Colocalized targeting of TGF-ß and PD-L1 by bintrafusp alfa elicits distinct antitumor responses.

BACKGROUND: Bintrafusp alfa (BA) is a bifunctional fusion protein designed for colocalized, simultaneous inhibition of two immunosuppressive pathways, transforming growth factor-ß (TGF-ß) and programmed death-ligand 1 (PD-L1), within the tumor microenvironment (TME). We hypothesized that targeting PD-L1 to the tumor by BA colocalizes the TGF-ß trap (TGF-ßRII) to the TME, enabling it to sequester TGF-ß in the tumor more effectively than systemic TGF-ß blockade, thereby enhancing antitumor activity. METHODS: Multiple technologies were used to characterize the TGF-ß trap binding avidity. BA versus combinations of anti-PD-L1 and TGF-ß trap or the pan-TGF-ß antibody fresolimumab were compared in proliferation and two-way mixed lymphocyte reaction assays. Immunophenotyping of tumor-infiltrating lymphocytes (TILs) and RNA sequencing (RNAseq) analysis assessing stromal and immune landscape following BA or the combination therapy were performed in MC38 tumors. TGF-ß and PD-L1 co-expression and their associated gene signatures in MC38 tumors and human lung carcinoma tissue were studied with single-cell RNAseq (scRNAseq) and immunostaining. BA-induced internalization, degradation, and depletion of TGF-ß were investigated in vitro. RESULTS: BA and fresolimumab had comparable intrinsic binding to TGF-ß1, but there was an ~80× avidity-based increase in binding affinity with BA. BA inhibited cell proliferation in TGF-ß-dependent and PD-L1-expressing cells more potently than TGF-ß trap or fresolimumab. Compared with the combination of anti-PD-L1 and TGF-ß trap or fresolimumab, BA enhanced T cell activation in vitro and increased TILs in MC38 tumors, which correlated with efficacy. BA induced distinct gene expression in the TME compared with the combination therapy, including upregulation of immune-related gene signatures and reduced activities in TGF-ß-regulated pathways, such as epithelial-mesenchymal transition, extracellular matrix deposition, and fibrosis. Regulatory T cells, macrophages, immune cells of myeloid lineage, and fibroblasts were key PD-L1/TGF-ß1 co-expressing cells in the TME. scRNAseq analysis suggested BA modulation of the macrophage phenotype, which was confirmed by histological assessment. PD-L1/TGF-ß1 co-expression was also seen in human tumors. Finally, BA induced TGF-ß1 internalization and degradation in the lysosomes. CONCLUSION: BA more effectively blocks TGF-ß by targeting TGF-ß trap to the tumor via PD-L1 binding. Such colocalized targeting elicits distinct and superior antitumor responses relative to single agent combination therapy.

Enhanced preclinical antitumor activity of M7824, a bifunctional fusion protein simultaneously targeting PD-L1 and TGF-ß.

Antibodies targeting immune checkpoints are emerging as potent and viable cancer therapies, but not all patients respond to these as single agents. Concurrently targeting additional immunosuppressive pathways is a promising approach to enhance immune checkpoint blockade, and bifunctional molecules designed to target two pathways simultaneously may provide a strategic advantage over the combination of two single agents. M7824 (MSB0011359C) is a bifunctional fusion protein composed of a monoclonal antibody against programmed death ligand 1 (PD-L1) fused to the extracellular domain of human transforming growth factor-ß (TGF-ß) receptor II, which functions as a "trap" for all three TGF-ß isoforms. We demonstrate that M7824 efficiently, specifically, and simultaneously binds PD-L1 and TGF-ß. In syngeneic mouse models, M7824 suppressed tumor growth and metastasis more effectively than treatment with either an anti-PD-L1 antibody or TGF-ß trap alone; furthermore, M7824 extended survival and conferred long-term protective antitumor immunity. Mechanistically, the dual anti-immunosuppressive function of M7824 resulted in activation of both the innate and adaptive immune systems, which contributed to M7824's antitumor activity. Finally, M7824 was an effective combination partner for radiotherapy or chemotherapy in mouse models. Collectively, our preclinical data demonstrate that simultaneous blockade of the PD-L1 and TGF-ß pathways by M7824 elicits potent and superior antitumor activity relative to monotherapies.

