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Design of split superantigen fusion proteins for cancer immunotherapy.

ABSTRACT: Several antibody-targeting cancer immunotherapies have been developed based on T cell activation at the target cells. One of the most potent activators of T cells are bacterial superantigens, which bind to major histocompatibility complex class II on antigen-presenting cells and activate T cells through T cell receptor. Strong T cell activation is also one of the main weaknesses of this strategy as it may lead to systemic T cell activation. To overcome the limitation of conventional antibody-superantigen fusion proteins, we have split a superantigen into two fragments, individually inactive, until both fragments came into close proximity and reassembled into a biologically active form capable of activating T cell response. A screening method based on fusion between SEA and coiled-coil heterodimers was developed that enabled detection of functional split SEA designs. The split SEA design that demonstrated efficacy in fusion with coiled-coil dimer forming polypeptides was fused to a single chain antibody specific for tumor antigen CD20. This design selectively activated T cells by split SEA-scFv fusion binding to target cells.

SUBMITTER: Golob-Urbanc A

PROVIDER: S-EPMC6484123 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Design of split superantigen fusion proteins for cancer immunotherapy.

Golob-Urbanc Anja A Rajčević Uroš U Strmšek Žiga Ž Jerala Roman R

The Journal of biological chemistry 20190219 16

Several antibody-targeting cancer immunotherapies have been developed based on T cell activation at the target cells. One of the most potent activators of T cells are bacterial superantigens, which bind to major histocompatibility complex class II on antigen-presenting cells and activate T cells through T cell receptor. Strong T cell activation is also one of the main weaknesses of this strategy as it may lead to systemic T cell activation. To overcome the limitation of conventional antibody-sup ...[more]

PMID: 30782846

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Project description:Many pathogenic viruses, including influenza virus, Ebola virus, coronaviruses, and Pneumoviruses, rely on class I fusion proteins to fuse viral and cellular membranes. To drive the fusion process, class I fusion proteins undergo an irreversible conformational change from a metastable prefusion state to an energetically more favorable and stable postfusion state. An increasing amount of evidence exists highlighting that antibodies targeting the prefusion conformation are the most potent. However, many mutations have to be evaluated before identifying prefusion-stabilizing substitutions. We therefore established a computational design protocol that stabilizes the prefusion state while destabilizing the postfusion conformation. As a proof of concept, we applied this principle to the fusion protein of the RSV, hMPV, and SARS-CoV-2 viruses. For each protein, we tested less than a handful of designs to identify stable versions. Solved structures of designed proteins from the three different viruses evidenced the atomic accuracy of our approach. Furthermore, the immunological response of the RSV F design compared to a current clinical candidate in a mouse model. While the parallel design of two conformations allows identifying and selectively modifying energetically less optimized positions for one conformation, our protocol also reveals diverse molecular strategies for stabilization. We recaptured many approaches previously introduced manually for the stabilization of viral surface proteins, such as cavity-filling, optimization of polar interactions, as well as postfusion-disruptive strategies. Using our approach, it is possible to focus on the most impacting mutations and potentially preserve the immunogen as closely as possible to its native version. The latter is important as sequence re-design can cause perturbations to B and T cell epitopes. Given the clinical significance of viruses using class I fusion proteins, our algorithm can substantially contribute to vaccine development by reducing the time and resources needed to optimize these immunogens.

Dataset Information

Design of split superantigen fusion proteins for cancer immunotherapy.

Publications

Design of split superantigen fusion proteins for cancer immunotherapy.

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