Computer search algorithms in protein modification and design

Current Opinion in Structural Biology

Volumn 8, Issue 4, 1998, Pages 471-475

  Desjarlais, John R ^a   Clarke, Neil D ^b  

^a PENNSYLVANIA STATE UNIVERSITY   (United States)

^b JOHNS HOPKINS UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; AMINO ACID SEQUENCE; ARTICLE; COMPUTER AIDED DESIGN; PRIORITY JOURNAL; PROTEIN MODIFICATION; PROTEIN STRUCTURE;

EID: 0032144120     PISSN: 0959440X     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0959-440X(98)80125-5     Document Type: Article

References (40)

1. Lee C, Levitt M. Accurate prediction of the stability and activity effects of site-directed mutagenesis on a protein core. Nature. 352:1991;448-451.
2. Hellinga HW, Richards FM. Optimal sequence selection in proteins of known structure by simulated evolution. Proc Natl Acad Sci USA. 91:1994;5803-5807.
3. Holland JH. Adaptation in Natural and Artificial Systems. 1992;The MIT Press, Cambridge, MA.
4. Tuffery P, Etchebest C, Hazout S, Lavery R. A new approach to the rapid determination of protein sidechain conformations. J Biomol Struct Dyn. 8:1991;1267-1289.
5. Desjarlais JR, Handel TM. De novo design of the hydrophobic cores of proteins. Protein Sci. 4:1995;2006-2018.
6. Pedersen JT, Moult J. Genetic algorithms for protein structure prediction. Curr Opin Struct Biol. 6:1996;227-231.
7. Desmet J, De Maeyer M, Hazes B, Lasters I. The dead-end elimination theorem and its use in protein side-chain positioning. Nature. 356:1992;539-542.
8. Dahiyat BI, Mayo SL. Protein design automation. of outstanding interest Protein Sci. 5:1996;895-903 This paper demonstrates the potential of dead end elimination in designing complete protein sequences that are consistent with a fixed backbone template. The structure of the designed protein is very similar to the zinc-finger template that was used for the sequence optimization and it folds with a stability that is considered to be substantial given the small size of the domain. There are, however, interesting differences between the backbone and sidechain structures determined for the real protein and the designed structure.
9. Dahiyat BI, Mayo SL. De novo protein design: fully automated sequence selection. Science. 278:1997;82-87.
10. Hellinga HW, Richards FM. Construction of new ligand binding sites in proteins of known structure. I. Computer-aided modeling of sites with pre-defined geometry. J Mol Biol. 222:1991;763-785.
11. Clarke ND, Yuan SM. Metal search: a computer program that helps design tetrahedral metal-binding sites. Proteins. 23:1995;256-263.
12. Klemba M, Regan L. Characterization of metal binding by a designed protein: single ligand substitutions at a tetrahedral Cys2His2 site. Biochemistry. 34:1995;10094-10100.
13. Klemba M, Gardner KH, Marino S, Clarke ND, Regan L. Novel metal-binding proteins by design. Nat Struct Biol. 2:1995;368-373.
14. Hellinga HW, Caradonna JP, Richards FM. Construction of new ligand binding sites in proteins of known structure. II. Grafting of a buried transition metal binding site into Escherichia coli thioredoxin. J Mol Biol. 222:1991;787-803.
15. Regan L, Clarke ND. A tetrahedral zinc(II)-binding site introduced into a designed protein. Biochemistry. 29:1990;10878-10883.
16. Hellinga HW. Metalloprotein design. Curr Opin Biotechnol. 7:1996;437-441.
17. Regan L. Protein design: novel metal-binding sites. Trends Biochem Sci. 20:1995;280-285.
18. Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH. Equation of state calculations by fast computing machines. J Chem Phys. 21:1953;1087-1092.
19. Goldstein RF. Efficient rotamer elimination applied to protein sidechains and related spin glasses. Biophys J. 66:1994;1335-1340.
20. Lasters I, De Maeyer M, Desmet J. Enhanced dead-end elimination in the search for the global minimum energy conformation of a collection of protein sidechains. Protein Eng. 8:1995;815-822.
