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-β (TGF-β) receptor II, which functions as a "trap" for all three TGF-β isoforms. We demonstrate that M7824 efficiently, specifically, and simultaneously binds PD-L1 and TGF-β. In syngeneic mouse models, M7824 suppressed tumor growth and metastasis more effectively than treatment with either an anti-PD-L1 antibody or TGF-β 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-β pathways by M7824 elicits potent and superior antitumor activity relative to monotherapies.

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 Å with 80% of the targets having an rmsd lower than 2.0 Å. The median backbone heavy atom global rmsd of the CDR H3 loop prediction was 1.6 Å, 1.9 Å, 2.4 Å, 3.1 Å and 6.0 Å 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 towards protein engineering applications.

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.

SUMMARY 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.