Protein visdom; Workbench for

Biology

Your institution must subscribe to JoVE's Biology section to access this content.

Fill out the form below to receive a free trial or learn more about access:

 

Cite this Article

Copy Citation | Download Citations | Reprints and Permissions

Smadbeck, J., Peterson, M. B., Khoury, G. A., Taylor, M. S., Floudas, C. A. Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules. J. Vis. Exp. (77), e50476, doi:10.3791/50476 (2013).

Please note that all translations are automatically generated.

Click here for the english version. For other languages click here.

Abstract

Målet med de novo protein design er å finne de aminosyresekvensene som vil foldes sammen til en ønsket 3-dimensjonal struktur og forbedringen i bestemte egenskaper, for eksempel bindingsaffinitet, agonist eller antagonist oppførsel, eller stabilitet, i forhold til den opprinnelige sekvens. Protein utforming ligger i sentrum av strøm fremskritt drug design og oppdagelse. Ikke bare gir protein utforming spådommer for potensielt nyttige narkotika mål, men også forbedrer vår forståelse av protein folding prosessen og protein-protein interaksjoner. Eksperimentelle metoder som anvist utvikling viser suksess i protein utforming. Men slike metoder begrenset av den begrensede sekvens plass som kan søkes tractably. I kontrast,-bearbeiding design strategier tillate for screening av et mye større sett med sekvenser som dekker et bredt spekter av egenskaper og funksjonalitet. Vi har utviklet en rekke beregningsorientert de novo protein utforming methods i stand til å takle flere viktige områder av protein design. Disse inkluderer utformingen av monomere proteiner for økt stabilitet og komplekser for økt bindingsaffinitet.

Å spre disse metodene for bredere bruker vi presentere Protein WISDOM ( http://www.proteinwisdom.org ), et verktøy som gir automatiserte metoder for en rekke protein design problemer. Strukturelle maler er presentert for å initialisere designprosessen. Den første fasen av design er en optimalisering sekvens utvalg scenen som tar sikte på å bedre stabilitet gjennom minimering av potensielle energien i sekvensen plass. Valgte sekvensene blir deretter kjørt gjennom en fold spesifisitet scene og en bindende affinitet scenen. En rang sortert liste av sekvensene for hvert trinn i prosessen, sammen med relevante designet strukturer, gir brukeren en omfattende kvantitativ vurdering av design. Her gir vi detaljene of hver design metode, så vel som flere kjente eksperimentelle suksesser oppnådd gjennom bruk av metodene.

