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The discretizable molecular distance geometry problem. (English) Zbl 1259.90153

Summary: Given a simple weighted undirected graph \(G=(V,E,d)\) with \(d:E\rightarrow \mathbb R_+\), the Molecular distance geometry problem (MDGP) consists in finding an embedding \(x:V\rightarrow \mathbb R^3\) such that \(\| x_u-x_v\| =d_{uv}\) for each \(\{u,v\}\in E\). We show that under a few assumptions usually satisfied in proteins, the MDGP can be formulated as a search in a discrete space. We call this MDGP subclass the Discretizable MDGP (DMDGP). We show that the DMDGP is NP-hard and we propose a solution algorithm called branch-and-prune (BP). The BP algorithm performs remarkably well in practice in terms of speed and solution accuracy, and can be easily modified to find all incongruent solutions to a given DMDGP instance. We show computational results on several artificial and real-life instances.

MSC:

90C35 Programming involving graphs or networks

References:

[1] An, L.T.H.: Solving large scale molecular distance geometry problems by a smoothing technique via the Gaussian transform and d.c. programming. J. Glob. Optim. 27, 375–397 (2003) · Zbl 1064.90036 · doi:10.1023/A:1026016804633
[2] An, L.T.H., Tao, P.D.: Large-scale molecular optimization from distance matrices by a d.c. optimization approach. SIAM J. Optim. 14, 77–114 (2003) · Zbl 1075.90071 · doi:10.1137/S1052623498342794
[3] Bachrach, J., Taylor, C.: Localization in sensor networks. In: Stojmenović, I. (ed.) Handbook of Sensor Networks, pp. 3627–3643. Wiley, New York (2005)
[4] Barker, G.: The lattice of faces of a finite dimensional cone. Linear Algebra Appl. 7(1), 71–82 (1973) · Zbl 0249.15010 · doi:10.1016/0024-3795(73)90038-4
[5] Berger, B., Kleinberg, J., Leighton, T.: Reconstructing a three-dimensional model with arbitrary errors. J. ACM 46(2), 212–235 (1999) · Zbl 1065.68574 · doi:10.1145/301970.301972
[6] Berman, H.M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T.N., Weissig, H., Shindyalov, I.N., Bourne, P.E.: The protein data bank. Nucleic Acids Res. 28, 235–242 (2000) · doi:10.1093/nar/28.1.235
[7] Biswas, P.: Semidefinite programming approaches to distance geometry problems. Ph.D. thesis, Stanford University (2007)
[8] Biswas, P., Ye, Y.: Semidefinite programming for ad hoc wireless sensor network localization. In: Proceedings of the 3rd International Symposium on Information Processing in Sensor Networks (IPSN04), New York, NY, USA, pp. 46–54. ACM, New York (2004)
[9] Biswas, P., Ye, Y.: A distributed method for solving semidefinite programs arising from ad hoc wireless sensor network localization. In: Multiscale Optimization Methods and Applications, vol. 82, pp. 69–84. Springer, Berlin (2006) · Zbl 1100.90029
[10] Biswas, P., Lian, T., Wang, T., Ye, Y.: Semidefinite programming based algorithms for sensor network localization. ACM Trans. Sens. Networks 2, 188–220 (2006) · doi:10.1145/1149283.1149286
[11] Biswas, P., Liang, T.-C., Toh, K.-C., Wang, T.-C., Ye, Y.: Semidefinite programming approaches for sensor network localization with noisy distance measurements. IEEE Trans. Autom. Sci. Eng. 3, 360–371 (2006) · doi:10.1109/TASE.2006.877401
[12] Biswas, P., Toh, K.C., Ye, Y.: A distributed SDP approach for large-scale noisy anchor-free graph realization with applications to molecular conformation. SIAM J. Sci. Comput. 30(3), 1251–1277 (2008) · Zbl 1161.49028 · doi:10.1137/05062754X
[13] Blumenthal, L.: Theory and Applications of Distance Geometry. Oxford University Press, London (1953) · Zbl 0050.38502
[14] Burkowski, F.: Structural Bioinformatics: An Algorithmic Approach. CRC Press, Boca Raton (2009) · Zbl 1259.92029
