Ph.D., Duke University, 2012
firstname.lastname@example.org | (516) 367-5286
Some mutations are harmful but others are benign. How can we predict the effects of mutations, both singly and in combination? Using data from experiments that simultaneously measure the effects of thousands of mutations, I develop computational tools to predict the functional impact of mutations in protein coding sequences.
The McCandlish lab develops computational and mathematical tools to analyze and exploit data from high-throughput functional assays. The current focus of the lab is on analyzing data from so-called “deep mutational scanning” experiments. These experiments simultaneously determine, for a single protein, the functional effects of thousands of mutations. By aggregating information across the proteins assayed using this technique, we seek to develop data-driven insights into basic protein biology, improved models of molecular evolution, and more accurate methods for predicting the functional effects of mutations in human genome sequences.
Critically, these data also show that the functional effects of mutations often depend on which other mutations are present in the sequence. We are developing new techniques in statistics and machine learning to infer and interpret the complex patterns of genetic interaction observed in these experiments. Our ultimate goal is to be able to model these sequence-function relationships with sufficient accuracy to guide the construction of a new generation of designed enzymes and drugs, and to be able to predict the evolution of antigenic and drug resistance phenotypes in rapidly evolving microbial pathogens.
Peter Koo wants to understand how machines learn biology
September 20, 2019
Dr. Peter Koo joins the CSHL faculty as an assistant professor. His focus is on exploring how artificial intelligence integrates with biology and genomics.
Public Lecture: Seeing With Sequencing
August 8, 2019
David McCandlish named Sloan Research Fellow
February 19, 2019
Assistant Professor David McCandlish has been named a 2019 Sloan Research Fellow for his promising work in the field of quantitative biology.