Biophysical Reviews

, Volume 10, Issue 2, pp 411–420 | Cite as

Creation of artificial protein–protein interactions using α-helices as interfaces

  • Sota Yagi
  • Satoshi Akanuma
  • Akihiko Yamagishi


Designing novel protein–protein interactions (PPIs) with high affinity is a challenging task. Directed evolution, a combination of randomization of the gene for the protein of interest and selection using a display technique, is one of the most powerful tools for producing a protein binder. However, the selected proteins often bind to the target protein at an undesired surface. More problematically, some selected proteins bind to their targets even though they are unfolded. Current state-of-the-art computational design methods have successfully created novel protein binders. These computational methods have optimized the non-covalent interactions at interfaces and thus produced artificial protein complexes. However, to date there are only a limited number of successful examples of computationally designed de novo PPIs. De novo design of coiled-coil proteins has been extensively performed and, therefore, a large amount of knowledge of the sequence–structure relationship of coiled-coil proteins has been accumulated. Taking advantage of this knowledge, de novo design of inter-helical interactions has been used to produce artificial PPIs. Here, we review recent progress in the in silico design and rational design of de novo PPIs and the use of α-helices as interfaces.


Protein–protein interactions Computational design Novel protein binding De novo interactions Interface 



The work was supported by JSPS KAKENHI Grant Number 16K14494 to S.A. and by MEXT-Supported Program for the Strategic Research Foundation at Private Universities (S1512002), 2015–2017 to A.Y.

Compliance with ethical standards

Conflict of interest

Sota Yagi declares that he has no conflicts of interest. Satoshi Akanuma declares that he has no conflicts of interest. Akihiko Yamagishi declares that he has no conflicts of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.


