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Physics-Based Modeling of Side Chain—Side Chain Interactions in the UNRES Force Field

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Part of the book series: Springer Series on Bio- and Neurosystems ((SSBN,volume 8))

Abstract

Work on a development of a new model of side-chain—side-chain interactions for side-chains of amino acids, to be used in the UNRES force-field and in other large-scale simulations, has been described in this chapter. In the presented model a polar/charged side chain consists of two sites of interaction, nonpolar and polar. General expressions for the effective energy of interaction between amino acids are given depending on the kind of interacting pair. Results of tests with the new UNRES force-field parameters have also been shown together with an extension of the force-field for the phosphorylated amino-acids in this chapter. The results of the studies on the influence of particle size on the free-energy profile of hydrophobic interactions, and the temperature dependence of the potential of mean force for side chain—side chain interactions are also presented.

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References

  1. Lee, J., Scheraga, H.A., Rackovsky, S.: Conformational analysis of the 20-residue membrane-bound portion of melittin by conformational space annealing. Biopolymers 46, 103–115 (1998)

    Article  Google Scholar 

  2. Lee, J., Liwo, A., Scheraga, H.A.: Energy-based de novo protein folding by conformational space annealing and an off-lattice united-residue force field: application to the 10-55 fragment of staphylococcal protein A and to apo calbindin D9K. Proc. Natl. Acad. Sci. U.S.A. 96, 2025–2030 (1999)

    Article  Google Scholar 

  3. Pillardy, J., Czaplewski, C., Wedemeyer, W.J., Scheraga, H.A.: Conformation-Family Monte Carlo (CFMC): an efficient computational method for identifying the low-energy states of a macromolecule. Helv. Chim. Acta 83, 2214–2230 (2000)

    Article  Google Scholar 

  4. Levitt, M.: Simplified representation of protein conformations for rapid simulation of protein folding. J. Mol. Biol. 104, 59–107 (1976)

    Article  Google Scholar 

  5. Crippen, G.M., Ponnuswamy, P.K.: Determination of an empirical energy function for protein conformational-analysis by energy embedding. J. Comput. Chem. 8, 972–981 (1987)

    Article  Google Scholar 

  6. Scheraga, H.A.: Calculations of stable conformations of polypeptides, proteins, and protein complexes. Chem. Scr. 29A, 3–13 (1989)

    Google Scholar 

  7. Dill, K.A.: Dominant forces in protein folding. Biochemistry 29, 7133–7155 (1990)

    Article  Google Scholar 

  8. Scheraga, H.A.: Some approaches to the multiple-minima problem structures. Int. J. Quant. Chem. 42, 1529–1536 (1992)

    Article  Google Scholar 

  9. Scheraga, H.A.: Predicting three-dimensional Structures of Oligopeptides. In: Lipkowitz, K., Boyd, D.B. (eds.) Reviews of Computational Chemistry, vol. 3, pp. 73–142. VCH Publ, New York (1992)

    Google Scholar 

  10. Seetharamulu, P., Crippen, G.M.: A potential function for protein folding. J. Math. Chem. 6, 91–110 (1991)

    Article  Google Scholar 

  11. Godzik, A., Koliński, A., Skolnick, J.: De-novo and inverse folding predictions of protein-structure and dynamics. J. Comput. Aided Mol. Des. 7, 397–438 (1993)

    Article  Google Scholar 

  12. Koliński, A., Godzik, A., Skolnick, J.: A general-method for the prediction of the 3-dimensional structure and folding pathway of globular-proteins—application to designed helical proteins. J. Chem. Phys. 98, 7420–7433 (1993)

    Article  Google Scholar 

  13. Sippl, M.J.: Boltzmann principle knowledge-based mean fields and protein-folding—an approach to the computational determination of protein structures. J. Comput. Aided Mol. Des. 7, 473–501 (1993)

    Article  Google Scholar 

  14. Koliński, A., Skolnick, J.: Monte-Carlo simulations of protein-folding. Lattice model and interaction scheme. Proteins 18, 338–352 (1994)

    Article  Google Scholar 

  15. Koliński, A., Skolnick, J.: Monte-Carlo simulations of protein-folding. 2. Application to protein-A, ROP, and crambin. Proteins 18, 353–366 (1994)

