Structure-function relationships of the soluble form of the antiaging protein Klotho have therapeutic implications for managing kidney disease.

The fortuitously discovered antiaging membrane protein αKlotho (Klotho) is highly expressed in the kidney, and deletion of the Klotho gene in mice causes a phenotype strikingly similar to that of chronic kidney disease (CKD). Klotho functions as a co-receptor for fibroblast growth factor 23 (FGF23) signaling, whereas its shed extracellular domain, soluble Klotho (sKlotho), carrying glycosidase activity, is a humoral factor that regulates renal health. Low sKlotho in CKD is associated with disease progression, and sKlotho supplementation has emerged as a potential therapeutic strategy for managing CKD. Here, we explored the structure-function relationship and post-translational modifications of sKlotho variants to guide the future design of sKlotho-based therapeutics. Chinese hamster ovary (CHO)- and human embryonic kidney (HEK)-derived WT sKlotho proteins had varied activities in FGF23 co-receptor and ß-glucuronidase assays in vitro and distinct properties in vivo Sialidase treatment of heavily sialylated CHO-sKlotho increased its co-receptor activity 3-fold, yet it remained less active than hyposialylated HEK-sKlotho. MS and glycopeptide-mapping analyses revealed that HEK-sKlotho is uniquely modified with an unusual N-glycan structure consisting of N,N'-di-N-acetyllactose diamine at multiple N-linked sites, one of which at Asn-126 was adjacent to a putative GalNAc transfer motif. Site-directed mutagenesis and structural modeling analyses directly implicated N-glycans in Klotho's protein folding and function. Moreover, the introduction of two catalytic glutamate residues conserved across glycosidases into sKlotho enhanced its glucuronidase activity but decreased its FGF23 co-receptor activity, suggesting that these two functions might be structurally divergent. These findings open up opportunities for rational engineering of pharmacologically enhanced sKlotho therapeutics for managing kidney disease.

Augmented Binary Substitution: Single-pass CDR germ-lining and stabilization of therapeutic antibodies.

Although humanized antibodies have been highly successful in the clinic, all current humanization techniques have potential limitations, such as: reliance on rodent hosts, immunogenicity due to high non-germ-line amino acid content, v-domain destabilization, expression and formulation issues. This study presents a technology that generates stable, soluble, ultrahumanized antibodies via single-step complementarity-determining region (CDR) germ-lining. For three antibodies from three separate key immune host species, binary substitution CDR cassettes were inserted into preferred human frameworks to form libraries in which only the parental or human germ-line destination residue was encoded at each position. The CDR-H3 in each case was also augmented with 1 ± 1 random substitution per clone. Each library was then screened for clones with restored antigen binding capacity. Lead ultrahumanized clones demonstrated high stability, with affinity and specificity equivalent to, or better than, the parental IgG. Critically, this was mainly achieved on germ-line frameworks by simultaneously subtracting up to 19 redundant non-germ-line residues in the CDRs. This process significantly lowered non-germ-line sequence content, minimized immunogenicity risk in the final molecules and provided a heat map for the essential non-germ-line CDR residue content of each antibody. The ABS technology therefore fully optimizes the clinical potential of antibodies from rodents and alternative immune hosts, rendering them indistinguishable from fully human in a simple, single-pass process.

Low-data interpretable deep learning prediction of antibody viscosity using a biophysically meaningful representation.

Deep learning, aided by the availability of big data sets, has led to substantial advances across many disciplines. However, many scientific problems of practical interest lack sufficiently large datasets amenable to deep learning. Prediction of antibody viscosity is one such problem where deep learning methods have not yet been explored due to the relative scarcity of relevant training data. In this work, we overcome this limitation using a biophysically meaningful representation that enables us to develop generalizable models even under limited training data. We present, PfAbNet-viscosity, a 3D convolutional neural network architecture, to predict high-concentration viscosity of therapeutic antibodies. We show that with the electrostatic potential surface of the antibody variable region as the only input to the network, the models trained on as few as couple dozen datapoints can generalize with high accuracy. Our feature attribution analysis shows that PfAbNet-viscosity has learned key biophysical drivers of viscosity. The applicability of our approach to other biological systems is discussed.

