Structural Insights into Transient States in HIV-1 Broadly Neutralizing Antibody Interactions

About This Grant

Abstract The development of effective vaccines against complex pathogens like HIV-1 requires immunogens that elicit broadly neutralizing antibodies (bnAbs). Traditional vaccine development strategies, including live- attenuated or subunit vaccines, and newer approaches leveraging genomic data, have not consistently induced bnAbs against HIV-1. Challenges stem from the unique structural and functional properties of bnAbs, such as improbable somatic mutations, autoreactivity, and long heavy-chain complementarity determining region-3 (HCDR3) loops. While priming immunogens have shown promise in selecting bnAb precursors, the production of mature bnAbs remains elusive, even in models engineered to ensure the presence of the relevant B-cell receptors. Our preliminary findings highlight the critical role of the transition state between unbound and bound antibody-antigen interactions in determining HIV-1 neutralizing antibody affinities. These transient structural states, which influence affinity maturation, neutralization breadth, and viral evolution, are poorly understood. Current methodologies primarily focus on static structural determinants of binding, neglecting the dynamic encounter complexes and intermediates that govern antibody-antigen association. This proposal aims to elucidate the association transition states and related intermediates for V2 apex- and CD4- binding site-directed bnAbs. Using structural and kinetic analyses, we will determine how variations in HIV-1 Env influence these transition states and how bnAbs overcome association barriers posed by Env sequence diversity. By dissecting the residue-level processes involved in antibody-antigen association, we will identify critical factors driving bnAb maturation and viral escape mechanisms. Using this information and large affinity datasets based on antibody clone HIV-1 Envelope interactions, we will develop cutting edge artificial intelligence/machine learning models to design immunogens with favorable affinity gradients for maturing bnAbs. Our work will address significant gaps in the understanding of antibody-antigen association, transitioning from phenomenological models to precise structural definitions of these poorly understood states and will directly use this information to inform immunogen development.