Structure-based design of supercharged, highly thermoresistant antibodies.

Mutation of surface residues to charged amino acids increases resistance to aggregation and can enable reversible unfolding. We have developed a protocol using the Rosetta computational design package that "supercharges" proteins while considering the energetic implications of each mutation. Using a homology model, a single-chain variable fragment antibody was designed that has a markedly enhanced resistance to thermal inactivation and displays an unanticipated ≈30-fold improvement in affinity. Such supercharged antibodies should prove useful for assays in resource-limited settings and for developing reagents with improved shelf lives.

IBC's 22nd Annual Antibody Engineering and 9th Annual Antibody Therapeutics International Conferences and the 2011 Annual Meeting of The Antibody Society, December 5-8, 2011, San Diego, CA.

The 22nd Annual Antibody Engineering and 9th Annual Antibody Therapeutics international conferences, and the 2011 Annual Meeting of The Antibody Society, organized by IBC Life Sciences with contributions from The Antibody Society and two Scientific Advisory Boards, were held December 5-8, 2011 in San Diego, CA. The meeting drew ~800 participants who attended sessions on a wide variety of topics relevant to antibody research and development. As a preview to the main events, a pre-conference workshop held on December 4, 2011 focused on antibodies as probes of structure. The Antibody Engineering Conference comprised eight sessions: (1) structure and dynamics of antibodies and their membrane receptor targets; (2) model-guided generation of binding sites; (3) novel selection strategies; (4) antibodies in a complex environment: targeting intracellular and misfolded proteins; (5) rational vaccine design; (6) viral retargeting with engineered binding molecules; (7) the biology behind potential blockbuster antibodies and (8) antibodies as signaling modifiers: where did we go right, and can we learn from success? The Antibody Therapeutics session comprised five sessions: (1)Twenty-five years of therapeutic antibodies: lessons learned and future challenges; (2) preclinical and early stage development of antibody therapeutics; (3) next generation anti-angiogenics; (4) updates of clinical stage antibody therapeutics and (5) antibody drug conjugates and bispecific antibodies.

Methods for the homology modeling of antibody variable regions.

Antibodies are one of the critical molecules of our immune system and are unique in their enormous diversity required for recognizing various antigens. Antibodies are protein molecules and their antigen interacting region, the fragment variable (F (V)), is typically composed of a light (V (L)) and heavy (V (H)) chain. In particular, three loops each at the tip of the V (L) and the V (H), known as the complementarity determining region (CDR) loops, are responsible for binding to the antigen. While the framework regions of the V (L) and V (H) are relatively constant across the entire repertoire of antibodies, the conformation of the CDR loops varies extensively to enable the antibody to recognize different antigens. Three-dimensional structures of antibodies illustrating the V (L)-V (H) relative orientation and the CDR conformations are needed to gain insight into antibody stability, immunogenicity, and antibody-antigen interactions. Computational modeling provides a fast and inexpensive route for generating antibody structural models. This chapter highlights the various features crucial for creating a successful antibody homology model.

Analysis and modeling of the variable region of camelid single-domain antibodies.

Camelids have a special type of Ab, known as heavy chain Abs, which are devoid of classical Ab light chains. Relative to classical Abs, camelid heavy chain Abs (cAbs) have comparable immunogenicity, Ag recognition diversity and binding affinities, higher stability and solubility, and better manufacturability, making them promising candidates for alternate therapeutic scaffolds. Rational engineering of cAbs to improve therapeutic function requires knowledge of the differences of sequence and structural features between cAbs and classical Abs. In this study, amino acid sequences of 27 cAb variable regions (V(H)H) were aligned with the respective regions of 54 classical Abs to detect amino acid differences, enabling automatic identification of cAb V(H)H CDRs. CDR analysis revealed that the H1 often (and sometimes the H2) adopts diverse conformations not classifiable by established canonical rules. Also, although the cAb H3 is much longer than classical H3 loops, it often contains common structural motifs and sometimes a disulfide bond to the H1. Leveraging these observations, we created a Monte Carlo-based cAb V(H)H structural modeling tool, where the CDR H1 and H2 loops exhibited a median root-mean-square deviation to natives of 3.1 and 1.5 Å, respectively. The protocol generated 8-12, 14-16, and 16-24 residue H3 loops with a median root-mean-square deviation to natives of 5.7, 4.5, and 6.8 Å, respectively. The large deviation of the predicted loops underscores the challenge in modeling such long loops. cAb V(H)H homology models can provide structural insights into interaction mechanisms to enable development of novel Abs for therapeutic and biotechnological use.

