Comprehensive analysis of loops at protein-protein interfaces for macrocycle design

Journal name:
Nature Chemical Biology
Year published:
Published online


Inhibiting protein-protein interactions (PPIs) with synthetic molecules remains a frontier of chemical biology. Many PPIs have been successfully targeted by mimicking α-helices at interfaces, but most PPIs are mediated by nonhelical, nonstrand peptide loops. We sought to comprehensively identify and analyze these loop-mediated PPIs by writing and implementing LoopFinder, a customizable program that can identify loop-mediated PPIs within all of the protein-protein complexes in the Protein Data Bank. Comprehensive analysis of the entire set of 25,005 interface loops revealed common structural motifs and unique features that distinguish loop-mediated PPIs from other PPIs. 'Hot loops', named in analogy to protein hot spots, were identified as loops with favorable properties for mimicry using synthetic molecules. The hot loops and their binding partners represent new and promising PPIs for the development of macrocycle and constrained peptide inhibitors.

At a glance


  1. Identification of hot loops.
    Figure 1: Identification of hot loops.

    Hot loops are identified as those loops that satisfy one or more of three criteria: the average ΔΔGresidue over the entire loop is greater than 1 kcal mol−1, the loop has three or more hot-spot residues (ΔΔGresidue ≥ 1 kcal mol−1) and the loop has two or more consecutive hot-spot residues. Representative loops that satisfy each of these criteria are shown within the blue, red and yellow circles (structures with PDB codes 1AXI, 1GK9 and 1L2U, respectively). Some hot loops satisfy two of these criteria, with representative loops from these categories shown in the purple, orange and green boxes (2QNR, 1GK9 and 2FPF, respectively). In addition, 67 hot loops satisfy all three criteria, an example of which is shown in the gray box to the left (PDB code 2AST). All structures, rendered in PyMOL42, show the chain at the interface in blue, the binding partner as a gray surface, the hot loop in green and hot spots in orange (ΔΔGresidue ≥ 1 kcal mol−1) and yellow (ΔΔGresidue ≥ 2 kcal mol−1). Representative hot loops display a wide range of loop structures and modes of interaction with the partner surface.

  2. Visualization of different loop structures observed among the hot loops.
    Figure 2: Visualization of different loop structures observed among the hot loops.

    Representative examples of each type of loop are shown within each circle, including β-turns (PDB code 2ZZC); Schellman loops (PDB code 2OL1); αβ-motifs (PDB code 2DVT); β-bulges (PDB code 3GBT); β-hairpins (PDB code 1T3I); Asx turns and motifs (PDB code 1LIA); S/T-turns, motifs and staples (PDB code 1Y1X); and γ-turns (PDB code 2IX5). The remaining two categories shown above are α-helical regions identified by their backbone torsional angles (PDB code 2BM8) and loops lacking canonical structural motifs (PDB code 3KYH). All structures, rendered in PyMOL42, show the hot loop in green and hot spots in orange (ΔΔGresidue ≥ 1 kcal mol−1) or yellow (ΔΔGresidue ≥ 2 kcal mol−1).

  3. Interface loops use a unique set of amino acids to recognize their binding partners.
    Figure 3: Interface loops use a unique set of amino acids to recognize their binding partners.

    The percent abundance values of each amino acid were normalized relative to propensity to reside on a protein surface20. These normalized values were further broken down into all residues (blue), hot spot residues (purple) and non-hot-spot residues (green).

  4. Established and new targets for inhibitor design.
    Figure 4: Established and new targets for inhibitor design.

    (a) LoopFinder identified a hot loop on the surface of ​hGH that is known to be essential for binding of ​hGHbp (PDB code 1HWG)24. (b) Hot loop within the transcription factor ​Nrf2 that binds its repressor, ​Keap1 (PDB code 2FLU)26. (c) The sC-connector loop of ​TIMP-3 is a hot loop that binds the S2 pocket of ​TACE (PDB code 3CKI)43. (d) The interaction between ​Skp2 and ​Cks1 is essential for the formation of the SCFSkp2 complex and its ubiquitin E3 ligase activity (PDB code 2AST)31. (e) Inhibition of the histone acetyltransferase (HAT) MSL complex is a nw target identified by LoopFinder (PDB code 2Y0N)37. All structures, rendered in PyMOL42, show the hot loop in green and hot spots in orange (ΔΔGresidue ≥ 1 kcal mol−1) or yellow (ΔΔGresidue ≥ 2 kcal mol−1).