References

  1. Drexler, K. Molecular engineering: An approach to the development of general capabilities for molecular manipulation. Proc. Natl Acad. Sci. U.S.A. 78, 5275-5278 (1981).
  2. Pabo, C. Molecular technology: Designing proteins and peptides. Nature. 301, 200 (1983).
  3. Floudas, C. A. Research challenges, opportunities and synergism in systems engineering and computational biology. AIChE J. 51, 1872-1884 (2005).
  4. Fung, H. K., Welsh, W. J., Floudas, C. A. Computational de novo peptide and protein design: Rigid templates versus flexible templates. Ind. Eng. Chem. Res. 47, 993-1001 (2008).
  5. Ponder, J., Richards, F. Tertiary templates for proteins. J. Mol. Biol. 193, 775-791 (1987).
  6. Dahiyat, B. I., Mayo, S. L. Protein design automation. Protein Sci. 5, 895-903 (1996).
  7. Dahiyat, B. I., Gordon, D. B., Mayo, S. L. Automated design of the surface positions of protein helices. Protein Sci. 6, 1333-1337 (1997).
  8. Su, A., Mayo, S. L. Coupling backbone flexibility and amino acid sequence selection in protein design. Protein Sci. 6, 1701-1707 (1997).
  9. Desjarlais, J., Handel, T. Side chain and backbone flexibility in protein core design. J. Mol. Biol. 290, 305-318 (1999).
  10. Farinas, E., Regan, L. The de novo design of a rubredoxin-like Fe site. Protein Sci. 7, 1939-1946 (1998).
  11. Harbury, P. B., Plecs, J. J., Tidor, B., Alber, T., Kim, P. S. High-resolution protein design with backbone freedom. Science. 282, 1462-1467 (1998).
  12. Koehl, P., Levitt, M. De novo protein design: I. In search of stability and specificity. J. Mol. Biol. 293, 1161-1181 (1999).
  13. Koehl, P., Levitt, M. De novo protein design. II. Plasticity in sequence space. J. Mol. Biol. 293, 1183-1193 (1999).
  14. Kuhlman, B., Dantae, G., Ireton, G., Verani, G., Stoddard, B., Baker, D. Design of a novel globular protein fold with atomic-level accuracy. Science. 302, 1364-1368 (2003).
  15. Klepeis, J. L., Floudas, C. A. Integrated structural, computational and experimental approach for lead optimization: Design of compstatin variants with improved activity. J. Am. Chem. Soc. 125, 8422-8423 (2003).
  16. Klepeis, J. L., Floudas, C. A., Morikis, D., Tsokos, C. G., Lambris, J. D. Design of peptide analogs with improved activity using a novel de novo protein design approach. Ind. Eng. Chem. Res. 43, 3817-3826 (2004).
  17. Fung, H. K., Floudas, C. A., Taylor, M. S., Zhang, L., Morikis, D. Toward full-sequence de novo protein design with flexible templates for human beta-defensin-2. Biophys. J. 94, 584-599 (2008).
  18. Bellows, M. L., Fung, H. K., Floudas, C. A., López de Victoria, A., Morikis, D. New compstatin variants through two de novo protein design frameworks. Biophys. J. 98, 2337-2346 (2010).
  19. López de Victoria, A., Gorham, R. D. Jr A new generation of potent complement inhibitors of the compstatin family. Chem. Biol. Drug Des. 77, 431-440 (2011).
  20. Tamamis, P., López de Victoria, A. Molecular dynamics in drug design: New generations of compstatin analogs. Chem. Biol. Drug Des. 79, 703-718 (2012).
  21. Bellows-Peterson, M. L., Fung, H. K. De novo peptide design with c3a receptor agonist and antagonist activities: Theoretical predictions and experimental validation. J. Med. Chem. 55, 4159-4168 (2012).
  22. Bellows, M. L., Taylor, M. S. Discovery of entry inhibitors for HIV-1 via a new de novo protein design framework. Biophys. J. 99, 3445-3453 (2010).
  23. Sun, J. -J., Abdeljabbar, D. M., Clarke, N. L., Bellows, M. L., Floudas, C. A., Link, A. J. Reconstitution and engineering of apoptotic protein interactions on the bacterial cell surface. J. Mol. Biol. 394, 297-305 (2009).
  24. Smadbeck, J., Bellows-Peterson, M. L. De novo protein design and validation of histone methyltranferase inhibitors. In Preparation (2013).
  25. Bellows, M. L., Fung, H. K., Floudas, C. A. Molecular Systems Engineering, Process Systems Engineering. Adjiman, C. S., Galindo, A. 6, Wiley-VCH Verlag GmbH & Co. KGaA. 207-232 (2010).
  26. Rajgaria, R., McAllister, S. R., Floudas, C. A. A novel high resolution Cα-Cα distance dependent force field based on a high quality decoy set. Proteins. 65, 726-741 (2006).
  27. Rajgaria, R., McAllister, S. R., Floudas, C. A. Distance dependent centroid to centroid force fields using high resolution decoys. Proteins. 70, 950-970 (2008).
  28. Fung, H. K., Taylor, M. S., Floudas, C. A. Novel formulations for the sequence selection problem in de novo protein design with flexible templates. Optim. Method. Softw. 22, 51-71 (2007).
  29. Fung, H. K., Rao, S., Floudas, C. A., Prokopyev, O., Pardalos, P. M., Rendl, F. Computational comparison studies of quadratic assignment like formulations for the in silico sequence selection problem in de novo protein design. J. Comb. Optim. 10, 41-60 (2005).
  30. CPLEX. Using the CPLEX Callable Library. ILOG, Inc. (1997).
  31. Klepeis, J. L., Floudas, C. A. Free energy calculations for peptides via deterministic global optimization. J. Chem. Phys. 110, 7491-7512 (1999).
  32. Klepeis, J. L., Floudas, C. A., Morikis, D., Lambris, J. D. Predicting peptide structures using NMR data and deterministic global optimization. J. Comput. Chem. 20, 1354-1370 (1999).