    Article  Google Scholar 

  16. Vasquez, M., Nemethy, G., Scheraga, H.A.: Chem. Rev. 94, 2183–2239 (1994)

    Article  Google Scholar 

  17. Skolnick, J., Koliński, A., Ortiz, A.R.: MONSSTER: a method for folding globular proteins with a small number of distance restraints. J. Mol. Biol. 265, 217–241 (1997)

    Article  Google Scholar 

  18. Bystroff, C., Baker, D.: Prediction of local structure in proteins using a library of sequence-structure motifs. J. Mol. Biol. 281, 565–577 (1998)

    Article  Google Scholar 

  19. Moult, J.: Predicting protein three-dimensional structure. Curr. Opin. Biotechnol. 10, 583–588 (1999)

    Article  Google Scholar 

  20. Scheraga, H.A., Lee, J., Pillardy, J., Ye, Y.-J., Liwo, A., Ripoll, D.R.: Surmounting the multiple-minima problem in protein folding. J. Glob. Optim. 15, 235–260 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  21. Samudrala, R., Xia, Y., Huang, E., Levitt, M.: Ab initio protein structure prediction using a combined hierarchical approach. Proteins 37(suppl 3), 194–198 (1999)

    Article  Google Scholar 

  22. Simons, K.T., Bonneau, R., Ruczinski, I., Baker, D.: Ab initio protein structure prediction of CASP III targets using ROSETTA. Proteins 37(suppl 3), 171–176 (1999)

    Article  Google Scholar 

  23. Skolnick, J., Fetrow, J., Ortiz, A.R., Koliński, A.: New methods for the prediction of protein structure and function from sequence. FASEB J. Suppl. S 13, A1584–A1584 (1999)

    Google Scholar 

  24. Lazaridis, T., Karplus, M.: Effective energy functions for protein structure prediction. Curr. Opin. Struct. Biol. 10, 139–145 (2000)

    Article  Google Scholar 

  25. Osguthorpe, D.J.: Ab initio protein folding. Curr. Opin. Struct. Biol. 10, 146–152 (2000)

    Article  Google Scholar 

  26. Murzin, A.G.: Progress in protein structure prediction. Nat. Struct. Biol. 8, 110–112 (2001)

    Article  Google Scholar 

  27. Warme, P.K., Momany, F.A., Rumball, S.V., Tuttle, R.W., Scheraga, H.A.: Computation of structures of homologous proteins—alpha-lactalbumin from lysozyme. Biochemistry 13, 768–782 (1974)

    Article  Google Scholar 

  28. Clark, D.A., Shirazi, J., Rawlings, C.J.: Protein topology prediction through constraint-based search and the evaluation of topological folding rules. Protein Eng. 7, 751–760 (1991)

    Article  Google Scholar 

  29. Rooman, M.J., Wodak, S.J.: Extracting information on folding from the amino-acid-sequence—consensus regions with preferred conformation in homologous proteins. Biochemistry 31, 10239–10249 (1992)

    Article  Google Scholar 

  30. Jones, T.A., Thirup, S.: Using known substructures in protein model-building and crystallography. EMBO J. 5, 819–822 (1986)

    Article  Google Scholar 

  31. Ginalski, K., Elofsson, A., Fischer, D., Rychlewski, D.: 3D-Jury: a simple approach to improve protein structure predictions. Bioinformatics 19, 1015–1018 (2003)

    Article  Google Scholar 

  32. Bujnicki, J.M., Elofsson, A., Fischer, D., Rychlewski, L.: LiveBench-1: continuous benchmarking of protein structure prediction servers. Protein Sci. 10, 352–361 (2001)

    Article  Google Scholar 

  33. Kosiński, J., Cymerman, I.A., Feder, M., Kurowski, M.A., Sasin, J.M., Bujnicki, J.M.: A “Frankenstein’s monster” approach to comparative modeling: merging the finest fragments of fold-recognition models and iterative model refinement aided by 3D structure evaluation. Proteins 53, 369–379 (2003)

    Article  Google Scholar 

  34. Johnson, M.S., Overington, J.P., Blundell, T.L.: Alignment and searching for common protein folds using a Data-Bank of structural templates. J. Mol. Biol. 231, 735–752 (1993)

