Deep mutational scanning is a powerful technique that enables the assessment of the functional consequences of a vast number of protein variants in a single experiment. This approach involves generating a library of protein variants, introducing them into a system where genotype is linked to a selectable phenotype, and then selecting for the protein's function. High-throughput DNA sequencing is used to measure the frequency of each variant before and after selection, allowing the generation of functional scores for each variant. This method provides large-scale datasets that can reveal intrinsic protein properties, protein behavior within cells, and the consequences of human genetic variation. Deep mutational scanning has transformed the study of proteins by enabling the unbiased examination of the impact of mutations. It can reveal unexpected functional consequences and address complex cases where the activity of a large number of variants must be measured. The technique is applicable to a wide range of protein activities, including binding to other molecules, enzymatic activities, and cellular processes. It has been used to study various proteins, including those involved in disease, and has provided insights into protein structure, function, and evolution. The data generated by deep mutational scanning can be used to infer fundamental protein properties, such as thermodynamic stability and catalytic mechanisms. These data can also be used to identify stabilizing mutations, understand enzyme function, and study protein evolution. However, the interpretation of these data remains challenging, requiring both experimental and computational approaches. The development of computational models and analytical tools is essential for extracting meaningful insights from the large-scale mutational data. Deep mutational scanning also has significant implications for protein engineering and the study of human genetics. It can help identify mutations that improve protein function, which is crucial for designing new proteins or improving existing ones. In the context of human genetics, it can provide insights into the functional consequences of rare mutations, which is critical for understanding disease risk and developing diagnostic tools. Despite its potential, deep mutational scanning faces challenges in terms of data analysis, assay development, and the integration of results into clinical applications. However, ongoing advancements in sequencing technology, computational methods, and experimental design are helping to overcome these challenges. The technique holds great promise for advancing our understanding of protein function and its role in health and disease.Deep mutational scanning is a powerful technique that enables the assessment of the functional consequences of a vast number of protein variants in a single experiment. This approach involves generating a library of protein variants, introducing them into a system where genotype is linked to a selectable phenotype, and then selecting for the protein's function. High-throughput DNA sequencing is used to measure the frequency of each variant before and after selection, allowing the generation of functional scores for each variant. This method provides large-scale datasets that can reveal intrinsic protein properties, protein behavior within cells, and the consequences of human genetic variation. Deep mutational scanning has transformed the study of proteins by enabling the unbiased examination of the impact of mutations. It can reveal unexpected functional consequences and address complex cases where the activity of a large number of variants must be measured. The technique is applicable to a wide range of protein activities, including binding to other molecules, enzymatic activities, and cellular processes. It has been used to study various proteins, including those involved in disease, and has provided insights into protein structure, function, and evolution. The data generated by deep mutational scanning can be used to infer fundamental protein properties, such as thermodynamic stability and catalytic mechanisms. These data can also be used to identify stabilizing mutations, understand enzyme function, and study protein evolution. However, the interpretation of these data remains challenging, requiring both experimental and computational approaches. The development of computational models and analytical tools is essential for extracting meaningful insights from the large-scale mutational data. Deep mutational scanning also has significant implications for protein engineering and the study of human genetics. It can help identify mutations that improve protein function, which is crucial for designing new proteins or improving existing ones. In the context of human genetics, it can provide insights into the functional consequences of rare mutations, which is critical for understanding disease risk and developing diagnostic tools. Despite its potential, deep mutational scanning faces challenges in terms of data analysis, assay development, and the integration of results into clinical applications. However, ongoing advancements in sequencing technology, computational methods, and experimental design are helping to overcome these challenges. The technique holds great promise for advancing our understanding of protein function and its role in health and disease.