Emerging technologies in protein interface engineering for biomedical applications

Mechanistic computational modeling of monospecific and bispecific antibodies targeting interleukin-6/8 receptors

The spread of cancer from organ to organ (metastasis) is responsible for the vast majority of cancer deaths; however, most current anti-cancer drugs are designed to arrest or reverse tumor growth without directly addressing disease spread. It was recently discovered that tumor cell-secreted interleukin-6 (IL-6) and interleukin-8 (IL-8) synergize to enhance cancer metastasis in a cell-density dependent manner, and blockade of the IL-6 and IL-8 receptors (IL-6R and IL-8R) with a novel bispecific antibody, BS1, significantly reduced metastatic burden in multiple preclinical mouse models of cancer. Bispecific antibodies (BsAbs), which combine two different antigen-binding sites into one molecule, are a promising modality for drug development due to their enhanced avidity and dual targeting effects. However, while BsAbs have tremendous therapeutic potential, elucidating the mechanisms underlying their binding and inhibition will be critical for maximizing the efficacy of new BsAb treatments. Here, we describe a quantitative, computational model of the BS1 BsAb, exhibiting how modeling multivalent binding provides key insights into antibody affinity and avidity effects and can guide therapeutic design. We present detailed simulations of the monovalent and bivalent binding interactions between different antibody constructs and the IL-6 and IL-8 receptors to establish how antibody properties and system conditions impact the formation of binary (antibody-receptor) and ternary (receptor-antibody-receptor) complexes. Model results demonstrate how the balance of these complex types drives receptor inhibition, providing important and generalizable predictions for effective therapeutic design.

Mechanistic computational modeling of monospecific and bispecific antibodies targeting interleukin-6/8 receptors

The spread of cancer from organ to organ (metastasis) is responsible for the vast majority of cancer deaths; however, most current anti-cancer drugs are designed to arrest or reverse tumor growth without directly addressing disease spread. It was recently discovered that tumor cell-secreted interleukin-6 (IL-6) and interleukin-8 (IL-8) synergize to enhance cancer metastasis in a cell-density dependent manner, and blockade of the IL-6 and IL-8 receptors (IL-6R and IL-8R) with a novel bispecific antibody, BS1, significantly reduced metastatic burden in multiple preclinical mouse models of cancer. Bispecific antibodies (BsAbs), which combine two different antigen-binding sites into one molecule, are a promising modality for drug development due to their enhanced avidity and dual targeting effects. However, while BsAbs have tremendous therapeutic potential, elucidating the mechanisms underlying their binding and inhibition will be critical for maximizing the efficacy of new BsAb treatments. Here, we describe a quantitative, computational model of the BS1 BsAb, exhibiting how modeling multivalent binding provides key insights into antibody affinity and avidity effects and can guide therapeutic design. We present detailed simulations of the monovalent and bivalent binding interactions between different antibody constructs and the IL-6 and IL-8 receptors to establish how antibody properties and system conditions impact the formation of binary (antibody-receptor) and ternary (receptor-antibody-receptor) complexes. Model results demonstrate how the balance of these complex types drives receptor inhibition, providing important and generalizable predictions for effective therapeutic design.

What Have We Learned from Design of Function in Large Proteins?

The overarching goal of computational protein design is to gain complete control over protein structure and function. The majority of sophisticated binders and enzymes, however, are large and exhibit diverse and complex folds that defy atomistic design calculations. Encouragingly, recent strategies that combine evolutionary constraints from natural homologs with atomistic calculations have significantly improved design accuracy. In these approaches, evolutionary constraints mitigate the risk from misfolding and aggregation, focusing atomistic design calculations on a small but highly enriched sequence subspace. Such methods have dramatically optimized diverse proteins, including vaccine immunogens, enzymes for sustainable chemistry, and proteins with therapeutic potential. The new generation of deep learning-based ab initio structure predictors can be combined with these methods to extend the scope of protein design, in principle, to any natural protein of known sequence. We envision that protein engineering will come to rely on completely computational methods to efficiently discover and optimize biomolecular activities.

Structure-Guided Molecular Engineering of a Vascular Endothelial Growth Factor Antagonist to Treat Retinal Diseases

Background

Ocular neovascularization is a hallmark of retinal diseases including neovascular age-related macular degeneration and diabetic retinopathy, two leading causes of blindness in adults. Neovascularization is driven by the interaction of soluble vascular endothelial growth factor (VEGF) ligands with transmembrane VEGF receptors (VEGFR), and inhibition of the VEGF pathway has shown tremendous clinical promise. However, anti-VEGF therapies require invasive intravitreal injections at frequent intervals and high doses, and many patients show incomplete responses to current drugs due to the lack of sustained VEGF signaling suppression.

Methods

We synthesized insights from structural biology with molecular engineering technologies to engineer an anti-VEGF antagonist protein. Starting from the clinically approved decoy receptor protein aflibercept, we strategically designed a yeast-displayed mutagenic library of variants and isolated clones with superior VEGF affinity compared to the clinical drug. Our lead engineered protein was expressed in the choroidal space of rat eyes via nonviral gene delivery.

Results

Using a structure-informed directed evolution approach, we identified multiple promising anti-VEGF antagonist proteins with improved target affinity. Improvements were primarily mediated through reduction in dissociation rate, and structurally significant convergent sequence mutations were identified. Nonviral gene transfer of our engineered antagonist protein demonstrated robust and durable expression in the choroid of treated rats one month post-injection.

Conclusions

We engineered a novel anti-VEGF protein as a new weapon against retinal diseases and demonstrated safe and noninvasive ocular delivery in rats. Furthermore, our structure-guided design approach presents a general strategy for discovery of targeted protein drugs for a vast array of applications.

An Integrated Approach for Determining a Protein-Protein Binding Interface in Solution and an Evaluation of Hydrogen-Deuterium Exchange Kinetics for Adjudicating Candidate Docking Models

We describe an integrated approach of using hydrogen-deuterium exchange mass spectrometry (HDX-MS), chemical cross-linking mass spectrometry (XL-MS), and molecular docking to characterize the binding interface and to predict the three-dimensional quaternary structure of a protein-protein complex in solution. Interleukin 7 (IL-7) and its α-receptor, IL-7Rα, serving as essential mediators in the immune system, are the model system. HDX kinetics reports widespread protection on IL-7Rα but shows no differential evidence of binding-induced protection or remote conformational change. Cross-linking with reagents that differ in spacer lengths and targeting residues increases the spatial resolution. Using five cross-links as distance restraints for protein-protein docking, we generated a high-confidence model of the IL-7/IL-7Rα complex. Both the predicted binding interface and regions with direct contacts agree well with those in the solid-state structure, as confirmed by previous X-ray crystallography. An additional binding region was revealed to be the C-terminus of helix B of IL-7, highlighting the value of solution-based characterization. To generalize the integrated approach, protein-protein docking was executed with a different number of cross-links. Combining cluster analysis and HDX kinetics adjudication, we found that two intermolecular cross-link-derived restraints are sufficient to generate a high-confidence model with root-mean-square distance (rmsd) value of all alpha carbons below 2.0 Å relative to the crystal structure. The remarkable results of binding-interface determination and quaternary structure prediction highlight the effectiveness and capability of the integrated approach, which will allow more efficient and comprehensive analysis of interprotein interactions with broad applications in the multiple stages of design, implementation, and evaluation for protein therapeutics.