A key challenge in B-cell lineage-based vaccine design is understanding the "inducibility" of target neutralizing antibodies-the ability of these antibodies to be elicited by presentation of an immunogen. Induction relies on a combination of stochastic diversification and immunogen-based selection, and as a result is heavily dependent on the probabilistic accessibility of induction pathways. We explored inducibility using a detailed stochastic model of the somatic hypermutation process-which captures the critical "context-dependence" of sequence mutation rates-coupled to a stochastic population model for B-cell clonal maturation. The model is used to calculate inducibilities for a set of critical mutations required by the HIV broadly neutralizing antibody (bnAb) CH235.12. The results provide insight into barriers to the elicitation of HIV bnAbs and explain experimental results observed in mouse models. Our models enable detailed analysis of maturation pathway probabilities, allowing us to identify opportunities for the design of boosting immunogens aimed at elicitation of CH235.12 via a sequential vaccination regimen.
Keywords: broadly neutralizing antibody; computational immunology; somatic hypermutation.
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