Our preprint describing a new statistical model for getting the most out of deep mutational scanning data is available. We implemented the model in Enrich2, which we hope will be a useful for the DMS community.
Nick Hasle, an M.D./Ph.D. student at UW has joined us. Nick is interested in exploring new, high-throughput methods to link genotype and phenotype.
Genomic alterations that confer enhanced growth, such as missense mutations in the p53 gene, are at the root of cancer. Identification of such cancer-driving mutations, as well as bystander mutations, has been greatly accelerated by sequencing advances, but the functional relevance of these mutations is often unknown. Mutations also frequently underlie the failure of targeted cancer therapeutics (e.g. mutations in BCR-Abl that confer resistance to imatinib). Therefore, understanding how mutations affect cancer-related protein activity and inhibitor efficacy is critical to make effective use of personal and cancer genomics data, as well as to develop and deploy of targeted therapies. We are using deep mutational scanning for the functional characterization of hundreds of thousands of mutants of cancer-related proteins simultaneously. For example, we are currently measuring the consequences for function, regulation and resistance to inhibition of nearly all 10,720 single mutants of Src kinase in parallel and in vivo.
Protein aggregation plays a critical role in many common diseases including Alzheimer’s and Parkinson’s, each of which afflicts tens of millions of individuals. These diseases remain largely intractable, with grim, inexorable clinical courses. The protein aggregates found in these diseases are called amyloid and have a highly organized, fibrous structure known as the cross-β sheet. We are using deep mutational scanning to understand the molecular determinants of amyloid formation, infer the structure of disease-related amyloid aggregates in vivo and delineate the biological interactions that influence amyloidogenesis.