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      Peptide-scFv antigen recognition domains effectively confer CAR T cell multiantigen specificity

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          Summary

          The emergence of immune escape is a significant roadblock to developing effective chimeric antigen receptor (CAR) T cell therapies against hematological malignancies, including acute myeloid leukemia (AML). Here, we demonstrate feasibility of targeting two antigens simultaneously by combining a GRP78-specific peptide antigen recognition domain with a CD123-specific scFv to generate a peptide-scFv bispecific antigen recognition domain (78.123). To achieve this, we test linkers with varying length and flexibility and perform immunophenotypic and functional characterization. We demonstrate that bispecific CAR T cells successfully recognize and kill tumor cells that express GRP78, CD123, or both antigens and have improved antitumor activity compared to their monospecific counterparts when both antigens are expressed. Protein structure prediction suggests that linker length and compactness influence the functionality of the generated bispecific CARs. Thus, we present a bispecific CAR design strategy to prevent immune escape in AML that can be extended to other peptide-scFv combinations.

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          Highlights

          • The most effective strategy to target multiple antigens remains elusive

          • Peptide-scFv antigen recognition domains allow for effective dual-antigen targeting

          • Structural configuration impacts antigen accessibility and CAR effector function

          Abstract

          Zoine et al. engineer a peptide-scFv antigen recognition domain as part of a bispecific chimeric antigen receptor (CAR). They utilize a systematic approach to design and test combinations by performing structural and functional evaluation. Their bispecific CAR demonstrates antitumor activity in vitro and in vivo while maintaining dual-antigen specificity.

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          Most cited references68

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          Fiji: an open-source platform for biological-image analysis.

          Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.
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            Highly accurate protein structure prediction with AlphaFold

            Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.
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              VMD: Visual molecular dynamics

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                Author and article information

                Contributors
                Journal
                Cell Rep Med
                Cell Rep Med
                Cell Reports Medicine
                Elsevier
                2666-3791
                12 February 2024
                20 February 2024
                12 February 2024
                : 5
                : 2
                : 101422
                Affiliations
                [1 ]Department of Bone Marrow Transplantation and Cellular Therapy, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA
                [2 ]Department of Structural Biology and Center of Excellence for Data Driven Discovery, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA
                [3 ]Department of Pathology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA
                Author notes
                []Corresponding author paulina.velasquez@ 123456stjude.org
                [4]

                Lead contact

                Article
                S2666-3791(24)00045-4 101422
                10.1016/j.xcrm.2024.101422
                10897625
                38350450
                9e050a3c-0d30-4150-a066-0ba793087844
                © 2024 The Author(s)

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 13 April 2023
                : 6 October 2023
                : 19 January 2024
                Categories
                Article

                car t cell therapy,chimeric antigen receptor,immune escape,aml,leukemia,bispecific car,structure prediction,grp78,cd123,b7h3

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