  33. Klepeis, J. L., Schafroth, H. D., Westerberg, K. M., Floudas, C. A. Deterministic global optimization and ab initio approaches for the structure prediction of polypeptides, dynamics of protein folding and protein-protein interactions. Adv. Chem. Phys. 120, 265-457 (2002).
  34. Klepeis, J. L., Floudas, C. A. Ab initio prediction of helical segments of polypeptides. J. Comput. Chem. 23, 246-266 (2002).
  35. Klepeis, J. L., Floudas, C. A. Prediction of beta-sheet topology and disulfide bridges in polypeptides. J. Comput. Chem. 24, 191-208 (2003).
  36. Klepeis, J. L., Floudas, C. A. ASTRO-FOLD: A combinatorial and global optimization framework for ab initio prediction of three-dimensional structures of proteins from the amino acid sequence. Biophys. J. 85, 2119-2146 (2003).
  37. Klepeis, J. L., Pieja, M. T., Floudas, C. A. A new class of hybrid global optimization algorithms for peptide structure prediction: Integrated hybrids. Comput. Phys. Commun. 151, 121-140 (2003).
  38. Klepeis, J., Pieja, M., Floudas, C. Hybrid global optimization algorithms for protein structure prediction : Alternating hybrids. Biophys. J. 84, 869-882 (2003).
  39. Klepeis, J. L., Floudas, C. Analysis and prediction of loop segments in protein structures. Comput. Chem. Eng. 29, 423-436 (2005).
  40. Mo¨nnigmann, M., Floudas, C. Protein loop structure prediction with flexible stem geometries. Proteins. 61, 748-762 (2005).
  41. McAllister, S. R., Mickus, B. E., Klepeis, J. L., Floudas, C. A. A novel approach for alpha-helical topology prediction in globular proteins: Generation of interhelical restraints. Proteins. 65, 930-952 (2006).
  42. Floudas, C. A., Fung, H. K., McAllister, S. R., Mönnigmann, M., Rajgaria, R. Advances in protein structure prediction and de novo protein design: A review. Chem. Eng. Sci. 61, 966-988 (2006).
  43. Subramani, A., Wei, Y., Floudas, C. A. ASTRO-FOLD 2.0: An enhanced framework for protein structure prediction. AIChE J. 58, 1619-1637 (2012).
  44. Wei, Y., Thompson, J., Floudas, C. Concord: a consensus method for protein secondary structure prediction via mixed integer linear optimization. P. Roy. Soc. A-Math. Phy. 468, 831-850 (2011).
  45. Subramani, A., Floudas, C. β-sheet topology prediction with high precision and recall for β and mixed α/β proteins. PLoS One. 7, e32461 (2012).
  46. Rajgaria, R., Wei, Y., Floudas, C. A. Contact prediction for beta and alpha-beta proteins using integer linear optimization and its impact on the first principles 3D structure prediction method ASTRO-FOLD. Proteins. 78, 1825-1846 (2010).
  47. Subramani, A., Floudas, C. A. Structure prediction of loops with fixed and flexible stems. J. Phys. Chem. B. 116, 6670-6682 (2012).
  48. Güntert, P., Mumenthaler, C., Wüthrich, K. Torsion angle dynamics for NMR structure calculation with the new program DYANA. J. Mol. Biol. 273, 283-298 (1997).
  49. Güntert, P. Automated NMR structure calculation with CYANA. Methods Mol. Biol. 278, 353-378 (2004).
  50. Ponder, J. TINKER, software tools for molecular design. Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine. Louis, MO. (1998).
  51. Cornell, W. D., Cieplak, P. A 2nd generation forcefield for the simulation of proteins, nucleic acids, and organic molecules. J. Am. Chem. Soc. 117, 5179-5197 (1995).
  52. Lilien, R. H., Stevens, B. W., Anderson, A. C., Donald, B. R. 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. J. Comput. Biol. 12, 740-761 (2005).
  53. Lee, M. R., Baker, D., Kollman, P. A. 2.1 and 1.8 A°Cα RMSD structure predictions on two small proteins, HP-36 and S15. J. Am. Chem. Soc. 123, 1040-1046 (2001).
  54. Rohl, C. A., Baker, D. De novo determination of protein backbone structure from residual dipolar couplings using rosetta. J. Am. Chem. Soc. 124, 2723-2729 (2002).
  55. Rohl, C. A., Strauss, C. E. M., Misura, K. M. S., Baker, D. Protein structure prediction using rosetta. Methods Enzymol. 383, 66-93 (2004).
  56. DiMaggio, P. A., McAllister, S. R., Floudas, C. A., Feng, X. J., Rabinowitz, J. D., Rabitz, H. A. Biclustering via optimal re-ordering of data matrices in systems biology: Rigorous methods and comparative studies. BMC Bioinformatics. 9, (458), (2008).
  57. DiMaggio, P. A., McAllister, S. R., Floudas, C. A., Feng, X. J., Rabinowitz, J. D., Rabitz, H. A. A network flow model for biclustering via optimal re-ordering of data matrices. J Global Optimization. 47, 343-354 (2010).
  58. Daily, M. D., Masica, D., Sivasubramanian, A., Somarouthu, S., Gray, J. J. CAPRI rounds 3-5 reveal promising successes and future challenges for RosettaDock. Proteins. 60, 181-186 (2005).
  59. Gray, J. J., Moughon, S., et al. Protein-protein docking with simultaneous optimization of rigid-body displacement and side-chain conformations. J. Mol. Biol. 331, 281-299 (2003).
  60. Gray, J. J., Moughon, S. E., et al. Protein-protein docking predictions for the CAPRI experiment. Proteins. 52, 118-122 (2003).
  61. Kuhlman, B., Baker, D. Native protein sequences are close to optimal for their structures. Proc. Natl Acad. Sci. U.S.A. 97, 10383-10388 (2000).
  62. Jmol: an open-source java viewer for chemical structures in 3d. Available from: http://www.jmol.org (2013).

Comments

0 Comments


    Post a Question / Comment / Request

    You must be signed in to post a comment. Please or create an account.

    Usage Statistics