Computational protein design: Advances in the design and redesign of biomolecular nanostructures

Current Opinion in Colloid and Interface Science

Volumn 15, Issue 1-2, 2010, Pages 13-17

  Saven, Jeffery G ^a  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

Cofactor; Computational protein design; Membrane protein; Nanoparticle; Nanostructures; Optoelectronic; Porphyrin; Protein library; Protein symmetry; Self assembly; Structural biology

Indexed keywords

COFACTORS; COMPUTATIONAL PROTEIN DESIGN; MEMBRANE PROTEIN; MEMBRANE PROTEINS; PROTEIN LIBRARY; STRUCTURAL BIOLOGY;

BIOLOGY; DESIGN; MEMBRANE STRUCTURES; NANOPARTICLES; PORPHYRINS; SELF ASSEMBLY;

PROTEINS;

MEMBRANE PROTEIN; NANOMATERIAL;

AMINO ACID SEQUENCE; PROTEIN ASSEMBLY; PROTEIN FUNCTION; PROTEIN STRUCTURE; REVIEW;

EID: 75849152422     PISSN: 13590294     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cocis.2009.06.002     Document Type: Review

References (35)

1. Park S., Fu X., Wang W., Yang X., and Saven J.G. Computational protein design and discovery. Annu Rep Prog Chem Sect C 100 (2004) 195-236
2. Floudas C.A., Fung H.K., McAllister S.R., Monnigmann M., and Rajgaria R. Advances in protein structure prediction and de novo protein design: a review. Chem Eng Sci 61 (2006) 966-988
3. Saven J.G. Computational protein design. In: Schwede T., and Peitsch M.C. (Eds). Computational structural biology: methods and applications (2008), World Scientific Publishing Co 401-424
4. Lehmann A., Lanci C.L., Petty II T.J., Kang S.G., and Saven J.G. Protein design: tailoring sequence, structure and folding properties. In: Muñoz V. (Ed). Protein folding, misfolding and aggregation: classical themes and novel approaches (2008), Royal Society of Chemistry Biomolecular Sciences 188-213
5. Park S., Kono H., Wang W., Boder E.T., and Saven J.G. Progress in the development and application of computational methods for probabilistic protein design. Comput Chem Eng 29 (2005) 407-421
6. Yang X., and Saven J.G. Computational methods for protein design and protein sequence variability: biased Monte Carlo and replica exchange. Chem Phys Lett 401 (2005) 205-210
7. Dantas G., Corrent C., Reichow S.L., Havranek J.J., Eletr Z.M., Isern N.G., et al. High-resolution structural and thermodynamic analysis of extreme stabilization of human procarboxypeptidase by computational protein design. J Mol Biol 366 (2007) 1209-1221 The authors show that computational design is capable of essentially redesigning the sequence of an enzyme fragment. Excellent agreement between the computational and experimentally determined structures is obtained
8. Shah P.S., Hom G.K., Ross S.A., Lassila J.K., Crowhurst K.A., and Mayo S.L. Full-sequence computational design and solution structure of a thermostable protein variant. J Mol Biol 372 (2007) 1-6
9. Nanda V., Rosenblatt M.M., Osyczka A., Kono H., Getahun Z., Dutton P.L., et al. De novo design of a redox-active minimal rubredoxin mimic. J Am Chem Soc 127 (2005) 5804-5805
10. Hu X.Z., Wang H.C., Ke H.M., and Kuhlman B. Computer-based redesign of a beta sandwich protein suggests that extensive negative design is not required for de novo beta sheet design. Structure 16 (2008) 1799-1805 Many current methods for computational design of sequence succeed despite the fact that they do not explicitly consider tertiary structures that may compete with the targeted structure. The authors speculate on why such methods are effective and provide an example of the application of computational design to a beta structured protein
11. Grigoryan G., Reinke A.W., and Keating A.E. Design of protein-interaction specificity gives selective bZIP-binding peptides. Nature 458 (2009) 859-864 The authors probe specificity of protein interactions via protein design. An elegant use of computational and micro-array data explores the effectiveness of design and the range of possible interactions between bZIP domain partners
12. Treynor T.P., Vizcarra C.L., Nedelcu D., and Mayo S.L. Computationally designed libraries of fluorescent proteins evaluated by preservation and diversity of function. Proc Natl Acad Sci U S A 104 (2007) 48-53
13. Choi E.J., Mao J., and Mayo S.L. Computational design and biochemical characterization of maize nonspecific lipid transfer protein variants for biosensor applications. Protein Sci 16 (2007) 582-588
14. Maglio O., Nastri F., Calhoun J.R., Lahr S., Wade H., Pavone V., et al. Artificial di-iron proteins: solution characterization of four helix bundles containing two distinct types of inter-helical loops. J Biol Inorg Chem 10 (2005) 539-549
15. Calhoun J.R., Nastri F., Maglio O., Pavone V., Lombardi A., and DeGrado W.F. Artificial diiron proteins: from structure to function. Biopolymers 80 (2005) 264-278
16. Wei P.P., Skulan A.J., Wade H., DeGrado W.F., and Solomon E.I. Spectroscopic and computational studies of the de novo designed protein DF2t: correlation to the biferrous active site of ribonucleotide reductase and factors that affect O-2 reactivity. J Am Chem Soc 127 (2005) 16098-16106
17. Wade H., Stayrook S.E., and DeGrado W.F. The structure of a designed diiron(III) protein: implications for cofactor stabilization and catalysis. Angew Chem Int Ed 45 (2006) 4951-4954
