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Structure Prediction of Binding Sites of MHC Class II Molecules based on the Crystal of HLA-DRB1 and Global Optimization

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Optimization in Computational Chemistry and Molecular Biology

Part of the book series: Nonconvex Optimization and Its Applications ((NOIA,volume 40))

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Abstract

Class II histocompatibility molecules are cell surface molecules that form complexes with self and non-self peptides and present them to T-cells that activate the immune response. A number of class II histocompatibility molecules have been analyzed by crystallography and include the molecules HLA-DR1 [59], HLA-DR3 [22], and I-E k [21].

A novel theoretical predictive approach is presented that can determine three dimensional structures of the binding sites of the HLA-II molecules based on the crystallographic data of previously characterized HLA class II molecules. The proposed approach uses the ECEPP/3 detailed potential energy model for describing the energetics of the atomic interactions in the space of substituted residues dihedral angles and employs a rigorous deterministic global optimization algorithm αBB [1, 6, 2, 3, 4] to obtain the global minimum energy conformation of the binding site. The binding sites of the HLA—DR3 and I-E k molecules are predicted based on the crystallographic data of HLADR1 [59]. The predicted structures of the binding sites of these molecules exhibit small root mean square differences that range between 1.09–2.03Å (based on all atoms) in comparison to the reported crystallographic data [21, 22]. The energetic driving forces for binding of the predicted structures are also studied using the decomposition-based approach of Androulakis et al. [28] and found to provide very good agreement with the results of the crystallographically obtained binding sites.

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References

  1. C. S. Adjiman, and C. A. Floudas. Rigorous Convex Underestimators for General Twice-Differentiable Problems. Jl. Global Opt., 9, 23–40, 1996.

    Article  MathSciNet  MATH  Google Scholar 

  2. C. S. Adjiman, I. P. Androulakis, C. D. Maranas, and C. A. Floudas. A global optimization method, αbb, for process design. Comp. Chem. Engng., 20, 419–418, 1996.

    Article  Google Scholar 

  3. C. S. Adjiman, S. Dallwig, C. A. Floudas and A. Neumaier. Global Optimization Method, αbb, for Twice-Differentiable Constrained NLPs — I Theoretical Advances Comp. Chem. Engng., 22, 1137–1158, 1998.

    Article  Google Scholar 

  4. C. S. Adjiman, I. P. Androulakis, and C. A. Floudas. Global Optimization Method, αbb, for Twice-Differentiable Constrained NLPs — II Implementation and Computational Results Comp. Chem. Engng., 22, 1159–418, 1998.

    Article  Google Scholar 

  5. N.L. Allinger, Y.H. Yuh, and J.-H. Liu. Molecular mechanics. the mm3 force field for hydrocarbons. J. Am. Chem. Soc., 111:8551, 1989.

    Article  Google Scholar 

  6. I. P. Androulakis, C. D. Maranas, and C. A. Floudas. αbb: A global optimization method for general constrained nonconvex problems. Journal of Global Optimization, 7:337–363, 1995.

    Article  MathSciNet  MATH  Google Scholar 

  7. I.P. Androulakis, C.D. Maranas, and C.A. Floudas. Prediction of oligopeptide conformations via deterministic global optimization. Journal of Global Optimization, 11:1–34, 1997.

    Article  MathSciNet  MATH  Google Scholar 

  8. D.J. Bacon and J. Moult. Docking by least-square fitting of molecular surface patterns. Jl. Mol. Biol., 225:849–858, 1992.

    Article  Google Scholar 

  9. T.L. Blundell, B.L. Sibanda, M.J.E. Sternberg, and J.M. Thornton. Knowledge-based prediction of protein structures and the design of novel molecules. Nature, 326:347, 1987.

    Article  Google Scholar 

  10. B. Brooks, R. Bruccoleri, B. Olafson, D. States, S. Swaminathan, and M. Karplus. Charm: A program for macromolecular energy minimization and dynamics calculation. J. Comp. Chem, 8:132, 1983.

    Google Scholar 

  11. A. Calfisch, P. Niederer, and M. Anliker. Monte carlo docking of oligopeptides to proteins. Proteins, 13:223–230, 1992.

    Article  Google Scholar 

  12. R. Chandrasekaran and G.N. Ramachandran. Studies on the conformation of amino acids, xi. analysis of the observed side group conformations in proteins. Int. J. Protein Res., 2:223, 1970.