Combining random mutagenesis, structure-guided design and next-generation sequencing to mitigate polyreactivity of an anti-IL-21R antibody.

Despite substantial technological advances in antibody library and display platform development, the number of approved biotherapeutics from displayed libraries remains limited. In vivo, 20-50% of peripheral B cells undergo a process of receptor editing, which modifies the variable and junctional regions of light chains to delete auto-reactive clones. However, in vitro antibody evolution relies primarily on interaction with antigen, with no in-built checkpoints to ensure that the selected antibodies have not acquired additional specificities or biophysical liabilities during the optimization process. We had previously observed an enrichment of positive charge in the complementarity-determining regions of an anti-IL-21 R antibody during affinity optimization, which correlated with more potent IL-21 neutralization, but poor in vivo pharmacokinetics (PK). There is an emerging body of data that has correlated antibody nonspecificity with poor PK in vivo, and established a series of screening assays that are predictive of this behavior. In this study we revisit the challenge of developing an anti-IL-21 R antibody that can effectively compete with IL-21 for its highly negatively charged paratope while maintaining favorable biophysical properties. In vitro deselection methods that included an excess of negatively charged membrane preparations, or deoxyribonucleic acid, during phage selection of optimization libraries were unsuccessful in avoiding enrichment of highly charged, nonspecific antibody variants. However, a combination of structure-guided rational library design, next-generation sequencing of library outputs and application of linear regression models resulted in the identification of an antibody that maintained high affinity for IL-21 R and exhibited a desirable stability and biophysical profile.

Discovery and optimization of a novel anti-GUCY2c x CD3 bispecific antibody for the treatment of solid tumors.

We report here the discovery and optimization of a novel T cell retargeting anti-GUCY2C x anti-CD3ε bispecific antibody for the treatment of solid tumors. Using a combination of hybridoma, phage display and rational design protein engineering, we have developed a fully humanized and manufacturable CD3 bispecific antibody that demonstrates favorable pharmacokinetic properties and potent in vivo efficacy. Anti-GUCY2C and anti-CD3ε antibodies derived from mouse hybridomas were first humanized into well-behaved human variable region frameworks with full retention of binding and T-cell mediated cytotoxic activity. To address potential manufacturability concerns, multiple approaches were taken in parallel to optimize and de-risk the two antibody variable regions. These approaches included structure-guided rational mutagenesis and phage display-based optimization, focusing on improving stability, reducing polyreactivity and self-association potential, removing chemical liabilities and proteolytic cleavage sites, and de-risking immunogenicity. Employing rapid library construction methods as well as automated phage display and high-throughput protein production workflows enabled efficient generation of an optimized bispecific antibody with desirable manufacturability properties, high stability, and low nonspecific binding. Proteolytic cleavage and deamidation in complementarity-determining regions were also successfully addressed. Collectively, these improvements translated to a molecule with potent single-agent in vivo efficacy in a tumor cell line adoptive transfer model and a cynomolgus monkey pharmacokinetic profile (half-life>4.5 days) suitable for clinical development. Clinical evaluation of PF-07062119 is ongoing.

Modeling and mitigation of high-concentration antibody viscosity through structure-based computer-aided protein design.

For an antibody to be a successful therapeutic many competing factors require optimization, including binding affinity, biophysical characteristics, and immunogenicity risk. Additional constraints may arise from the need to formulate antibodies at high concentrations (>150 mg/ml) to enable subcutaneous dosing with reasonable volume (ideally <1.0 mL). Unfortunately, antibodies at high concentrations may exhibit high viscosities that place impractical constraints (such as multiple injections or large needle diameters) on delivery and impede efficient manufacturing. Here we describe the optimization of an anti-PDGF-BB antibody to reduce viscosity, enabling an increase in the formulated concentration from 80 mg/ml to greater than 160 mg/ml, while maintaining the binding affinity. We performed two rounds of structure guided rational design to optimize the surface electrostatic properties. Analysis of this set demonstrated that a net-positive charge change, and disruption of negative charge patches were associated with decreased viscosity, but the effect was greatly dependent on the local surface environment. Our work here provides a comprehensive study exploring a wide sampling of charge-changes in the Fv and CDR regions along with targeting multiple negative charge patches. In total, we generated viscosity measurements for 40 unique antibody variants with full sequence information which provides a significantly larger and more complete dataset than has previously been reported.