  1. Abrusán G, Marsh JA (2016) Alpha helices are more robust to mutations than beta strands. PLoS Comput Biol 12:e1005242CrossRefPubMedPubMedCentralGoogle Scholar
  2. Arai R, Ueda H, Kitayama A, Kamiya N, Nagamune T (2001) Design of the linkers which effectively separate domains of a bifunctional fusion protein. Protein Eng 14:529–532CrossRefPubMedGoogle Scholar
  3. Arai R, Kobayashi N, Kimura A, Sato T, Matsuo K, Wang AF, Platt JM, Bradley LH, Hecht MH (2012) Domain-swapped dimeric structure of a stable and functional de novo four-helix bundle protein, WA20. J Phys Chem B 116:6789–6797CrossRefPubMedGoogle Scholar
  4. Argos P (1990) An investigation of oligopeptides linking domains in protein tertiary structures and possible candidates for general gene fusion. J Mol Biol 211:943–958CrossRefPubMedGoogle Scholar
  5. Azoitei ML, Correia BE, Ban YE, Carrico C, Kalyuzhniy O, Chen L, Schroeter A, Huang PS, McLellan JS, Kwong PD, Baker D, Strong RK, Scief WR (2011) Computation-guided backbone grafting of a discontinuous motif onto a protein scaffold. Science 334:373–376CrossRefPubMedGoogle Scholar
  6. Azoitei ML, Ban YE, Julien JP, Bryson S, Schroeter A, Kalyuzhniy O, Porter JR, Adachi Y, Baker D, Pai EF, Schoef WR (2012) Computational design of high-affinity epitope scaffolds by backbone grafting of a linear epitope. J Mol Biol 415:175–192CrossRefPubMedGoogle Scholar
  7. Azoitei ML, Ban YA, Kalyuzhny O, Guenaga J, Schroeter A, Porter J, Wyatt R, Schief WR (2014) Computational design of protein antigens that interact with the CDR H3 loop of HIV broadly neutralizing antibody 2F5. Proteins 82:2770–2782CrossRefPubMedPubMedCentralGoogle Scholar
  8. Bale JB, Gonen S, Liu Y, Sheffler W, Ellis D, Thomas C, Cascio D, Yeates TO, Gonen T, King NP, Baker D (2016) Accurate design of megadalton-scale two-component icosahedral protein complexes. Science 353:389–394CrossRefPubMedPubMedCentralGoogle Scholar
  9. Berger S, Procko E, Margineantu D, Lee EF, Shen BW, Zelter A, Silva DA, Chawla K, Herold MJ, Garnier JM, Johnson R, MacCoss MJ, Lessene G, Davis TN, Stayton PS, Stoddard BL, Fairlie WD, Hockenbery DM, Baker D (2016) Computationally designed high specificity inhibitors delineate the roles of BCL2 family proteins in cancer. elife 5:e20352CrossRefPubMedPubMedCentralGoogle Scholar
  10. Bogan AA, Thorn KS (1998) Anatomy of hot spots in protein interfaces. J Mol Biol 280:1–9CrossRefPubMedGoogle Scholar
  11. Boyken SE, Chen Z, Groves B, Langan RA, Oberdorfer G, Ford A, Gilmore JM, Xu C, DiMaio F, Pereira JH, Sankaran B, Seelig G, Zwart PH, Baker D (2016) De novo design of protein homo-oligomers with modular hydrogen-bond network-mediated specificity. Science 352:680–687CrossRefPubMedPubMedCentralGoogle Scholar
  12. Brunette TJ, Parmeggiani F, Huang PS, Bhabha G, Ekiert DC, Tsutakawa SE, Hura GL, Tainer JA, Baker D (2015) Exploring the repeat protein universe through computational protein design. Nature 528:580–584CrossRefPubMedPubMedCentralGoogle Scholar
  13. Burgess NC, Sharp TH, Thomas F, Wood CW, Thomson AR, Zaccai NR, Brady RL, Serpell LC, Woolfson DN (2015) Modular design of self-assembling peptide-based nanotubes. J Am Chem Soc 137:10554–10562CrossRefPubMedGoogle Scholar
  14. Butz M, Kast P, Hilvert D (2014) Affinity maturation of a computationally designed binding protein affords a functional but disordered polypeptide. J Struct Biol 185:168–177CrossRefPubMedGoogle Scholar
  15. Chevalier A, Silva DA, Rocklin GJ, Hicks DR, Vergara R, Murapa P, Bernard SM, Zhang L, Lam KH, Yao G, Bahl CD, Miyashita SI, Goreshnik I, Fuller JT, Koday MT, Jenkins CM, Colvin T, Carter L, Bohn A, Bryan CM, Fernández-Velasco DA, Stewart L, Dong M, Huang X, Jin R, Wilson IA, Fuller DH, Baker D (2017) Massively parallel de novo protein design for targeted therapeutics. Nature 550:74–79. doi:  10.1038/nature23912
  16. Chin JW, Schepartz A (2001) Design and evolution of a miniature Bcl-2 binding protein. Angew Chem Int Ed 40:3806–3809CrossRefGoogle Scholar
  17. Clackson T, Wells JA (1995) A hot spot of binding energy in a hormone–receptor interface. Science 267:383–386CrossRefPubMedGoogle Scholar