    Article  Google Scholar 

  13. S.Y. Chung and S. Subbiah. A structural explanation for the twilight zone of protein sequence homology. Structure, 4:1123, 1996.

    Article  Google Scholar 

  14. M. L. Connolly. Analytical molecular surface calculations. J. Appl. Cryst., 16:548–558, 1983.

    Article  Google Scholar 

  15. M.L. Connolly. Solvent-accessible surfaces of proteins and nucleic acids. Science, 221:709, 1983.

    Article  Google Scholar 

  16. P. Dauber-Osguthorpe, V.A. Roberts, D.J. Osguthorpe, J. Wolff, M. Genest, and A.T. Hagler. Structure and energetics of ligand binding to peptides: Escherichia coli dihydrofolate reductase—trimethoprim, a drug receptor system. Proteins, 4:31, 1988.

    Article  Google Scholar 

  17. R. Diamond. On the comparison of conformations using linear and quadratic transformations. ACTA Cryst., 1, 1976.

    Google Scholar 

  18. R.L. Dunbrack and M. Karplus. Backbone-dependent rotamer library for proteins: Application to side-chain prediction. J. Mol. Biol., 230:543, 1993.

    Article  Google Scholar 

  19. C.A. Floudas. Deterministic global optimization in design, control, and computational chemistry. In L.T. Biegler, T.F. Coleman, A.R. Conn, and F.N. Santosa, editors, Large Scale Optimization with Applications, Part II: Optimal Design and Control, volume 93, pages 129–184. IMA Volumes in Mathematics and its Applications, Springer—Verlag, 1997.

    Chapter  Google Scholar 

  20. C.A. Floudas, P.M. Pardalos, C.S. Adjiman, W.R. Esposito, Z. Gumus, S.T Harding, J.L. Klepeis, C.A. Meyer and C.A. Schweiger. Handbook of Test Problems for Local and Global Optimization. Kluwer Academic Publishers, (1999).

    Book  Google Scholar 

  21. D. H. Fremont, W.A. Hendrickson, P. Marrack, and J. Kappler. Structures of an mhc class ii molecule with covalently bound single peptides. Science, 272:1001–1004, 1996.

    Article  Google Scholar 

  22. P. Ghosh, M. Amaya, E. Mellins, and D.C. Wiley. The structure of an intermediate in class ii mhc maturation: Clip bound to hla-dr3. Nature, 378:457–462, 1995.

    Article  Google Scholar 

  23. D.S. Goodsell and A.J. Olson. Automated docking of substrates to proteins by simulated annealing. Proteins, 8:195–202, 1990.

    Article  Google Scholar 

  24. T.N. Hart and R.J. Read. A multiple-start monte-carlo docking method. Proteins, 13:206–222, 1992.

    Article  Google Scholar 

  25. L. Holm and C. Sander. Fast and simple monte-carlo algorithm for side-chain optimization in proteins: application to model building by homology. Proteins: Sruct. Funct. Genet., 14:213, 1994.

    Article  Google Scholar 

  26. J.K. Hwang and W.F. Liao. Side-chain prediction by neural networks and simulated annealing optimization. Protein Eng., 8:363, 1995.

    Article  Google Scholar 

  27. I.D. Kuntz, J.M. Blaney, S.J. Oatley, R. Langridge, and T.E. Ferrin. A geometric approach to macromolecule-ligand interactions. Jl. Mol. Biol., 161:269–288, 1982.

    Article  Google Scholar 

  28. I.P. Androulakis, N.N. Nayak, M.G. Ierapetritou, D.S. Monos, and C.A. Floudas. A predictive method for the evaluation of peptide binding in pocket 1 of hla-drbl via global minimization of energy interactions. Proteins, 29:87–102, 1997.

    Article  Google Scholar 

  29. F. Jiang and S.H. Kim. Soft docking: Matching of molecular surface cubes. Jl. Mol. Biol., 219:79–102, 1991.

    Article  Google Scholar 

  30. W. Kabsh. A solution for the best rotation to relate two sets of vectors. ACTA Cryst., page 922, 1976.

    Google Scholar 

  31. W. Kabsh. A discussion of the solution for the best rotation to relate two sets of vectors. ACTA Cryst., page 827, 1978.

