Antibody-peptide conjugates deliver covalent inhibitors blocking oncogenic cathepsins.

Cysteine cathepsins are a family of proteases that are relevant therapeutic targets for the treatment of different cancers and other diseases. However, no clinically approved drugs for these proteins exist, as their systemic inhibition can induce deleterious side effects. To address this problem, we developed a modular antibody-based platform for targeted drug delivery by conjugating non-natural peptide inhibitors (NNPIs) to antibodies. NNPIs were functionalized with reactive warheads for covalent inhibition, optimized with deep saturation mutagenesis and conjugated to antibodies to enable cell-type-specific delivery. Our antibody-peptide inhibitor conjugates specifically blocked the activity of cathepsins in different cancer cells, as well as osteoclasts, and showed therapeutic efficacy in vitro and in vivo. Overall, our approach allows for the rapid design of selective cathepsin inhibitors and can be generalized to inhibit a broad class of proteases in cancer and other diseases.

De novo design of protein interactions with learned surface fingerprints.

Physical interactions between proteins are essential for most biological processes governing life1. However, the molecular determinants of such interactions have been challenging to understand, even as genomic, proteomic and structural data increase. This knowledge gap has been a major obstacle for the comprehensive understanding of cellular protein-protein interaction networks and for the de novo design of protein binders that are crucial for synthetic biology and translational applications2-9. Here we use a geometric deep-learning framework operating on protein surfaces that generates fingerprints to describe geometric and chemical features that are critical to drive protein-protein interactions10. We hypothesized that these fingerprints capture the key aspects of molecular recognition that represent a new paradigm in the computational design of novel protein interactions. As a proof of principle, we computationally designed several de novo protein binders to engage four protein targets: SARS-CoV-2 spike, PD-1, PD-L1 and CTLA-4. Several designs were experimentally optimized, whereas others were generated purely in silico, reaching nanomolar affinity with structural and mutational characterization showing highly accurate predictions. Overall, our surface-centric approach captures the physical and chemical determinants of molecular recognition, enabling an approach for the de novo design of protein interactions and, more broadly, of artificial proteins with function.

Reversible Shielding and Immobilization of Liposomes and Viral Vectors by Tailored Antibody-Ligand Interactions.

Controlling the time and dose of nanoparticulate drug delivery by administration of small molecule drugs holds promise for efficient and safer therapies. This study describes a versatile approach of exploiting antibody-ligand interactions for the design of small molecule-responsive nanocarrier and nanocomposite systems. For this purpose, antibody fragments (scFvs) specific for two distinct small molecule ligands are designed. Subsequently, the surface of nanoparticles (liposomes or adeno-associated viral vectors, AAVs) is modified with these ligands, serving as anchor points for scFv binding. By modifying the scFvs with polymer tails, they can act as a non-covalently bound shielding layer, which is recruited to the anchor points on the nanoparticle surface and prevents interactions with cultured mammalian cells. Administration of an excess of the respective ligand triggers competitive displacement of the shielding layer from the nanoparticle surface and restores nanoparticle-cell interactions. The same principle is applied for developing hydrogel depots that can release integrated AAVs or liposomes in response to small molecule ligands. The liberated nanoparticles subsequently deliver their cargoes to cells. In summary, the utilization of different antibody-ligand interactions, different nanoparticles, and different release systems validates the versatility of the design concept described herein.

Designs and Characterization of Subunit Ebola GP Vaccine Candidates: Implications for Immunogenicity.

The humoral responses of Ebola virus (EBOV) survivors mainly target the surface glycoprotein GP, and anti-GP neutralizing antibodies have been associated with protection against EBOV infection. In order to elicit protective neutralizing antibodies through vaccination a native-like conformation of the antigen is required. We therefore engineered and expressed in CHO cells several GP variants from EBOV (species Zaire ebolavirus, Mayinga variant), including a soluble GP ΔTM, a mucin-like domain-deleted GP ΔTM-ΔMUC, as well as two GP ΔTM-ΔMUC variants with C-terminal trimerization motifs in order to favor their native trimeric conformation. Inclusion of the trimerization motifs resulted in proteins mimicking GP metastable trimer and showing increased stability. The mucin-like domain appeared not to be critical for the retention of the native conformation of the GP protein, and its removal unmasked several neutralizing epitopes, especially in the trimers. The soluble GP variants inhibited mAbs neutralizing activity in a pseudotype transduction assay, further confirming the proteins' structural integrity. Interestingly, the trimeric GPs, a native-like GP complex, showed stronger affinity for antibodies raised by natural infection in EBOV disease survivors rather than for antibodies raised in volunteers that received the ChAd3-EBOZ vaccine. These results support our hypothesis that neutralizing antibodies are preferentially induced when using a native-like conformation of the GP antigen. The soluble trimeric recombinant GP proteins we developed represent a novel and promising strategy to develop prophylactic vaccines against EBOV and other filoviruses.

Cathepsin S Regulates Antigen Processing and T Cell Activity in Non-Hodgkin Lymphoma.