Cluster expansion models for flexible-backbone protein energetics.

Protein structure prediction and design often involve discrete modeling of side-chain conformations on structural templates. Introducing backbone flexibility into such models has proven important in many different applications. Backbone flexibility improves model accuracy and provides access to larger sequence spaces in computational design, although at a cost in complexity and time. Here, we show that the influence of backbone flexibility on protein conformational energetics can be treated implicitly, at the level of sequence, using the technique of cluster expansion. Cluster expansion provides a way to convert structure-based energies into functions of sequence alone. It leads to dramatic speed-ups in energy evaluation and provides a convenient functional form for the analysis and optimization of sequence-structure relationships. We show that it can be applied effectively to flexible-backbone structural models using four proteins: alpha-helical coiled-coil dimers and trimers, zinc fingers, and Bcl-xL/peptide complexes. For each of these, low errors for the sequence-based models when compared with structure-based evaluations show that this new way of treating backbone flexibility has considerable promise, particularly for protein design.

Predicting helix orientation for coiled-coil dimers.

The alpha-helical coiled coil is a structurally simple protein oligomerization or interaction motif consisting of two or more alpha helices twisted into a supercoiled bundle. Coiled coils can differ in their stoichiometry, helix orientation, and axial alignment. Because of the near degeneracy of many of these variants, coiled coils pose a challenge to fold recognition methods for structure prediction. Whereas distinctions between some protein folds can be discriminated on the basis of hydrophobic/polar patterning or secondary structure propensities, the sequence differences that encode important details of coiled-coil structure can be subtle. This is emblematic of a larger problem in the field of protein structure and interaction prediction: that of establishing specificity between closely similar structures. We tested the behavior of different computational models on the problem of recognizing the correct orientation--parallel vs. antiparallel--of pairs of alpha helices that can form a dimeric coiled coil. For each of 131 examples of known structure, we constructed a large number of both parallel and antiparallel structural models and used these to assess the ability of five energy functions to recognize the correct fold. We also developed and tested three sequence-based approaches that make use of varying degrees of implicit structural information. The best structural methods performed similarly to the best sequence methods, correctly categorizing approximately 81% of dimers. Steric compatibility with the fold was important for some coiled coils we investigated. For many examples, the correct orientation was determined by smaller energy differences between parallel and antiparallel structures distributed over many residues and energy components. Prediction methods that used structure but incorporated varying approximations and assumptions showed quite different behaviors when used to investigate energetic contributions to orientation preference. Sequence based methods were sensitive to the choice of residue-pair interactions scored.

Modeling backbone flexibility to achieve sequence diversity: the design of novel alpha-helical ligands for Bcl-xL.

Computational protein design can be used to select sequences that are compatible with a fixed-backbone template. This strategy has been used in numerous instances to engineer novel proteins. However, the fixed-backbone assumption severely restricts the sequence space that is accessible via design. For challenging problems, such as the design of functional proteins, this may not be acceptable. Here, we present a method for introducing backbone flexibility into protein design calculations and apply it to the design of diverse helical BH3 ligands that bind to the anti-apoptotic protein Bcl-xL, a member of the Bcl-2 protein family. We demonstrate how normal mode analysis can be used to sample different BH3 backbones, and show that this leads to a larger and more diverse set of low-energy solutions than can be achieved using a native high-resolution Bcl-xL complex crystal structure as a template. We tested several of the designed solutions experimentally and found that this approach worked well when normal mode calculations were used to deform a native BH3 helix structure, but less well when they were used to deform an idealized helix. A subsequent round of design and testing identified a likely source of the problem as inadequate sampling of the helix pitch. In all, we tested 17 designed BH3 peptide sequences, including several point mutants. Of these, eight bound well to Bcl-xL and four others showed weak but detectable binding. The successful designs showed a diversity of sequences that would have been difficult or impossible to achieve using only a fixed backbone. Thus, introducing backbone flexibility via normal mode analysis effectively broadened the set of sequences identified by computational design, and provided insight into positions important for binding Bcl-xL.