  18. Crick FHC (1953) The packing of alpha-helices–simple coiled-coils. Acta Crystallogr 6:689–697CrossRefGoogle Scholar
  19. Der BS, Machius M, Miley MJ, Mills JL, Szyperski T, Kuhlman B (2012) Metal-mediated affinity and orientation specificity in a computationally designed protein homodimer. J Am Chem Soc 134:375–385CrossRefPubMedGoogle Scholar
  20. Doyle L, Hallinan J, Bolduc J, Parmeggiani F, Baker D, Stoddard BL, Bradley P (2015) Rational design of α-helical tandem repeat proteins with closed architectures. Nature 528:585–588CrossRefPubMedPubMedCentralGoogle Scholar
  21. Egelman EH, Xu C, DiMaio F, Magnotti E, Modlin C, Yu X, Wright E, Baker D, Conticello VP (2015) Structural plasticity of helical nanotubes based on coiled-coil assemblies. Structure 23:280–289CrossRefPubMedPubMedCentralGoogle Scholar
  22. Fallas JA, Ueda G, Sheffler W, Nguyen V, McNamara DE, Sankaran B, Pereira JH, Parmeggiani F, Brunette TJ, Cascio D, Yeates TR, Zwart P, Baker D (2016) Computational design of self-assembling cyclic protein homo-oligomers. Nat Chem 9:353–360CrossRefPubMedPubMedCentralGoogle Scholar
  23. Fleishman SJ, Whitehead TA, Ekiert DC, Dreyfus C, Corn JE, Strauch E, Wilson IA, Baker D (2011) Computational design of proteins targeting the conserved stem region of influenza hemagglutinin. Science 332:816–821CrossRefPubMedPubMedCentralGoogle Scholar
  24. Fletcher JM, Harniman RL, Barnes FRH, Boyle AL, Collins A, Mantell J, Sharp TH, Antognozzi M, Booth PJ, Linden N, Miles MJ, Sessions RB, Verkade P, Woolfson DN (2013) Self-assembling cages from coiled-coil peptide modules. Science 340:595–599CrossRefPubMedGoogle Scholar
  25. Gemperli AC, Rutledge SE, Maranda A, Gemperli AS (2005) Paralog-selective ligands for Bcl-2 proteins. J Am Chem Soc 127:1596–1597CrossRefPubMedGoogle Scholar
  26. George RA, Heringa J (2002) An analysis of protein domain linkers: their classification and role in protein folding. Protein Eng 15(11):871–879CrossRefPubMedGoogle Scholar
  27. Gonen S, Dimaio F, Gonen T, Baker D (2015) Design of ordered two-dimensional arrays mediated by noncovalent protein–protein interfaces. Science 348:1365–1368CrossRefPubMedGoogle Scholar
  28. Gradisar H, Bozic S, Doles T, Vengust D, Hafner-Bratkovic I, Mertelj A, Webb B, Sali A, Klavzar S, Jerala R (2013) Design of a single-chain polypeptide tetrahedron assembled from coiled-coil segments. Nat Chem Biol 9:362–366CrossRefPubMedPubMedCentralGoogle Scholar
  29. Grigoryan G, Degrado WF (2011) Probing designability via a generalized model of helical bundle geometry. J Mol Biol 405:1079–1100CrossRefPubMedGoogle Scholar
  30. Guharoy M, Chakrabarti P (2007) Secondary structure based analysis and classification of biological interfaces: identification of binding motifs in protein–protein interactions. Bioinformatics 23:1909–1918CrossRefPubMedGoogle Scholar
  31. Hsia Y, Bale JB, Gonen S, Shi D, Sheffler W, Fong KK, Nattermann U, Xu C, Huang PS, Ravichandran R, Yi S, Davis TN, Gonen T, King NP, Baker D (2016) Design of a hyperstable 60-subunit protein icosahedron. Nature 535:136–139CrossRefPubMedPubMedCentralGoogle Scholar
  32. Huang PS, Love JJ, Mayo SL (2007) A de novo designed protein protein interface. Protein Sci 16:2770–2774CrossRefPubMedPubMedCentralGoogle Scholar
  33. Huang PS, Oberdorfer G, Xu CF, Pei XY, Nannenga BL, Rogers JM, DiMaio F, Gonen T, Luisi B, Baker D (2014) High thermodynamic stability of parametrically designed helical bundles. Science 346:481–485CrossRefPubMedPubMedCentralGoogle Scholar
  34. Hume J, Sun J, Jacquet R, Renfrew PD, Martin JA, Bonneau R, Gilchrist ML, Montclare JK (2014) Engineered coiled-coil protein microfibers. Biomacromolecules 15:3503–3510CrossRefPubMedGoogle Scholar
  35. Jäckel C, Kast P, Hilvert D (2008) Protein design by directed evolution. Annu Rev Biophys 37:153–173CrossRefPubMedGoogle Scholar
  36. Jacobs TM, Williams B, Williams T, Xu X, Eletsky A, Federizon JF, Szyperski T, Kuhlman B (2016) Design of structurally distinct proteins using strategies inspired by evolution. Science 352:687–690CrossRefPubMedPubMedCentralGoogle Scholar
