2024 AIChE Annual Meeting

Computationally Motivated Mutagenesis for Intelligent Incorporation of Noncanonical Amino Acids

Recombinantly expressed variable heavy chain regions of heavy chain antibodies, otherwise referred to as nanobodies, condense the highly selective characteristics of the larger antibody into a smaller package. In turn, these proteins maintain the ability to serve as a vehicle for intelligent drug design, something commonly found in cancer and other rare disease therapeutics. Despite nanobody’s major presence in immunoengineering, there lacks a distinct set of guidelines to rationally select conjugation sites on these nanobodies, resulting in effectively blind labeling and loading of drugs. Noncannonical amino acids (ncAAs) extend the preexisting 20 canonically encoded amino acids, introducing novel interactions, specifically the capacity to perform click chemistry, a common method in drug loading. Improper incorporation of these noncannonicals can yield detrimental results, reducing or even preventing binding. Due to the lack of guidelines, many incorporation attempts result in trivial results, due to nonbinding or inactivity. By providing intuition of incorporation sites, ncAAs become more accessible as a technology platform, and thus synthetic biology as a whole can be expanded to solve relevant issues beyond immunoengineering, such as biocatalysis. Additionally, intelligent selection of conjugation sites may elevate the time and financial expense of blind mutagenesis.

We seek to define these guidelines, creating a workflow in silico, in which conjugation sites can be rationally selected based off a variety of parameters. By this introduction of computational methods, we quickly screen and select promising conjugation site candidates for more informed mutagenesis rounds. The selection explores (1) the surface exposure of the various residues on the nanobody, (2) the distance between conjugation site and binding regions, and (3) the relative hydrophobicity of the side chains. We create various scripts interfacing PyMol and Python to extract and screen the nanobody. Each trait was screened and mutated independently to one another, with combination of screens tested afterwards. After selection in silico, we transition to in vitro, experimentally validating our findings through site directed mutagenesis, and expression of our mutated nanobodies. We then characterize the function of resultant nanobodies through examination of cell viability and target receptor binding affinity in vitro using relevant disease model cells. Upon collection of data, we plan to revisit the original computational model, leveraging statistical analysis to identify key features that have the highest correlation to both the binding interaction and intercellular activity, with the end goal of creating a predictive model accounting for necessary parameters.