Proteins are the basic building blocks necessary for the operation and regulation of virtually all functions in living organisms. Over millions of years, evolution has created a vast repertoire of proteins finely tuned to execute their biological functions. However, biomedical and biotechnological applications often require protein properties and functions beyond the natural scope. To overcome this limitation, protein engineering methods have emerged which has enabled the targeted alteration and optimization of proteins. Driven by advances in structural biology and growing understanding of the structure-function relationship, the development of computational design methods has opened new possibilities for the design of functional proteins. Since many proteins carry out their functions through physical interactions with other proteins, the alteration or creation of protein-protein interactions (PPIs) represents a crucial aspect of functional protein design. The design of PPIs from scratch remains a substantial challenge, particularly when no structural data is available. To tackle this challenge, we showcase a novel strategy for the design of de novo PPIs by identifying complementary buried surfaces to a target interface using geometric deep learning, leveraging our knowledge from known protein complexes. Using this approach, we were able to show the design of de novo protein binders to two highly relevant targets. Computation-based functional protein design also holds great potential for the development of novel and more efficient vaccines to combat pathogens, like the influenza virus, that have yet been resisting traditional vaccine development efforts. The isolation of broadly neutralizing antibodies targeting the influenza hemagglutinin (HA) have guided vaccine development towards a conserved epitope on the stem domain. However, the low immunogenicity and complexity of this epitope hinders the robust and focused induction of these antibodies. Computational design of immunogens allows us to spotlight this epitope to elicit a targeted antibody response. By using a design approach that focuses on the mimicry of the molecular surface of the epitope, we were able to design an immunogen that elicits focused immune response cross-reactive to HA and able to activate cellular cytotoxicity in vitro. While this immunogen design focused on the induction of antibodies against the conserved stem domain of influenza HA, we also explored design routes to engineer antigens based on the variable head domain, known to induce very potent neutralizing antibodies. By exploiting the high structural similarity between head domains variable in sequence, we aim to construct novel chimeric head domains combining semi-conserved sites from two or more relevant HA subtypes. These immunogens bear great potential to induce heterosubtypic antibody responses by a single molecule, but also to further elucidate the immunodominance hierarchies on the HA head domain. Altogether, this work demonstrates the utilization of different computation-guided design strategies in combination with experimental fine-tuning and validation to develop new or re-engineered protein binders. The implemented strategies represent general frameworks that can be applied to the development of immunogens and other protein-based therapeutics.