforms on the rotation group. Numer Algorithms 2009; 52: 355–384.
- 18Hielscher R, Potts D, Prestin J, Schaeben H, Schmalz M. The Radon transform on SO(3): a Fourier slice theorem and numerical inversion. Inverse Problems 2008; 24: 025011.
- 19Lipson H, Taylor CA. Fourier transforms and X-ray diffraction. London: Bell; 1958.
- 20Löwe J, Li H, Downing KH, Nogales E. Refined structure of alpha beta-tubulin at 3.5 A resolution. J Mol Biol 2001; 313: 1045–1057.
- 21Arfken JB, Weber HB. Mathematical methods for physicists, 6th ed. Burlington, MA: Elsevier; 2005.
- 22Curtis CW. Linear Algebra. Undergraduate texts in mathematics. New York: Springer-Verlag; 1984.
- 23Titchmarsh EC. Introduction to theory of the Fourier integrals, 2nd ed. London: Oxford University Press; 1948.
- 24Huldt G, Szoke A, Hajdu J. Diffraction imaging of single particles and biomolecules. J Struct Biol 2003; 144: 219–227.
- 25Wilson AJC. The probability distribution of X-ray intensities. Acta Crystallogr 1949; 2: 318–321.
- 26Ritchie DW, Kemp GJ. Protein docking using spherical polar Fourier correlations. Proteins 2000; 39: 178–194.
10.1002/(SICI)1097-0134(20000501)39:2<178::AID-PROT8>3.0.CO;2-6 CAS PubMed Web of Science® Google Scholar
- 27Novotni M, Klein R. Shape retrieval using 3D Zernike descriptors. Comput Aided Des 2004; 36: 1047–1062.
- 28Sael L, Li B, La D, Fang Y, Ramani K, Rustamov R, Kihara D. Fast protein tertiary structure retrieval based on global surface shape similarity. Proteins 2008; 72: 1259–1273.
- 29Venkatraman V, Yang YD, Sael L, Kihara D. Protein-protein docking using region-based 3D Zernike descriptors. BMC Bioinformatics 2009; 10: 407.
- 30Chikhi R, Sael L, Kihara D. Protein binding ligand prediction using moments-based methods. In: D Kihara, editor. Protein function prediction for omics era. Netherlands: Springer; 2011. pp 145–163.
- 31An J, Totrov M, Abagyan R. Pocketome via comprehensive identification and classification of ligand binding envelopes. Mol Cell Proteomics 2005; 4: 752–761.
- 32Brunet D, Vrscay ER, Wang Z. On the mathematical properties of the structural similarity index. IEEE Trans Image Process 2012; 21: 1488–1499.
- 33Heyne S, Costa F, Rose D, Backofen R. GraphClust: alignment-free structural clustering of local RNA secondary structures. Bioinformatics 2012; 28: i224–i232.
- 34Feng J, Meyer CA, Wang Q, Liu JS, Shirley Liu X, Zhang Y. GFOLD: a generalized fold change for ranking differentially expressed genes from RNA-seq data. Bioinformatics (Oxford, England) 2012; 28: 2782–2788.
- 35Carugo O, Pongor S. Protein fold similarity estimated by a probabilistic approach based on C(alpha)-C(alpha) distance comparison. J Mol Biol 2002; 315: 887–898.
- 36Huang C-D, Lin C-T, Pal NR. Hierarchical learning architecture with automatic feature selection for multiclass protein fold classification. IEEE Trans Nanobiosci 2003; 2: 221–232.
- 37Rogen P, Fain B. Automatic classification of protein structure by using Gauss integrals. Proc Natl Acad Sci USA 2003; 100: 119–124.
- 38An J, Totrov M, Abagyan R. Comprehensive identification of “druggable” protein ligand binding sites. Genome Inform 2004; 15: 31–41.
- 39Kazhdan M, Funkhouser T, Rusinkiewicz S. Rotation invariant spherical harmonic representation of 3D shape descriptors. In: Proceedings of the 2003 Eurographics/ACM SIGGRAPH symposium on Geometry processing. SGP'03. Aire-la-Ville, Switzerland, Switzerland: Eurographics Association; 2003. pp 156–164.
- 40Zhang Y, Skolnick J. TM-align: a protein structure alignment algorithm based on the TM-score. Nucleic Acids Res 2005; 33: 2302–2309.
- 41Betancourt MR, Skolnick J. Universal similarity measure for comparing protein structures. Biopolymers 2001; 59: 305–309.
- 42Zhang Y, Skolnick J. Scoring function for automated assessment of protein structure template quality. Proteins 2004; 57: 702–710.
- 43Levitt M, Gerstein M. A unified statistical framework for sequence comparison and structure comparison. Proc Natl Acad Sci 1998; 95: 5913–5920.
- 44Lathrop RH. The protein threading problem with sequence amino acid interaction preferences is NP-complete. Protein Eng 1994; 7: 1059–1068.
- 45Holm L, Sander C. Protein structure comparison by alignment of distance matrices. J Mol Biol 1993; 233: 123–138.
- 46Holm L, Sander C. Dali: a network tool for protein structure comparison. Trends Biochem Sci 1995; 20: 478–480.
- 47Shindyalov IN, Bourne PE. Protein structure alignment by incremental combinatorial extension (CE) of the optimal path. Protein Eng 1998; 11: 739–747.
- 48Kihara D, Skolnick J. The PDB is a covering set of small protein structures. J Mol Biol 2003; 334: 793–802.
- 49Mavridis L, Venkatraman V, Ritchie D, Morikawa H, Andonov R, Cornu A, Malod-Dognin N, Nicolas J, Temerinac-Ott M, Reisert M, Burkhardt H, Axenopoulos A, Daras P. SHREC'10 Track: Protein Models; 2010.
- 50McLachlan A. Gene duplications in the structural evolution of chymotrypsin. J Mol Biol 1979; 128: 49–79.
- 51Zhang Y. I-TASSER server for protein 3D structure prediction. BMC Bioinformatics 2008; 9: 101186.
- 52Pandit S, Skolnick J. Fr-TM-align: a new protein structural alignment method based on fragment alignments and the TM-score. BMC Bioinformatics 2008; 9: 101186.