Proteus and the Design of Ligand Binding Sites

  • Savvas Polydorides
  • Eleni Michael
  • David Mignon
  • Karen Druart
  • Georgios ArchontisEmail author
  • Thomas SimonsonEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1414)


This chapter describes the organization and use of Proteus, a multitool computational suite for the optimization of protein and ligand conformations and sequences, and the calculation of pKα shifts and relative binding affinities. The software offers the use of several molecular mechanics force fields and solvent models, including two generalized Born variants, and a large range of scoring functions, which can combine protein stability, ligand affinity, and ligand specificity terms, for positive and negative design. We present in detail the steps for structure preparation, system setup, construction of the interaction energy matrix, protein sequence and structure optimizations, pKα calculations, and ligand titration calculations. We discuss illustrative examples, including the chemical/structural optimization of a complex between the MHC class II protein HLA-DQ8 and the vinculin epitope, and the chemical optimization of the compstatin analog Ac-Val4Trp/His9Ala, which regulates the function of protein C3 of the complement system.

Key words

Protein design Ligand design Monte Carlo Implicit solvent Generalized Born model 



GA, SP, and EM acknowledge financial support through a grant offered by the University of Cyprus.


  1. 1.
    Kortemme T, Baker D (2004) Computational design of protein–protein interactions. Curr Opin Chem Biol 8(1):91–97CrossRefPubMedGoogle Scholar
  2. 2.
    Floudas C, Fung H, McAllister SR, Monnigmann M, Rajgaria R (2006) Advances in protein structure prediction and de novo protein design: a review. Chem Eng Sci 61:966–988CrossRefGoogle Scholar
  3. 3.
    Boas EF, Harbury PB (2007) Potential energy functions for protein design. Curr Opin Struct Biol 17(2):199–204CrossRefPubMedGoogle Scholar
  4. 4.
    Lippow SM, Tidor B (2007) Progress in computational protein design. Curr Opin Biotechnol 18:305–311CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Das R, Baker D (2008) Macromolecular modeling with Rosetta. Biochemistry 77(1):363–382CrossRefGoogle Scholar
  6. 6.
    Karanicolas J, Kuhlman B (2009) Computational design of affinity and specificity at protein-protein interfaces. Curr Opin Struct Biol 13:26–34CrossRefGoogle Scholar
  7. 7.
    Damborsky J, Brezovsky J (2009) Computational tools for designing and engineering biocatalysts. Curr Opin Struct Biol 19:458–463CrossRefGoogle Scholar
  8. 8.
    Mandell DJ, Kortemme T (2009) Backbone flexibility in computational protein design. Curr Opin Biotechnol 20:420–428CrossRefPubMedGoogle Scholar
  9. 9.
    Suarez M, Jaramillo A (2009) Challenges in the computational design of proteins. J R Soc Interface 6:477–491CrossRefGoogle Scholar
  10. 10.
    Saven JG (2010) Computational protein design: advances in the design and redesign of biomolecular nanostructures. Curr Opin Colloid Interface Sci 15:13–17CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Pantazes RJ, Greenwood MJ, Maranas CD (2011) Recent advances in computational protein design. Curr Opin Struct Biol 21:467–472CrossRefPubMedGoogle Scholar
  12. 12.
    Der BS, Kuhlman B (2013) Strategies to control the binding mode of de novo designed protein interactions. Curr Opin Struct Biol 23(4):639–646CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Moal IH, Moretti R, Baker D, Fernandez-Recio J (2013) Scoring functions for protein-protein interactions. Curr Opin Struct Biol 23(6)Google Scholar
  14. 14.
    Zanghellini A (2014) de novo computational enzyme design. Curr Opin Biotechnol 29:132–138CrossRefPubMedGoogle Scholar
  15. 15.
    Khoury GA, Smadbeck J, Kieslich CA, Floudas CA (2014) Protein folding and de novo protein design for biotechnological applications. Trends Biotechnol 32(2):9099–9109CrossRefGoogle Scholar
  16. 16.
    Schmidt am Busch M, Lopes A, Mignon D, Simonson T (2008) Computational protein design: software implementation, parameter optimization, and performance of a simple model. J Comput Chem 29:1092–1102CrossRefPubMedGoogle Scholar
