(333e) Multiscale Affinity Maturation Simulations to Elicit Broadly Neutralizing Antibodies Against HIV

Vaccines play a critical role in both preventing and eliminating global pandemics. This is especially true in the case of highly mutable pathogens like HIV and influenza, which present unique challenges to vaccine design. One promising strategy for designing vaccines against highly mutable pathogens is to target regions on the surfaces of the pathogenic proteins that are functionally important (e.g., receptor binding sites for adhering to/entering host cells), and thus cannot be so easily mutated by the pathogen. Antibodies that can bind to these conserved regions on pathogenic proteins are called broadly neutralizing antibodies, or bnAbs. BnAbs have now been isolated from people naturally infected with many different highly mutable pathogens. However, in most cases it remains unclear how to elicit these same antibodies by vaccination. In this presentation, we present an agent-based model of affinity maturation – the Darwinian process by which antibodies evolve against a pathogen – that, for the first time, enables the in silico investigation of real germline nucleotide sequences of antibodies known to evolve into potent bnAbs, evolving against real amino acid sequences of HIV-based vaccine-candidate proteins (Ags). These high-resolution simulations are enabled by the use of a fast and reasonably accurate free energy function to calculate the binding affinity between the evolved B cell receptor (BCR) sequences and Ags. We find that sequentially administering Ags that are increasingly diverse in the variable residues surrounding conserved residues will maximize bnAb production. Additionally, by analyzing and comparing crystal structures of bnAbs and germline antibodies bound to HIV-based Ags, we identified a novel mechanism through which bnAbs may evolve against the CD4 binding site of HIV. Lastly, we find that in response to specific vaccination protocols, the BCR population evolving in silico employs this same mechanism, recapitulating a key aspect of biology. In the future, our model may be used to iteratively design protein sequences that will maximize bnAb formation, thus forming the basis of vaccines against highly mutable pathogens.