Deep mutational scanning is a novel approach in protein science that couples genotype to phenotype to assess the activities of a large number of mutant protein variants in a single experiment. This method generates large-scale datasets that reveal intrinsic protein properties, cellular behavior, and the consequences of human genetic variation. The key challenge is identifying informative mutations to analyze, as changes distant from binding or active sites can have significant effects on protein stability or activity. Deep mutational scanning can address this by examining the impact of mutations unbiasedly, revealing unexpected effects and providing insights into protein structure and function. The process involves creating a library of mutated variants, introducing them into a system where genotype is linked to phenotype, and then measuring the frequency of each variant before and after selection using high-throughput DNA sequencing. This data can be organized into sequence-function maps, which show how different mutations affect protein function. Deep mutational scanning has applications in inferring fundamental protein properties, understanding protein behavior in cells, studying protein evolution and engineering, and interpreting human genetic variation. However, challenges remain in developing appropriate assays, ensuring data quality, and interpreting complex datasets. Despite these challenges, deep mutational scanning holds great potential for transforming protein science and addressing the complex roles of disease proteins.Deep mutational scanning is a novel approach in protein science that couples genotype to phenotype to assess the activities of a large number of mutant protein variants in a single experiment. This method generates large-scale datasets that reveal intrinsic protein properties, cellular behavior, and the consequences of human genetic variation. The key challenge is identifying informative mutations to analyze, as changes distant from binding or active sites can have significant effects on protein stability or activity. Deep mutational scanning can address this by examining the impact of mutations unbiasedly, revealing unexpected effects and providing insights into protein structure and function. The process involves creating a library of mutated variants, introducing them into a system where genotype is linked to phenotype, and then measuring the frequency of each variant before and after selection using high-throughput DNA sequencing. This data can be organized into sequence-function maps, which show how different mutations affect protein function. Deep mutational scanning has applications in inferring fundamental protein properties, understanding protein behavior in cells, studying protein evolution and engineering, and interpreting human genetic variation. However, challenges remain in developing appropriate assays, ensuring data quality, and interpreting complex datasets. Despite these challenges, deep mutational scanning holds great potential for transforming protein science and addressing the complex roles of disease proteins.