  17. 17.
    Polydorides S, Amara N, Simonson T, Archontis G (2011) Computational protein design with a generalized Born solvent model: application to asparaginyl-tRNA synthetase. Proteins 79:3448–3468CrossRefPubMedGoogle Scholar
  18. 18.
    Simonson T, Gaillard T, Mignon D, Schmidt am Busch M, Lopes A, Amara N, Polydorides S, Sedano A, Druart K, Archontis G (2013) Computational protein design: the Proteus software and selected applications. J Comput Chem 34:2472–2484CrossRefPubMedGoogle Scholar
  19. 19.
    Brünger AT (1992) X-plor version 3.1, A System for X-ray crystallography and NMR. Yale University Press, New HavenGoogle Scholar
  20. 20.
    Srinivasan J, Cheatham T, Cieplak P, Kollman P, Case DA (1998) Continuum solvent studies of the stability of DNA, RNA, and phosphoramidate-DNA helices. J Am Chem Soc 120:9401–9409CrossRefGoogle Scholar
  21. 21.
    Simonson T (2013) Protein-ligand recognition: simple models for electrostatic effects. Curr Pharm Des 19:4241–4256CrossRefPubMedGoogle Scholar
  22. 22.
    Brooks B, Bruccoleri R, Olafson B, States D, Swaminathan S, Karplus M (1983) Charmm: a program for macromolecular energy, minimization, and molecular dynamics calculations. J Comput Chem 4:187–217CrossRefGoogle Scholar
  23. 23.
    Cornell W, Cieplak P, Bayly C, Gould I, Merz K, Ferguson D, Spellmeyer D, Fox T, Caldwell J, Kollman P (1995) A second generation force field for the simulation of proteins, nucleic acids, and organic molecules. J Am Chem Soc 117:5179–5197CrossRefGoogle Scholar
  24. 24.
    Pokala N, Handel TM (2005) Energy functions for protein design: adjustment with protein–protein complex affinities, models for the unfolded state, and negative design of solubility and specificity. J Mol Biol 347:203–227CrossRefPubMedGoogle Scholar
  25. 25.
    Dahiyat BI, Mayo SL (1997) De novo protein design: fully automated sequence selection. Science 278:82–87CrossRefPubMedGoogle Scholar
  26. 26.
    Wernisch L, Hery S, Wodak S (2000) Automatic protein design with all atom force fields by exact and heuristic optimization. J Mol Biol 301:713–736CrossRefPubMedGoogle Scholar
  27. 27.
    Pace CN, Grimsley GR, Scholtz JM (2009) Protein ionizable groups: pKa values and their contribution to protein stability and solubility. J Biol Chem 284:13285–13289CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Aleksandrov A, Thompson D, Simonson T (2010) Alchemical free energy simulations for biological complexes: powerful but temperamental. J Mol Recognit 23:117–127PubMedGoogle Scholar
  29. 29.
    Tuffery P, Etchebest C, Hazout S, Lavery R (1991) A new approach to the rapid determination of protein side chain conformations. J Biomol Struct Dyn 8(6)Google Scholar
  30. 30.
    Gaillard T, Simonson T (2014) Pairwise decomposition of an mmgbsa energy function for computational protein design. J Comput Chem 35:1371–1387CrossRefPubMedGoogle Scholar
  31. 31.
    Koehl P, Delarue M (1994) Application of a self-consistent mean field theory to predict protein sidechain conformations and estimate their conformational entropy. J Mol Biol 239:249–275CrossRefPubMedGoogle Scholar
  32. 32.
    Zou BJ, Saven JG (2005) Statistical theory for protein ensembles with designed energy landscapes. J Chem Phys 123:154908CrossRefPubMedGoogle Scholar
  33. 33.
    Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH, Teller E (1953) Equation of state calculations by fast computing machines. J Chem Phys 21:1087–1092CrossRefGoogle Scholar
  34. 34.
    Frenkel D, Smit B (1996) Understanding molecular simulation. Academic, New YorkGoogle Scholar
  35. 35.