21. Keller DA, Shibata M, Marcus E, Ornstein RL, Rein R. Finding the global minimum: a fuzzy end elimination implementation. Protein Eng. 8:1995;893-904.
22. De Maeyer M, Desmet J, Lasters I. All in one: a highly detailed rotamer library improves both accuracy and speed in the modelling of sidechains by dead-end elimination. Fold Des. 2:1997;53-66.
23. Lee C. Testing homology modeling on mutant proteins: predicting structural and thermodynamic effects in the Ala98→Val mutants of T4 lysozyme. Fold Des. 1:1996;1-12.
24. Hurley JH, Baase WA, Matthews BW. Design and structural analysis of alternative hydrophobic core packing arrangements in bacteriophage T4 lysozyme. J Mol Biol. 224:1992;1143-1159.
25. Dahiyat BI, Mayo SL. Probing the role of packing specificity in protein design. Proc Natl Acad Sci USA. 94:1997;10172-10177.
26. Eisenberg D, McLachlan AD. Solvation energy in protein folding and binding. Nature. 319:1986;199-203.
27. Street AG, Mayo SL. Pairwise calculation of protein solvent-accessible surface area. of special interest Fold Des. 3:1998;253-258 The authors present the parametrization of scaling factors for pairwise calculations of solvent-accessible surface areas, a requirement for using dead end elimination. Excellent correlations between the resulting approximations and the true surface areas are demonstrated.
28. Pinto AL, Hellinga HW, Caradonna JP. Construction of a catalytically active iron superoxide dismutase by rational protein design. Proc Natl Acad Sci USA. 94:1997;5562-5567.
29. Coldren CD, Hellinga HW, Caradonna JP. The rational design and construction of a cuboidal iron-sulfur protein. Proc Natl Acad Sci USA. 94:1997;6635-6640.
30. Dalal S, Balasubramanian S, Regan L. Protein alchemy: changing beta-sheet into alpha-helix. Nat Struct Biol. 4:1997;548-552.
31. Regan L, DeGrado WF. Characterization of a helical protein designed from first principles. Science. 241:1988;976-978.
32. Handel TM, Williams SA, DeGrado WF. Metal ion-dependent modulation of the dynamics of a designed protein. Science. 261:1993;879-885.
33. Koehl P, Delarue M. Application of a self-consistent mean field theory to predict protein side-chains conformation and estimate their conformational entropy. J Mol Biol. 239:1994;249-275.
34. Lee C. Predicting protein mutant energetics by self-consistent ensemble optimization. J Mol Biol. 236:1994;918-939.
35. Koehl P, Delarue M. Mean-field minimization methods for biological macromolecules. Curr Opin Struct Biol. 6:1996;222-226.
36. Kono H, Doi J. Energy minimization method using automata network for sequence and sidechain conformation prediction from given backbone geometry. Proteins. 19:1994;244-255.
37. Kono H, Nishiyama M, Tanokura M, Doi J. Design of hydrophobic core of E. coli malate dehydrogenase based on the sidechain packing. of special interest Pac Symp Biocomput. 1997;210-221 This work demonstrates the use of a novel automata network method, similar to mean-field approaches [35], for hydrophobic core design.
38. Harbury PB, Tidor B, Kim PS. Repacking protein cores with backbone freedom: structure prediction for coiled coils. Proc Natl Acad Sci USA. 92:1995;8408-8412.
39. Su A, Mayo SL. Coupling backbone flexibility and amino acid sequence selection in protein design. Protein Sci. 6:1997;1701-1707.
40. Lazar GA, Desjarlais JR, Handel TM. De novo design of the hydrophobic core of ubiquitin. of special interest Protein Sci. 6:1997;1167-1178 The paper represents the most recent application of a genetic algorithm for hydrophobic core design. Several multiply sustituted variants of ubiquitin were designed and experimentally characterized. The use of different types of sidechain rotamer libraries, atomic-potential functions and levels of conformational discreteness are explored.