MAY 14, 2015 10:30 AM PDT

Deep mutational scanning to measure the impact of mutations in proteins on the molecular and organismal level

Speaker
  • Principal Investigator, Department of Genome Sciences, University of Washington
    Biography
      Dr. Fowler focuses on developing and implementing new technologies to address difficult problems in protein science and genomics. He is currently an Assistant Professor of Genome Sciences and Affiliate Assistant Professor of Bioengineering at the University of Washington in Seattle. His lab works to understand how variation in protein coding regions of the genome relates to disease, as well as to develop new methods for understanding proteins. Dr. Fowler's did his graduate work at The Scripps Research Institute, where he discovered and characterized the first mammalian functional amyloid protein, Pmel17. During his postdoctoral work at the University of Washington, he developed high-throughput methods for analyzing the effect of mutations on protein function.

    Abstract

    Deep mutational scanning is a method that marries selection for protein function amongst a large library of protein variants with high-throughput DNA sequencing to measure the activity of hundreds of thousands of variants simultaneously. The result is a sequence-function map that describes the impact of all possible single and many double mutants on protein function. We have shown that sequence-function maps have many uses. For example, we are analyzing them to learn about protein properties like structure, aggregation, stability and enzyme mechanism; to guide the interpretation of coding variants in genomes; and to better understand protein evolution. 1. Describe progress in computational and experimental methods for interpreting the consequences of variation in genomes. 2. Evaluate the strengths and weaknesses of different approaches for interpreting variation.


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    MAY 14, 2015 10:30 AM PDT

    Deep mutational scanning to measure the impact of mutations in proteins on the molecular and organismal level


    Specialty

    Gene Expression

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    Dna

    Big Data

    Cancer Research

    Tumor

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