In silico protein design: fitting sequence onto structure.

In the last 10 yr, efforts have begun to combine the goals and approaches of computational molecular design and protein sequence analysis to provide tools for the rational mutagenesis and functional modification of proteins. These approaches use analysis of the three-dimensional structure of a protein to guide the selection of appropriate amino acid sequences to create desired properties or functions. The convergence of low-cost, high-speed computers, a tremendous increase in protein structure information, and a growing understanding of the forces that control protein structure has resulted in dramatic advances in the ability to control protein function and structure and to create the first truly artificial proteins. Various academic software packages have been developed for in silico protein design. The methods for selecting the protein structure, defining the portion to be designed, and choosing the input parameters for the software are described in this chapter.

The INNs and outs of antibody nonproprietary names.

An important step in drug development is the assignment of an International Nonproprietary Name (INN) by the World Health Organization (WHO) that provides healthcare professionals with a unique and universally available designated name to identify each pharmaceutical substance. Monoclonal antibody INNs comprise a -mab suffix preceded by a substem indicating the antibody type, e.g., chimeric (-xi-), humanized (-zu-), or human (-u-). The WHO publishes INN definitions that specify how new monoclonal antibody therapeutics are categorized and adapts the definitions to new technologies. However, rapid progress in antibody technologies has blurred the boundaries between existing antibody categories and created a burgeoning array of new antibody formats. Thus, revising the INN system for antibodies is akin to aiming for a rapidly moving target. The WHO recently revised INN definitions for antibodies now to be based on amino acid sequence identity. These new definitions, however, are critically flawed as they are ambiguous and go against decades of scientific literature. A key concern is the imposition of an arbitrary threshold for identity against human germline antibody variable region sequences. This leads to inconsistent classification of somatically mutated human antibodies, humanized antibodies as well as antibodies derived from semi-synthetic/synthetic libraries and transgenic animals. Such sequence-based classification implies clear functional distinction between categories (e.g., immunogenicity). However, there is no scientific evidence to support this. Dialog between the WHO INN Expert Group and key stakeholders is needed to develop a new INN system for antibodies and to avoid confusion and miscommunication between researchers and clinicians prescribing antibodies.

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 degrees 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 protein's potential for antigenicity. Using a structure that was homology modeled from bovine G-CSF, core designs of 25-34 residues were completed, corresponding to 10(21)-10(28) 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 degrees 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.

PanR1, a dominant negative missense allele of the gene encoding TNF-alpha (Tnf), does not impair lymphoid development.

A dominant hypomorphic allele of Tnf, PanR1, was identified in a population of G(1) mice born to N-ethyl-N-nitrosourea-mutagenized sires. Macrophages from homozygotes produced no detectable TNF bioactivity, although normal quantities of immunoreactive TNF were secreted. The phenotype was confined to a critical region on mouse chromosome 17, and then ascribed to a C-->A transversion at position 3480 of the Tnf gene, corresponding to the amino acid substitution P138T. As a result of subunit exchange, the protein exerts a dominant-negative effect on normal TNF trimers, interfering with the trimer/receptor interaction. Homozygotes are highly susceptible to infection by Listeria monocytogenes, confirming the essential role of TNF in innate immune defense. However, PanR1 mutant mice show normal architecture of the spleen and Peyer's patches, suggesting that TNF is not essential for the formation of these lymphoid structures.

Engineered antibody Fc variants with enhanced effector function.

Antibody-dependent cell-mediated cytotoxicity, a key effector function for the clinical efficacy of monoclonal antibodies, is mediated primarily through a set of closely related Fcgamma receptors with both activating and inhibitory activities. By using computational design algorithms and high-throughput screening, we have engineered a series of Fc variants with optimized Fcgamma receptor affinity and specificity. The designed variants display >2 orders of magnitude enhancement of in vitro effector function, enable efficacy against cells expressing low levels of target antigen, and result in increased cytotoxicity in an in vivo preclinical model. Our engineered Fc regions offer a means for improving the next generation of therapeutic antibodies and have the potential to broaden the diversity of antigens that can be targeted for antibody-based tumor therapy.

JNK and tumor necrosis factor-alpha mediate free fatty acid-induced insulin resistance in 3T3-L1 adipocytes.

Lipid infusion and high fat feeding are established causes of systemic and adipose tissue insulin resistance. In this study, we treated 3T3-L1 adipocytes with a mixture of free fatty acids (FFAs) to investigate the molecular mechanisms underlying fat-induced insulin resistance. FFA treatment impaired insulin receptor-mediated signal transduction and decreased insulin-stimulated GLUT4 translocation and glucose transport. FFAs activated the stress/inflammatory kinases c-Jun N-terminal kinase (JNK) and IKKbeta, and the suppressor of cytokine signaling protein 3, increased secretion of the inflammatory cytokine tumor necrosis factor (TNF)-alpha, and decreased secretion of adiponectin into the medium. RNA interference-mediated down-regulation of JNK blocked JNK activation and prevented most of the FFA-induced defects in insulin action. Blockade of TNF-alpha signaling with neutralizing antibodies to TNF-alpha or its receptors or with a dominant negative TNF-alpha peptide had a partial effect to inhibit FFA-induced cellular insulin resistance. We found that JNK activation by FFAs was not inhibited by blocking TNF-alpha signaling, whereas the FFA-induced increase in TNF-alpha secretion was inhibited by RNA interference-mediated JNK knockdown. Together, these results indicate that 1) JNK can be activated by FFAs through TNF-alpha-independent mechanisms, 2) activated JNK is a major contributor to FFA-induced cellular insulin resistance, and 3) TNF-alpha is an autocrine/paracrine downstream effector of activated JNK that can also mediate insulin resistance.

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 beta-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 approximately equal 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 beta-lactamases, the most prevalent type of Gram-negative beta-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.

Inactivation of TNF signaling by rationally designed dominant-negative TNF variants.

Tumor necrosis factor (TNF) is a key regulator of inflammatory responses and has been implicated in many pathological conditions. We used structure-based design to engineer variant TNF proteins that rapidly form heterotrimers with native TNF to give complexes that neither bind to nor stimulate signaling through TNF receptors. Thus, TNF is inactivated by sequestration. Dominant-negative TNFs represent a possible approach to anti-inflammatory biotherapeutics, and experiments in animal models show that the strategy can attenuate TNF-mediated pathology. Similar rational design could be used to engineer inhibitors of additional TNF superfamily cytokines as well as other multimeric ligands.