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 (DDG) 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 DDG values were low (r 5 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 |DDG| > 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 DDG < 21.0 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.Abbreviation and Symbols: DDG, change in free energy of binding; Ab, antibody; mAbs, monoclonal antibodies; Fab, fragment antigen binding; CDR, complementarity determining region; MD, molecular dynamics; KIC, kinematic closure; ROC, receiver operator characteristic; AUC, area under the curve; SPM, single point mutation; SPR, surface plasmon resonance; Yeast Disp. Flow Cyt, yeast surface display analyzed using flow cytometry; ELISA, enzyme-linked immunosorbent assay; phage ELISA, phage display ELISA; KinExA, kinetic exclusion assay; ITC, isothermal titration calorimetry; ASA, accessible surface area; SASA, solvent-accessible surface area; bASA, buried accessible surface area; VdW, van der Waals; CI, confidence interval; D. Studio, Discovery Studio.Additional Supporting Information may be found in the online version of this article.Short Statement: We report a data set of 1101 antibody and antibody-like interface mutations with experimentally determined free energies of binding and at least one experimental structure that enables structure-based modeling. The database, AB-Bind, was used to benchmark computational scoring potentials for their ability to predict observed changes in binding free energies. Although there was a clear signal in tests discriminating mutations that improved/reduced binding, the prediction performance of all methods was modest, indicating a continued need to i...

Implementation of in vitro assays that correlate with in vivo human pharmacokinetics (PK) would provide desirable preclinical tools for the early selection of therapeutic monoclonal antibody (mAb) candidates with minimal non-target-related PK risk. Use of these tools minimizes the likelihood that mAbs with unfavorable PK would be advanced into costly preclinical and clinical development. In total, 42 mAbs varying in isotype and soluble versus membrane targets were tested in in vitro and in vivo studies. MAb physicochemical properties were assessed by measuring non-specific interactions (DNA- and insulin-binding ELISA), self-association (affinity-capture self-interaction nanoparticle spectroscopy) and binding to matrix-immobilized human FcRn (surface plasmon resonance and column chromatography). The range of scores obtained from each in vitro assay trended well with in vivo clearance (CL) using both human FcRn transgenic (Tg32) mouse allometrically projected human CL and observed human CL, where mAbs with high in vitro scores resulted in rapid CL in vivo. Establishing a threshold value for mAb CL in human of 0.32 mL/hr/kg enabled refinement of thresholds for each in vitro assay parameter, and using a combinatorial triage approach enabled the successful differentiation of mAbs at high risk for rapid CL (unfavorable PK) from those with low risk (favorable PK), which allowed mAbs requiring further characterization to be identified. Correlating in vitro parameters with in vivo human CL resulted in a set of in vitro tools for use in early testing that would enable selection of mAbs with the greatest likelihood of success in the clinic, allowing costly late-stage failures related to an inadequate exposure profile, toxicity or lack of efficacy to be avoided.

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 (T m 1 > 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.

Computational protein design can be used to select sequences that are compatible with a fixedbackbone 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. In this paper, 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-x L , 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-x L 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 seventeen designed BH3 peptide sequences, including several point mutants. Of these, eight bound well to Bcl-x L 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-x L .

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.