Sutzl, L. | Search bioRxiv

bioRxiv2025

MiniCARbids: Minimalistic human binding domains specifically tailored to CAR T applications

Traditionally, chimeric antigen receptor (CAR) T cells employ single-chain variable fragments (scFvs) as binding entities. While scFvs represent a convenient option due to their broad availability, they also come with drawbacks, in particular their tendency to cluster and their relatively large size. Moreover, most scFvs used in the CAR field are of non-human origin, potentially causing immunogenicity. Therefore, we established an engineering platform for minimalistic CAR binding domains (miniCARbids), which combine several critical advantages: (i) human origin, (ii) small size, (iii) efficient expression in T cells and (iv) single-domain architecture, among others. We demonstrate that miniCARbids can be engineered to recognize various antigens with antibody-like affinities, while being stable and aggregation-resistant. When miniCARbids are incorporated into CARs, they induce high anti-tumor potency in both adapter and conventional CAR formats. Remarkably, CD22-directed miniCARbid-based CARs showed similar or even more efficient tumor clearance in leukemia-bearing mice when compared with a CAR comprising the clinically tested m971-1xG4S scFv. Together, we introduce the miniCARbid engineering platform, enabling the generation of small, human antigen-binding domains with high potency in CAR T cells against virtually any target antigen.

synthetic biology↗

bioRxiv2019

Identifying and engineering ancient variants of enzymes using Graphical Representation of Ancestral Sequence Predictions (GRASP)

Ancestral sequence reconstruction is a technique that is gaining widespread use in molecular evolution studies and protein engineering. Accurate reconstruction requires the ability to handle appropriately large numbers of sequences, as well as insertion and deletion ("indel") events, but available approaches exhibit limitations. To address these limitations, we developed Graphical Representation of Ancestral Sequence Predictions (GRASP), which efficiently implements maximum likelihood methods to enable the inference of ancestors of families with more than 10,000 members. GRASP implements partial order graphs (POGs) to represent and infer insertion and deletion events across ancestors, enabling the identification of building blocks for protein engineering. To validate the capacity to engineer novel proteins from realistic data, we predicted ancestor sequences across three distinct enzyme families: glucose-methanol-choline (GMC) oxidoreductases, cytochromes P450, and dihydroxy/sugar acid dehydratases (DHAD). All tested ancestors demonstrated enzymatic activity. Our study demonstrates the ability of GRASP (1) to support large data sets over 10,000 sequences and (2) to employ insertions and deletions to identify building blocks for engineering biologically active ancestors, by exploring variation over evolutionary time. Author summaryMassive sequencing projects expose the extent of natural, genetic diversity. Here, we describe a method with capacity to perform ancestor sequence reconstruction from data sets in excess of 10,000 sequences, poised to recover ancestral diversity, including the evolutionary events that determine present-time biological function and structure. We introduce a novel strategy for suggesting "indel variants" that are distinct from, but can be explored alongside, substitution variants for creating ancestral libraries. We demonstrate how indels can be used as building blocks to form "hybrid ancestors"; based on this strategy, we synthesise ancestor variants, with varying enzymatic activities, for wide-ranging applications in the biotechnology sector.

bioinformatics↗