PROJECT SUMMARY This isolation of exceptionally broad anti-HIV antibodies has revealed sites of vulnerability on the virus’s surface protein, Env. Major efforts are now underway to elicit such antibodies with vaccines, or to use the antibodies directly as therapeutics. The design of such vaccines and therapies requires carefully characterizing how monoclonal antibodies and polyclonal sera recognize Env. We have recently developed a new deep-sequencing based approach to functionally characterize antibody-Env interactions on a large scale. Here we will greatly extend the utility of this approach by making it possible to easily and completely map the effects of all amino-acid mutations on viral recognition by both neutralizing and non- neutralizing antibodies or sera. Specifically, we will: 1) Create libraries of viruses carrying all single amino-acid mutations to Env, as well as many combinations of mutations. These libraries will be designed in a way that enables them to be easily and cheaply characterized by deep sequencing. 2) Develop methods to use the libraries to efficiently map how mutations to Env affect virus recognition by neutralizing and non-neutralizing antibodies and sera. 3) Create algorithms and software to analyze and visualize the “Big Data” sets generated by the mappings. We will use these tools to completely map how Env mutations affect recognition by important monoclonal antibodies, as well as natural and vaccine-induced plasma responses. We will also distribute the experimental and computational tools so that they can be easily used by the entire HIV research community. Overall, this work will develop powerful methods to functionally map the antigenic effects of mutations to HIV. Such maps will aid in the basic study of HIV and inform the design of vaccines and therapeutics.