David Savage
University of California-Berkeley, CA·Biochemistry
Every protein's function depends on its amino acid sequence. Even a single change can make it work better, worse or not at all.
- Fields
Biochemistry
Genetics
Biophysics
- Topics
protein engineering
sequence-function landscape
mutational scanning
- Grants
- 1 known grant, through Jun 2030
What they work on
- Develop experimental methods to systematically map how mutations change the function of biomedically relevant proteins, such as CRISPR-Cas genome editing enzymes.
- Create new assays that yield robust measurements of real biochemical parameters, rather than the relative measurements commonly used, across many protein variants.
- Combine large-scale mutational data with mechanistic studies and machine learning to build methods for predicting and engineering protein function.
More about the research
Mapping how sequence relates to function links genotype to phenotype at the molecular level. Such maps could improve basic understanding of how proteins work in the cell, clarify how genetic variants cause disease and help design better protein-based drugs.
Why it matters
Better maps of how mutations change protein function could help clarify which genetic variants cause disease. Patients and clinicians interpreting genetic tests could benefit. The same knowledge could guide the design of biological therapeutics, including CRISPR-Cas genome editing enzymes, with improved potency and specificity.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Justin Meyer
University of California-San Diego, CA·Ecology & Evolution
Test the two-step model of how bacteriophage λ's receptor-binding protein gains new functions by finding which mutations, first destabilizing ones and then binding-loop changes, let it use new E. coli receptors, using high-throughput gene editing and phenotyping.
Adriana San Miguel
North Carolina State University, NC·Biomedical Engineering
Develop BOWiD components, including inducible promoters that drive Cre recombinase, robust cellular landmarks and labeling approaches, and machine learning pipelines that read random fluorescent cell-labeling barcodes in individual C. elegans.
Sidi Chen
Yale University, CT·Biomedical Engineering
Advance and validate MAJESTIC, a gene delivery system combining mRNA, AAV vectors and the Sleeping Beauty transposon, testing efficient and stable cargo delivery with minimal toxicity across diverse immune cell types.
The school
University of California-Berkeley: 46 labs on PI Pages, $1.1B a year on research, 825 research doctorates a year.
Reach out
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