  37. Jha RK, Leaver-Fay A, Yin S, Wu Y, Butterfoss GL, Szyperski T, Dokholyan NV, Kuhlman B (2010) Computational design of a PAK1 binding protein. J Mol Biol 400:257–270CrossRefPubMedPubMedCentralGoogle Scholar
  38. Karanicolas J, Corn J, Chen I, Joachimiak L, Dym O, Peck S, Albeck S, Unger T, Hu W, Liu G, Debecq S, Montelione GT, Spiegel CP, Liu DR, Baker D (2011) A de novo protein binding pair by computational design and directed evolution. Mol Cell 42:250–260CrossRefPubMedPubMedCentralGoogle Scholar
  39. King NP, Sheffler W, Sawaya MR, Vollmar BS, Sumida JP, Andre I, Gonen T, Yeates TO, Baker D (2012) Computational design of self-assembling protein nanomaterials with atomic level accuracy. Science 336:1171–1174CrossRefPubMedPubMedCentralGoogle Scholar
  40. King NP, Bale JB, Sheffler W, McNamara DE, Gonen S, Gonen T, Yeates TO, Baker D (2014) Accurate design of co-assembling multi-component protein nanomaterials. Nature 510:103–108CrossRefPubMedPubMedCentralGoogle Scholar
  41. Kobayashi N, Yanase K, Sato T, Unzai S, Hecht MH, Arai R (2015) Self-assembling Nano-architectures created from a protein Nano-building block using an Intermolecularly folded Dimeric de novo protein. J Am Chem Soc 137:11285–11293CrossRefPubMedGoogle Scholar
  42. Leaver-Fay A, Tyka M, Lewis SM, Lange OF, Thompson J, Jacak R, Kaufman K, Renfrew PD, Smith CA, Sheffler W, Davis IW, Cooper S, Treuille A, Mandell DJ, Richter F, Ban YE, Fleishman SJ, Corn JE, Kim DE, Lyskov S, Berrondo M, Mentzer S, Popović Z, Havranek JJ, Karanicolas J, Das R, Meiler J, Kortemme T, Gray JJ, Kuhlman B, Baker D, Bradley P (2011) ROSETTA3: an object-oriented software suite for the simulation and design of macromolecules. Methods Enzymol 487:545–574CrossRefPubMedPubMedCentralGoogle Scholar
  43. Li G, Huang Z, Zhang C, Dong BJ, Guo RH, Yue HW, Yan LT, Xing XH (2016) Construction of a linker library with widely controllable flexibility for fusion protein design. Appl Microbiol Biotechnol 100:215–225CrossRefPubMedGoogle Scholar
  44. Liu JF, Rost B (2001) Comparing function and structure between entire proteomes. Protein Sci 10:1970–1979CrossRefPubMedPubMedCentralGoogle Scholar
  45. Liu X, Taylor RD, Griffin L, Coker SF, Adams R, Ceska T, Shi J, Lawson ADG, Baker T (2017) Computational design of an epitope-specific Keap1 binding antibody using hotspot residues grafting and CDR loop swapping. Sci Rep 7:41306CrossRefPubMedPubMedCentralGoogle Scholar
  46. McManus JJ, Charbonneau P, Zaccarelli E (2016) The physics of protein self-assembly. Cur Opin Colloid Interface Sci 22:73–79Google Scholar
  47. Mills JH, Sheffler W, Ener ME, Almhjell PJ, Oberdorfer G, Pereira JH, Parmeggiani F, Sankaran B, Zwart PH, Baker D (2016) Computational design of a homotrimeric metalloprotein with a trisbipyridyl core. Proc Natl Acad Sci USA 113:15012–15017CrossRefPubMedPubMedCentralGoogle Scholar
  48. Mou Y, Huang PS, Hsu FC, Huang SJ, Mayo SL (2015a) Computational design and experimental verification of a symmetric protein homodimer. Proc Natl Acad Sci USA 112:10714–10719CrossRefPubMedPubMedCentralGoogle Scholar
  49. Mou Y, Yu JY, Wannier TM, Guo CL, Mayo SL (2015b) Computational design of co-assembling protein-DNA nanowires. Nature 525:230–233CrossRefPubMedGoogle Scholar
  50. Pandya MJ, Spooner GM, Sunde M, Thorpe JR, Rodger A, Woolfson DN (2000) Sticky-end assembly of a designed peptide fiber provides insight into protein fibrillogenesis. Biochemistry 39:8728–8734CrossRefPubMedGoogle Scholar
  51. Patterson DP, Desai AM, Holl MM, Marsh EN (2011) Evaluation of a symmetry-based strategy for assembling protein complexes. RSC Adv 1:1004–1012CrossRefPubMedPubMedCentralGoogle Scholar
  52. Patterson DP, Su M, Franzmann TM, Sciore A, Skiniotis G, Marsh EN (2013) Characterization of a highly flexible self-assembling protein system designed to form nanocages. Protein Sci 23:190–199CrossRefPubMedPubMedCentralGoogle Scholar
  53. Procko E, Hedman R, Hamilton K, Seetharaman J, Fleishman SJ, Su M, Aramini M, Kornhaber G, Hunt JF, Tong L, Montelione GT, Baker D (2013) Computational design of a protein-based enzyme inhibitor. J Mol Biol 425:3563–3575CrossRefPubMedGoogle Scholar