[15] Carvalho, R.S., Lavor, C., Protti, F.: Extending the geometric build-up algorithm for the molecular distance geometry problem. Inf. Process. Lett. 108, 234–237 (2008) · Zbl 1191.68772 · doi:10.1016/j.ipl.2008.05.009
[16] Connelly, R.: Generic global rigidity. Discrete Comput. Geom. 33, 549–563 (2005) · Zbl 1072.52016 · doi:10.1007/s00454-004-1124-4
[17] Creighton, T.: Proteins: Structures and Molecular Properties. Freeman, New York (1993)
[18] Crippen, G.M., Havel, T.F.: Distance Geometry and Molecular Conformation. Wiley, New York (1988) · Zbl 1066.51500
[19] Dattorro, J.: Convex Optimization and Euclidean Distance Geometry. Annual Reviews, Palo Alto (2005) · Zbl 1142.15014
[20] Dong, Q., Wu, Z.: A linear-time algorithm for solving the molecular distance geometry problem with exact inter-atomic distances. J. Glob. Optim. 22, 365–375 (2002) · Zbl 1045.90093 · doi:10.1023/A:1013857218127
[21] Dong, Q., Wu, Z.: A geometric build-up algorithm for solving the molecular distance geometry problem with sparse distance data. J. Glob. Optim. 26, 321–333 (2003) · Zbl 1032.92036 · doi:10.1023/A:1023221624213
[22] Eren, T., Goldenberg, D.K., Whiteley, W., Yang, Y.R., Morse, A.S., Anderson, B.D.O., Belhumeur, P.N.: Rigidity, computation, and randomization in network localization. In: IEEE Infocom Proceedings, pp. 2673–2684 (2004)
[23] Floyd, R.W.: Algorithm 97: Shortest path. Commun. ACM 5(6), 345 (1962) · doi:10.1145/367766.368168
[24] Glunt, W., Hayden, T.H., Hong, S., Wells, J.: An alternating projection algorithm for computing the nearest Euclidean distance matrix. SIAM J. Matrix Anal. Appl. 11(4), 589–600 (1990) · Zbl 0728.65034 · doi:10.1137/0611042
[25] Grosso, A., Locatelli, M., Schoen, F.: Solving molecular distance geometry problems by global optimization algorithms. Comput. Optim. Appl. 43, 23–27 (2009) · Zbl 1176.90567 · doi:10.1007/s10589-007-9127-8
[26] Havel, T.: Distance geometry. In: Grant, D., Harris, R. (eds.) Encyclopedia of Nuclear Magnetic Resonance, pp. 1701–1710. Wiley, New York (1995)
[27] Hendrickson, B.A.: The molecule problem: exploiting structure in global optimization. SIAM J. Optim. 5, 835–857 (1995) · Zbl 0844.05093 · doi:10.1137/0805040
[28] Huang, H.-X., Liang, Z.-A., Pardalos, P.: Some properties for the Euclidean distance matrix and positive semidefinite matrix completion problems. J. Glob. Optim. 25, 3–21 (2003) · Zbl 1037.15015 · doi:10.1023/A:1021336413386
[29] Johnson, C., Kroschel, B., Wolkowicz, H.: An interior-point method for approximate positive semidefinite completions. Comput. Optim. Appl. 9, 175–190 (1998) · Zbl 0907.90207 · doi:10.1023/A:1018363021404
[30] Kearsley, A., Tapia, R., Trosset, M.: The solution of the metric stress and stress problems in multidimensional scaling by newton’s method. Comput. Stat. 13, 369–396 (1998) · Zbl 0926.92002
[31] Krislock, N.: Semidefinite facial reduction for low-rank Euclidean distance matrix completion. Ph.D. thesis, University of Waterloo (2010) · Zbl 1229.90250
[32] Krislock, N., Wolkowicz, H.: Explicit sensor network localization using semidefinite representations and facial reductions. SIAM J. Optim. 20, 2679–2708 (2010) · Zbl 1229.90250 · doi:10.1137/090759392
[33] Laurent, M.: Matrix completion problems. In: Floudas, C., Pardalos, P. (eds.) Encyclopedia of Optimization, 2nd edn., pp. 1967–1975. Springer, New York (2009)
[34] Lavor, C.: On generating instances for the molecular distance geometry problem. In: Liberti, L., Maculan, N. (eds.) Global Optimization: From Theory to Implementation, pp. 405–414. Springer, Berlin (2006) · Zbl 1100.90034
[35] Lavor, C., Liberti, L., Maculan, N.: Grover’s algorithm applied to the molecular distance geometry problem. In: Proc. of VII Brazilian Congress of Neural Networks, Natal, Brazil (2005) · Zbl 1136.92037
