The Community for Technology Leaders
RSS Icon
Issue No.06 - Nov.-Dec. (2012 vol.9)
pp: 1621-1628
G. Cavuslar , Univ. of Wisconsin-Madison, Madison, WI, USA
B. Catay , Sabanci Univ., Istanbul, Turkey
M. S. Apaydin , Istanbul Sehir Univ., Istanbul, Turkey
Nuclear Magnetic Resonance (NMR) (Abbreviations used: NMR, Nuclear Magnetic Resonance; NOE, Nuclear Overhauser Effect; RDC, Residual Dipolar Coupling; PDB, Protein Data Bank; SBA, Structure-Based Assignments; NVR, Nuclear Vector Replacement; BIP, Binary Integer Programming; TS, Tabu Search; QAP, Quadratic Assignment Problem; ff2, the FF Domain 2 of human transcription elongation factor CA150 (RNA polymerase II C-terminal domain interacting protein); SPG, Streptococcal Protein G; hSRI, Human Set2-Rpb1 Interacting Domain; MBP, Maltose Binding Protein; EIN, Amino Terminal Domain of Enzyme I from Escherichia Coli; EM, expectation maximization) Spectroscopy is an experimental technique which exploits the magnetic properties of specific nuclei and enables the study of proteins in solution. The key bottleneck of NMR studies is to map the NMR peaks to corresponding nuclei, also known as the assignment problem. Structure-Based Assignment (SBA) is an approach to solve this computationally challenging problem by using prior information about the protein obtained from a homologous structure. NVR-BIP used the Nuclear Vector Replacement (NVR) framework to model SBA as a binary integer programming problem. In this paper, we prove that this problem is NP-hard and propose a tabu search (TS) algorithm (NVR-TS) equipped with a guided perturbation mechanism to efficiently solve it. NVR-TS uses a quadratic penalty relaxation of NVR-BIP where the violations in the Nuclear Overhauser Effect constraints are penalized in the objective function. Experimental results indicate that our algorithm finds the optimal solution on NVRBIP's data set which consists of seven proteins with 25 templates (31 to 126 residues). Furthermore, it achieves relatively high assignment accuracies on two additional large proteins, MBP and EIN (348 and 243 residues, respectively), which NVR-BIP failed to solve. The executable and the input files are available for download at
Proteins, Nuclear magnetic resonance, Amino acids, Accuracy, Linear programming, Protons, Bioinformatics,structural bioinformatics, Automated NMR assignments, tabu search, NMR structural biology
G. Cavuslar, B. Catay, M. S. Apaydin, "A Tabu Search Approach for the NMR Protein Structure-Based Assignment Problem", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.9, no. 6, pp. 1621-1628, Nov.-Dec. 2012, doi:10.1109/TCBB.2012.122
[1] M.G. Rossmann and D.M. Blow, “The Detection of Sub-Units within the Crystallographic Asymmetric Unit,” Acta Crystallographica, vol. 15, no. 1, pp. 24-31, Jan. 1962.
[2] H.M. Al-Hashimi, A. Gorin, A. Majumdar, Y. Gosser, and D.J. Patel, “Towards Structural Genomics of RNA: Rapid NMR Resonance Assignment and Simultaneous RNA Tertiary Structure Determination Using Residual Dipolar Couplings,” J. Computational Biology, vol. 318, no. 3, pp. 637-649, 2002.
[3] J. Hus, J. Prompers, and R. Bruschweiler, “Assignment Strategy for Proteins of Known Structure,” J. Magnetic Resonance, vol. 157, no. 1, pp. 119-125, 2002.
[4] Y. Jung and M. Zweckstetter, “Backbone Assignment of Proteins with Known Structure Using Residual Dipolar Couplings,” J. Biomolecular NMR, vol. 30, no. 1, pp. 25-35, 2004.
[5] F. Xiong and C. Bailey-Kellogg, “A Hierarchical Grow-and-Match Algorithm for Backbone Resonance Assignments Given 3D Structure,” Proc. Int'l Conf. Bioinformatics and Bioeng. (BIBE), pp. 403-410, 2007.
[6] F. Xiong, G. Pandurangan, and C. Bailey-Kellogg, “Contact Replacement for NMR Resonance Assignment,” Bioinformatics, vol. 24, no. 13, pp. 205-213, 2008.
[7] R. Jang, X. Gao, and M. Li, “Towards Fully Automated Structure-Based NMR Resonance Assignment of 15N-Labeled Proteins from Automatically Picked Peaks,” J. Computational Biology, vol. 18, no. 3, pp. 347-363, 2011.
[8] R. Jang, X. Gao, and M. Li, “Combining Automated Peak Tracking in SAR by NMR with Structure-Based Backbone Assignment from 15N-NOESY,” BMC Bioinformatics, vol. 13, no. Suppl 3, article S4, 2012.
[9] D. Stratmann, E. Guittet, and C. van Heijenoort, “Robust Structure-Based Resonance Assignment for Functional Protein Studies by NMR,” J. Biomolecular NMR, vol. 46, no. 2, pp. 157-173, 2010.
[10] D. Stratmann, C. van Heijenoort, and E. Guittet, “NOEnet-Use of NOE Networks for NMR Resonance Assignment of Proteins with Known 3D Structure,” Bioinformatics, vol. 25, no. 4, pp. 474-481, 2009.
[11] C. Bartels, M. Billeter, P. Güntert, and K. Wütrich, “Automated Sequence-Specific NMR Assignment of Homologous Proteins Using the Program GARANT,” J. Biomolecular NMR, vol. 7, no. 3, pp. 207-213, 1996.
