Protein design simulations suggest that side-chain conformational entropy is not a strong determinant of amino acid environmental preferences

Proteins: Structure, Function and Genetics

Volumn 62, Issue 3, 2006, Pages 739-748

  Hu, Xiaozhen ^a   Kuhlman, Brian ^a  

^a UNIVERSITY OF NORTH CAROLINA SCHOOL OF MEDICINE   (United States)

Author keywords

Computational protein design; Protein stability; Side chain entropy

Indexed keywords

AMINO ACID; ARGININE; METHIONINE; SOLVENT;

ALGORITHM; AMINO ACID SEQUENCE; ARTICLE; ENTROPY; HYDROGEN BOND; MOLECULAR MODEL; MONTE CARLO METHOD; PRIORITY JOURNAL; PROTEIN CONFORMATION; PROTEIN FOLDING; SOLVATION;

AMINO ACID SEQUENCE; AMINO ACID SUBSTITUTION; AMINO ACIDS; COMPUTER SIMULATION; ENTROPY; MONTE CARLO METHOD; POLYMORPHISM, SINGLE NUCLEOTIDE; PROTEIN CONFORMATION; PROTEIN FOLDING; PROTEINS; RECOMBINANT PROTEINS;

EID: 31944436020     PISSN: 08873585     EISSN: 10970134     Source Type: Journal    
DOI: 10.1002/prot.20786     Document Type: Article

References (55)

