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Design of protein-interaction specificity gives selective bZIP-binding peptides

Abstract

Interaction specificity is a required feature of biological networks and a necessary characteristic of protein or small-molecule reagents and therapeutics. The ability to alter or inhibit protein interactions selectively would advance basic and applied molecular science. Assessing or modelling interaction specificity requires treating multiple competing complexes, which presents computational and experimental challenges. Here we present a computational framework for designing protein-interaction specificity and use it to identify specific peptide partners for human basic-region leucine zipper (bZIP) transcription factors. Protein microarrays were used to characterize designed, synthetic ligands for all but one of 20 bZIP families. The bZIP proteins share strong sequence and structural similarities and thus are challenging targets to bind specifically. Nevertheless, many of the designs, including examples that bind the oncoproteins c-Jun, c-Fos and c-Maf (also called JUN, FOS and MAF, respectively), were selective for their targets over all 19 other families. Collectively, the designs exhibit a wide range of interaction profiles and demonstrate that human bZIPs have only sparsely sampled the possible interaction space accessible to them. Our computational method provides a way to systematically analyse trade-offs between stability and specificity and is suitable for use with many types of structure-scoring functions; thus, it may prove broadly useful as a tool for protein design.

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Figure 1: Designing specific peptides using CLASSY.
Figure 2: Experimental testing of anti-bZIP designs.
Figure 3: Properties of designed peptides compared to human bZIP leucine zippers.

References

  1. Stiffler, M. A. et al. PDZ domain binding selectivity is optimized across the mouse proteome. Science 317, 364–369 (2007)

    ADS  CAS  Article  Google Scholar 

  2. Jones, R. B., Gordus, A., Krall, J. A. & MacBeath, G. A quantitative protein interaction network for the ErbB receptors using protein microarrays. Nature 439, 168–174 (2006)

    ADS  CAS  Article  Google Scholar 

  3. Wiedemann, U. et al. Quantification of PDZ domain specificity, prediction of ligand affinity and rational design of super-binding peptides. J. Mol. Biol. 343, 703–718 (2004)

    CAS  Article  Google Scholar 

  4. Newman, J. R. & Keating, A. E. Comprehensive identification of human bZIP interactions with coiled-coil arrays. Science 300, 2097–2101 (2003)

    ADS  CAS  Article  Google Scholar 

  5. Landgraf, C. et al. Protein interaction networks by proteome peptide scanning. PLoS Biol. 2, E14 (2004)

    Article  Google Scholar 

  6. Skerker, J. M., Prasol, M. S., Perchuk, B. S., Biondi, E. G. & Laub, M. T. Two-component signal transduction pathways regulating growth and cell cycle progression in a bacterium: a system-level analysis. PLoS Biol. 3, e334 (2005)

    Article  Google Scholar 

  7. Havranek, J. J. & Harbury, P. B. Automated design of specificity in molecular recognition. Nature Struct. Biol. 10, 45–52 (2003)

    CAS  Article  Google Scholar 

  8. Kortemme, T. et al. Computational redesign of protein–protein interaction specificity. Nature Struct. Mol. Biol. 11, 371–379 (2004)

    CAS  Article  Google Scholar 

  9. Ali, M. H. et al. Design of a heterospecific, tetrameric, 21-residue miniprotein with mixed α/β structure. Structure 13, 225–234 (2005)

    CAS  Article  Google Scholar 

  10. van der Sloot, A. M. et al. Designed tumor necrosis factor-related apoptosis-inducing ligand variants initiating apoptosis exclusively via the DR5 receptor. Proc. Natl Acad. Sci. USA 103, 8634–8639 (2006)

    ADS  CAS  Article  Google Scholar 

  11. Yin, H. et al. Computational design of peptides that target transmembrane helices. Science 315, 1817–1822 (2007)

    ADS  CAS  Article  Google Scholar 

  12. Reina, J. et al. Computer-aided design of a PDZ domain to recognize new target sequences. Nature Struct. Biol. 9, 621–627 (2002)

    CAS  PubMed  Google Scholar 

  13. Shifman, J. M. & Mayo, S. L. Exploring the origins of binding specificity through the computational redesign of calmodulin. Proc. Natl Acad. Sci. USA 100, 13274–13279 (2003)

    ADS  CAS  Article  Google Scholar 

  14. Fu, X., Apgar, J. R. & Keating, A. E. Modeling backbone flexibility to achieve sequence diversity: the design of novel α-helical ligands for Bcl-xL. J. Mol. Biol. 371, 1099–1117 (2007)

    CAS  Article  Google Scholar 

  15. Bolon, D. N., Grant, R. A., Baker, T. A. & Sauer, R. T. Specificity versus stability in computational protein design. Proc. Natl Acad. Sci. USA 102, 12724–12729 (2005)

    ADS  CAS  Article  Google Scholar 

  16. Kangas, E. & Tidor, B. Electrostatic specificity in molecular ligand design. J. Comput. Phys. 112, 9120–9131 (2000)

    CAS  Google Scholar 

  17. Deutsch, J. M. & Kurosky, T. New algorithm for protein design. Phys. Rev. Lett. 76, 323–326 (1996)

    ADS  CAS  Article  Google Scholar 

  18. Mason, J. M., Schmitz, M. A., Muller, K. M. & Arndt, K. M. Semirational design of Jun-Fos coiled coils with increased affinity: universal implications for leucine zipper prediction and design. Proc. Natl Acad. Sci. USA 103, 8989–8994 (2006)

    ADS  CAS  Article  Google Scholar 

  19. Vinson, C., Acharya, A. & Taparowsky, E. J. Deciphering B-ZIP transcription factor interactions in vitro and in vivo . Biochim. Biophys. Acta 1759, 4–12 (2006)