18. Calhoun J.R., Kono H., Lahr S., Wang W., DeGrado W.F., and Saven J.G. Computational design and characterization of a monomeric helical dinuclear metalloprotein. J Mol Biol 334 (2003) 1101-1115
19. Calhoun J.R., Liu W., Spiegel K., Dal Peraro M., Klein M.L., Valentine K.G., et al. Solution NMR structure of a designed metalloprotein and complementary molecular dynamics refinement. Structure 16 (2008) 210-215 This work results in an experimentally determined structure of a di-nuclear metalloprotein that is in excellent agreement with that resulting from the computational design. In addition, the authors show how quantum-mechanics/molecular-mechanics (QM/MM) modeling can be used to obtained high quality models consistent with NMR spectra, particularly for cases where the quality of the molecular force field used in refinement may be poor or incomplete
20. Cochran F.V., Wu S.P., Wang W., Nanda V., Saven J.G., Therien M.J., et al. Computational de novo design and characterization of a four-helix bundle protein that selectively binds a nonbiological cofactor. J Am Chem Soc 127 (2005) 1346-1347
21. McAllister K.A., Zou H.L., Cochran F.V., Bender G.M., Senes A., Fry H.C., Nanda V., Keenan P.A., Lear J.D., Saven J.G., et al. Using alpha-helical coiled-coils to design nanostructured metalloporphyrin arrays. J Am Chem Soc 130 (2008) 11921-11927 The authors expand upon the computational design of helix bundle proteins to obtain extended systems will well positioned redox-active nonbiological chromophores
22. Bender G.M., Lehmann A., Zou H., Cheng H., Fry H.C., Engel D., Therien M.J., Blasie J.K., Roder H., Saven J.G., et al. De novo design of a single-chain diphenylporphyrin metalloprotein. J Am Chem Soc 129 (2007) 10732-10740 By designing helical structures, loop conformation, and sequence, the authors arrive at an asymmetric, single-chain structure and sequence that binds two equivalents of a nonbiological porphyrin cofactor. The resulting protein is more stable than similar oligomeric proteins and exhibits a dramatic increase in folded state stability upon cofactor binding
23. Zou H.L., Therien M.J., and Blasie J.K. Structure and dynamics of an extended conjugated NLO chromophore within an amphiphilic 4-helix bundle peptide by molecular dynamics simulation. J Phys Chem B 112 (2008) 1350-1357
24. Zou H.L., Strzalka J., Xu T., Tronin A., and Blasie J.K. Three-dimensional structure and dynamics of a de novo designed, amphiphilic, metallo-porphyrin-binding protein maquette at soft interfaces by molecular dynamics simulations. J Phys Chem B 111 (2007) 1823-1833
25. Andre I., Strauss C.E.M., Kaplan D.B., Bradley P., and Baker D. Emergence of symmetry in homooligomeric biological assemblies. Proc Natl Acad Sci U S A 105 (2008) 16148-16152
26. Chowdhury P., Wang W., Lavender S., Bunagan M.R., Klemke J.W., Tang J., et al. Fluorescence correlation spectroscopic study of serpin depolymerization by computationally designed peptides. J Mol Biol 369 (2007) 462-473
27. Huang P.S., Love J.J., and Mayo S.L. A de novo designed protein-protein interface. Protein Sci 16 (2007) 2770-2774
28. Swift J., Wehbi W.A., Kelly B.D., Stowell X.F., Saven J.G., and Dmochowski I.J. Design of functional ferritin-like proteins with hydrophobic cavities. J Am Chem Soc 128 (2006) 6611-6619
29. Butts C.A., Swift J., Kang S.G., Di Costanzo L., Christianson D.W., Saven J.G., et al. Directing noble metal ion chemistry within a designed ferritin protein. Biochemistry 47 (2008) 12729-12739 The authors use computational design to arrive at human H ferritin variants that promote the formation of Ag and Au nanoparticles on their interiors, yielding bio-nano hybrid systems. The high-resolution crystallographic structures of the 24 subunit assemblies are in good agreement with the targeted protein structural modifications
30. Slovic A.M., Kono H., Lear J.D., Saven J.G., and DeGrado W.F. Computational design of water-soluble analogues of the potassium channel KcsA. Proc Natl Acad Sci U S A 101 (2004) 1828-1833
31. Ma D.J., Tillman T.S., Tang P., Meirovitch E., Eckenhoff R., Carnini A., et al. NMR studies of a channel protein without membranes: structure and dynamics of water-solubilized KcsA. Proc Natl Acad Sci U S A 105 (2008) 16537-16542 NMR based studies of a computationally designed water soluble variant of a bacterial potassium ion channel is in excellent agreement with the structure of the parent membrane bound form
32. Taylor M.S., Fung H.K., Rajgaria R., Filizola M., Weinstein H., and Floudas C.A. Mutations affecting the oligomerization interface of G-protein-coupled receptors revealed by a novel de novo protein design framework. Biophys J 94 (2008) 2470-2481
33. Yin H., Slusky J.S., Berger B.W., Walters R.S., Vilaire G., Litvinov R.I., et al. Computational design of peptides that target transmembrane helices. Science 315 (2007) 1817-1822 Computational protein design is used to obtain membrane embedded proteins that selectively bind to integral membrane proteins, in this case integrins, and modulate their functions
34. Caputo G.A., Litvinov R.I., Li W., Bennett J.S., DeGrado W.F., and Yin H. Computationally designed peptide inhibitors of protein-protein interactions in membranes. Biochemistry 47 (2008) 8600-8606
35. Signarvic R.S., and DeGrado W.F. Metal-binding dependent disruption of membranes by designed helices. J Am Chem Soc 131 (2009) 3377-3384