Assisted Design of Antibody and Protein Therapeutics (ADAPT)

Effective biologic therapeutics require binding affinities that are fine-tuned to their disease-related molecular target. The ADAPT (Assisted Design of Antibody and Protein Therapeutics) platform aids in the selection of mutants that improve/modulate the affinity of antibodies and other biologics. It uses a consensus z-score from three scoring functions and interleaves computational predictions with experimental validation, significantly enhancing the robustness of the design and selection of mutants. The platform was tested on three antibody Fab-antigen systems that spanned a wide range of initial binding affinities: bH1-VEGF-A (44 nM), bH1-HER2 (3.6 nM) and Herceptin-HER2 (0.058 nM). Novel triple mutants were obtained that exhibited 104-, 46- and 32-fold improvements in binding affinity for each system, respectively. Moreover, for all three antibody-antigen systems over 90% of all the intermediate single and double mutants that were designed and tested showed higher affinities than the parent sequence. The contributions of the individual mutants to the change in binding affinity appear to be roughly additive when combined to form double and triple mutants. The new interactions introduced by the affinity-enhancing mutants included long-range electrostatics as well as short-range nonpolar interactions. This diversity in the types of new interactions formed by the mutants was reflected in SPR kinetics that showed that the enhancements in affinities arose from increasing on-rates, decreasing off-rates or a combination of the two effects, depending on the mutation. ADAPT is a very focused search of sequence space and required only 20–30 mutants for each system to be made and tested to achieve the affinity enhancements mentioned above

Additivity of contribution of mutations to binding affinity.

<p>Shown is a scatter plot of the experimentally measured relative binding affinities of double and triple mutants versus the sum of independently measured relative binding affinities of the component single/double mutants. The dashed line is the linear regression line for the entire set.</p

Sensorgrams of the parent Fabs and best triple mutant for each complex.

<p>The red curves represent the global fits of the data to a 1:1 bimolecular interaction model. The slow off rates of Herceptin and its triple mutant required longer data acquisition times in the dissociation phase to obtain reliable kinetics. The insets in panels (C) and (F) are expanded views of the association phase of these sensorgrams.</p

Fold improvements in binding affinity and relative changes in binding free energy (kcal/mol) relative to the parent Fab during three rounds of mutations.

<p>(a) bH1-VEGF. (b) bH1-HER2. (c) Herceptin-HER2. H and L designate the mutation as being in the heavy or light chain, respectively. Standard deviations of ΔΔG are based on 3 or more replicates, typically. NB = no binding. WT = parent sequence. Residue numbering for bH1 follows that of Bostrom et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0181490#pone.0181490.ref008" target="_blank">8</a>] Residue numbering for Herceptin follows that in the 1n8z PDB entry.</p

SPR-measured k_on and k_off rates for the parent sequence and triple mutants.

<p>SPR-measured k_on and k_off rates for the parent sequence and triple mutants.</p

Binding Properties of the Transforming Growth Factor‑β Coreceptor Betaglycan: Proposed Mechanism for Potentiation of Receptor Complex Assembly and Signaling

Transforming growth factor (TGF) β1, β2, and β3 (TGF-β1–TGF-β3, respectively) are small secreted signaling proteins that each signal through the TGF-β type I and type II receptors (TβRI and TβRII, respectively). However, TGF-β2, which is well-known to bind TβRII several hundred-fold more weakly than TGF-β1 and TGF-β3, has an additional requirement for betaglycan, a membrane-anchored nonsignaling receptor. Betaglycan has two domains that bind TGF-β2 at independent sites, but how it binds TGF-β2 to potentiate TβRII binding and how the complex with TGF-β, TβRII, and betaglycan undergoes the transition to the signaling complex with TGF-β, TβRII, and TβRI are not understood. To investigate the mechanism, the binding of the TGF-βs to the betaglycan extracellular domain, as well as its two independent binding domains, either directly or in combination with the TβRI and TβRII ectodomains, was studied using surface plasmon resonance, isothermal titration calorimetry, and size-exclusion chromatography. These studies show that betaglycan binds TGF-β homodimers with a 1:1 stoichiometry in a manner that allows one molecule of TβRII to bind. These studies further show that betaglycan modestly potentiates the binding of TβRII and must be displaced to allow TβRI to bind. These findings suggest that betaglycan functions to bind and concentrate TGF-β2 on the cell surface and thus promote the binding of TβRII by both membrane-localization effects and allostery. These studies further suggest that the transition to the signaling complex is mediated by the recruitment of TβRI, which simultaneously displaces betaglycan and stabilizes the bound TβRII by direct receptor–receptor contact