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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
C. S. Adjiman, and C. A. Floudas. Rigorous Convex Underestimators for General Twice-Differentiable Problems. Jl. Global Opt., 9, 23–40, 1996.
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.
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.
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.
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.
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.
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.
D.J. Bacon and J. Moult. Docking by least-square fitting of molecular surface patterns. Jl. Mol. Biol., 225:849–858, 1992.
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.
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.
A. Calfisch, P. Niederer, and M. Anliker. Monte carlo docking of oligopeptides to proteins. Proteins, 13:223–230, 1992.
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.
S.Y. Chung and S. Subbiah. A structural explanation for the twilight zone of protein sequence homology. Structure, 4:1123, 1996.
M. L. Connolly. Analytical molecular surface calculations. J. Appl. Cryst., 16:548–558, 1983.
M.L. Connolly. Solvent-accessible surfaces of proteins and nucleic acids. Science, 221:709, 1983.
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.
R. Diamond. On the comparison of conformations using linear and quadratic transformations. ACTA Cryst., 1, 1976.
R.L. Dunbrack and M. Karplus. Backbone-dependent rotamer library for proteins: Application to side-chain prediction. J. Mol. Biol., 230:543, 1993.
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.
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).
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.
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.
D.S. Goodsell and A.J. Olson. Automated docking of substrates to proteins by simulated annealing. Proteins, 8:195–202, 1990.
T.N. Hart and R.J. Read. A multiple-start monte-carlo docking method. Proteins, 13:206–222, 1992.
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.
J.K. Hwang and W.F. Liao. Side-chain prediction by neural networks and simulated annealing optimization. Protein Eng., 8:363, 1995.
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.
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.
F. Jiang and S.H. Kim. Soft docking: Matching of molecular surface cubes. Jl. Mol. Biol., 219:79–102, 1991.
W. Kabsh. A solution for the best rotation to relate two sets of vectors. ACTA Cryst., page 922, 1976.
W. Kabsh. A discussion of the solution for the best rotation to relate two sets of vectors. ACTA Cryst., page 827, 1978.
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.
J.L. Klepeis, and C. A. Floudas. Free Energy Calculations for Peptides via Deterministic Global Optimization. Jl. Chem. Phys., 110:7491–7512, 1999.
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.
B. Lee and F.M. Richards. The interpretation of protein structures: Estimation of static accessibility. Jl. Mol. Biol., 55:379–400, 1971.
M. Levitt. Protein folding by restrained energy minimization and molecular dynamics. J. Mol. Biol., 170:723, 1983.
A. L. Mackay. The generalized inverse and inverse structure. ACTA Cryst., page 212, 1977.
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.
C. D. Maranas and C. A. Floudas. A global optimization approach for lennard-jones microclusters. J. Chem. Phys., 97(10):7667–7677, 1992.
C. D. Maranas and C. A. Floudas. Global optimization for molecular conformation problems. Annals of Operations Research, 42:85–117, 1993.
C. D. Maranas and C. A. Floudas. A deterministic global optimization approach for molecular structure determination. J. Chem. Phys., 100(2):1247–1261, 1994.
C. D. Maranas and C. A. Floudas. Global minimum potential energy conformations of small molecules. Journal of Global Optimization, 4:135–170, 1994.
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.
C.D. Maranas and C.A. Floudas. Global minimum potential energy conformations of small molecules. Journal of Global Optimization, 4:135–170, 1994.
A. D. McLachlan. A mathematical procedure for superimposing atomic coordinates of proteins. ACTA Cryst., page 656, 1972.
A. D. McLachlan. Gene duplications in the structural evolution of chymotrypsin. J. Mol. Biol., 128:49, 1979.
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.
F. A. Momany, L. M. Carruthers, and H. A. Scheraga. Intermolecular potential from crystal data. iv. J. Phys. Chem., 78:1621–1630, 1974.
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.
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.
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.
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.
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.
S. T. Rao and M.G. Rossmann. Comparison of Super-Secondary Structures in Proteins. J. Mol. Biol., 76:241, 1973.
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.
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.
H.A. Scheraga. ECEPP/3 USER GUIDE. Cornell University Department of Chemistry, January 1993.
H.A. Scheraga. PACK: Programs for Packing Polypeptide Chains, 1996. online documentation.
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.
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.
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.
W. F. van Gunsteren and H. J. C. Berendsen. GROMOS. Groningen Molecular Simulation, Groningen, The Netherlands, 1987.
M. Vasquez. An evaluation of discrete and continuous search techniques for conformational analysis of side-chains in proteins. Biopolymers, 36:53, 1995.
M. Vásquez, G. Némethy, and H. A. Scheraga. Conformational energy calculations on polypeptides and proteins. Chemical Reviews, 94:2183–2239, 1994.
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.
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.
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.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer Science+Business Media Dordrecht
About this chapter
Cite this chapter
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
Download citation
DOI: https://doi.org/10.1007/978-1-4757-3218-4_10
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4419-4826-7
Online ISBN: 978-1-4757-3218-4
eBook Packages: Springer Book Archive