Advertisement

Comets (Constrained Optimization of Multistate Energies by Tree Search): A Provable and Efficient Algorithm to Optimize Binding Affinity and Specificity with Respect to Sequence

  • Mark A. Hallen
  • Bruce R. DonaldEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9029)

Abstract

Practical protein design problems require designing sequences with a combination of affinity, stability, and specificity requirements. Multistate protein design algorithms model multiple structural or binding “states" of a protein to address these requirements. comets provides a new level of versatile, efficient, and provable multistate design. It provably returns the minimum with respect to sequence of any desired linear combination of the energies of multiple protein states, subject to constraints on other linear combinations. Thus, it can target nearly any combination of affinity (to one or multiple ligands), specificity, and stability (for multiple states if needed). Empirical calculations on 52 protein design problems showed comets is far more efficient than the previous state of the art for provable multistate design (exhaustive search over sequences). comets can handle a very wide range of protein flexibility and can enumerate a gap-free list of the best constraint-satisfying sequences in order of objective function value.

Keywords

Protein Design Sequence Space Exhaustive Search Conformational Space Unbind State 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Arnold, F.H.: Design by directed evolution. Accounts of Chemical Research 31(3), 125–131 (1998)CrossRefGoogle Scholar
  2. 2.
    Chen, C.-Y., et al.: Computational structure-based redesign of enzyme activity. PNAS 106(10), 3764–3769 (2009)CrossRefGoogle Scholar
  3. 3.
    Davey, J.A., et al.: Multistate approaches in computational protein design. Protein Science 21(9), 1241–1252 (2012)CrossRefGoogle Scholar
  4. 4.
    Desmet, J., et al.: The dead-end elimination theorem and its use in protein side-chain positioning. Nature 356, 539–542 (1992)CrossRefGoogle Scholar
  5. 5.
    Donald, B.R.: Algorithms in Structural Molecular Biology. MIT Press (2011)Google Scholar
  6. 6.
    Frey, K.M., et al.: Predicting resistance mutations using protein design algorithms. PNAS 107(31), 13707–13712 (2010)CrossRefGoogle Scholar
  7. 7.
    Fromer, M.: A Probabilistic Approach to the Design of Structural Selectivity of Proteins. PhD thesis, Hebrew University of Jerusalem (2010)Google Scholar
  8. 8.
    Fromer, M., et al.: SPRINT: Side-chain prediction inference toolbox for multistate protein design. Bioinformatics 26(19), 2466–2467 (2010)CrossRefGoogle Scholar
  9. 9.
    Fromer, M., et al.: Design of multispecific protein sequences using probabilistic graphical modeling. Proteins: Structure, Function, and Bioinformatics 78(3), 530–547 (2010)Google Scholar
  10. 10.
    Gainza, P., et al.: Protein design using continuous rotamers. PLoS Computational Biology 8(1), e1002335 (2012)CrossRefGoogle Scholar
  11. 11.
    Gainza, P., et al.: osprey: Protein design with ensembles, flexibility, and provable algorithms. Methods in Enzymology 523, 87–107 (2013)CrossRefGoogle Scholar
  12. 12.
    Georgiev, I., et al.: Design of epitope-specific probes for sera analysis and antibody isolation. Retrovirology 9(Suppl. 2), P50 (2012)CrossRefMathSciNetGoogle Scholar
  13. 13.
    Georgiev, I., et al.: Dead-end elimination with backbone flexibility. Bioinformatics 23(13), i185–i194 (2007)CrossRefMathSciNetGoogle Scholar
  14. 14.
    Georgiev, I., et al.: Improved pruning algorithms and divide-and-conquer strategies for dead-end elimination, with application to protein design. Bioinformatics 22(14), e174–e183 (2006)CrossRefMathSciNetGoogle Scholar
  15. 15.
    Georgiev, I., et al.: The minimized dead-end elimination criterion and its application to protein redesign in a hybrid scoring and search algorithm for computing partition functions over molecular ensembles. Journal of Computational Chemistry 29(10), 1527–1542 (2008)CrossRefMathSciNetGoogle Scholar
  16. 16.
    Georgiev, I., et al.: osprey (Open Source Protein Redesign for You) user manual (2009). http://www.cs.duke.edu/donaldlab/software.php; Updated, 2015. 96 pages
  17. 17.
    Georgiev, I.S., et al.: Antibodies VRC01 and 10E8 neutralize HIV-1 with high breadth and potency even with Ig-framework regions substantially reverted to germline. The Journal of Immunology 192(3), 1100–1106 (2014)CrossRefGoogle Scholar