Mechanism of polyubiquitin chain recognition by the human ubiquitin conjugating enzyme Ube2g2.

Ube2g2 is a human ubiquitin conjugating (E2) enzyme involved in the endoplasmic reticulum-associated degradation pathway, which is responsible for the identification and degradation of unfolded and misfolded proteins in the endoplasmic reticulum compartment. The Ube2g2-specific role is the assembly of Lys-48-linked polyubiquitin chains, which constitutes a signal for proteasomal degradation when attached to a substrate protein. NMR chemical shift perturbation and paramagnetic relaxation enhancement approaches were employed to characterize the binding interaction between Ube2g2 and ubiquitin, Lys-48-linked diubiquitin, and Lys-63-linked diubiquitin. Results demonstrate that ubiquitin binds to Ube2g2 with an affinity of 90 µM in two different orientations that are rotated by 180° in models generated by the RosettaDock modeling suite. The binding of Ube2g2 to Lys-48- and Lys-63-linked diubiquitin is primarily driven by interactions with individual ubiquitin subunits, with a clear preference for the subunit containing the free Lys-48 or Lys-63 side chain (i.e. the distal subunit). This preference is particularly striking in the case of Lys-48-linked diubiquitin, which exhibits an ∼3-fold difference in affinities between the two ubiquitin subunits. This difference can be attributed to the partial steric occlusion of the subunit whose Lys-48 side chain is involved in the isopeptide linkage. As such, these results suggest that Lys-48-linked polyubiquitin chains may be designed to bind certain proteins like Ube2g2 such that the terminal ubiquitin subunit carrying the reactive Lys-48 side chain can be positioned properly for chain elongation regardless of chain length.

A generalized approach to sampling backbone conformations with RosettaDock for CAPRI rounds 13-19.

In CAPRI rounds 13-19, the most native-like structure predicted by RosettaDock resulted in two high, one medium, and one acceptable accuracy model out of 13 targets. The current rounds of CAPRI were especially challenging with many unbound and homology modeled starting structures. Novel docking methods, including EnsembleDock and SnugDock, allowed backbone conformational sampling during docking and enabled the creation of more accurate models. For Target 32, α-amylase/subtilisin inhibitor-subtilisin savinase, we sampled different backbone conformations at an interfacial loop to produce five high-quality models including the most accurate structure submitted in the challenge (2.1 Å ligand rmsd, 0.52 Å interface rmsd). For Target 41, colicin-immunity protein, we used EnsembleDock to sample the ensemble of nuclear magnetic resonance (NMR) models of the immunity protein to generate a medium accuracy structure. Experimental data identifying the catalytic residues at the binding interface for Target 40 (trypsin-inhibitor) were used to filter RosettaDock global rigid body docking decoys to determine high accuracy predictions for the two distinct binding sites in which the inhibitor interacts with trypsin. We discuss our generalized approach to selecting appropriate methods for different types of docking problems. The current toolset provides some robustness to errors in homology models, but significant challenges remain in accommodating larger backbone uncertainties and in sampling adequately for global searches.

SnugDock: paratope structural optimization during antibody-antigen docking compensates for errors in antibody homology models.

High resolution structures of antibody-antigen complexes are useful for analyzing the binding interface and to make rational choices for antibody engineering. When a crystallographic structure of a complex is unavailable, the structure must be predicted using computational tools. In this work, we illustrate a novel approach, named SnugDock, to predict high-resolution antibody-antigen complex structures by simultaneously structurally optimizing the antibody-antigen rigid-body positions, the relative orientation of the antibody light and heavy chains, and the conformations of the six complementarity determining region loops. This approach is especially useful when the crystal structure of the antibody is not available, requiring allowances for inaccuracies in an antibody homology model which would otherwise frustrate rigid-backbone docking predictions. Local docking using SnugDock with the lowest-energy RosettaAntibody homology model produced more accurate predictions than standard rigid-body docking. SnugDock can be combined with ensemble docking to mimic conformer selection and induced fit resulting in increased sampling of diverse antibody conformations. The combined algorithm produced four medium (Critical Assessment of PRediction of Interactions-CAPRI rating) and seven acceptable lowest-interface-energy predictions in a test set of fifteen complexes. Structural analysis shows that diverse paratope conformations are sampled, but docked paratope backbones are not necessarily closer to the crystal structure conformations than the starting homology models. The accuracy of SnugDock predictions suggests a new genre of general docking algorithms with flexible binding interfaces targeted towards making homology models useful for further high-resolution predictions.

