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Computational Protein Design Under a Given Backbone Structure with the ABACUS Statistical Energy Function

Part of the Methods in Molecular Biology book series (MIMB,volume 1529)

Abstract

An important objective of computational protein design is to identify amino acid sequences that stably fold into a given backbone structure. A general approach to this problem is to minimize an energy function in the sequence space. We have previously reported a method to derive statistical energies for fixed-backbone protein design and showed that it led to de novo proteins that fold as expected. Here, we present the usage of the program that implements this method, which we now name as ABACUS (A Backbone-based Amino aCid Usage Survey).

Key words

  • Protein design
  • Statistical energy function
  • Backbone structure
  • Mutation analysis

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References

  1. Kamtekar S, Schiffer JM, Xiong H, Babik JM, Hecht MH (1993) Protein design by binary patterning of polar and nonpolar amino acids. Science 262(5140):1680–1685

    CAS  CrossRef  PubMed  Google Scholar 

  2. Fedorov AN, Dolgikh DA, Chemeris VV, Chernov BK, Finkelstein AV, Schulga AA, Alakhov Yu B, Kirpichnikov MP, Ptitsyn OB (1992) De novo design, synthesis and study of albebetin, a polypeptide with a predetermined three-dimensional structure. Probing the structure at the nanogram level. J Mol Biol 225(4):927–931

    CAS  CrossRef  PubMed  Google Scholar 

  3. Marshall SA, Mayo SL (2001) Achieving stability and conformational specificity in designed proteins via binary patterning. J Mol Biol 305(3):619–631. doi:10.1006/jmbi.2000.4319

    CAS  CrossRef  PubMed  Google Scholar 

  4. Dahiyat BI, Mayo SL (1997) De novo protein design: fully automated sequence selection. Science 278(5335):82–87

    CAS  CrossRef  PubMed  Google Scholar 

  5. Kuhlman B, Dantas G, Ireton GC, Varani G, Stoddard BL, Baker D (2003) Design of a novel globular protein fold with atomic-level accuracy. Science 302(5649):1364–1368. doi:10.1126/science.1089427

    CAS  CrossRef  PubMed  Google Scholar 

  6. Gainza P, Roberts KE, Georgiev I, Lilien RH, Keedy DA, Chen CY, Reza F, Anderson AC, Richardson DC, Richardson JS, Donald BR (2013) OSPREY: protein design with ensembles, flexibility, and provable algorithms. Methods Enzymol 523:87–107. doi:10.1016/B978-0-12-394292-0.00005-9

    CAS  CrossRef  PubMed  PubMed Central  Google Scholar 

  7. Li Z, Yang Y, Zhan J, Dai L, Zhou Y (2013) Energy functions in de novo protein design: current challenges and future prospects. Annu Rev Biophys 42:315–335. doi:10.1146/annurev-biophys-083012-130315

    CAS  CrossRef  PubMed  Google Scholar 

  8. Xiong P, Wang M, Zhou X, Zhang T, Zhang J, Chen Q, Liu H (2014) Protein design with a comprehensive statistical energy function and boosted by experimental selection for foldability. Nat Commun 5:5330. doi:10.1038/ncomms6330

    CAS  CrossRef  PubMed  Google Scholar 

  9. Foit L, Morgan GJ, Kern MJ, Steimer LR, von Hacht AA, Titchmarsh J, Warriner SL, Radford SE, Bardwell JC (2009) Optimizing protein stability in vivo. Mol Cell 36(5):861–871. doi:10.1016/j.molcel.2009.11.022

    CAS  CrossRef  PubMed  PubMed Central  Google Scholar 

  10. 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(1):203–227. doi:10.1016/j.jmb.2004.12.019

    CAS  CrossRef  PubMed  Google Scholar 

  11. Frishman D, Argos P (1995) Knowledge-based protein secondary structure assignment. Proteins 23(4):566–579. doi:10.1002/prot.340230412

    CAS  CrossRef  PubMed  Google Scholar 

  12. Crooks GE, Hon G, Chandonia JM, Brenner SE (2004) WebLogo: a sequence logo generator. Genome Res 14(6):1188–1190. doi:10.1101/gr.849004

    CAS  CrossRef  PubMed  PubMed Central  Google Scholar 

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Acknowledgments

This work has been supported by Chinese Ministry of Science and Technology (2011CBA00803 to Q.C. and 2012AA02A704 to H.L.) and National Natural Science Foundation of China (31200546 to Q.C. and 31370755 to H.L.).

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Correspondence to Quan Chen or Haiyan Liu .

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Xiong, P., Chen, Q., Liu, H. (2017). Computational Protein Design Under a Given Backbone Structure with the ABACUS Statistical Energy Function. In: Samish, I. (eds) Computational Protein Design. Methods in Molecular Biology, vol 1529. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-6637-0_10

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  • DOI: https://doi.org/10.1007/978-1-4939-6637-0_10

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