Nanobodies are single-domain fragments of camelid heavy chain-only antibodies. Due to their size, nanobodies possess several key advantages over classical antibodies and are used in research and medicine. Nanobodies are usually generated using camelid immunization which is time-consuming and expensive. Beyond animal immunization, multiple methods for identifying nanobodies have emerged with machine learning–guided design becoming increasingly popular.
In this master's thesis we designed new nanobodies against BcII, a β-lactamase from Bacillus cereus, based on the cAbBCII10 scaffold. We used AlphaFold 3 to predict the structure of the complex between cAbBCII10 and BcII. Next, we used machine learning to design new nanobodies targeting the same epitope and constructed a DNA-library based on the designed nanobody sequences, which we assembled using polymerase chain assembly. Subsequently, we evaluated nanobody binding to BcII and used next generation sequencing to validate the quality of the constructed library. Due to mistakes that were introduced into the nanobody sequences during library assembly, we were not able to screen all the designed nanobodies for their ability to bind BcII. Despite this, we isolated three new nanobodies with low affinity for BcII.
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