1. Pickett SD, Sternberg MJ. Empirical scale of side-chain conformational entropy in protein folding. J Mol Biol 1993;231(3):825-839.
2. Doig AJ, Sternberg MJ. Side-chain conformational entropy in protein folding. Protein Sci 1995;4(11):2247-2251.
3. Abagyan R, Totrov M. Biased probability Monte Carlo conformational searches and electrostatic calculations for peptides and proteins. J Mol Biol 1994;235(3):983-1002.
4. 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 1994;239(2):249-275.
5. Blaber M, Zhang X, Lindstrom JD, Pepiot SD, Baase WA, Matthews BW. Determination of á-helix propensity within the context of a folded protein. Sites 44 and 131 in bacteriophage T4 lysozyme. J Mol Biol 1994;235:600-624.
6. Creamer TP, Rose GD. Alpha-helix-forming propensities in peptides and proteins. Proteins 1994;19(2):85-97.
7. Lee KH, Xie D, Freire E, Amzel LM. Estimation of changes in side chain configurational entropy in binding and folding: general methods and application to helix formation. Proteins 1994;20(1):68-84.
8. Creamer TP, Rose GD. Side-chain entropy opposes alpha-helix formation but rationalizes experimentally determined helix-forming propensities. Proc Natl Acad Sci USA 1992;89(13):5937-5941.
9. Tuffery P, Etchebest C, Hazout S. Prediction of protein side chain conformations: a study on the influence of backbone accuracy on conformation stability in the rotamer space. Protein Eng 1997;10(4):361-372.
10. Street AG, Mayo SL. Intrinsic beta-sheet propensities result from van der Waals interactions between side chains and the local backbone. Proc Natl Acad Sci USA 1999;96(16):9074-9076.
11. Vasquez M. Modeling side-chain conformation. Curr Opin Struct Biol 1996;6(2):217-221.
12. Liang S, Grishin NV. Side-chain modeling with an optimized scoring function. Protein Sci 2002;11(2):322-331.
13. Gautier R, Tuffery P. Critical assessment of side-chain conformational space sampling procedures designed for quantifying the effect of side-chain environment. J Comput Chem 2003;24(15):1950-1961.
14. Schafer H, Smith LJ, Mark AE, van Gunsteren WF. Entropy calculations on the molten globule state of a protein: side-chain entropies of alpha-lactalbumin. Proteins 2002;46(2):215-224.
15. Kussell E, Shimada J, Shakhnovich EI. Excluded volume in protein side-chain packing. J Mol Biol 2001;311(1):183-193.
16. Creamer TP. Side-chain conformational entropy in protein unfolded states. Proteins 2000;40(3):443-450.
17. Cole C, Warwicker J. Side-chain conformational entropy at protein-protein interfaces. Protein Sci 2002;11(12):2860-2870.
18. Doig AJ, Gardner M, Searle MS, Williams DH. Thermodynamics of side chain internal rotations: effects on protein structure and stability. San Diego, CA: Academic Press; 1993. p 557-566.
19. Doig AJ. Thermodynamics of amino acid side-chain internal rotations. Biophys Chem 1996;61:131-141.
20. Dahiyat BI, Mayo SL. De novo protein design: fully automated sequence selection. Science 1997;278:82-87.
21. Desjarlais JR, Handel TM. De novo design of the hydrophobic cores of proteins. Protein Sci 1995;4:2006-2018.
22. Ponder JW, Richards FM. Tertiary templates for proteins. Use of packing criteria in the enumeration of allowed sequences for different structural classes. J Mol Biol 1987;193:775-791.
23. Koehl P, Levitt M. De novo protein design. I. In search of stability and specificity. J Mol Biol 1999;293(5):1161-1181.
24. Havranek JJ, Harbury PB. Automated design of specificity in molecular recognition. Nat Struct Biol 2003;10(1):45-52.
25. Harbury PB, Plecs JJ, Tidor B, Alber T, Kim PS. High-resolution protein design with backbone freedom. Science 1998;282:1462-1467.
26. Richardson JM, Lopez MM, Makhatadze GI. Enthalpy of helix-coil transition: missing link in rationalizing the thermodynamics of helix-forming propensities of the amino acid residues. Proc Natl Acad Sci USA 2005;102(5):1413-1418.
27. Penel S, Doig AJ. Rotamer strain energy in protein helices: quantification of a major force opposing protein folding. J Mol Biol 2001;305(4):961-968.
28. Koehl P, Levitt M. Structure-based conformational preferences of amino acids. Proc Natl Acad Sci USA 1999;96(22):12524-12529.
29. Kinnear BS, Jarrold MF. Helix formation in unsolvated peptides: side chain entropy is not the determining factor. J Am Chem Soc 2001;123(32):7907- 7908.
30. Avbelj F, Fele L. Role of main-chain electrostatics, hydrophobic effect and side-chain conformational entropy in determining the secondary structure of proteins. J Mol Biol 1998;279(3):665-684.
31. Filikov AV, Hayes RJ, Luo P, et al. Computational stabilization of human growth hormone. Protein Sci 2002;11(6):1452-1461.
32. Looger LL, Hellinga HW. Generalized dead-end elimination algorithms make large-scale protein side-chain structure prediction tractable: implications for protein design and structural genomics. J Mol Biol 2001;307(1):429-445.
33. Angrand I, Serrano L, Lacroix E. Computer-assisted re-design of spectrin SH3 residue clusters. Biomol Eng 2001;18(3):125-134.
34. Lopez de la Paz M, Lacroix E, Ramirez-Alvarado M, Serrano L. Computer-aided design of beta-sheet peptides. J Mol Biol 2001;312(1):229-246.
35. Kono H, Saven JG. Statistical theory for protein combinatorial libraries. Packing interactions, backbone flexibility, and the sequence variability of a main-chain structure. J Mol Biol 2001;306(3):607-628.
36. Mendes J, Baptista AM, Carrondo MA, Soares CM. Implicit solvation in the self-consistent mean field theory method: sidechain modelling and prediction of folding free energies of protein mutants. J Comput Aided Mol Des 2001;15(8):721-740.
37. Shenkin PS, Farid H, Fetrow JS. Prediction and evaluation of side-chain conformations for protein backbone structures. Proteins 1996;26(3):323-352.
38. Kuhlman B, Baker D. Native protein sequences are close to optimal for their structures. Proc Natl Acad Sci USA 2000;97(19):10383-10388.
39. Kuhlman B, Dantas G, Ireton G, Varani G, Stoddard B, Baker D. Design of a novel globular protein fold with atomic level accuracy. Science 2003;302:1364-1368.
40. Dunbrack RL Jr, Karplus M. Conformational analysis of the backbone-dependent rotamer preferences of protein sidechains. Nat Struct Biol 1994;1(5):334-340.
41. Dunbrack RL, Cohen FE. Bayesian statistical analysis of protein side-chain rotamer preferences. Protein Sci 1997;6:1661-1681.
42. Lazaridis T, Karplus M. Effective energy function for proteins in solution. Proteins 1999;35:132-152.
43. Kortemme T, Morozov AV, Baker D. An orientation-dependent hydrogen bonding potential improves prediction of specificity and structure for proteins and protein-protein complexes. J Mol Biol 2003;326(4):1239-1259.
44. Simons KT, Ruczinski I, Kooperberg C, Fox BA, Bystroff C, Baker D. Improved recognition of native-like protein structures using a combination of sequence-dependent and sequence-independent features of proteins. Proteins 1999;34:82-95.
45. Bower MJ, Cohen FE, Dunbrack RL Jr. Prediction of protein side-chain rotamers from a backbone-dependent rotamer library: a new homology modeling tool. J Mol Biol 1997;267(5):1268-1282.
46. Peterson RW, Dutton PL, Wand AJ. Improved side-chain prediction accuracy using an ab initio potential energy function and a very large rotamer library. Protein Sci 2004;13(3):735-751.
47. Xiang Z, Honig B. Extending the accuracy limits of prediction for side-chain conformations. J Mol Biol 2001;311(2):421-430.
48. Dunbrack RL, Karplus M. Backbone-dependent rotamer library for proteins: an application to side-chain prediction. J Mol Biol 1993;230:543-574.
49. Hu H, Clarkson MW, Hermans J, Lee AL. Increased rigidity of eglin c at acidic pH: evidence from NMR spin relaxation and MD simulations. Biochemistry 2003;42(47):13856-13868.
50. Leach AH, Lemon AP. Exploring the conformational space of protein side chains using dead-end elimination and the A* algorithm. Proteins 1998;33(2):227-239.
51. Butterfoss GL, Hermans J. Boltzmann-type distribution of side-chain conformation in proteins. Protein Sci 2003;12(12):2719-2731.
52. Lazaridis T, Karplus M. Effective energy functions for protein structure prediction. Curr Opin Struct Biol 2000;10(2):139-145.
53. Best RB, Clarke J, Karplus M. What contributions to protein side-chain dynamics are probed by NMR experiments? A molecular dynamics simulation analysis. J Mol Biol 2005;349(1):185-203.
54. Prabhu NV, Lee AL, Wand AJ, Sharp KA. Dynamics and entropy of a calmodulin-peptide complex studied by NMR and molecular dynamics. Biochemistry 2003;42(2):562-570.
55. Lindorff-Larsen K, Best RB, Depristo MA, Dobson CM, Vendruscolo M. Simultaneous determination of protein structure and dynamics. Nature 2005;433(7022):128-132.