different methods, pointing to the inuence of antigen presentation on immune response specicity. Remarkably, sequences derived from soluble immunogen methods showed more similarity across FACS methods compared to those from cell/DNA methods, suggesting differences in immune response elicitation. This study exemplies how integrating NGS with innovative immunization strategies can streamline the antibody discovery process, enhance the functional relevance of identied antibodies, and expe‑ dite the development of new therapeutics.

Antibody 3D structure prediction is the domain which has beneted the most from the advent of ML, and methods of antibody structures have evolved rapidly. Much of this evolution is owed to the development of AlphaFold2,60 which has revolutionized protein structure prediction. However, despite AlphaFold2’s remarkable accuracy, the method still struggles with antibody structure prediction. AlphaFold2 requires a mul‑ tiple alignment step using various homologues, which is problematic in the context of antibodies due to overall diversity of the HCDR3 region. This has led to the develop‑ ment of antibody‑specic protein predictors such as ABodyBuilder261 and DeepAb,62 both of which are DL approaches capable of accurately predicting the entire Fv mod‑ elling region,61 whereas ABlooper specializes in CDR loop prediction by leveraging graph neural networks.63 Protein language models have also shown success in predicting antibody structure and function by assigning probabilities to predicted amino acids or ‘tokens.’64 IgFold is an example of a structure prediction tool that leverages this type of approach and utilizes a model trained on embeddings generated from AntiBERTy, a natural antibody dataset consisting of over 500million sequences.65 Unlike ABlooper and DeepAb, however, IgFold is able to make use of template structures, which results in more accurate and faster predictions.66 Lastly, EquiFold attempts to represent anti‑ bodies as geometrical structures, avoiding the need for either protein language model embeddings or multiple sequence alignments.67 Obtaining accurate predicted structural representations is crucial for not only understanding the binding potential of candidate antibodies but also their biophysical properties.68 Features such as solubility, hydro‑ phobicity, shelf‑life, viscosity, and immunogenicity have major roles in determining whether an antibody is suitable for large‑scale manufacturing and commercialization.69 Indeed, poor developability in post‑Phase 1 antibodies can largely be attributed to a small number of these features70; therefore, determining developability concerns early in the development cycle is key. In‑silico screening tools like FreeSASA,71 PROPKA3,72 and TAP73 can leverage predicted structures to identify major developability concerns, thus addressing these problems much earlier in development when compared with clas‑ sical approaches. Lastly, structure prediction can also help guide selection to a more diverse set of candidates. Tools such as SPACE2leverage predicted structures to cluster antibodies based on their predicted epitope of binding,74 thus allowing therapeutic can‑ didates to target a wider range of epitopes.