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Computational Design of an Allosteric Antibody Switch by Deletion and Rescue of a Complex Structural Constellation.

ABSTRACT: Therapeutic monoclonal antibodies have transformed medicine, especially with regards to treating cancers and disorders of the immune system. More than 50 antibody-derived drugs have already reached the clinic, the majority of which target cytokines or cell-surface receptors. Unfortunately, many of these targets have pleiotropic functions: they serve multiple different roles, and often not all of these roles are disease-related. This can be problematic because antibodies act throughout the body, and systemic neutralization of such targets can lead to safety concerns. To address this, we have developed a strategy whereby an antibody's ability to recognize its antigen is modulated by a second layer of control, relying on addition of an exogenous small molecule. In previous studies, we began to explore this idea by introducing a deactivating tryptophan-to-glycine mutation in the domain-domain interface of a single-chain variable fragment (scFv), and then restoring activity by adding back indole to fit the designed cavity. Here, we now describe a novel computational strategy for enumerating larger cavities that can be formed by simultaneously introducing multiple adjacent large-to-small mutations; we then carry out a complementary virtual screen to identify druglike compounds to match each candidate cavity. We first demonstrate the utility of this strategy in a fluorescein-binding single-chain variable fragment (scFv) and experimentally characterize a triple mutant with reduced antigen-binding (Rip-3) that can be rescued using a complementary ligand (Stitch-3). Because our design is built upon conserved residues in the antibody framework, we then show that the same mutation/ligand pair can also be used to modulate antigen-binding in an scFv build from a completely unrelated framework. This set of residues is present in many therapeutic antibodies as well, suggesting that this mutation/ligand pair may serve as a general starting point for introducing ligand-dependence into many clinically relevant antibodies.

SUBMITTER: Khowsathit J

PROVIDER: S-EPMC7099597 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Computational Design of an Allosteric Antibody Switch by Deletion and Rescue of a Complex Structural Constellation.

Khowsathit Jittasak J Bazzoli Andrea A Cheng Hong H Karanicolas John J

ACS central science 20200311 3

Therapeutic monoclonal antibodies have transformed medicine, especially with regards to treating cancers and disorders of the immune system. More than 50 antibody-derived drugs have already reached the clinic, the majority of which target cytokines or cell-surface receptors. Unfortunately, many of these targets have pleiotropic functions: they serve multiple different roles, and often not all of these roles are disease-related. This can be problematic because antibodies act throughout the body, ...[more]

PMID: 32232139

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Project description:A structural-bioinformatics-based computational methodology and framework have been developed for the design of antibodies to targets of interest. RosettaAntibodyDesign (RAbD) samples the diverse sequence, structure, and binding space of an antibody to an antigen in highly customizable protocols for the design of antibodies in a broad range of applications. The program samples antibody sequences and structures by grafting structures from a widely accepted set of the canonical clusters of CDRs (North et al., J. Mol. Biol., 406:228-256, 2011). It then performs sequence design according to amino acid sequence profiles of each cluster, and samples CDR backbones using a flexible-backbone design protocol incorporating cluster-based CDR constraints. Starting from an existing experimental or computationally modeled antigen-antibody structure, RAbD can be used to redesign a single CDR or multiple CDRs with loops of different length, conformation, and sequence. We rigorously benchmarked RAbD on a set of 60 diverse antibody-antigen complexes, using two design strategies-optimizing total Rosetta energy and optimizing interface energy alone. We utilized two novel metrics for measuring success in computational protein design. The design risk ratio (DRR) is equal to the frequency of recovery of native CDR lengths and clusters divided by the frequency of sampling of those features during the Monte Carlo design procedure. Ratios greater than 1.0 indicate that the design process is picking out the native more frequently than expected from their sampled rate. We achieved DRRs for the non-H3 CDRs of between 2.4 and 4.0. The antigen risk ratio (ARR) is the ratio of frequencies of the native amino acid types, CDR lengths, and clusters in the output decoys for simulations performed in the presence and absence of the antigen. For CDRs, we achieved cluster ARRs as high as 2.5 for L1 and 1.5 for H2. For sequence design simulations without CDR grafting, the overall recovery for the native amino acid types for residues that contact the antigen in the native structures was 72% in simulations performed in the presence of the antigen and 48% in simulations performed without the antigen, for an ARR of 1.5. For the non-contacting residues, the ARR was 1.08. This shows that the sequence profiles are able to maintain the amino acid types of these conserved, buried sites, while recovery of the exposed, contacting residues requires the presence of the antigen-antibody interface. We tested RAbD experimentally on both a lambda and kappa antibody-antigen complex, successfully improving their affinities 10 to 50 fold by replacing individual CDRs of the native antibody with new CDR lengths and clusters.

Dataset Information

Computational Design of an Allosteric Antibody Switch by Deletion and Rescue of a Complex Structural Constellation.

Publications

Computational Design of an Allosteric Antibody Switch by Deletion and Rescue of a Complex Structural Constellation.

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