[36] Lavor, C., Liberti, L., Maculan, N.: Computational experience with the molecular distance geometry problem. In: Pintér, J. (ed.) Global Optimization: Scientific and Engineering Case Studies, pp. 213–225. Springer, Berlin (2006) · Zbl 1129.90389
[37] Lavor, C., Liberti, L., Maculan, N.: The discretizable molecular distance geometry problem. Technical Report, arXiv: q-bio/0608012 (2006) · Zbl 1129.90389
[38] Lavor, C., Liberti, L., Maculan, N.: Molecular distance geometry problem. In: Floudas, C., Pardalos, P. (eds.) Encyclopedia of Optimization, 2nd edn., pp. 2305–2311. Springer, New York (2009)
[39] Lavor, C., Liberti, L., Mucherino, A., Maculan, N.: On a discretizable subclass of instances of the molecular distance geometry problem. In: Shin, D. (ed.) Proceedings of the 24th Annual ACM Symposium on Applied Computing, pp. 804–805. ACM, New York (2009) · Zbl 1190.92009
[40] Lavor, C., Mucherino, A., Liberti, L., Maculan, N.: Computing artificial backbones of hydrogen atoms in order to discover protein backbones. In: Proceedings of the International Multiconference on Computer Science and Information Technology, Mragowo, Poland, pp. 751–756. IEEE Press, New York (2009)
[41] Lavor, C., Mucherino, A., Liberti, L., Maculan, N.: On the solution of molecular distance geometry problems with interval data. In: Proceedings of the International Workshop on Computational Proteomics, Hong Kong. IEEE Press, New York (2010) · Zbl 1253.05132
[42] Lavor, C., Lee, J., Lee-St. John, A., Liberti, L., Mucherino, A., Sviridenko, M.: Discretization orders for distance geometry problems. Optim. Lett. (accepted for publication) · Zbl 1258.90096
[43] Lavor, C., Mucherino, A., Liberti, L., Maculan, N.: On the computation of protein backbones by using artificial backbones of hydrogens. J. Glob. Optim. (accepted). doi: 10.1007/s10898-010-9584-y · Zbl 1219.90209
[44] Liberti, L., Lavor, C., Maculan, N.: Double VNS for the molecular distance geometry problem. In: Proc. of Mini Euro Conference on Variable Neighbourhood Search, Tenerife, Spain (2005) · Zbl 1129.90389
[45] Liberti, L., Lavor, C., Maculan, N.: A branch-and-prune algorithm for the molecular distance geometry problem. Int. Trans. Oper. Res. 15, 1–17 (2008) · Zbl 1136.92037 · doi:10.1111/j.1475-3995.2007.00622.x
[46] Liberti, L., Lavor, C., Maculan, N., Marinelli, F.: Double variable neighbourhood search with smoothing for the molecular distance geometry problem. J. Glob. Optim. 43, 207–218 (2009) · Zbl 1169.90470 · doi:10.1007/s10898-007-9218-1
[47] Liberti, L., Lavor, C., Mucherino, A., Maculan, N.: Molecular distance geometry methods: from continuous to discrete. Int. Trans. Oper. Res. 18, 33–51 (2010) · Zbl 1219.90177 · doi:10.1111/j.1475-3995.2009.00757.x
[48] Liberti, L., Masson, B., Lavor, C., Lee, J., Mucherino, A.: On the number of solutions of the discretizable molecular distance geometry problem. Technical Report, arXiv: 1010.1834v1 [cs.DM] (2010) · Zbl 1342.90168
[49] Moré, J.J., Wu, Z.: Global continuation for distance geometry problems. SIAM J. Optim. 7(3), 814–846 (1997) · Zbl 0891.90168 · doi:10.1137/S1052623495283024
[50] Moré, J.J., Wu, Z.: Distance geometry optimization for protein structures. J. Glob. Optim. 15, 219–234 (1999) · Zbl 0944.92012 · doi:10.1023/A:1008380219900
[51] Mucherino, A., Lavor, C.: The branch and prune algorithm for the molecular distance geometry problem with inexact distances. In: Proceedings of the International Conference on Computational Biology. World Academy of Science, Engineering and Technology, vol. 58, pp. 349–353 (2009) · Zbl 1272.90074
[52] Mucherino, A., Lavor, C., Maculan, N.: The molecular distance geometry problem applied to protein conformations. In: Cafieri, S., Mucherino, A., Nannicini, G., Tarissan, F., Liberti, L. (eds.) Proceedings of the 8th Cologne-Twente Workshop on Graphs and Combinatorial Optimization, pp. 337–340. École Polytechnique, Paris (2009)