    Article  Google Scholar 

  35. Fischer, D., Rice, D., Bowie, J.U., Eisenberg, D.: Assigning amino acid sequences to 3-dimensional protein folds. FASEB J 10, 126–136 (1996)

    Article  Google Scholar 

  36. Simons, K.T., Koopernberg, C., Huang, E., Baker, D.: Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions. J. Mol. Biol. 268, 209–225 (1997)

    Article  Google Scholar 

  37. Rohl, C.A., Strauss, C.E., Misura, K.M., Baker, D.: Protein structure prediction using rosetta. Methods Enzymol. 383, 66–93 (2004)

    Article  Google Scholar 

  38. Liwo, A., Ołdziej, S., Pincus, M.R., Wawak, R.J., Rackovsky, S., Scheraga, H.A.: A united-residue force field for off-lattice protein-structure simulations. 1. Functional forms and parameters of long-range side-chain interaction potentials from protein crystal data. J. Comput. Chem. 18, 849–873 (1997)

    Article  Google Scholar 

  39. Anfinsen, C.B.: Principles that govern folding of protein chains. Science 181, 223–230 (1973)

    Article  Google Scholar 

  40. Jansen, K., Schafer, O., Birkmann, E., Post, K., Serban, H., Prusiner, S.B., Riesner, D.: Structural intermediates in the putative pathway from the cellular prion protein to the pathogenic form. Biol. Chem. 382, 683–691 (2001)

    Article  Google Scholar 

  41. Morillas, M., Vanik, D.L., Surewicz, W.K.: On the mechanism of alpha-helix to beta-sheet transition in the recombinant prion protein. Biochemistry 40, 6982–6987 (2001)

    Article  Google Scholar 

  42. Shaw, D.E., Maragakis, P., Lindorff-Larsen, K., Piana, S., Dror, R.O., Eastood, M.P., Bank, J.A., Jumper, J.M., Salmon, J.K., Yibing, S., Wriggers, W.: Atomic-level characterization of the structural dynamics of proteins. Science 330, 341–346 (2010)

    Article  Google Scholar 

  43. Lindorff-Larsen, K., Piana, S., Dror, R.O., Shaw, D.E.: How fast-folding proteins fold. Science 334, 517–520 (2011)

    Article  Google Scholar 

  44. Gay, J.G., Berne, B.J.: Modification of the overlap potential to mimic a linear site-site potential. J. Chem. Phys. 74, 3316–3319 (1981)

    Article  Google Scholar 

  45. Pearlman, D.A., Case, D.A., Caldwell, J.W., Ross, W.S., Cheatham III, T.E., DeBolt, S., Ferguson, D., Seibel, G., Kollman, P.A.: AMBER, a package of computer-programs for applying molecular mechanics, normal-mode analysis, molecular-dynamics and free-energy calculations to simulate the structural and energetic properties of molecules. Comput. Phys. Commun. 91, 1–41 (1995)

    Article  MATH  Google Scholar 

  46. Liwo, A., Lee, J., Ripoll, D.R., Pillardy, J., Scheraga, H.A.: Protein structure prediction by global optimization of a potential energy function. Proc. Natl. Acad. Sci. U.S.A. 96, 5482–5485 (1999)

    Article  Google Scholar 

  47. Lee, J., Liwo, A., Ripoll, D.R., Pillardy, J., Scheraga, H.A.: Calculation of protein conformation by global optimization of a potential energy function. Proteins Struct. Funct. Genet. 37(Suppl. 3), 204–208 (1999)

    Article  Google Scholar 

  48. Lee, J., Liwo, A., Ripoll, D.R., Pillardy, J., Saunders, J.A., Gibson, K.D., Scheraga, H.A.: Hierarchical energy-based approach to protein-structure prediction: blind-test evaluation with CASP3 targets. Int. J. Quantum Chem. 71, 90–117 (2000)

    Article  Google Scholar 

  49. Liwo, A., Pincus, M.R., Wawak, R.J., Rackovsky, S., Scheraga, H.A.: Calculation of protein backbone geometry from beta-carbon coordinates based on peptide-group dipole alignment. Protein Sci. 2, 1697–1714 (1993)