  1. Wells, J.A. & McClendon, C.L. Reaching for high-hanging fruit in drug discovery at protein–protein interfaces. Nature 450, 10011009 (2007).
  2. Marsault, E. & Peterson, M.L. Macrocycles are great cycles: applications, opportunities, and challenges of synthetic macrocycles in drug discovery. J. Med. Chem. 54, 19612004 (2011).
  3. Bock, J.E., Gavenonis, J. & Kritzer, J.A. Getting in shape: controlling peptide bioactivity and bioavailability using conformational constraints. ACS Chem. Biol. 8, 488499 (2013).
  4. Vagner, J., Qu, H. & Hruby, V.J. Peptidomimetics, a synthetic tool of drug discovery. Curr. Opin. Chem. Biol. 12, 292296 (2008).
  5. Nowick, J.S. Exploring β-sheet structure and interactions with chemical model systems. Acc. Chem. Res. 41, 13191330 (2008).
  6. Azzarito, V., Long, K., Murphy, N.S. & Wilson, A.J. Inhibition of α-helix–mediated protein-protein interactions using designed molecules. Nat. Chem. 5, 161173 (2013).
  7. Guharoy, M. & Chakrabarti, P. Secondary structure based analysis and classification of biological interfaces: identification of binding motifs in protein–protein interactions. Bioinformatics 23, 19091918 (2007).
  8. Guney, E., Tuncbag, N., Keskin, O. & Gursoy, A. HotSprint: database of computational hot spots in protein interfaces. Nucleic Acids Res. 36, D662D666 (2008).
  9. Koes, D. et al. Enabling large-scale design, synthesis and validation of small molecule protein-protein antagonists. PLoS ONE 7, e32839 (2012).
  10. Jochim, A.L. & Arora, P.S. Systematic analysis of helical protein interfaces reveals targets for synthetic inhibitors. ACS Chem. Biol. 5, 919923 (2010).
  11. Bergey, C.M., Watkins, A.M. & Arora, P.S. HippDB: a database of readily targeted helical protein-protein interactions. Bioinformatics 29, 28062807 (2013).
  12. London, N., Raveh, B., Movshovitz-Attias, D. & Schueler-Furman, O. Can self-inhibitory peptides be derived from the interfaces of globular protein-protein interactions? Proteins 78, 31403149 (2010).
  13. Chrencik, J.E. et al. Structure and thermodynamic characterization of the EphB4/ephrin-B2 antagonist peptide complex reveals the determinants for receptor specificity. Structure 14, 321330 (2006).
  14. Kortemme, T. & Baker, D. A simple physical model for binding energy hot spots in protein–protein complexes. Proc. Natl. Acad. Sci. USA 99, 1411614121 (2002).
  15. Kortemme, T., Kim, D. & Baker, D. Computational alanine scanning of protein-protein interfaces. Sci. STKE 2004, pl2 (2004).
  16. Chaudhury, S., Lyskov, S. & Gray, J.J. PyRosetta: a script-based interface for implementing molecular modeling algorithms using Rosetta. Bioinformatics 26, 689691 (2010).
  17. Shulman-Peleg, A., Shatsky, M., Nussinov, R. & Wolfson, H.J. Spatial chemical conservation of hot spot interactions in protein-protein complexes. BMC Biol. 5, 43 (2007).
  18. Golovin, A. & Henrick, K. MSDmotif: exploring protein sites and motifs. BMC Bioinformatics 9, 312 (2008).
  19. Bogan, A.A. & Thorn, K.S. Anatomy of hot spots in protein interfaces. J. Mol. Biol. 280, 19 (1998).
  20. Janin, J., Miller, S. & Chothia, C. Surface, subunit interfaces and interior of oligomeric proteins. J. Mol. Biol. 204, 155164 (1988).
  21. Tsai, C.-J., Lin, S.L., Wolfson, H.J. & Nussinov, R. Studies of protein-protein interfaces: a statistical analysis of the hydrophobic effect. Protein Sci. 6, 5364 (1997).
  22. Cunningham, B.C. & Wells, J.A. Rational design of receptor-specific variants of human growth hormone. Proc. Natl. Acad. Sci. USA 88, 34073411 (1991).
  23. Cunningham, B.C. & Wells, J.A. Comparison of a structural and a functional epitope. J. Mol. Biol. 234, 554563 (1993).
  24. Sundström, M. et al. Crystal structure of an antagonist mutant of human growth hormone, G120R, in complex with its receptor at 2.9 Å resolution. J. Biol. Chem. 271, 3219732203 (1996).
  25. Hong, D.S. et al. A phase I first-in-human trial of bardoxolone methyl in patients with advanced solid tumors and lymphomas. Clin. Cancer Res. 18, 33963406 (2012).
  26. Lo, S.-C., Li, X., Henzl, M.T., Beamer, L.J. & Hannink, M. Structure of the Keap1: Nrf2 interface provides mechanistic insight into Nrf2 signaling. EMBO J. 25, 36053617 (2006).