  18. 18.
    Gorczynski, M.J., et al.: Allosteric inhibition of the protein-protein interaction between the leukemia-associated proteins Runx1 and CBF\(\beta \). Chemistry and Biology 14, 1186–1197 (2007)CrossRefGoogle Scholar
  19. 19.
    Hallen, M.A., et al.: Dead-end elimination with perturbations (DEEPer): A provable protein design algorithm with continuous sidechain and backbone flexibility. Proteins: Structure, Function and Bioinformatics 81(1), 18–39 (2013)Google Scholar
  20. 20.
  21. 21.
    Hart, P.E., et al.: A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems Science and Cybernetics 4(2), 100–107 (1968)CrossRefGoogle Scholar
  22. 22.
    Karanicolas, J., et al.: Computational design of affinity and specificity at protein-protein interfaces. Current Opinion in Structural Biology 19(4), 458–463 (2009)CrossRefGoogle Scholar
  23. 23.
    Kingsford, C.L., et al.: Solving and analyzing side-chain positioning problems using linear and integer programming. Bioinformatics 21(7), 1028–1039 (2005)CrossRefGoogle Scholar
  24. 24.
    Kuhlman, B., et al.: Native protein sequences are close to optimal for their structures. PNAS 97(19), 10383–10388 (2000)CrossRefGoogle Scholar
  25. 25.
    Leach, A.R., et al.: Exploring the conformational space of protein side chains using dead-end elimination and the A* algorithm. Proteins: Structure, Function, and Bioinformatics 33(2), 227–239 (1998)CrossRefGoogle Scholar
  26. 26.
    Leaver-Fay, A., et al.: A generic program for multistate protein design. PLoS One 6(7), e20937 (2011)CrossRefGoogle Scholar
  27. 27.
    Lee, C., et al.: Accurate prediction of the stability and activity effects of site-directed mutagenesis on a protein core. Nature 352, 448–451 (1991)CrossRefGoogle Scholar
  28. 28.
    Leech, J., et al.: SMD: Visual steering of molecular dynamics for protein design. Computational Science and Engineering 3(4), 38–45 (1996)CrossRefGoogle Scholar
  29. 29.
    Lewis, S.M., et al.: Generation of bispecific IgG antibodies by structure-based design of an orthogonal Fab interface. Nature Biotechnology 32, 191–198 (2014)CrossRefGoogle Scholar
  30. 30.
    Lilien, R.H., et al.: A novel ensemble-based scoring and search algorithm for protein redesign and its application to modify the substrate specificity of the gramicidin synthetase A phenylalanine adenylation enzyme. Journal of Computational Biology 12(6), 740–761 (2005)CrossRefGoogle Scholar
  31. 31.
    Pierce, N.A., et al.: Protein design is NP-hard. Protein Engineering 15(10), 779–782 (2002)CrossRefGoogle Scholar
  32. 32.
    Qi, S., et al.: Crystal structure of the Caenorhabditis elegans apoptosome reveals an octameric assembly of CED-4. Cell 141(3), 446–457 (2010)CrossRefGoogle Scholar
  33. 33.
    Roberts, K.E.: Novel Computational Protein Design Algorithms with Applications to Cystic Fibrosis and HIV. PhD thesis, Duke University (2014)Google Scholar
  34. 34.
    Roberts, K.E., et al.: Computational design of a PDZ domain peptide inhibitor that rescues CFTR activity. PLoS Computational Biology 8(4), e1002477 (2012)CrossRefGoogle Scholar
  35. 35.
    Rudicell, R.S., et al.: Enhanced potency of a broadly neutralizing HIV-1 antibody in vitro improves protection against lentiviral infection in vivo. Journal of Virology (2014); Published online, 2014Google Scholar
  36. 36.
    Sitkoff, D., et al.: Accurate calculation of hydration free energies using macroscopic solvent models. Journal of Physical Chemistry 98, 1978–1988 (1994)CrossRefGoogle Scholar
  37. 37.
    Stevens, B.W., et al.: Redesigning the PheA domain of gramicidin synthetase leads to a new understanding of the enzyme’s mechanism and selectivity. Biochemistry 45(51), 15495–15504 (2006)CrossRefGoogle Scholar
  38. 38.
    Yan, N., et al.: Structure of the CED-4-CED-9 complex provides insights into programmed cell death in Caenorhabditis elegans. Nature 437, 831–837 (2005)CrossRefGoogle Scholar
  39. 39.
    Yanover, C., et al.: Dead-end elimination for multistate protein design. Journal of Computational Chemistry 28(13), 2122–2129 (2007)CrossRefGoogle Scholar
  40. 40.
    Zheng, F., et al.: Most efficient cocaine hydrolase designed by virtual screening of transition states. Journal of the American Chemical Society 130, 12148–12155 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Computer ScienceDuke UniversityDurhamUSA
  2. 2.Department of BiochemistryDuke University Medical CenterDurhamUSA

Personalised recommendations