Biomedical subjects

Geoffrey K Hom

Publications and source records attributed to Geoffrey K Hom.

4 recordsLinked to original sources

PubMed2006

A search algorithm for fixed-composition protein design.

We present a computational protein design algorithm for finding low-energy sequences of fixed amino acid composition. The search algorithms used in protein design typically do not restrict amino acid composition. However, the random energy model of Shakhnovich suggests that the use of fixed-composition sequences may circumvent defects in the modeling of the denatured state. Our algorithm, FC_FASTER, links fixed-composition versions of Monte Carlo and the FASTER algorithm. As proof of principle, FC_FASTER was tested on an experimentally validated, full-sequence design of the beta1 domain of protein G. For the wild-type composition, FC_FASTER found a lower energy sequence than the experimentally validated sequence. Also, for a different composition, FC_FASTER found the hypothetical lowest-energy sequence in 14 out of 32 trials.

Algorithms↗

PubMed2005

Dioxane contributes to the altered conformation and oligomerization state of a designed engrailed homeodomain variant.

Our goal was to compute a stable, full-sequence design of the Drosophila melanogaster engrailed homeodomain. Thermal and chemical denaturation data indicated the design was significantly more stable than was the wild-type protein. The data were also nearly identical to those for a similar, later full-sequence design, which was shown by NMR to adopt the homeodomain fold: a three-helix, globular monomer. However, a 1.65 A crystal structure of the design described here turned out to be of a completely different fold: a four-helix, rodlike tetramer. The crystallization conditions included approximately 25% dioxane, and subsequent experiments by circular dichroism and sedimentation velocity analytical ultracentrifugation indicated that dioxane increases the helicity and oligomerization state of the designed protein. We attribute at least part of the discrepancy between the target fold and the crystal structure to the presence of a high concentration of dioxane.

Animals↗

PubMed2004

Preprocessing of rotamers for protein design calculations.

We have developed a process that significantly reduces the number of rotamers in computational protein design calculations. This process, which we call Vegas, results in dramatic computational performance increases when used with algorithms based on the dead-end elimination (DEE) theorem. Vegas estimates the energy of each rotamer at each position by fixing each rotamer in turn and utilizing various search algorithms to optimize the remaining positions. Algorithms used for this context specific optimization can include Monte Carlo, self-consistent mean field, and the evaluation of an expression that generates a lower bound energy for the fixed rotamer. Rotamers with energies above a user-defined cutoff value are eliminated. We found that using Vegas to preprocess rotamers significantly reduced the calculation time of subsequent DEE-based algorithms while retaining the global minimum energy conformation. For a full boundary design of a 51 amino acid fragment of engrailed homeodomain, the total calculation time was reduced by 12-fold.

Algorithms↗

PubMed2003

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 10(128) conformations, a full core/boundary design of the beta1 domain of protein G with 25 residues and 10(53) conformations, and a full surface design of the beta1 domain of protein G with 27 residues and 10(60) conformations. In addition, a full sequence design of the beta1 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.

Algorithms↗