Antibody recognition of antigens is a unique class of protein-protein interactions, and increased knowledge regarding the determinants of these interactions has advanced fields such as computational vaccine design and protein docking. However, the diversity and flexibility of antibodies and antigens can hinder generation of potent vaccine immunogens or prediction of correct antibody-antigen interfaces, slowing progress in the design of vaccines and antibody therapeutics. In this thesis, we present strategies to design vaccine candidates for a difficult viral target and describe expanded resources for benchmarking and training antibody-antigen docking and affinity prediction algorithms.We utilized rational design to develop candidate immunogens for a vaccine against hepatitis C virus (HCV), which represents a global disease burden despite recent advances in antiviral treatments. This design strategy produced a soluble and secreted E1E2 glycoprotein heterodimer with native-like antigenicity and immunogenicity by fusing ectodomains with a leucine zipper scaffold and a furin cleavage site. We developed additional constructs that incorporated synthetic or non-eukaryotic scaffolds or alternative ectodomains that included consensus sequences designed using a large reference database. Finally, we utilized previously published data on HCV antibody neutralization and E1E2 mutagenesis to predict residues that impact antibody neutralization and E1E2 heterodimerization, offering potential insights that can aid vaccine design. To improve our knowledge of and accuracy in modeling antibody-antigen recognition, we assembled a set of antibody-antigen complex structures from the Protein Data Bank (PDB) that expanded Docking Benchmark 5, a widely used benchmark for protein docking. These complexes more than doubled the number of antibody-antigen structures in the benchmark and, based on tests of current algorithms, highlight significant challenges for docking and affinity prediction. Building on this resource, we assembled and curated a dataset of ~400 antibody-antigen affinities and corresponding structures, forming an expanded and updated benchmark to guide ΔG prediction of antibody-antigen interactions. Using this dataset, we retrained combinations of terms from existing scoring functions and potentials, demonstrating that this resource can be used to improve antibody-antigen ΔG prediction. Overall, these findings can advance HCV vaccine design and antibody-antigen docking and affinity prediction, helping to better elucidate the determinants of antibody-antigen interactions and to better display vaccine immunogens for induction of neutralizing antibodies.