Waiting
Login processing...

Trial ends in Request Full Access Tell Your Colleague About Jove
Click here for the English version

Biology

プロテインWISDOM:ベンチ用 Published: July 25, 2013 doi: 10.3791/50476

Abstract

デノボタンパク質設計の目的は、天然の配列に対して、例えば結合親和性、アゴニストまたはアンタゴニストの動作、又は安定性などの特定の性質、所望の改善を持つ3次元構造に折り畳まになるアミノ酸配列を見つけることである。タンパク質の設計には、現在の進歩の薬物設計と発見の中心に位置しています。だけでなく、タンパク質の設計は潜在的に有用な薬物標的の予測を提供し、それはまた、タンパク質の折り畳みプロセスおよびタンパク質 - タンパク質相互作用の理解を高めるない。このような定方向進化のような実験方法は、タンパク質の設計の成功を示している。しかし、そのような方法はtractably検索できる限られた配列空間によって制限されています。対照的に、計算設計戦略は、特性及び機能の様々な被覆配列の非常に大きなセットのスクリーニングを可能にする。我々は、計算のde novoタンパク質設計メトキシの範囲を開発しているタンパク質設計のいくつかの重要な分野に取り組むことのできるDS。これらには、増大した結合親和性のために増加した安定性との複合体のための単量体のタンパク質の設計が含まれています。

より広範な使用のためのこれらの方法我々現在のタンパク質WISDOM(普及にhttp://www.proteinwisdom.org )、タンパク質設計に関するさまざまな問題のために自動化された方法を提供するツールです。構造テンプレートは、設計プロセスを初期化するために提出されます。設計の第一段階は、シーケンス空間にポテンシャルエネルギーの最小化を介して安定性を改善することを目的とする最適化シーケンスを選択する段階である。選択された配列は、その後、折り目特異ステージと結合親和性の段階を介して実行されています。プロセスの各ステップのための配列のランク順序付きリストは、関連する設計された構造に加えて、設計の包括的な定量的評価をユーザに提供します。ここでは詳細をOを提供fは、それぞれ設計方法、並びに方法の使用によって達成いくつかの注目すべき実験成功。

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).
プロテインWISDOM:ベンチ用<em&gt;インシリコで</em&gt;<em&gt;デノボ</em生体分子の&gt;デザイン
Play Video
PDF DOI

Cite this Article

Smadbeck, J., Peterson, M. B.,More

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).

Less
Copy Citation Download Citation Reprints and Permissions
View Video

Get cutting-edge science videos from JoVE sent straight to your inbox every month.

Waiting X
Simple Hit Counter