[12] M.A. Erdmann and G.S. Rule, “Rapid Protein Structure Detection and Assignment Using Residual Dipolar Couplings,” Technical Report CMU-CS-02-195, School of Computer Science, Carnegie Mellon Univ., Dec. 2002.
[13] C.J. Langmead, A. Yan, R. Lilien, L. Wang, and B.R. Donald, “A Polynomial-Time Nuclear Vector Replacement Algorithm for Automated NMR Resonance Assignments,” RECOMB '03: Proc. Seventh Ann. Int'l Conf. Research in Computational Molecular Biology, pp. 176-187, 2003.
[14] C.J. Langmead and B.R. Donald, “An Expectation/Maximization Nuclear Vector Replacement Algorithm for Automated NMR Resonance Assignments,” J. Biomolecular NMR, vol. 29, no. 2, pp. 111-138, June 2004.
[15] M.S. Apaydin, V. Conitzer, and B.R. Donald, “Structure-Based Protein NMR Assignments Using Native Structural Ensembles,” J. Biomolecular NMR, vol. 40, no. 4, pp. 263-276, 2008.
[16] M.S. Apaydin, B. Çatay, N. Patrick, and B.R. Donald, “NVR-BIP: Nuclear Vector Replacement Using Binary Integer Programming for NMR Structure-Based Assignments,” The Computer J., vol. 54, no. 5, pp. 708-716, 2011.
[17] G. Kochenberger and F. Glover, “A Unified Framework for Modeling and Solving Combinatorial Optimization Problems: A Tutorial,” Multiscale Optimization Methods and Applications, pp. 101-124, Springer, 2006.
[18] H.R. Eghbalnia, A. Bahrami, L. Wang, A. Assadi, and J.L. Markley, “Probabilistic Identification of Spin Systems and Their Assignments Including Coil-Helix Inference as Output (PISTACHIO),” J. Biomolecular NMR, vol. 32, pp. 219-233, 2005.
[19] R. Battiti and G. Tecchiolli, “The Reactive Tabu Search,” INFORMS J. Computing, vol. 6, no. 2, pp. 126-140, 1994.
[20] J. Chakrapani and J. Skorin-Kapov, “Massively Parallel Tabu Search for the Quadratic Assignment Problem,” Annals Operations Research, vol. 41, nos. 1-4, pp. 327-341, 1993.
[21] J. Skorin-Kapov, “Tabu Search Applied to the Quadratic Assignment Problem,” ORSA J. Computing, vol. 2, no. 1, pp. 33-45, 1990.
[22] J. Skorin-Kapov, “Extensions of a Tabu Search Adaptation to the Quadratic Assignment Problem,” Computers Operations Research, vol. 21, no. 8, pp. 855-865, 1994.
[23] E. Taillard, “Robust Taboo Search for the Quadratic Assignment Problem,” Parallel Computing, vol. 17, pp. 443-455, 1991.
[24] R.K. Ahuja, K.C. Jha, J.B. Orlin, and D. Sharma, “Very Large-Scale Neighborhood Search for the Quadratic Assignment Problem,” INFORMS J. Computing, vol. 19, no. 4, pp. 646-657, 2007.
[25] Z. Drezner, “Extensive Experiments with Hybrid Genetic Algorithms for the Solution of the Quadratic Assignment Problem,” Computers Operations Research, vol. 35, no. 3, pp. 717-736, 2008.
[26] T. James, C. Rego, and F. Glover, “Multistart Tabu Search and Diversification Strategies for the Quadratic Assignment Problem,” IEEE Trans. Systems Man Cybernetics Part A, vol. 39, no. 3, pp. 579-596, May 2009.
[27] A. Misevicius, “A Tabu Search Algorithm for the Quadratic Assignment Problem,” Computational Optimization and Applications, vol. 30, no. 1, pp. 95-111, 2005.
[28] A. Misevicius, “A Fast Hybrid Genetic Algorithm for the Quadratic Assignment Problem,” GECCO '06: Proc. Eighth Ann. Conf. Genetic and Evolutionary Computation, pp. 1257-1264, 2006.
[29] J.P. Kelly, M. Laguna, and F. Glover, “A Study of Diversification Strategies for the Quadratic Assignment Problem,” Computers Operation Research, vol. 21, no. 8, pp. 885-893, 1994.
[30] F. Glover, “Tabu Search—Part I,” ORSA J. Computing, vol. 1, no. 3, pp. 190-206, 1989.
[31] F. Glover, “Tabu Search—Part II,” ORSA J. Computing, vol. 2, no. 1, pp. 4-32, 1990.
[32] F. Glover and M. Laguna, Tabu Search. Kluwer Academic, 1997.
[33] R. Koradi, M. Billeter, and K. Wutrich, “Molmol: A Program for Display and Analysis of Macromolecular Structures,” J. Molecular Graphics, vol. 14, no. 1, pp. 51-55, 1996.
[34] C. Dominguez, R. Boelens, and A. Bonvin, “HADDOCK: A Protein-Protein Docking Approach Based on Biochemical or Biophysical Information,” J. Am. Chemical Soc., vol. 125, no. 7, pp. 1731-1737, 2003.
[35] S. de Vries, A. van Dijk, M. Krzeminski, M. van Dijk, A. Thureau, V. Hsu, T. Wassenaar, and A. Bonvin, “HADDOCK versus HADDOCK: New Features and Performance of HADDOCK2.0 on the CAPRI Targets,” Proteins, vol. 69, no. 4, pp. 726-733, 2007.
[36] M. Resende, C.C. Ribeiro, F. Glover, and R. Martí, “Scatter Search and Path-Relinking: Fundamentals, Advances, and Applications,” Handbook of Metaheuristics, International Series in Operations Research & Management Science, vol. 146, pp. 87-107, Springer, 2010.
83 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool