Global Optimization of Protein–peptide Docking by a Filling Function Method

Journal of Optimization Theory and Applications

Volumn 164, Issue 3, 2015, Pages 1090-1108

  Lampariello, Francesco ^a   Liuzzi, Giampaolo ^a  

^a CNR   (Italy)

Author keywords

Continuous global optimization; Potential free energy model; Protein peptide docking

Indexed keywords

FILLING; FREE ENERGY; FUNCTIONS; MOLECULAR MODELING; PEPTIDES; PROBLEM SOLVING;

CONTINUOUS GLOBAL OPTIMIZATION; DOCKING POSITIONS; DOCKING PROGRAMS; FREE-ENERGY MODEL; FUNCTION METHODS; GLOBAL OPTIMISATION; MOLECULAR DOCKING; POTENTIAL FREE ENERGY MODEL; PROTEINS PEPTIDES; PROTEIN–PEPTIDE DOCKING;

GLOBAL OPTIMIZATION;

EID: 84958025648     PISSN: 00223239     EISSN: 15732878     Source Type: Journal    
DOI: 10.1007/s10957-014-0525-7     Document Type: Article

References (48)

1. Berman, H., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T., Weissig, H., Shindyalov, I., Bourne, P.: The protein data bank. Nucleic Acids Res. 28, 235–242 (2000)
2. Hartenfeller, M., Schneider, G.: De novo drug design. Methods Mol. Biol. 672, 299–323 (2011)
3. Dean, P., Lloyd, D., Todorov, N.: De novo drug design: integration of structure-based and ligand-based methods. Curr. Opin. Drug Discov. Dev. 7(3), 347–353 (2004)
4. Vaara, M.: A unified conformational selection and induced fit approach to protein–peptide docking. PLoS ONE 8(3), e58769 (2013)
5. Fjell, C., Hiss, J., Hancock, R., Schneider, G.: Designing antimicrobial peptides: form follows function. Nat. Rev. Drug Discov. 11, 37–51 (2012)
6. Scior, T., Bender, A., Tresadern, G., Medina-Franco, J., Martínez-Mayorga, K., Langer, T., Cuanalo-Contreras, K., Agrafiotis, D.: Recognizing pitfalls in virtual screening: a critical review. J. Chem. Inf. Model. 52(4), 867–881 (2012)
7. Cheng, T., Li, Q., Zhou, Z., Wang, Y., Bryant, S.: Structure-based virtual screening for drug discovery: a problem-centric review. AAPS J. 14(1), 133–141 (2012)
8. Trellet, M., Melquiond, A., Bonvin, A.: New approaches in peptide antibiotics. Curr. Opin. Pharmacol. 9, 571–576 (2009)
9. Antes, I.: A new molecular dynamics-based algorithm for proteinpeptide docking including receptor flexibility. Proteins 78, 1084–1104 (2010)
10. Duhovny, D., Nussinov, R., Wolfson, H.: Efficient unbound docking of rigid molecules. In: Gusfield et al. (ed.) Proceedings of the 2nd Workshop on Algorithms in Bioinformatics (WABI). Lecture Notes in Computer Science, 2452, pp. 185–200. Springer-Verlag, Rome (2002)
11. Schneidman-Duhovny, D., Inbar, Y., Nussinov, R., Wolfson, H.: Patchdock and symmdock: servers for rigid and symmetric docking. Nucleic Acids Res. 33, 363–367 (2005)
12. De Ruvo, M., Giuliani, A., Paci, P., Santoni, D., Paola, L.D.: Shedding light on protein ligand binding by graph theory: the topological nature of allostery. Biophys. Chem. 165, 21–29 (2012)
13. Di Paola, L., Ruvo, M.D., Paci, P., Santoni, D., Giuliani, A.: Protein contact networks: an emerging paradigm in chemistry. Chem. Rev. 113(3), 1598–1613 (2013)
14. Ewing, T., Makino, S., Skillman, A., Kuntz, I.: Search strategies for automated molecular docking of flexible molecule databases. J. Comput. Aided Mol. Des. 15, 411–428 (2001)
15. Neduva, V., Linding, R., Su-Angrand, I., Stark, A., De Masi, F., Gibson, T., Lewis, J., Serrano, L., Russell, R.: Systematic discovery of new recognition peptides mediating protein interaction networks. PLoS Biol. 3(12), e405 (2005). doi:10.1371/journal.pbio.0030405
16. Petsalaki, E., Russell, R.: Peptide-mediated interactions in biological systems: new discoveries and applications. Curr. Opin. Biotechnol. 19, 344–350 (2008)
17. Jones, G., Willett, P., Glen, R., Leach, A., Taylor, R.: Development and validation of a genetic algorithm for flexible docking. J. Mol. Biol. 267, 727–748 (1997)
18. Morris, G., Goodsell, D., Halliday, R., Huey, R., Hart, W., Belew, R., Olson, A.: Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J. Comput. Chem. 19, 1639–1662 (1998)
19. Prasad, P., Gautham, N.: A new peptide docking strategy using a mean field technique with mutually orthogonal latin square sampling. J. Comput. Aided Mol. Des. 22, 815–829 (2008)
20. Taylor, R.D., Jewsbury, P.J., Essex, J.W.: Flexible ligand and receptor docking with a continuum solvent model and soft-core energy function. J. Comput. Chem. 24, 1637–1656 (2003)
21. Apostolakis, J., Plückthun, A., Caflisch, A.: Docking small ligands in flexible binding sites. J. Comput. Chem. 19, 21–37 (1998)
22. Case, D., Darden, T., III, T.C., Simmerling, C., Wang, J., Duke, R., Luo, R., Walker, R., Zhang, W., Merz, K., Roberts, B., Hayik, S., Roitberg, A., Seabra, G., Swails, J., Goetz, A., Kolossváry, I., Wong, K., Paesani, F., Vanicek, J., Wolf, R., Liu, J., Wu, X., Brozell, S., Steinbrecher, T., Gohlke, H., Cai, Q., Ye, X., Wang, J., Hsieh, M., Cui, G., Roe, D., Mathews, D., M.G. Seetin and, R.S.F., Sagui, C., Babin, V., Luchko, T., Gusarov, S., Kovalenko, A., Kollman, P.: Amber 12. Technical Report, University of California, San Francisco (2012)
23. Lampariello, F.: A filling function method for continuos unconstrained global optimization: application to morse clusters. Technical Report R.615, IASI-CNR (2004)
24. Lampariello, F., Liuzzi, G.: A filling function method for unconstrained global optimization. Comput. Optim. Appl. (2013). (submitted)
25. Momany, F., McGuire, R., Burgess, A., Scheraga, H.: Energy parameters in polypeptides. vii. geometric parameters, partial atomic charges, nonbonded interactions, hydrogen bond interactions, and intrinsic torsional potentials for the naturally occurring amino acids. J. Phys. Chem. 79, 2361–2381 (1975)
26. Brooks, B.R., Brooks III, C.L., Mackerell, A.D., Nilsson, L., Petrella, R.J., Roux, B., Won, Y., Archontis, G., Bartels, C., Boresch, S., Caflisch, A., Caves, L., Cui, Q., Dinner, A.R., Fischer, M.F., Gao, J., Hodoscek, M., Im, W., Kuczera, K., Lazaridis, T., Ma, J., Ovchinnikov, V., Paci, E., Pastor, R.W., Post, C.B., Pu, J.Z., Schaefer, M., Tidor, B., Venable, R.M., Woodcock, H.L., Wu, X., Yang, W., York, D.M., Karplus, M.: Charmm: the biomolecular simulation program. J. Comput. Chem. 30, 1545–1615 (2009)
27. Bertolazzi, P., Guerra, C., Liuzzi, G.: A global optimization algorithm for protein surface alignment. BMC Bioinform. 11, 488–498 (2010)
28. London, N., Movshovitz-Attias, D., Schueler-Furman, O.: The structural basis of peptide–protein binding strategies. Structure 18(2), 188–189 (2010)
29. Connolly, M.: Analytical molecular surface calculation. J. Appl. Crystallogr. 16, 548–558 (1983)
30. Törn, A., Z̆ilinskas, A.: Global Optimization. Springer-Verlag, Berlin (1989)
31. Pintér, J.D.: Global Optimization in Action. Continuous and Lipschitz Optimization: Algorithms, Implementations and Applications. Series on Nonconvex Optimization and Its Applications. Kluwer Academic Publisher, Dordrecht (1996)
32. Horst, R., Pardalos, P.M., Thoai, N.V.: Introduction to Global Optimization. Series on Nonconvex Optimization and Its Applications, 2nd edn. Kluwer Academic Publisher, Dordrecht (2000)
33. Dzemyda, G., S̆altenis, V., Z̆ilinskas, A. (eds.): Stochastic and Global Optimization. Series on Nonconvex Optimization and Its Applications. Kluwer Academic Publisher, Dordrecht (2002)
34. Pardalos, P., Romeijn, H. (eds.): Handbook of Global Optimization, vol. 2. Series on Nonconvex Optimization and Its Applications. Kluwer Academic Publisher, Dordrecht (2002)
35. Liuzzi, G., Lucidi, S., Piccialli, V.: A direct-based approach exploiting local minimizations for the solution of large-scale global optimization problems. Comput. Optim. Appl. 45(2), 353–375 (2010)
36. Liuzzi, G., Lucidi, S., Piccialli, V.: A partition-based global optimization algorithm. J. Glob. Optim. 48, 113–128 (2010)
37. Levy, A., Montalvo, A.: The tunneling algorithm for the global minimization of functions. SIAM J. Sci. Stat. Comput. 6, 15–29 (1985)
38. Ge, R.: A filled function method for finding a global minimizer of a function of several variables. Math. Program. 46, 191–204 (1990)
39. Lucidi, S., Piccialli, V.: New classes of globally convexized filled functions for global optimization. J. Glob. Optim. 24(2), 219–236 (2002)
40. Liuzzi, G., Lucidi, S., Piccialli, V., Sotgiu, A.: A magnetic resonance device designed via global optimization techniques. Math. Program. 101(2), 339–364 (2004)
41. Campana, E., Liuzzi, G., Lucidi, S., Peri, D., Piccialli, V., Pinto, A.: New global optimization methods for ship design problems. Optim. Eng. 10, 533–555 (2009)
42. Jones, D., Perttunen, C., Stuckman, B.: Lipschitzian optimization without the Lipschitz constant. J. Optim. Theory Appl. 79(1), 157–181 (1993)
43. Thomas, P., Dill, K.: An iterative method for extracting energy-like quantities from protein structures. Proc. Natl. Acad. Sci. USA 93, 11628–11633 (1996)
44. Andreani, R., Birgin, E., Martinez, J., Schuverdt, M.: On augmented lagrangian methods with general lower-level constraints. SIAM J. Optim. 18, 1286–1309 (2007)
45. Andreani, R., Birgin, E., Martinez, J., Schuverdt, M.: Augmented lagrangian methods under the constant positive linear dependence constraint qualification. Math. Program. 111, 5–32 (2008)
46. Dolan, E., Moré, J.: Benchmarking optimization software with performance profiles. Math. Program. 91(2), 201–213 (2002)
47. Floudas, C.A., Klepeis, J., Pardalos P.: Global optimization approaches in protein folding and peptide docking. In: Farach-Colton, M., Roberts, F., Vingron, M., Waterman M. (eds.) Mathematical Support for Molecular Biology, DIMACS Series in Discrete Mathematics and Theoretical Computer Science, vol. 47. American Mathematical Society, Providence (1999)
48. Taylor, R., Jewsbury, P., Essex, J.: A review of protein-small molecule docking methods. J. Comput. Aided Mol. Des. 16(3), 151–166 (2002)