    Google Scholar 

  32. J.L. Klepeis, I. P. Androulakis, M. G. Ierapetritou, and C. A. Floudas. Predicting solvated peptide conformations via global minimization of energetic atom-to atom interactions. Comp. Chem. Engng., 22, 765–788, 1998.

    Article  Google Scholar 

  33. J.L. Klepeis, and C. A. Floudas. Free Energy Calculations for Peptides via Deterministic Global Optimization. Jl. Chem. Phys., 110:7491–7512, 1999.

    Article  Google Scholar 

  34. P. Koehl and M. Delarue. Application of a self-consistent mean field theory to predict protein side-chains conformation and estimate their conformational entropy. J. Mol. Biol., 239:249, 1994.

    Article  Google Scholar 

  35. B. Lee and F.M. Richards. The interpretation of protein structures: Estimation of static accessibility. Jl. Mol. Biol., 55:379–400, 1971.

    Article  Google Scholar 

  36. M. Levitt. Protein folding by restrained energy minimization and molecular dynamics. J. Mol. Biol., 170:723, 1983.

    Article  Google Scholar 

  37. A. L. Mackay. The generalized inverse and inverse structure. ACTA Cryst., page 212, 1977.

    Google Scholar 

  38. C. D. Maranas, I. P. Androulakis, and C. A. Floudas. A deterministic global optimization approach for the protein folding problem. In DIMA CS Series in Discrete Mathematics and Theoretical Computer Science, volume 23, pages 133–150. American Mathematical Society, 1996.

    Google Scholar 

  39. C. D. Maranas and C. A. Floudas. A global optimization approach for lennard-jones microclusters. J. Chem. Phys., 97(10):7667–7677, 1992.

    Article  Google Scholar 

  40. C. D. Maranas and C. A. Floudas. Global optimization for molecular conformation problems. Annals of Operations Research, 42:85–117, 1993.

    Article  MATH  Google Scholar 

  41. C. D. Maranas and C. A. Floudas. A deterministic global optimization approach for molecular structure determination. J. Chem. Phys., 100(2):1247–1261, 1994.

    Article  MathSciNet  Google Scholar 

  42. C. D. Maranas and C. A. Floudas. Global minimum potential energy conformations of small molecules. Journal of Global Optimization, 4:135–170, 1994.

    Article  MathSciNet  MATH  Google Scholar 

  43. C.D. Maranas, I.P. Androulakis, and C.A. Floudas. A deterministic global optimization approach for the protein folding problem. In P.M. Pardalos, D. Shalloway, and G. Xue, editors, DIMACS Series in Discrete Mathematics and Theoretical Computer Science, volume 23, pages 133–150. American Mathematical Society, 1995.

    Google Scholar 

  44. C.D. Maranas and C.A. Floudas. Global minimum potential energy conformations of small molecules. Journal of Global Optimization, 4:135–170, 1994.

    Article  MathSciNet  MATH  Google Scholar 

  45. A. D. McLachlan. A mathematical procedure for superimposing atomic coordinates of proteins. ACTA Cryst., page 656, 1972.

    Google Scholar 

  46. A. D. McLachlan. Gene duplications in the structural evolution of chymotrypsin. J. Mol. Biol., 128:49, 1979.

    Article  Google Scholar 

  47. F. A. Momany, L. M. Carruthers, R. F. McGuire, and H. A. Scheraga. Intermolecular potential from crystal data. iii. J. Phys. Chem., 78:1595–1620, 1974.

    Article  Google Scholar 

  48. F. A. Momany, L. M. Carruthers, and H. A. Scheraga. Intermolecular potential from crystal data. iv. J. Phys. Chem., 78:1621–1630, 1974.

    Article  Google Scholar 

  49. F.A. Momany, L.M. Carruthers, R.F. McGuire, and H.A. Scheraga. 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, 1975.

    Article  Google Scholar 

  50. D. Monos, A. Soulika, E. Argyris, J. Corga, L. Stern, V. Magafa, P. Cordopatis, I.P. Androulakis, and C.A. Floudas. HLA—Peptide Interactions: Theoretical and Experimental Approaches. Proceedings of the 12th International Histocompatibility Conference, Vol 12, 1996.

    Google Scholar 

  51. G. Némethy, K. D. Gibson, K. A. Palmer, C. N. Yoon, G. Paterlini, A. Zagari, S. Rumsey, and H. A. Scheraga. Energy parameters in polypeptides. 10. J. Phys. Chem., 96:6472–6484, 1992.

