Analysis of Anisotropic Side-chain Packing in Proteins and Application to High-resolution Structure Prediction

Misura, Kira M. S.; Morozov, Alexandre V.; Baker, David

doi:10.1016/j.jmb.2004.07.038

Cited by 48 publications

(42 citation statements)

References 34 publications

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“…having poorly packed native structures, rather than to the predictive power of the potentials themselves. OPUS-PSP's performance on the MOULDER decoy set collection is similar to that of DOPE, 20 Rosetta, 16,25,29 DFIRE, 19 and DFIRE-SCM. 30 The single structure that all of the tested energy functions fail to correctly identify is an NMR structure (PDB code 2pna).…”

Section: Decoy Set Recognitionmentioning

confidence: 76%

“…On the other hand, typical (semi)-residue-based potentials insufficiently describe side-chain packing due to coarse-graining. The literature documents extensive efforts to exploit orientation preference for improving the description of side-chain packing, [14][15][16][17][18] but much remains to be done to fully capture the orientation of side-chains when statistics are derived from known structures.…”

Section: Introductionmentioning

confidence: 99%

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OPUS-PSP: An Orientation-dependent Statistical All-atom Potential Derived from Side-chain Packing

Dousis

2008

Journal of Molecular Biology

171

240

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Here we report an orientation-dependent statistical all-atom potential derived from side-chain packing, named OPUS-PSP. It features a basis set of 19 rigid-body blocks extracted from the chemical structures of all 20 amino acid residues. The potential is generated from the orientation-specific packing statistics of pairs of those blocks in a non-redundant structural database. The purpose of such an approach is to capture the essential elements of orientation dependence in molecular packing interactions. Tests of OPUS-PSP on commonly used decoy sets demonstrate that it significantly outperforms most of the existing knowledge-based potentials in terms of both its ability to recognize native structures and consistency in achieving high Z-scores across decoy sets. As OPUS-PSP excludes interactions among main-chain atoms, its success highlights the crucial importance of side-chain packing in forming native protein structures. Moreover, OPUS-PSP does not explicitly include solvation terms, and thus the potential should perform well when the solvation effect is difficult to determine, such as in membrane proteins. Overall, OPUS-PSP is a generally applicable potential for protein structure modeling, especially for handling side-chain conformations, one of the most difficult steps in high-accuracy protein structure prediction and refinement.

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Section: Decoy Set Recognitionmentioning

confidence: 76%

Section: Introductionmentioning

confidence: 99%

OPUS-PSP: An Orientation-dependent Statistical All-atom Potential Derived from Side-chain Packing

Dousis

2008

Journal of Molecular Biology

171

240

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“…Finally, because stabilization of the transition state by charge delocalization is a key factor in catalysis of the Kemp elimination 5-7,10,14 , we chose to stack aromatic amino acid side chains on the planar transition state (Fig. 1b) using idealized p-stacking geometries 15 .…”

Section: Computational Design Methodsmentioning

confidence: 99%

“…Transition state geometries were computed at the B3LYP/6-31G(d) level for idealized active sites containing either a carboxylate or an imidazole-carboxylate dyad as the general base. Aromatic side chains were placed above and below the transition state using idealized p-stacking geometries 15 . A six-dimensional hashing procedure 4 was applied to find transition state placements in a large set of protein scaffolds (Supplementary Table 3) that were consistent with the catalytic geometry.…”

Section: Methods Summarymentioning

confidence: 99%

“…These side chains were placed using idealized p-stacking geometries 15 in a parallel configuration (4 Å separation) with the aromatic centre offset from the transition state rings by 1 Å . The aromatic groups were placed above either the five-or the six-membered ring and were allowed on both the top and the bottom faces of the transition state.…”

Section: Methodsmentioning

confidence: 99%

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Kemp elimination catalysts by computational enzyme design

Röthlisberger¹,

Khersonsky²,

Wollacott³

et al. 2008

Nature

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1,197

1,237

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The design of new enzymes for reactions not catalysed by naturally occurring biocatalysts is a challenge for protein engineering and is a critical test of our understanding of enzyme catalysis. Here we describe the computational design of eight enzymes that use two different catalytic motifs to catalyse the Kemp elimination-a model reaction for proton transfer from carbon-with measured rate enhancements of up to 10 5 and multiple turnovers. Mutational analysis confirms that catalysis depends on the computationally designed active sites, and a high-resolution crystal structure suggests that the designs have close to atomic accuracy. Application of in vitro evolution to enhance the computational designs produced a .200-fold increase in k cat /K m (k cat /K m of 2,600 M 21 s 21 and k cat /k uncat of .10 6 ). These results demonstrate the power of combining computational protein design with directed evolution for creating new enzymes, and we anticipate the creation of a wide range of useful new catalysts in the future.Naturally occurring enzymes are extraordinarily efficient catalysts 1 . They bind their substrates in a well-defined active site with precisely aligned catalytic residues to form highly active and selective catalysts for a wide range of chemical reactions under mild conditions. Nevertheless, many important synthetic reactions lack a naturally occurring enzymatic counterpart. Hence, the design of stable enzymes with new catalytic activities is of great practical interest, with potential applications in biotechnology, biomedicine and industrial processes. Furthermore, the computational design of new enzymes provides a stringent test of our understanding of how naturally occurring enzymes work. In the past several years, there has been exciting progress in designing new biocatalysts 2,3 .Here we describe the use of our recently developed computational enzyme design methodology 4 to create new enzyme catalysts for a reaction for which no naturally occurring enzyme exists: the Kemp elimination 5,6 . The reaction, shown in Fig. 1a, has been extensively studied as an activated model system for understanding the catalysis of proton abstraction from carbon-a process that is normally restricted by high activation-energy barriers 7,8 . Computational design methodThe first step in our protocol for designing new enzymes is to choose a catalytic mechanism and then to use quantum mechanical transition state calculations to create an idealized active site with protein functional groups positioned so as to maximize transition state stabilization (Fig. 1b). The key step for the Kemp elimination is deprotonation of a carbon by a general base. We chose two different catalytic bases for this purpose: first, the carboxyl group of an aspartate or glutamate side chain, and, second, the imidazole of a histidine positioned and polarized by the carboxyl group of an aspartate or glutamate (we refer to this combination as a His-Asp dyad). The two choices have complementary strengths and weaknesses. The advantage of the carboxylate...

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Homology Modeling in Biology and Medicine

Dunbrack¹

2007

Bioinformatics‐From Genomes to Therapies

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Analysis of Anisotropic Side-chain Packing in Proteins and Application to High-resolution Structure Prediction

Cited by 48 publications

References 34 publications

OPUS-PSP: An Orientation-dependent Statistical All-atom Potential Derived from Side-chain Packing

OPUS-PSP: An Orientation-dependent Statistical All-atom Potential Derived from Side-chain Packing

Kemp elimination catalysts by computational enzyme design

Homology Modeling in Biology and Medicine

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