    Article  Google Scholar 

  50. Ołdziej, S., Kozłowska, U., Liwo, A., Scheraga, H.A.: Determination of the potentials of mean force for rotation about C-alpha-C-alpha virtual bonds in polypeptides from the ab initio energy surfaces of terminally blocked glycine, alanine, and proline. J. Phys. Chem. A 107, 8035–8046 (2003)

    Article  Google Scholar 

  51. Liwo, A., Ołdziej, S., Czaplewski, C., Kozłowska, U., Scheraga, H.A.: Parametrization of backbone-electrostatic and multibody contributions to the UNRES force field for protein-structure prediction from ab initio energy surfaces of model systems. J. Phys. Chem. B 108, 9421–9438 (2004)

    Article  Google Scholar 

  52. Czaplewski, C., Liwo, A., Ołdziej, S., Scheraga, H.A.: Improved conformational space annealing method to treat beta-structure with the UNRES force-field and to enhance scalability of parallel implementation. Polymer 45, 677–686 (2004)

    Article  Google Scholar 

  53. Liwo, A., Arłukowicz, P., Czaplewski, C., Ołdziej, S., Pillardy, J., Scheraga, H.A.: A method for optimizing potential-energy functions by a hierarchical design of the potential-energy landscape: application to the UNRES force field. Proc. Natl. Acad. Sci. U.S.A. 99, 1937–1942 (2002)

    Article  Google Scholar 

  54. Liwo, A., Arłukowicz, P., Ołdziej, S., Czaplewski, C., Makowski, M., Scheraga, H.A.: Optimization of the UNRES force field by hierarchical design of the potential-energy landscape. 1. Tests of the approach using simple lattice protein models. J. Phys. Chem. B 108, 16918–16933 (2004)

    Article  Google Scholar 

  55. Ołdziej, S., Liwo, A., Czaplewski, C., Pillardy, J., Scheraga, H.A.: Optimization of the UNRES force field by hierarchical design of the potential-energy landscape. 2. Off-lattice tests of the method with single proteins. J. Phys. Chem. B 108, 16934–16949 (2004)

    Article  Google Scholar 

  56. Makowski, M., Liwo, A., Scheraga, H.A.: Simple physics-based analytical formulas for the potentials of mean force for the interaction of amino-acid side chains in water. 1. Approximate expression for the free energy of hydrophobic association based on a Gaussian-overlap model. J. Phys. Chem. B 111, 2910–2916 (2007). Erratum: J. Phys. Chem. B 114, 1226 (2010)

    Article  Google Scholar 

  57. Makowski, M., Liwo, A., Maksimiak, K., Makowska, J., Scheraga, H.A.: Simple physics-based analytical formulas for the potentials of mean force for the interaction of amino-acid side chains in water. 2. Tests with simple spherical systems. J. Phys. Chem. B 111, 2917–2924 (2007)

    Article  Google Scholar 

  58. Makowski, M., Sobolewski, E., Czaplewski, C., Liwo, A., Ołdziej, S., No, J.H., Scheraga, H.A.: Simple physics-based analytical formulas for the potentials of mean force for the interaction of amino-acid side chains in water. 3. Calculation and parameterization of the potentials of mean force of pairs of identical hydrophobic side chains. J. Phys. Chem. B 111, 2925–2931 (2007)

    Article  Google Scholar 

  59. Makowski, M., Sobolewski, E., Czaplewski, C., Ołdziej, S., Liwo, A., Scheraga, H.A.: Simple physics-based analytical formulas for the potentials of mean force for the interaction of amino-acid side chains in water. IV. Pairs of different hydrophobic side chains. J. Phys. Chem. B 112, 11385–11395 (2008)

    Article  Google Scholar 

  60. Makowski, M., Liwo, A., Sobolewski, E., Scheraga, H.A.: Simple physics-based analytical formulas for the potentials of mean force of the interaction of amino-acid side chains in water. V. Like-charged side chains. J. Phys. Chem. B 115, 6119–6129 (2011)