    Article  Google Scholar 

  52. G. Némethy, M.S. Pottle, and H.A. Scheraga. Energy parameters in polypeptides. 9. updating of geometrical parameters, nonbinded interaction and hydrogen bond interactions for the naturally occurring amino acids. J. Phys. Chem., 89:1883, 1983.

    Article  Google Scholar 

  53. G. Perrot, B. Cheng, K. D. Gibson, K. A. Palmer, J. Vila, A. Nayeem, B. Maigret, and H. A. Scheraga. Mseed: A program for the rapid analytical determination of accessible surface areas and their derivatives. J. Comp. Chem, 13:1–11, 1992.

    Article  Google Scholar 

  54. S. T. Rao and M.G. Rossmann. Comparison of Super-Secondary Structures in Proteins. J. Mol. Biol., 76:241, 1973.

    Article  Google Scholar 

  55. S.J. Remington and B.W. Matthews. General Method to assess similarity of protein structures, with applications to T4-Bacteriophage Lysozyme Proc. Nat. Acad. Sci. USA, 75:2180, 1978.

    Article  Google Scholar 

  56. H. Schauber, F. Eisenhaber, and P. Argos. Rotamers: to be or not to be? an analysis of amino acid side-chain conformations in globular proteins. J. Mol. Biol., 230:592, 1993.

    Article  Google Scholar 

  57. H.A. Scheraga. ECEPP/3 USER GUIDE. Cornell University Department of Chemistry, January 1993.

    Google Scholar 

  58. H.A. Scheraga. PACK: Programs for Packing Polypeptide Chains, 1996. online documentation.

    Google Scholar 

  59. L. Stern, J. Brown, T. Jardetzky, J. Gorga, R. Urban, L. Strominger, and D. Wiley. Crystal structure of the human class ii mhc protein hla-drl complexes with an influenza virus peptide. Nature, 368:215–221, 1994.

    Article  Google Scholar 

  60. M.J. Sutcliffe, I. Haneef, D. Carney, and T.L. Blundell. Knowledge-based modeling of homologous proteins, part i: three dimensional frameworks derived from the simultaneous superposition of multiple structures. Protein Eng., 1:377, 1987.

    Article  Google Scholar 

  61. P. Tuffery, C. Etchebest, S. Hazout, and R. Lavery. A new approach to the rabid determination of protein side-chain conformations. J. Biomol. Struct. Dynam., 8:1267, 1991.

    Article  Google Scholar 

  62. W. F. van Gunsteren and H. J. C. Berendsen. GROMOS. Groningen Molecular Simulation, Groningen, The Netherlands, 1987.

    Google Scholar 

  63. M. Vasquez. An evaluation of discrete and continuous search techniques for conformational analysis of side-chains in proteins. Biopolymers, 36:53, 1995.

    Article  Google Scholar 

  64. M. Vásquez, G. Némethy, and H. A. Scheraga. Conformational energy calculations on polypeptides and proteins. Chemical Reviews, 94:2183–2239, 1994.

    Article  Google Scholar 

  65. J. Vila, R.L. Williams, M. Vasquez, and H.A. Scheraga. Empirical solvation models can be used to differentiate native from non-native conformations of bovine pancreatic trypsin inhibitor. Proteins, pages 199–218, 1991.

    Google Scholar 

  66. S. Weiner, P. Kollmann, D.A. Case, U.C. Singh, C. Ghio, G. Alagona, S. Profeta, and P. Weiner. A new force field for molecular mechanical simulation of nucleic acids and proteins. J. Am. Chem. Soc., 106:765, 1984.

    Article  Google Scholar 

  67. S. Weiner, P. Kollmann, D. Nguyen, and D. Case. An all atom force field for simulations of proteins and nucleic acids. J. Comp. Chem., 7:230, 1986.

    Article  Google Scholar 

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Ierapetritou, M.G., Androulakis, I.P., Monos, D.S., Floudas, C.A. (2000). Structure Prediction of Binding Sites of MHC Class II Molecules based on the Crystal of HLA-DRB1 and Global Optimization. In: Floudas, C.A., Pardalos, P.M. (eds) Optimization in Computational Chemistry and Molecular Biology. Nonconvex Optimization and Its Applications, vol 40. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-3218-4_10

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  • DOI: https://doi.org/10.1007/978-1-4757-3218-4_10

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