  54. Procko E, Berguig GY, Shen BW, Song Y, Frayo S, Convertine AJ, Margineantu D, Booth G, Correia BE, Cheng Y, Schief WR, Hockenbery DM, Press OW, Stoddard BL, Stayton PS, Baker D (2014) A computationally designed inhibitor of an Epstein–Barr viral Bcl-2 protein induces apoptosis in infected cells. Cell 157:1644–1656CrossRefPubMedPubMedCentralGoogle Scholar
  55. Rackham OJL, Madera M, Armstrong CT, Vincent TL, Woolfson DN, Gough J (2010) The evolution and structure prediction of coiled coils across all genomes. J Mol Biol 403:480–493CrossRefPubMedGoogle Scholar
  56. Rose A, Schraegle SJ, Stahlberg EA, Meier I (2005) Coiled-coil protein composition of 22 proteomes-differences and common themes in subcellular infrastructure and traffic control. BMC Evol Biol 5:66CrossRefPubMedPubMedCentralGoogle Scholar
  57. Salgado EN, Ambroggio XI, Brodin JD, Lewis RA, Kuhlman B, Tezcan FA (2010) Metal templated design of protein interfaces. Proc Natl Acad Sci USA 107:1827–1832Google Scholar
  58. Sciore A, Su M, Koldewey P, Eschweiler JD, Diffley KA, Linhares BM, Ruotolo BT, Bardwell JC, Skiniotis G, Marsh EN (2016) Flexible, symmetry-directed approach to assembling protein cages. Proc Natl Acad Sci USA 113:8681–8686CrossRefPubMedPubMedCentralGoogle Scholar
  59. Stranges PB, Machius M, Miley MJ, Tripathy A, Kuhlman B (2011) Computational design of a symmetric homodimer using beta-strand assembly. Proc Natl Acad Sci USA 108:20562–20567CrossRefPubMedPubMedCentralGoogle Scholar
  60. Strauch EM, Bernard SM, La D, Bohn AJ, Lee PS, Anderson CE, Nieusma T, Holstein CA, Garcia NK, Hooper KA, Ravichandran R, Nelson JW, Sheffler W, Bloom JD, Lee KK, Ward AB, Yager P, Fuller DH, Wilson IA, Baker D (2017) Computational design of trimeric influenza-neutralizing proteins targeting the hemagglutinin receptor binding site. Nat Biotechnol 35:667–671CrossRefPubMedPubMedCentralGoogle Scholar
  61. Thomson AR, Wood CW, Burton AJ, Bartlett GJ, Sessions RB, Brady RL, Woolfson DN (2014) Computational design of water-soluble α-helical barrels. Science 346:485–488Google Scholar
  62. Whitehead TA, Chevalier A, Song Y, Dreyfus C, Fleishman SJ, De Mattos C, Myers CA, Kamisetty H, Blair P, Wilson IA, Baker D (2012) Optimization of affinity, specificity and function of designed influenza inhibitors using deep sequencing. Nat Biotechnol 30:543–548CrossRefPubMedPubMedCentralGoogle Scholar
  63. Wood CW, Woolfson DN (2017) CCBuilder 2.0: powerful and accessible coiled-coil modeling. Protein Sci. doi: 10.1002/pro.3279
  64. Wood CW, Bruning M, Ibarra AA, Bartlett GJ, Thomson AR, Sessions RB, Brady RL, Woolfson DN (2014) CCBuilder: an interactive web-based tool for building, designing and assessing coiled-coil protein assemblies. Bioinformatics 30:3029–3035CrossRefPubMedPubMedCentralGoogle Scholar
  65. Woolfson DN (2017) Coiled-coil design: updated and upgraded. Subcell Biochem 82:35–61CrossRefPubMedGoogle Scholar
  66. Xu C, Liu R, Mehta AK, Guerrero-Ferreira RC, Wright ER, Dunin-Horkawicz S, Morris K, Serpell LC, Zuo X, Wall JS, Conticello VP (2013) Rational design of helical nanotubes from self-assembly of coiled-coil lock washers. J Am Chem Soc 135:15565–15578CrossRefPubMedGoogle Scholar
  67. Yagi S, Akanuma S, Yamagishi M, Uchida T, Yamagishi A (2016) De novo design of protein–protein interactions through modification of inter-molecular helix–helix interface residues. Biochim Biophys Acta 1864:479–487CrossRefPubMedGoogle Scholar
  68. Zhu C, Zhang C, Zhang T, Zhang X, Shen Q, Tang B, Liang H, Lai L (2016) Rational design of TNFa binding proteins bas ed on the de novo designed protein DS119. Prot Sci 25:2066–2075CrossRefGoogle Scholar

Copyright information

© International Union for Pure and Applied Biophysics (IUPAB) and Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Department of Applied Life SciencesTokyo University of Pharmacy and Life SciencesTokyoJapan
  2. 2.Faculty of Human SciencesWaseda UniversitySaitamaJapan

Personalised recommendations