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Bohn, M.-F.

Publications and source records attributed to Bohn, M.-F..

2 recordsLinked to original sources

Phage display assisted discovery of a pH-dependent anti-alpha-cobratoxin antibody from a natural variable domain library

Recycling antibodies can bind to their target antigen at neutral pH in the blood stream and release them upon endocytosis when pH levels drop, allowing the antibodies to be recycled into circulation via FcRn-mediated pathway, while the antigens undergo lysosomal degradation. This enables recycling antibodies to achieve the same therapeutic effect at lower doses than their non-recyclable counterparts. The development of such antibodies is typically achieved by histidine doping of the variable regions of specific antibodies or by performing in vitro antibody selection campaigns utilizing histidine doped libraries. While often successful, these strategies may introduce sequence liabilities, as they often involve mutations that may render the resultant antibodies to be non-natural. Here, we present a methodology that employs a naive antibody phage display library, consisting of natural variable domains, to discover antibodies that bind -cobratoxin from the venom of Naja kaouthia in a pH-dependent manner. Upon screening of the discovered antibodies with immunoassays and bio-layer interferometry, a pH-dependent antibody was discovered that exhibits an 8-fold higher dissociation rate at pH 5.5 than 7.4. Interestingly, the variable domains of the pH-dependent antibody were found to be entirely devoid of histidines, demonstrating that pH-dependency may not always be driven by this amino acid. Further, given the high diversity available in a naive antibody library, the methodology presented here can likely be applied to discover pH-dependent antibodies against different targets ab initio without the need of histidine doping. For broader audienceHere, we present the discovery of an -cobratoxin targeting pH-dependent antibody, with a variable region devoid of histidines, from a naive antibody library with natural variable domains. Our findings suggest that the commonly taken approach of histidine doping to find pH-dependent antibodies may not always be required, and thus offer an alternative strategy for the discovery of pH-dependent antibodies.

bioengineering↗

A Comparative Study of Protein Structure Prediction Tools for Challenging Targets: Snake Venom Toxins

Protein structure determination is a critical aspect of biological research, enabling us to understand protein function and potential applications. Recent advances in deep learning and artificial intelligence have led to the development of several protein structure prediction tools, such as AlphaFold2 and ColabFold. However, their performance has primarily been evaluated on well-characterised proteins, and comparisons using proteins with poor reference templates are lacking. In this study, we evaluated three modelling tools on their prediction of over 1000 snake venom toxin structures with no reference templates. Our findings show that AlphaFold2 (AF2) performed the best across all assessed parameters. We also observed that ColabFold (CF) only scored slightly worse than AF2, while being computationally less intensive. All tools struggled with regions of intrinsic disorder, such as loops and propeptide regions, and performed well in predicting the structure of functional domains. Overall, our study highlights the importance of exercising caution when working with proteins that have poor reference templates, are large, and contain flexible regions. Nonetheless, leveraging computational structure prediction tools can provide valuable insights into the modelling of protein interactions with different targets and reveal potential binding sites, active sites, and conformational changes, as well as into the design of potential molecular binders for reagent, diagnostic, or therapeutic purposes. StatementRecent advances in machine learning have led to the development of new protein structure prediction tools. However, these tools have mainly been tested on well-known proteins and their performance on proteins without known templates is unclear. This study evaluated the performance of three tools on over 1000 snake venom toxins. We found that while caution is required when studying poorly characterised proteins, these tools offer valuable opportunities to understand protein function and applications.

bioinformatics↗