RosettaAntibody: antibody variable region homology modeling server.

The RosettaAntibody server (http://antibody.graylab.jhu.edu) predicts the structure of an antibody variable region given the amino-acid sequences of the respective light and heavy chains. In an initial stage, the server identifies and displays the most sequence homologous template structures for the light and heavy framework regions and each of the complementarity determining region (CDR) loops. Subsequently, the most homologous templates are assembled into a side-chain optimized crude model, and the server returns a picture and coordinate file. For users requesting a high-resolution model, the server executes the full RosettaAntibody protocol which additionally models the hyper-variable CDR H3 loop. The high-resolution protocol also relieves steric clashes by optimizing the CDR backbone torsion angles and by simultaneously perturbing the relative orientation of the light and heavy chains. RosettaAntibody generates 2000 independent structures, and the server returns pictures, coordinate files, and detailed scoring information for the 10 top-scoring models. The 10 models enable users to use rational judgment in choosing the best model or to use the set as an ensemble for further studies such as docking. The high-resolution models generated by RosettaAntibody have been used for the successful prediction of antibody-antigen complex structures.

Toward high-resolution homology modeling of antibody Fv regions and application to antibody-antigen docking.

High-resolution homology models are useful in structure-based protein engineering applications, especially when a crystallographic structure is unavailable. Here, we report the development and implementation of RosettaAntibody, a protocol for homology modeling of antibody variable regions. The protocol combines comparative modeling of canonical complementarity determining region (CDR) loop conformations and de novo loop modeling of CDR H3 conformation with simultaneous optimization of V(L)-V(H) rigid-body orientation and CDR backbone and side-chain conformations. The protocol was tested on a benchmark of 54 antibody crystal structures. The median root mean square deviation (rmsd) of the antigen binding pocket comprised of all the CDR residues was 1.5 A with 80% of the targets having an rmsd lower than 2.0 A. The median backbone heavy atom global rmsd of the CDR H3 loop prediction was 1.6, 1.9, 2.4, 3.1, and 6.0 A for very short (4-6 residues), short (7-9), medium (10-11), long (12-14) and very long (17-22) loops, respectively. When the set of ten top-scoring antibody homology models are used in local ensemble docking to antigen, a moderate-to-high accuracy docking prediction was achieved in seven of fifteen targets. This success in computational docking with high-resolution homology models is encouraging, but challenges still remain in modeling antibody structures for sequences with long H3 loops. This first large-scale antibody-antigen docking study using homology models reveals the level of "functional accuracy" of these structural models toward protein engineering applications.

Incorporating biochemical information and backbone flexibility in RosettaDock for CAPRI rounds 6-12.

In CAPRI rounds 6-12, RosettaDock successfully predicted 2 of 5 unbound-unbound targets to medium accuracy. Improvement over the previous method was achieved with computational mutagenesis to select decoys that match the energetics of experimentally determined hot spots. In the case of Target 21, Orc1/Sir1, this resulted in a successful docking prediction where RosettaDock alone or with simple site constraints failed. Experimental information also helped limit the interacting region of TolB/Pal, producing a successful prediction of Target 26. In addition, we docked multiple loop conformations for Target 20, and we developed a novel flexible docking algorithm to simultaneously optimize backbone conformation and rigid-body orientation to generate a wide diversity of conformations for Target 24. Continued challenges included docking of homology targets that differ substantially from their template (sequence identity <50%) and accounting for large conformational changes upon binding. Despite a larger number of unbound-unbound and homology model binding targets, Rounds 6-12 reinforced that RosettaDock is a powerful algorithm for predicting bound complex structures, especially when combined with experimental data.