    CAS  Article  Google Scholar 

  20. Gerdes, M. J. et al. Activator protein-1 activity regulates epithelial tumor cell identity. Cancer Res. 66, 7578–7588 (2006)

    CAS  Article  Google Scholar 

  21. Krylov, D., Olive, M. & Vinson, C. Extending dimerization interfaces: the bZIP basic region can form a coiled coil. EMBO J. 14, 5329–5337 (1995)

    CAS  Article  Google Scholar 

  22. Acharya, A., Rishi, V. & Vinson, C. Stability of 100 homo and heterotypic coiled-coil a–a′ pairs for ten amino acids (A, L, I, V, N, K, S, T, E, and R). Biochemistry 45, 11324–11332 (2006)

    CAS  Article  Google Scholar 

  23. Krylov, D., Barchi, J. & Vinson, C. Inter-helical interactions in the leucine zipper coiled coil dimer: pH and salt dependence of coupling energy between charged amino acids. J. Mol. Biol. 279, 959–972 (1998)

    CAS  Article  Google Scholar 

  24. Lupas, A. N. & Gruber, M. The structure of α-helical coiled coils. Adv. Protein Chem. 70, 37–78 (2005)

    CAS  Article  Google Scholar 

  25. Fong, J. H., Keating, A. E. & Singh, M. Predicting specificity in bZIP coiled-coil protein interactions. Genome Biol. 5, R11 (2004)

    Article  Google Scholar 

  26. Grigoryan, G. & Keating, A. E. Structure-based prediction of bZIP partnering specificity. J. Mol. Biol. 355, 1125–1142 (2006)

    CAS  Article  Google Scholar 

  27. Kingsford, C. L., Chazelle, B. & Singh, M. Solving and analyzing side-chain positioning problems using linear and integer programming. Bioinformatics 21, 1028–1036 (2005)

    CAS  Article  Google Scholar 

  28. Grigoryan, G. et al. Ultra-fast evaluation of protein energies directly from sequence. PLOS Comput. Biol. 2, e63 (2006)

    ADS  Article  Google Scholar 

  29. Zhou, F. et al. Coarse-graining protein energetics in sequence variables. Phys. Rev. Lett. 95, 148103 (2005)

    ADS  Article  Google Scholar 

  30. Mason, J. M., Muller, K. M. & Arndt, K. M. Positive aspects of negative design: simultaneous selection of specificity and interaction stability. Biochemistry 46, 4804–4814 (2007)

    CAS  Article  Google Scholar 

  31. Hadley, E. B., Testa, O. D., Woolfson, D. N. & Gellman, S. H. Preferred side-chain constellations at antiparallel coiled-coil interfaces. Proc. Natl Acad. Sci. USA 105, 530–535 (2008)

    ADS  CAS  Article  Google Scholar 

  32. Apgar, J. R., Hahn, S., Grigoryan, G. & Keating, A. E. Cluster-expansion models flexible-backbone protein energetics. J. Comput. Chem. (in the press)

  33. Sanchez, I. E. et al. Genome-wide prediction of SH2 domain targets using structural information and the FoldX algorithm. PLOS Comput. Biol. 4, e1000052 (2008)

    Article  Google Scholar 

  34. Kaplan, T., Friedman, N. & Margalit, H. Ab initio prediction of transcription factor targets using structural knowledge. PLOS Comput. Biol. 1, e1 (2005)

    ADS  Article  Google Scholar 

  35. Boas, F. E. & Harbury, P. B. Potential energy functions for protein design. Curr. Opin. Struct. Biol. 17, 199–204 (2007)

    CAS  Article  Google Scholar 

  36. Das, R. & Baker, D. Macromolecular modeling with ROSETTA. Annu. Rev. Biochem. 77, 363–382 (2008)

    CAS  Article  Google Scholar 

  37. Zhou, H. & Zhou, Y. Distance-scaled, finite ideal-gas reference state improves structure-derived potentials of mean force for structure selection and stability prediction. Protein Sci. 11, 2714–2726 (2002)

    CAS  Article  Google Scholar 

  38. Hoover, D. M. & Lubkowski, J. DNAWorks: an automated method for designing oligonucleotides for PCR-based gene synthesis. Nucleic Acids Res. 30, e43 (2002)

    Article  Google Scholar 

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Acknowledgements

This work was supported by the NIH award GM67681 and used computer equipment purchased under the NSF award 0216437. We thank the MIT BioMicro center for arraying instrumentation and R. T. Sauer, M. Singh, B. Tidor, M. Laub, T. A. Baker, J. H. Davis, M. S. Kay, J. R. S. Newman, W. F. DeGrado and members of the Keating laboratory, especially O. Ashenberg and T. C. S. Chen, for comments on the manuscript.

Author Contributions G.G., A.W.R. and A.E.K. conceived the project. G.G. developed, implemented and applied the CLASSY formalism and carried out all computational analyses. A.W.R. designed and performed all experiments. All authors analysed data and guided the research plan. G.G. and A.E.K. wrote the paper, in consultation with A.W.R.

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Correspondence to Amy E. Keating.

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This file contains Supplementary Methods, Supplementary Data, a Supplementary Discussion, Supplementary Figures 1-17 with Legends, Supplementary Tables 1-6 and Supplementary References. (PDF 7986 kb)

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Grigoryan, G., Reinke, A. & Keating, A. Design of protein-interaction specificity gives selective bZIP-binding peptides. Nature 458, 859–864 (2009). https://doi.org/10.1038/nature07885

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