AB-Bind: Antibody binding mutational database for computational affinity predictions.

Antibodies (Abs) are a crucial component of the immune system and are often used as diagnostic and therapeutic agents. The need for high-affinity and high-specificity antibodies in research and medicine is driving the development of computational tools for accelerating antibody design and discovery. We report a diverse set of antibody binding data with accompanying structures that can be used to evaluate methods for modeling antibody interactions. Our Antibody-Bind (AB-Bind) database includes 1101 mutants with experimentally determined changes in binding free energies (ΔΔG) across 32 complexes. Using the AB-Bind data set, we evaluated the performance of protein scoring potentials in their ability to predict changes in binding free energies upon mutagenesis. Numerical correlations between computed and observed ΔΔG values were low (r = 0.16-0.45), but the potentials exhibited predictive power for classifying variants as improved vs weakened binders. Performance was evaluated using the area under the curve (AUC) for receiver operator characteristic (ROC) curves; the highest AUC values for 527 mutants with |ΔΔG| > 1.0 kcal/mol were 0.81, 0.87, and 0.88 using STATIUM, FoldX, and Discovery Studio scoring potentials, respectively. Some methods could also enrich for variants with improved binding affinity; FoldX and Discovery Studio were able to correctly rank 42% and 30%, respectively, of the 80 most improved binders (those with ΔΔG < -1.0 kcal/mol) in the top 5% of the database. This modest predictive performance has value but demonstrates the continuing need to develop and improve protein energy functions for affinity prediction.

Beyond CDR-grafting: Structure-guided humanization of framework and CDR regions of an anti-myostatin antibody.

Antibodies are an important class of biotherapeutics that offer specificity to their antigen, long half-life, effector function interaction and good manufacturability. The immunogenicity of non-human-derived antibodies, which can be a major limitation to development, has been partially overcome by humanization through complementarity-determining region (CDR) grafting onto human acceptor frameworks. The retention of foreign content in the CDR regions, however, is still a potential immunogenic liability. Here, we describe the humanization of an anti-myostatin antibody utilizing a 2-step process of traditional CDR-grafting onto a human acceptor framework, followed by a structure-guided approach to further reduce the murine content of CDR-grafted antibodies. To accomplish this, we solved the co-crystal structures of myostatin with the chimeric (Protein Databank (PDB) id 5F3B) and CDR-grafted anti-myostatin antibody (PDB id 5F3H), allowing us to computationally predict the structurally important CDR residues as well as those making significant contacts with the antigen. Structure-based rational design enabled further germlining of the CDR-grafted antibody, reducing the murine content of the antibody without affecting antigen binding. The overall "humanness" was increased for both the light and heavy chain variable regions.

Development of PF-06671008, a Highly Potent Anti-P-cadherin/Anti-CD3 Bispecific DART Molecule with Extended Half-Life for the Treatment of Cancer.

Bispecific antibodies offer a promising approach for the treatment of cancer but can be challenging to engineer and manufacture. Here we report the development of PF-06671008, an extended-half-life dual-affinity re-targeting (DART®) bispecific molecule against P-cadherin and CD3 that demonstrates antibody-like properties. Using phage display, we identified anti-P-cadherin single chain Fv (scFv) that were subsequently affinity-optimized to picomolar affinity using stringent phage selection strategies, resulting in low picomolar potency in cytotoxic T lymphocyte (CTL) killing assays in the DART format. The crystal structure of this disulfide-constrained diabody shows that it forms a novel compact structure with the two antigen binding sites separated from each other by approximately 30 Å and facing approximately 90° apart. We show here that introduction of the human Fc domain in PF-06671008 has produced a molecule with an extended half-life (-4.4 days in human FcRn knock-in mice), high stability (Tm1 > 68 °C), high expression (>1 g/L), and robust purification properties (highly pure heterodimer), all with minimal impact on potency. Finally, we demonstrate in vivo anti-tumor efficacy in a human colorectal/human peripheral blood mononuclear cell (PBMC) co-mix xenograft mouse model. These results suggest PF-06671008 is a promising new bispecific for the treatment of patients with solid tumors expressing P-cadherin.