Bassil I. Dahiyat

Combining computational and experimental screening for rapid optimization of protein properties

We present a combined computational and experimental method for the rapid optimization of proteins. Using β-lactamase as a test case, we redesigned the active site region using our Protein Design Automation technology as a computational screen to search the entire sequence space. By eliminating sequences incompatible with the protein fold, Protein Design Automation rapidly reduced the number of sequences to a size amenable to experimental screening, resulting in a library of ≈200,000 mutants. These were then constructed and experimentally screened to select for variants with improved resistance to the antibiotic cefotaxime. In a single round, we obtained variants exhibiting a 1,280-fold increase in resistance. To our knowledge, all of the mutations were novel, i.e., they have not been identified as beneficial by random mutagenesis or DNA shuffling or seen in any of the naturally occurring TEM β-lactamases, the most prevalent type of Gram-negative β-lactamases. This combined approach allows for the rapid improvement of any property that can be screened experimentally and provides a powerful broadly applicable tool for protein engineering.

Computational stabilization of human growth hormone

Recombinant human growth hormone (hGH) is used worldwide for the treatment of pediatric hypopituitary dwarfism and in children suffering from low levels of hGH. It has limited stability in solution, and because of poor oral absorption, is administered by injection, typically several times a week. Development has therefore focused on more stable or sustained‐release formulations and alternatives to injectable delivery that would increase bioavailability and make it easier for patients to use. We redesigned hGH computationally to improve its thermostability. A more stable variant of hGH could have improved pharmacokinetics or enhanced shelf‐life, or be more amenable to use in alternate delivery systems and formulations. The computational design was performed using a previously developed combinatorial optimization algorithm based on the dead‐end elimination theorem. The algorithm uses an empirical free energy function for scoring designed sequences. This function was augmented with a term that accounts for the loss of backbone and side‐chain conformational entropy. The weighting factors for this term, the electrostatic interaction term, and the polar hydrogen burial term were optimized by minimizing the number of mutations designed by the algorithm relative to wild‐type. Forty‐five residues in the core of the protein were selected for optimization with the modified potential function. The proteins designed using the developed scoring function contained six to 10 mutations, showed enhancement in the melting temperature of up to 16°C, and were biologically active in cell proliferation studies. These results show the utility of our free energy function in automated protein design.

Development of a cytokine analog with enhanced stability using computational ultrahigh throughput screening

Granulocyte‐colony stimulating factor (G‐CSF) is used worldwide to prevent neutropenia caused by high‐dose chemotherapy. It has limited stability, strict formulation and storage requirements, and because of poor oral absorption must be administered by injection (typically daily). Thus, there is significant interest in developing analogs with improved pharmacological properties. We used our ultrahigh throughput computational screening method to improve the physicochemical characteristics of G‐CSF. Improving these properties can make a molecule more robust, enhance its shelf life, or make it more amenable to alternate delivery systems and formulations. It can also affect clinically important features such as pharmacokinetics. Residues in the buried core were selected for optimization to minimize changes to the surface, thereby maintaining the active site and limiting the designed proteins potential for antigenicity. Using a structure that was homology modeled from bovine G‐CSF, core designs of 25–34 residues were completed, corresponding to 1021–1028 sequences screened. The optimal sequence from each design was selected for biophysical characterization and experimental testing; each had 10–14 mutations. The designed proteins showed enhanced thermal stabilities of up to 13°C, displayed five‐to 10‐fold improvements in shelf life, and were biologically active in cell proliferation assays and in a neutropenic mouse model. Pharmacokinetic studies in monkeys showed that subcutaneous injection of the designed analogs results in greater systemic exposure, probably attributable to improved absorption from the subcutaneous compartment. These results show that our computational method can be used to develop improved pharmaceuticals and illustrate its utility as a powerful protein design tool.

Site-specific modification of α-helical peptides with electron donors and acceptors

We have prepared a histidine containing monomeric α-helical peptide, ETH6 (A_2KA_4KA_2HA_6HA_4KA_4K) in order to study long-range electron transfer (ET). This peptide was site-specifically labelled with a ruthenium (donor) at one histidine and a second ruthenium (acceptor) at a second histidine located on the same peptide. Both the unlabeled peptide and the singly labeled peptide-metal complex, Ru(bpy)_2(im)(His)-ETH6, were shown to form stable monomeric α-helical structures as determined by circular dichroism. Ru(NH_3)_4(py) was coupled to Ru(bpy_2)(im)(His)-ETH6, forming a Ru(bpy)_2(im)(His)-ETH6-(His)Ru(NH_3)_4(py) donor-acceptor complex. The synthesis and characterization of these peptides provide an entry into a series of molecules that are ideally suited to evaluate pathway differences such as H-bond mediated versus backbone-coupled long-range ET in protein α-helices.