[53] Mucherino, A., Lavor, C., Liberti, L., Talbi, E.-G.: A parallel version of the branch & prune algorithm for the molecular distance geometry problem. In: ACS/IEEE International Conference on Computer Systems and Applications (AICCSA10), Hammamet, Tunisia. IEEE Press, New York (2010) · Zbl 1294.68144
[54] Mucherino, A., Liberti, L., Lavor, C., Maculan, N.: Comparisons between an exact and a metaheuristic algorithm for the molecular distance geometry problem. In: Rothlauf, F. (ed.) Proceedings of the Genetic and Evolutionary Computation Conference, Montreal, pp. 333–340. ACM, New York (2009) · Zbl 1190.92009
[55] Mucherino, A., Liberti, L., Lavor, C.: MD-jeep: an implementation of a branch-and-prune algorithm for distance geometry problems. In: Fukuda, K., van der Hoeven, J., Joswig, M., Takayama, N. (eds.) Mathematical Software. LNCS, vol. 6327, pp. 186–197. Springer, New York (2010) · Zbl 1294.68144
[56] Phillips, A.T., Rosen, J.B., Walke, V.H.: Molecular structure determination by convex underestimation of local energy minima. In: Pardalos, P.M., Shalloway, D., Xue, G. (eds.) Global Minimization of Nonconvex Energy Functions: Molecular Conformation and Protein Folding, vol. 23, pp. 181–198. American Mathematical Society, Providence (1996) · Zbl 0848.65048
[57] Pogorelov, A.: Geometry. Mir, Moscow (1987)
[58] Roth, B.: Rigid and flexible frameworks. Am. Math. Mon. 88(1), 6–21 (1981) · Zbl 0455.51012 · doi:10.2307/2320705
[59] Santana, R., Larrañaga, P., Lozano, J.A.: Combining variable neighbourhood search and estimation of distribution algorithms in the protein side chain placement problem. In: Proc. of Mini Euro Conference on Variable Neighbourhood Search, Tenerife, Spain (2005) · Zbl 1211.90316
[60] Santana, R., Larra naga, P., Lozano, J.A.: Side chain placement using estimation of distribution algorithms. Artif. Intell. Med. 39, 49–63 (2007) · doi:10.1016/j.artmed.2006.04.004
[61] Saxe, J.B.: Embeddability of weighted graphs in k-space is strongly NP-hard. In: Proceedings of 17th Allerton Conference in Communications, Control and Computing, pp. 480–489 (1979)
[62] Schlick, T.: Molecular Modelling and Simulation: An Interdisciplinary Guide. Springer, New York (2002) · Zbl 1011.92019
[63] Schoenberg, I.J.: Remarks to Maurice Fréchet’s article ”sur la définition axiomatique d’une classe d’espaces distanciés vectoriellement applicable sur l’espace de Hilbert”. Ann. Math. 36(3), 724–732 (1935) · Zbl 0012.30703 · doi:10.2307/1968654
[64] So, M.-C., Ye, Y.: Theory of semidefinite programming for sensor network localization. Math. Program. 109, 367–384 (2007) · Zbl 1278.90482 · doi:10.1007/s10107-006-0040-1
[65] Trosset, M.: Applications of multidimensional scaling to molecular conformation. Comput. Sci. Stat. 29, 148–152 (1998)
[66] Wang, L., Mettu, R., Donald, B.R.: An algebraic geometry approach to protein structure determination from nmr data. In: Proceedings of the Computational Systems Bioinformatics Conference. IEEE Press, New York (2005)
[67] Wu, D., Wu, Z.: An updated geometric build-up algorithm for solving the molecular distance geometry problem with sparse distance data. J. Glob. Optim. 37, 661–673 (2007) · Zbl 1119.92032 · doi:10.1007/s10898-006-9080-6
[68] Wu, D., Wu, Z., Yuan, Y.: Rigid versus unique determination of protein structures with geometric buildup. Optim. Lett. 2(3), 319–331 (2008) · Zbl 1144.92017 · doi:10.1007/s11590-007-0060-7
[69] Zou, Z., Bird, R., Schnabel, R.: A stochastic/perturbation global optimization algorithm for distance geometry problems. J. Glob. Optim. 11, 91–105 (1997) · Zbl 0891.90173 · doi:10.1023/A:1008244930007
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