Finding low-energy conformations of lattice protein models by quantum annealing

Scientific Reports

Volumn 2, Issue , 2012, Pages

  Perdomo Ortiz, Alejandro ^a   Dickson, Neil ^b   Drew Brook, Marshall ^b   Rose, Geordie ^b   Aspuru Guzik, Alan ^a  

^a HARVARD UNIVERSITY   (United States)

^b D WAVE SYSTEMS INC   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

PROTEIN;

ALGORITHM; AMINO ACID SEQUENCE; ARTICLE; CHEMICAL STRUCTURE; CHEMISTRY; COMPUTER SIMULATION; PROTEIN CONFORMATION; PROTEIN FOLDING; QUANTUM THEORY;

ALGORITHMS; AMINO ACID SEQUENCE; COMPUTER SIMULATION; MODELS, MOLECULAR; PROTEIN CONFORMATION; PROTEIN FOLDING; PROTEINS; QUANTUM THEORY;

EID: 84866056973     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/srep00571     Document Type: Article

References (50)

1. Sali, A., Shakhnovich, E. & Karplus, M. How does a protein fold? Nature 369, 248-251 (1994). (Pubitemid 24154805)
2. Pande, V. S. Simple theory of protein folding kinetics. Phys. Rev. Lett. 105, 198101 (2010).
3. Dill, K. A., Ozkan, S. B., Shell, M. S. & Weikl, T. R. The protein folding problem. Ann. Rev. Biophys. 37, 289-316 (2008).
4. Mirny, L. & Shakhnovich, E. Protein folding theory: from lattice to all-atom models. Annu. Rev. Biophys. Bio. 30, 361-396 (2001). (Pubitemid 32566168)
5. Pande, V. S., Grosberg, A. Y. & Tanaka, T. Heteropolymer freezing and design: Towards physical models of protein folding. Rev. Mod. Phys. 72, 259 (2000).
6. Kolinski, A. & Skolnick, J. Lattice Models of Protein Folding, Dynamics and Thermodynamics (Chapman & Hall, 1996).
7. Shakhnovich, E. I. Proteins with selected sequences fold into unique native conformation. Phys. Rev. Lett. 72, 3907 (1994).
8. Bohannon, J. Distributed computing: Grassroots supercomputing. Science 308, 810 (2005).
9. Shaw, D. E. et al. Atomic-Level characterization of the structural dynamics of proteins. Science 330, 341-346 (2010).
10. Li, M. S. et al. Factors governing fibrillogenesis of polypeptide chains revealed by lattice models. Phys. Rev. Lett. 105, 218101 (2010).
11. Finnila, A. B., Gomez, M. A., Sebenik, C., Stenson, C. & Doll, J. D. Quantum annealing: A new method for minimizing multidimensional functions. Chem. Phys. Lett. 219, 343-348 (1994).
12. Kadowaki, T. & Nishimori, H. Quantum annealing in the transverse ising model. Phys. Rev. E. 58, 5355 (1998).
13. Santoro, G. E. & Tosatti, E. Optimization using quantum mechanics: quantum annealing through adiabatic evolution. J. Phys. A. 39, R393-R431 (2006). (Pubitemid 44261969)
14. Das, A. & Chakrabarti, B. K. Colloquium: Quantum annealing and analog quantum computation. Rev. Mod. Phys. 80, 1061-1081 (2008).
15. Ray, P., Chakrabarti, B. K. & Chakrabarti, A. Sherrington-Kirkpatrick model in a transverse field: Absence of replica symmetry breaking due to quantum fluctuations. Phys. Rev. B 39, 11828-11832 (1989).
16. Amara, P., Hsu, D. & Straub, J. E. Global energy minimum searches using an approximate solution of the imaginary time schroedinger equation. J. Phys. Chem. 97, 6715-6721 (1993).
17. Farhi, E. et al. A quantum adiabatic evolution algorithm applied to random instances of an NP-Complete problem. Science 292, 472-475 (2001).
18. Santoro, G.,Marton?a'k, R., Tosatti, E.& Car, R. Theory of quantum annealing of an ising spin glass. Science 295, 24272430 (2002).
19. Brooke, J., Bitko, D., Rosenbaum, T. F. & Aeppli, G. Quantum annealing of a disordered magnet. Science 284, 779-781 (1999). (Pubitemid 29291337)
20. Kirkpatrick, S., Gelatt, C. D.& Vecchi, M. P. Optimization by simulated annealing. Science 220, 671-680 (1983).
21. Perdomo, A., Truncik, C., Tubert-Brohman, I., Rose, G. & Aspuru-Guzik, A. Construction of model hamiltonians for adiabatic quantum computation and its application to finding low-energy conformations of lattice protein models. Phys. Rev. A 78, 012320-15 (2008).
22. Johnson, M. W. et al. Quantum annealing with manufactured spins. Nature 473, 194-198 (2011).