    Qu H, Ricklin D, Lambris JD (2009) Recent developments in low molecular weight complement inhibitors. Mol Immunol 47(2):185–195CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Tamamis P, Pierou P, Mytidou C, Floudas CA, Morikis D, Archontis G (2011) Design of a modified mouse protein with ligand binding properties of its human analog by molecular dynamics simulations: the case of c3 inhibition by compstatin. Proteins 79(11):3166–3179CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Tamamis P, Lopez de Victoria A, Gorham RD, Bellows ML, Pierou P, Floudas CA, Morikis D, Archontis G (2012) Molecular dynamics in drug design: new generations of compstatin analogs. Chem Biol Drug Des 79(5):703–718CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Gorham RD, Forest DL, Tamamis P, Lopez de Victoria A, Kraszni M, Kieslich CA, Banna CD, Bellows ML, Larive CK, Floudas CA, Archontis G, Johnson LV, Morikis D (2013) Novel compstatin family peptides inhibit complement activation by drusen-like deposits in human retinal pigmented epithelial cell cultures. Exp Eye Res 116:9096–9108CrossRefGoogle Scholar
  39. 39.
    Gorham RD, Forest DL, Khoury GA, Smadbeck J, Beecher CN, Healy ED, Tamamis P, Archontis G, Larive CK, Floudas CA, Radeke MJ, Johnson LV, Morikis D (2015) New compstatin peptides containing n-terminal extensions and non-natural amino acids exhibit potent complement inhibition and improved solubility characteristics. J Med Chem 58(2):814–826CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Hawkins GD, Cramer C, Truhlar D (1997) Parameterized model for aqueous free energies of solvation using geometry-dependent atomic surface tensions with implicit electrostatics. J Phys Chem B 101:7147–7157CrossRefGoogle Scholar
  41. 41.
    Schaefer M, Karplus M (1996) A comprehensive analytical treatment of continuum electrostatics. J Phys Chem 100:1578–1599CrossRefGoogle Scholar
  42. 42.
    Polydorides S, Simonson T (2013) Monte Carlo simulations of proteins at constant pH with generalized born solvent. J Phys Chem B 34:2742–2756Google Scholar
  43. 43.
    van Heemst J, Jansen DTSL, Polydorides S, Moustakas AK, Bax M, Feitsma AL, Bontrop-Elferink DG, Baarse M, van der Woude D, Wolbink G-J, Rispens T, Koning F, de Vries RRP, Papadopoulos GK, Archontis G, Huizinga TW, Toes RE (2015) Crossreactivity to vinculin and microbes provides a molecular basis for HLA-based protection against rheumatoid arthritis. Nat Commun 6:1–11Google Scholar
  44. 44.
    Lee K, Wucherpfennig K, Wiley D (2001) Structure of a human insulin peptide-HLA-DQ8 complex and susceptibility to type 1 diabetes. Nat Immunol 2(6):501–507CrossRefPubMedGoogle Scholar
  45. 45.
    Yaneva R, Springer S, Zacharias M (2009) Flexibility of the MHC class II peptide binding cleft in the bound, partially filled, and empty states: a molecular dynamics simulation study. Biopolymers 91(1):14–27CrossRefPubMedGoogle Scholar
  46. 46.
    Henderson KN, Tye-Din JA, Reid HH, Chen Z, Borg NA, Beissbarth T, Tatham A, Mannering SI, Purcell AW, Dudek NL, van Heel DA, McCluskey J, Rossjohn J, Anderson RP (2007) A structural and immunological basis for the role of human leukocyte antigen DQ8 in celiac disease. Immunity 27(1)Google Scholar
  47. 47.
    Bellows M, Fung H, Taylor M, Floudas C, Lopez de Victoria A, Morikis D (2010) New compstatin variants through two de novo protein design frameworks. Biophys J 98(10):2337–2346CrossRefPubMedPubMedCentralGoogle Scholar
  48. 48.
    Tamamis P, Morikis D, Floudas CA, Archontis G (2010) Species specificity of the complement inhibitor compstatin investigated by all-atom molecular dynamics simulations. Proteins 78(12):2655–2667PubMedPubMedCentralGoogle Scholar
  49. 49.
    Schmidt am Busch M, Mignon D, Simonson T (2009) Computational protein design as a tool for fold recognition. Proteins 77:139–158CrossRefGoogle Scholar
  50. 50.
    Schmidt am Busch M, Sedano A, Simonson T (2010) Computational protein design: validation and possible relevance as a tool for homology searching and fold recognition. PLoS One 5(5):10410CrossRefGoogle Scholar
  51. 51.
    Mignon D, Simonson T (2015) Sequence exploration in computational protein design with stochastic, heuristic and exact methods (in press)Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Savvas Polydorides
    • 1
  • Eleni Michael
    • 1
  • David Mignon
    • 2
  • Karen Druart
    • 2
  • Georgios Archontis
    • 1
    Email author
  • Thomas Simonson
    • 2
    Email author
  1. 1.Theoretical and Computational Biophysics Group, Department of PhysicsUniversity of CyprusNicosiaCyprus
  2. 2.Department of BiologyLaboratoire de Biochimie (CNRS UMR7654), Ecole PolytechniquePalaiseauFrance

Personalised recommendations