Genomic alterations in cancer cells can influence the immune system to favor tumor growth. In non-Hodgkin lymphoma, physiological interactions between B cells and the germinal center microenvironment are coopted to sustain cancer cell proliferation. We found that follicular lymphoma patients harbor a recurrent hotspot mutation targeting tyrosine 132 (Y132D) in cathepsin S (CTSS) that enhances protein activity. CTSS regulates antigen processing and CD4+ and CD8+ T cell-mediated immune responses. Loss of CTSS activity reduces lymphoma growth by limiting communication with CD4+ T follicular helper cells while inducing antigen diversification and activation of CD8+ T cells. Overall, our results suggest that CTSS inhibition has non-redundant therapeutic potential to enhance anti-tumor immune responses in indolent and aggressive lymphomas.

Structural basis for recognition of RALF peptides by LRX proteins during pollen tube growth.

Plant reproduction relies on the highly regulated growth of the pollen tube for sperm delivery. This process is controlled by secreted RALF signaling peptides, which have previously been shown to be perceived by Catharanthus roseus RLK1-like (CrRLK1Ls) membrane receptor-kinases/LORELEI-like GLYCOLPHOSPHATIDYLINOSITOL (GPI)-ANCHORED PROTEINS (LLG) complexes, or by leucine-rich repeat (LRR) extensin proteins (LRXs). Here, we demonstrate that RALF peptides fold into bioactive, disulfide bond-stabilized proteins that bind the LRR domain of LRX proteins with low nanomolar affinity. Crystal structures of LRX2-RALF4 and LRX8-RALF4 complexes at 3.2- and 3.9-Å resolution, respectively, reveal a dimeric arrangement of LRX proteins, with each monomer binding one folded RALF peptide. Structure-based mutations targeting the LRX-RALF4 complex interface, or the RALF4 fold, reduce RALF4 binding to LRX8 in vitro and RALF4 function in growing pollen tubes. Mutants targeting the disulfide-bond stabilized LRX dimer interface fail to rescue lrx infertility phenotypes. Quantitative biochemical assays reveal that RALF4 binds LLGs and LRX cell-wall modules with drastically different binding affinities, and with distinct and mutually exclusive binding modes. Our biochemical, structural, and genetic analyses reveal a complex signaling network by which RALF ligands instruct different signaling proteins using distinct targeting mechanisms.

Rosetta FunFolDes - A general framework for the computational design of functional proteins.

The robust computational design of functional proteins has the potential to deeply impact translational research and broaden our understanding of the determinants of protein function and stability. The low success rates of computational design protocols and the extensive in vitro optimization often required, highlight the challenge of designing proteins that perform essential biochemical functions, such as binding or catalysis. One of the most simplistic approaches for the design of function is to adopt functional motifs in naturally occurring proteins and transplant them to computationally designed proteins. The structural complexity of the functional motif largely determines how readily one can find host protein structures that are "designable", meaning that are likely to present the functional motif in the desired conformation. One promising route to enhance the "designability" of protein structures is to allow backbone flexibility. Here, we present a computational approach that couples conformational folding with sequence design to embed functional motifs into heterologous proteins-Rosetta Functional Folding and Design (FunFolDes). We performed extensive computational benchmarks, where we observed that the enforcement of functional requirements resulted in designs distant from the global energetic minimum of the protein. An observation consistent with several experimental studies that have revealed function-stability tradeoffs. To test the design capabilities of FunFolDes we transplanted two viral epitopes into distant structural templates including one de novo "functionless" fold, which represent two typical challenges where the designability problem arises. The designed proteins were experimentally characterized showing high binding affinities to monoclonal antibodies, making them valuable candidates for vaccine design endeavors. Overall, we present an accessible strategy to repurpose old protein folds for new functions. This may lead to important improvements on the computational design of proteins, with structurally complex functional sites, that can perform elaborate biochemical functions related to binding and catalysis.

A Two-Step Approach for the Design and Generation of Nanobodies.

Nanobodies, the smallest possible antibody format, have become of considerable interest for biotechnological and immunotherapeutic applications. They show excellent robustness, are non-immunogenic in humans, and can easily be engineered and produced in prokaryotic hosts. Traditionally, nanobodies are selected from camelid immune libraries involving the maintenance and treatment of animals. Recent advances have involved the generation of nanobodies from naïve or synthetic libraries. However, such approaches demand large library sizes and sophisticated selection procedures. Here, we propose an alternative, two-step approach for the design and generation of nanobodies. In a first step, complementarity-determining regions (CDRs) are grafted from conventional antibody formats onto nanobody frameworks, generating weak antigen binders. In a second step, the weak binders serve as templates to design focused synthetic phage libraries for affinity maturation. We validated this approach by grafting toxin- and hapten-specific CDRs onto frameworks derived from variable domains of camelid heavy-chain-only antibodies (VHH). We then affinity matured the hapten binder via panning of a synthetic phage library. We suggest that this strategy can complement existing immune, naïve, and synthetic library based methods, requiring neither animal experiments, nor large libraries, nor sophisticated selection protocols.