Exact rotamer optimization for protein design

Computational methods play a central role in the rational design of novel proteins. The present work describes a new hybrid exact rotamer optimization (HERO) method that builds on previous dead‐end elimination algorithms to yield dramatic performance enhancements. Measured on experimentally validated physical models, these improvements make it possible to perform previously intractable designs of entire protein core, surface, or boundary regions. Computational demonstrations include a full core design of the variable domains of the light and heavy chains of catalytic antibody 48G7 FAB with 74 residues and 10128 conformations, a full core/boundary design of the β1 domain of protein G with 25 residues and 1053 conformations, and a full surface design of the β1 domain of protein G with 27 residues and 1060 conformations. In addition, a full sequence design of the β1 domain of protein G is used to demonstrate the strong dependence of algorithm performance on the exact form of the potential function and the fidelity of the rotamer library. These results emphasize that search algorithm performance for protein design can only be meaningfully evaluated on physical models that have been subjected to experimental scrutiny. The new algorithm greatly facilitates ongoing efforts to engineer increasingly complex protein features. © 2002 Wiley Periodicals, Inc. J Comput Chem 24: 232–243, 2003

[1]  S. L. Mayo,et al.  Enzyme-like proteins by computational design , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[2]  J R Desjarlais,et al.  De novo design of the hydrophobic cores of proteins , 1995, Protein science : a publication of the Protein Society.

[3]  S L Mayo,et al.  Pairwise calculation of protein solvent-accessible surface areas. , 1998, Folding & design.

[4]  S. L. Mayo,et al.  Computational protein design. , 1999, Structure.

[5]  R. Jaenicke.  The protein folding problem and tertiary structure prediction , 1995 .

[6]  S. A. Marshall,et al.  Achieving stability and conformational specificity in designed proteins via binary patterning. , 2001, Journal of molecular biology.

[7]  G L Gilliland,et al.  Two crystal structures of the B1 immunoglobulin-binding domain of streptococcal protein G and comparison with NMR. , 1994, Biochemistry.

[8]  L L Looger,et al.  Generalized dead-end elimination algorithms make large-scale protein side-chain structure prediction tractable: implications for protein design and structural genomics. , 2001, Journal of molecular biology.

[9]  J Deisenhofer,et al.  Mechanism of ribonuclease inhibition by ribonuclease inhibitor protein based on the crystal structure of its complex with ribonuclease A. , 1996, Journal of molecular biology.

[10]  S L Mayo,et al.  The beta-beta-alpha fold: explorations in sequence space. , 2001, Journal of molecular biology.

[11]  S L Mayo,et al.  Structure of a protein G helix variant suggests the importance of helix propensity and helix dipole interactions in protein design , 2000, Protein science : a publication of the Protein Society.

[12]  P. S. Kim,et al.  High-resolution protein design with backbone freedom. , 1998, Science.

[13]  F. Young.  Biochemistry , 1955, The Indian Medical Gazette.

[14]  M. Levitt,et al.  Accurate prediction of the stability and activity effects of site-directed mutagenesis on a protein core , 1991, Nature.

[15]  Stephen L. Mayo,et al.  Designing protein β-sheet surfaces by Z-score optimization , 2000 .

[16]  D. Benjamin Gordon,et al.  Radical performance enhancements for combinatorial optimization algorithms based on the dead‐end elimination theorem , 1998 .

[17]  R. Goldstein.  Efficient rotamer elimination applied to protein side-chains and related spin glasses. , 1994, Biophysical journal.

[18]  P. Koehl,et al.  Application of a self-consistent mean field theory to predict protein side-chains conformation and estimate their conformational entropy. , 1994, Journal of molecular biology.

[19]  Christopher A. Voigt,et al.  Trading accuracy for speed: A quantitative comparison of search algorithms in protein sequence design. , 2000, Journal of molecular biology.

[20]  Niles A Pierce,et al.  Protein design is NP-hard. , 2002, Protein engineering.

[21]  C Venclovas,et al.  Some measures of comparative performance in the three CASPs , 1999, Proteins.

[22]  Junichi Takagi,et al.  Computational design of an integrin I domain stabilized in the open high affinity conformation , 2000, Nature Structural Biology.

[23]  S. L. Mayo,et al.  Designed protein G core variants fold to native‐like structures: Sequence selection by ORBIT tolerates variation in backbone specification , 2001, Protein science : a publication of the Protein Society.

[24]  S. L. Mayo,et al.  Protein design automation , 1996, Protein science : a publication of the Protein Society.

[25]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[26]  J R Desjarlais,et al.  Computer search algorithms in protein modification and design. , 1998, Current opinion in structural biology.

[27]  Peter G. Schultz,et al.  The Immunological Evolution of Catalysis , 1996, Science.

[28]  S J Wodak,et al.  Automatic protein design with all atom force-fields by exact and heuristic optimization. , 2000, Journal of molecular biology.

[29]  I Lasters,et al.  All in one: a highly detailed rotamer library improves both accuracy and speed in the modelling of sidechains by dead-end elimination. , 1997, Folding & design.

[30]  A R Leach,et al.  Exploring the conformational space of protein side chains using dead‐end elimination and the A* algorithm , 1998, Proteins.

[31]  J. Guss,et al.  The crystal structure of poplar apoplastocyanin at 1.8-A resolution. The geometry of the copper-binding site is created by the polypeptide. , 1984, The Journal of biological chemistry.

[32]  C. Lee,et al.  Predicting protein mutant energetics by self-consistent ensemble optimization. , 1994, Journal of molecular biology.

[33]  Roland L. Dunbrack,et al.  Backbone-dependent rotamer library for proteins. Application to side-chain prediction. , 1993, Journal of molecular biology.

[34]  S L Mayo,et al.  Coupling backbone flexibility and amino acid sequence selection in protein design , 1997, Protein science : a publication of the Protein Society.

[35]  F M Richards,et al.  Optimal sequence selection in proteins of known structure by simulated evolution. , 1994, Proceedings of the National Academy of Sciences of the United States of America.

[36]  G. A. Lazar,et al.  De novo design of the hydrophobic core of ubiquitin , 1997, Protein science : a publication of the Protein Society.

[37]  S. L. Mayo,et al.  Automated design of the surface positions of protein helices , 1997, Protein science : a publication of the Protein Society.

[38]  S. A. Marshall,et al.  Energy functions for protein design. , 1999, Current opinion in structural biology.

[39]  S. L. Mayo,et al.  De novo protein design: fully automated sequence selection. , 1997, Science.

[40]  D B Gordon,et al.  Branch-and-terminate: a combinatorial optimization algorithm for protein design. , 1999, Structure.

[41]  S. L. Mayo,et al.  Conformational splitting: A more powerful criterion for dead‐end elimination , 2000, J. Comput. Chem..

[42]  S. L. Mayo,et al.  Probing the role of packing specificity in protein design. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[43]  Johan Desmet,et al.  The dead-end elimination theorem and its use in protein side-chain positioning , 1992, Nature.

[44]  Kenneth Steiglitz,et al.  Combinatorial Optimization: Algorithms and Complexity , 1981 .

[45]  Stephen L. Mayo,et al.  Design, structure and stability of a hyperthermophilic protein variant , 1998, Nature Structural Biology.

[46]  Stephen L. Mayo,et al.  Rubredoxin Variant Folds without Iron , 1999 .

[47]  I. Lasters,et al.  The fuzzy-end elimination theorem: correctly implementing the side chain placement algorithm based on the dead-end elimination theorem. , 1993, Protein engineering.