  27. Chen, Y., Inoyama, D., Kong, A.-N.T., Beamer, L.J. & Hu, L. Kinetic analyses of Keap1–Nrf2 interaction and determination of the minimal Nrf2 peptide sequence required for Keap1 binding using surface plasmon resonance. Chem. Biol. Drug Des. 78, 10141021 (2011).
  28. Hancock, R., Schaap, M., Pfister, H. & Wells, G. Peptide inhibitors of the Keap1-Nrf2 protein-protein interaction with improved binding and cellular activity. Org. Biomol. Chem. 11, 35533557 (2013).
  29. Hörer, S., Reinert, D., Ostmann, K., Hoevels, Y. & Nar, H. Crystal-contact engineering to obtain a crystal form of the Kelch domain of human Keap1 suitable for ligand-soaking experiments. Acta Crystallogr. Sect. F Struct. Biol. Cryst. Commun. 69, 592596 (2013).
  30. Chen, Q. et al. Targeting the p27 E3 ligase SCFSkp2 results in p27-and Skp2-mediated cell-cycle arrest and activation of autophagy. Blood 111, 46904699 (2008).
  31. Hao, B. et al. Structural basis of the Cks1-dependent recognition of p27Kip1 by the SCFSkp2 ubipuitin ligase. Mol. Cell 20, 919 (2005).
  32. Sitry, D. et al. Three different binding sites of Cks1 are required for p27-ubiquitin ligation. J. Biol. Chem. 277, 4223342240 (2002).
  33. Huang, K.S. & Vassilev, L.T. High-throughput screening for inhibitors of the Cks1-Skp2 interaction. Methods Enzymol. 399, 717728 (2005).
  34. Ungermannova, D. et al. High-throughput screening AlphaScreen assay for identification of small-molecule inhibitors of ubiquitin E3 ligase SCFSkp2-Cks1. J. Biomol. Screen. 18, 910920 (2013).
  35. Wu, L. et al. Specific small molecule inhibitors of Skp2-mediated p27 degradation. Chem. Biol. 19, 15151524 (2012).
  36. Huang, J. et al. Structural insight into the regulation of MOF in the male-specific lethal complex and the non-specific lethal complex. Cell Res. 22, 10781081 (2012).
  37. Kadlec, J. et al. Structural basis for MOF and MSL3 recruitment into the dosage compensation complex by MSL1. Nat. Struct. Mol. Biol. 18, 142149 (2011).
  38. Clackson, T. & Wells, J. A hot spot of binding energy in a hormone-receptor interface. Science 267, 383386 (1995).
  39. DeLano, W.L. Unraveling hot spots in binding interfaces: progress and challenges. Curr. Opin. Struct. Biol. 12, 1420 (2002).
  40. Gohlke, H., Kiel, C. & Case, D.A. Insights into protein–protein binding by binding free energy calculation and free energy decomposition for the Ras–Raf and Ras–RalGDS complexes. J. Mol. Biol. 330, 891913 (2003).
  41. Moreira, I.S., Fernandes, P.A. & Ramos, M.J. Hot spots—a review of the protein–protein interface determinant amino-acid residues. Proteins 68, 803812 (2007).
  42. DeLano, W.L. The PyMOL Molecular Graphic System, Version 0.99 (DeLano Scientific LLC, 2006).
  43. Wisniewska, M. et al. Structural determinants of the ADAM inhibition by TIMP-3: crystal structure of the TACE-N-TIMP-3 complex. J. Mol. Biol. 381, 13071319 (2008).
  44. Fersht, A.R. et al. Hydrogen bonding and biological specificity analysed by protein engineering. Nature 314, 235238 (1985).

Download references

Author information

  1. These authors contributed equally to this work.

    • Jason Gavenonis,
    • Bradley A Sheneman &
    • Timothy R Siegert


  1. Department of Chemistry, Tufts University, Medford, Massachusetts, USA.

    • Jason Gavenonis,
    • Bradley A Sheneman,
    • Timothy R Siegert,
    • Matthew R Eshelman &
    • Joshua A Kritzer


B.A.S. wrote the LoopFinder code. J.G., B.A.S. and J.A.K. performed troubleshooting and debugged and parameterized Loopfinder and Rosetta-based computational alanine scanning. J.G., T.R.S., M.R.E. and J.A.K. analyzed and contextualized data. J.G., T.R.S. and J.A.K. produced figures and tables and wrote the paper.

Competing financial interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to:

Author details

Supplementary information

PDF files

  1. Supplementary Text and Figures (1,744 KB)

    Supplementary Note, Supplementary Results, Supplementary Figures 1–8 and Supplementary Tables 1–6.

Excel files

  1. Supplementary Data Set 1 (315 KB)

    The entire set of hot loops generated by LoopFinder.

  2. Supplementary Data Set 2 (51 KB)

    The subset of 364 hot loops that do not contain two or more consecutive hot spots.

Additional data