    Article  Google Scholar 

  61. Makowski, M., Liwo, A., Scheraga, H.A.: Simple physics-based analytical formulas for the potentials of mean force of the interaction of amino-acid side chains in water. VI. Oppositely-charged side chains. J. Phys. Chem. B 115, 6130–6137 (2011)

    Article  Google Scholar 

  62. Makowski, M., Liwo, A., Scheraga, H.A.: Simple physics-based analytical formulas for the potentials of mean force of the interaction of amino-acid side chains in water. VII. Charged—hydrophobic/polar and polar—hydrophobic/polar side chains. J. Phys. Chem. B 121, 379–390 (2017)

    Article  Google Scholar 

  63. Lewandowska, A., Ołdziej, S., Liwo, A., Scheraga, H.A.: Beta-hairpin-forming peptides; models of early stages of protein folding. Biophys. Chem. 151, 1–9 (2010)

    Article  Google Scholar 

  64. Sobolewski, E., Makowski, M., Czaplewski, C., Liwo, A., Ołdziej, S., Scheraga, H.A.: Potential of mean force of hydrophobic association: dependence on solute size. J. Phys. Chem. B 111, 10765–10774 (2007)

    Article  Google Scholar 

  65. Makowski, M., Czaplewski, C., Liwo, A., Scheraga, H.A.: Potential of mean force of large hydrophobic particles: towards nanoscale limit. J. Phys. Chem. B 114, 993–1003 (2010)

    Article  Google Scholar 

  66. Sobolewski, E., Makowski, M., Ołdziej, S., Czaplewski, C., Liwo, A., Scheraga, H.A.: Towards temperature-dependent coarse-grained potentials of side-chain interactions. I. Molecular dynamics study a pair of methane molecules in water at various temperatures. Protein Des. Eng. Sel. (PEDS) 22, 547–552 (2009)

    Article  Google Scholar 

  67. Sobolewski, E., Ołdziej, S., Wiśniewska, M., Liwo, A., Makowski, M.: Toward temperature-dependent coarse-grained potentials of side-chain interactions for protein folding simulations. II. Molecular dynamics study of pairs of different types of interactions in water at various temperatures. J. Phys. Chem. B 116, 6844–6853 (2012)

    Article  Google Scholar 

  68. Liwo, A., Khalili, M., Czaplewski, C., Kalinowski, S., Ołdziej, S., Wachucik, K., Scheraga, H.A.: Modification and optimization of the united-residue (UNRES) potential energy function for canonical simulations. I. Temperature dependence of the effective energy function and tests of the optimization method with single training proteins. J. Phys. Chem. B 111, 260–285 (2007)

    Article  Google Scholar 

  69. Paschek, D.: Temperature dependence of the hydrophobic hydration and interaction of simple solutes: an examination of five popular water models. J. Chem. Phys. 120, 6674–6690 (2004)

    Article  Google Scholar 

  70. Wiśniewska, M., Sobolewski, E., Ołdziej, S., Liwo, A., Scheraga, H.A., Makowski, M.: Theoretical studies of interactions between O-phosphorylated and standard amino-acid side-chain models in water. J. Phys. Chem. B 119, 8526–8534 (2015)

    Article  Google Scholar 

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Acknowledgements

This research was conducted by using the resources of (a) our 818-processor Beowulf cluster at the Baker Laboratory of Chemistry and Chemical Biology, Cornell University, (b) the National Science Foundation Terascale Computing System at the Pittsburgh Supercomputer Center, (c) 45-processor Beowulf cluster at the Faculty of Chemistry, University of Gdańsk, (d) the Informatics Center of the Metropolitan Academic Network (IC MAN) in Gdańsk. This work was supported by grants from the U.S. National Institutes of Health (GM-14312), the U.S. National Science Foundation (MCB05-41633), the Polish Ministry of Science and Education (N N204 152836), and the Polish National Science Centre (UMO-2013/10/E/ST4/00755).

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Makowski, M. (2019). Physics-Based Modeling of Side Chain—Side Chain Interactions in the UNRES Force Field. In: Liwo, A. (eds) Computational Methods to Study the Structure and Dynamics of Biomolecules and Biomolecular Processes. Springer Series on Bio- and Neurosystems, vol 8. Springer, Cham. https://doi.org/10.1007/978-3-319-95843-9_4

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