23. Farhi, E., Goldstone, J., Gutmann, S. & Sipser, M. Quantum computation by adiabatic evolution. arXiv:quant-ph/0001106 (2000).
24. Hogg, T. Adiabatic quantum computing for random satisfiability problems. Phys. Rev. A. 67, 022314 (2003).
25. Steffen, M., van Dam, W., Hogg, T., Breyta, G. & Chuang, I. Experimental implementation of an adiabatic quantum optimization algorithm. Phys. Rev. Lett. 90, 067903 (2003).
26. Xu, N. et al. Quantum factorization of 143 on a dipolar-coupling nmr system. arXiv:1111.3726v1 (2011).
27. Ancona-Torres, C., Silevitch, D. M., Aeppli, G. & Rosenbaum, T. F. Quantum and classical glass transitions in LiHo-xY-1-xF-4. Phys. Rev. Lett. 101, 057201 (2008).
28. Wernsdorfer, W. Molecular nanomagnets: towards molecular spintronics. Int. J. Nanotechnol. 7, 497-522 (2010).
29. Vion, D. et al.Manipulating the quantum state of an electrical circuit. Science 296, 886-889 (2002). (Pubitemid 34464889)
30. You, J. Q. & Nori, F. Superconducting circuits and quantum information. Phys. Today. 58, 42-47 (2005). (Pubitemid 41541350)
31. Lupascu, A. et al. Quantum non-demolition measurement of a superconducting two-level system. Nat. Phys. 3, 119-125 (2007).
32. Hofheinz, M. et al. Synthesizing arbitrary quantum states in a superconducting resonator. Nature 459, 546-549 (2009).
33. DiCarlo, L. et al. Demonstration of two-qubit algorithms with a superconducting quantum processor. Nature 460, 240-244 (2009).
34. Neeley, M. et al.Generation of three-qubit entangled states using superconducting phase qubits. Nature 467, 570-573 (2010).
35. DiCarlo, L. et al. Preparation and measurement of three-qubit entanglement in a superconducting circuit. Nature 467, 574-578 (2010).
36. Kaminsky, W. M., Lloyd, S. & Orlando, T. P. Scalable superconducting architecture for adiabatic quantum computation. arXiv:quant-ph/0403090 (2004).
37. Bian, Z., Chudak, F., Macready, W. G., Clark, L. & Gaitan, F. Experimental determination of ramsey numbers with quantum annealing. arXiv:1201.1842v2 (2012).
38. Miyazawa, S. & Jernigan, R. L. Residue-residue potentials with a favorable contact pair term and an unfavorable high packing density term, for simulation and threading. J. Mol. Biol. 256, 623-644 (1996). (Pubitemid 26107211)
39. Harris, R. et al. Experimental investigation of an eight-qubit unit cell in a superconducting optimization processor. Phys. Rev. B. 82, 024511 (2010).
40. Barahona, F. On the computational complexity of ising spin glass models. J. Phys. A: Math. Gen. 15, 3241-3253 (1982).
41. Anfinsen, C. B. Principles that govern the folding of protein chains. Science 181, 223-230 (1973).
42. Berger, B. & Leighton, T. Protein folding in the hydrophobic- hydrophilic (HP) model is NP-complete. J. Comput. Biol. 5, 27-40 (SPR 1998). (Pubitemid 28153003)
43. Crescenzi, P., Goldman, D., Papadimitriou, C., Piccolboni, A. & Yannakakis, M. On the complexity of protein folding. J. Comput. Biol. 5, 597-603 (1998).
44. Hart, W. E.& Istrail, S. Robust proofs of NP-Hardness for protein folding: General lattices and energy potentials. J. Comput. Biol. 4, 1-22 (1997). (Pubitemid 27209249)
45. Lau, K. F. & Dill, K. A. A lattice statistical-mechanics model of the conformational and sequence-spaces of proteins. Macromolecules. 22, 3986-3997 (1989).
46. Yue, K. & Dill, K. A. Forces of tertiary structural organization in globular proteins. Proc. Natl. Acad. Sci. USA. 92, 146-150 (1995).
47. Amin, M. H. S., Truncik, C. J. S. & Averin, D. V. Role of single-qubit decoherence time in adiabatic quantum computation. Phys. Rev. A 80, 022303 (2009).
48. Amin, M. H. S., Averin, D. V.& Nesteroff, J. A.Decoherence in adiabatic quantum computation. Phys. Rev. A 79, 022107 (2009).
49. Camacho, C. J. & Thirumalai, D.Minimum energy compact structures of random sequences of heteropolymers. Phys. Rev. Lett. 71, 2505-2508 (1993).
50. Hartmann, A. K. & Rieger, H. New Optimization Algorithms in Physics (Wiley-VCH, 2004).